From aa3676202ecd4fc9400796fef113e68c36a52393 Mon Sep 17 00:00:00 2001 From: benherry Date: Tue, 12 Nov 2024 16:50:20 +0100 Subject: [PATCH 01/13] feat: perform optimization at the 8 poles. Use trust-constr optimizer with more robust options. Initialize input data with data from the datamanager of the usecase_iea_nze --- data_energy/fitting/data_in_iea_nze.pkl | Bin 0 -> 156296 bytes .../fitting/data_in_iea_nze_after_exec.pkl | Bin 0 -> 156296 bytes data_energy/fitting/dm_iea_nze.pkl | Bin 0 -> 9247489 bytes data_energy/fitting/gaseous_bioenergy.py | 62 ++++++++++-------- data_energy/fitting/hydropower.py | 29 ++++---- data_energy/fitting/windpower.py | 2 +- 6 files changed, 48 insertions(+), 45 deletions(-) create mode 100644 data_energy/fitting/data_in_iea_nze.pkl create mode 100644 data_energy/fitting/data_in_iea_nze_after_exec.pkl create mode 100644 data_energy/fitting/dm_iea_nze.pkl diff --git a/data_energy/fitting/data_in_iea_nze.pkl b/data_energy/fitting/data_in_iea_nze.pkl new file mode 100644 index 0000000000000000000000000000000000000000..3440b57b67065c576137f19f4254baa8a852b600 GIT binary patch literal 156296 zcmeHw36LGdnP%%~NeF>B1eTD21_TIjb=RxoRn_1{jZT=hS{5?HXjuHHBdha0-qC)C zq}FfqH^nQXlbgbq)moug4Qq`; 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z3d`Nk@OS>bx+msz&*wkA@pbOwyB>SjU55EO78UXt*=`2IxQ^~^eewU@_!RzI<}Szl z=8BWN`EEC4$uzB>dC|fr-g(OXEZlQ1hi_r+D?h4$z*r!}itV^iLd!GF;h}{$^`2E3 GHvSJoHPqk$ literal 0 HcmV?d00001 diff --git a/data_energy/fitting/gaseous_bioenergy.py b/data_energy/fitting/gaseous_bioenergy.py index 74d1750e..c1da3bf3 100644 --- a/data_energy/fitting/gaseous_bioenergy.py +++ b/data_energy/fitting/gaseous_bioenergy.py @@ -17,7 +17,7 @@ import numpy as np import pandas as pd -from climateeconomics.glossarycore import GlossaryCore +import pickle from scipy.interpolate import interp1d from scipy.optimize import minimize from sostrades_core.execution_engine.execution_engine import ExecutionEngine @@ -27,6 +27,8 @@ ) from energy_models.glossaryenergy import GlossaryEnergy +from climateeconomics.glossarycore import GlossaryCore +from copy import deepcopy """ This script is used to calibrate the gaseous bioenergy invest so that the energy production matches the IEA NZE scenario @@ -54,12 +56,13 @@ prod_IEA_interpolated = f(years) # increase discretization in order to smooth production between 2020 and 2030 -years_optim = np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) +years_optim = np.linspace(year_start, year_end, 8) #np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) invest_year_start = 3.432 #G$ -name = 'Test' -model_name = GlossaryEnergy.AnaerobicDigestion +# chose the name so that it mathes the datamanager of the IEA vs NZE study +name = 'usecase_witness_optim_nze_eval' +model_name = f"WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.biogas.{GlossaryEnergy.AnaerobicDigestion}" ns_dict = {'ns_public': name, 'ns_energy': name, 'ns_energy_study': f'{name}', @@ -76,34 +79,38 @@ ee.configure() ee.display_treeview_nodes() - -def run_model(x: list, year_end: int = year_end): - init_prod = x[0] - invest_before_year_start = x[1:1 + construction_delay] - invest_years_optim = x[1 + construction_delay:] +# recover the input data of the discipline from the iea nze scenario +with open('dm_iea_nze.pkl', 'rb') as f: + dm = pickle.load(f) +f.close() +inputs_dict = deepcopy(dm) +inputs_dict.update({f'{name}.{GlossaryEnergy.CO2TaxesValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.{GlossaryEnergy.CO2TaxesValue}')}) +inputs_dict.update({f'{name}.{GlossaryEnergy.StreamsCO2EmissionsValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.{GlossaryEnergy.StreamsCO2EmissionsValue}')}) +inputs_dict.update({f'{name}.{GlossaryEnergy.StreamPricesValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.{GlossaryEnergy.StreamPricesValue}')}) +inputs_dict.update({f'{name}.{GlossaryEnergy.ResourcesPriceValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.{GlossaryEnergy.ResourcesPriceValue}')}) +inputs_dict.update({f'{name}.{GlossaryEnergy.TransportCostValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.biogas.{GlossaryEnergy.TransportCostValue}')}) + + +def run_model(x: list, inputs_dict: dict = inputs_dict, year_end: int = year_end, + ee: ExecutionEngine = ee): + init_prod = x[0] * initial_production + invest_before_year_start = x[1:1 + construction_delay] * invest_year_start + invest_years_optim = x[1 + construction_delay:] * invest_year_start # interpolate on missing years f = interp1d(years_optim, invest_years_optim, kind='linear') invests = f(years) invest_df = pd.DataFrame({GlossaryEnergy.Years: years, GlossaryCore.InvestValue: list(invests)}) - inputs_dict = { + inputs_dict.update({ f'{name}.{GlossaryEnergy.YearStart}': year_start, f'{name}.{GlossaryEnergy.YearEnd}': year_end, f'{name}.{model_name}.{GlossaryEnergy.InvestLevelValue}': invest_df, - f'{name}.{GlossaryEnergy.CO2TaxesValue}': pd.DataFrame( - {GlossaryEnergy.Years: years, GlossaryEnergy.CO2Tax: np.linspace(0., 0., len(years))}), - f'{name}.{GlossaryEnergy.StreamsCO2EmissionsValue}': pd.DataFrame({GlossaryEnergy.Years: years, GlossaryEnergy.electricity: np.zeros_like(years), GlossaryEnergy.WetBiomassResource: np.zeros_like(years)}), - f'{name}.{GlossaryEnergy.StreamPricesValue}': pd.DataFrame({GlossaryEnergy.Years: years, GlossaryEnergy.electricity: np.zeros_like(years), GlossaryEnergy.WetBiomassResource: np.zeros_like(years)}), - f'{name}.{GlossaryEnergy.ResourcesPriceValue}': pd.DataFrame({GlossaryEnergy.Years: years, GlossaryEnergy.electricity: np.zeros_like(years), GlossaryEnergy.WetBiomassResource: np.zeros_like(years)}), - f'{name}.{GlossaryEnergy.TransportCostValue}': pd.DataFrame({GlossaryEnergy.Years: years, 'transport': np.zeros(len(years))}), #f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, f'{name}.{model_name}.initial_production': init_prod, f'{name}.{model_name}.{GlossaryEnergy.InvestmentBeforeYearStartValue}': pd.DataFrame({GlossaryEnergy.Years: np.arange(year_start - construction_delay, year_start), GlossaryEnergy.InvestValue: invest_before_year_start}), - } + }) - # must load the dict twice, otherwise values are not taken into account - ee.load_study_from_input_dict(inputs_dict) ee.load_study_from_input_dict(inputs_dict) ee.execute() @@ -117,22 +124,21 @@ def fitting_renewable(x: list): prod_df, invest_df = run_model(x) prod_values_model = prod_df.loc[prod_df[GlossaryEnergy.Years].isin( years_IEA_interpolated), "biogas (TWh)"].values * 1000. # TWh - return (((prod_values_model - prod_IEA_interpolated)) ** 2).mean() + return (((prod_values_model - prod_IEA_interpolated) / (invest_year_start * np.ones_like(prod_values_model))) ** 2).mean() # Initial guess for the variables invest from year 2025 to 2100. -x0 = np.concatenate((np.array([initial_production]), invest_year_start * np.ones(construction_delay), invest_year_start * np.ones(len(years_optim)))) -bounds = [(initial_production * 0.87, initial_production * 0.87)] + [(invest_year_start/2.4, invest_year_start/2.4)] * construction_delay + (len(years_optim)) * [(invest_year_start/3., 3. * invest_year_start)] - +x0 = np.concatenate((np.array([0.87]), 1/2.4 * np.ones(construction_delay), np.ones(len(years_optim)))) +bounds = [(0.87, 0.87)] + [(1./2.4, 1./2.4)] * construction_delay + (len(years_optim)) * [(1./3., 3. * 1.)] # Use minimize to find the minimum of the function -result = minimize(fitting_renewable, x0, bounds=bounds, options={'disp': True, 'maxiter': 500, 'maxfun': 500, 'method': 'trust-constr', 'FACTR': 1.e-7}) +result = minimize(fitting_renewable, x0, bounds=bounds, method='trust-constr', options={'disp': True, 'maxiter': 500}) #, 'maxfun': 500, 'ftol': 1.e-6, 'maxls': 50}) prod_df, invest_df = run_model(result.x) # Print the result print("Function value at the optimum:", result.fun) -print("initial production", result.x[0]) -print("invest before year start", result.x[1:1+construction_delay]) -print("invest at the optimum", result.x[1+construction_delay:]) +print("initial production", result.x[0] * initial_production) +print("invest before year start", result.x[1:1+construction_delay] * invest_year_start) +print("invest at the optimum", result.x[1+construction_delay:] * invest_year_start) new_chart = TwoAxesInstanciatedChart('years', 'biogas production (TWh)', @@ -151,7 +157,7 @@ def fitting_renewable(x: list): new_chart = TwoAxesInstanciatedChart('years', 'biogas invest (G$)', chart_name='investments') -serie = InstanciatedSeries(list(years_optim), list(result.x)[1+construction_delay:], 'invests_at_poles', 'lines+markers') +serie = InstanciatedSeries(list(years_optim), list(result.x[1+construction_delay:] * invest_year_start), 'invests_at_poles', 'lines+markers') new_chart.series.append(serie) serie = InstanciatedSeries(list(years), list(invest_df[GlossaryEnergy.InvestValue]), 'invests', 'lines') new_chart.series.append(serie) diff --git a/data_energy/fitting/hydropower.py b/data_energy/fitting/hydropower.py index eb74e832..32e564ce 100644 --- a/data_energy/fitting/hydropower.py +++ b/data_energy/fitting/hydropower.py @@ -50,7 +50,7 @@ prod_IEA_interpolated = f(years_IEA_interpolated) # increase discretization in order to smooth production between 2020 and 2030 -years_optim = np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #years_IEA_interpolated #sorted(list(set(years_IEA_interpolated + list(np.arange(year_start, max(year_start, 2030) + 1))))) +years_optim = np.linspace(year_start, year_end, 8) #np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #years_IEA_interpolated #sorted(list(set(years_IEA_interpolated + list(np.arange(year_start, max(year_start, 2030) + 1))))) invest_year_start = 18.957 #G$ name = 'Test' @@ -75,9 +75,9 @@ def run_model(x: list, year_end: int = year_end): - init_prod = x[0] - invest_before_year_start = x[1:1 + construction_delay] - invest_years_optim = x[1 + construction_delay:] + init_prod = x[0] * initial_production + invest_before_year_start = x[1:1 + construction_delay] * invest_year_start + invest_years_optim = x[1 + construction_delay:] * invest_year_start # interpolate on missing years f = interp1d(years_optim, invest_years_optim, kind='linear') invests = f(years) @@ -100,8 +100,6 @@ def run_model(x: list, year_end: int = year_end): {GlossaryEnergy.Years: np.arange(year_start - construction_delay, year_start), GlossaryEnergy.InvestValue: invest_before_year_start}), } - # bug: must load the study twice so that modifications are taked into accout - ee.load_study_from_input_dict(inputs_dict) ee.load_study_from_input_dict(inputs_dict) ee.execute() @@ -115,25 +113,24 @@ def fitting_renewable(x: list): prod_df, invest_df = run_model(x) prod_values_model = prod_df.loc[prod_df[GlossaryEnergy.Years].isin( years_IEA_interpolated), "electricity (TWh)"].values * 1000. # TWh - return (((prod_values_model - prod_IEA_interpolated)) ** 2).mean() + return (((prod_values_model - prod_IEA_interpolated) / (initial_production * np.ones_like(prod_values_model))) ** 2).mean() # Initial guess for the variables invest from year 2025 to 2100. -# Initial guess for the variables invest from year 2025 to 2100. -x0 = np.concatenate((np.array([initial_production]), invest_year_start * np.ones(construction_delay), invest_year_start * np.ones(len(years_optim)))) -bounds = [(initial_production, initial_production)] + [(invest_year_start/1., invest_year_start/1.)] * construction_delay + (len(years_optim)) * [(invest_year_start/10., 10. * invest_year_start)] - +x0 = np.concatenate((np.array([1.]), np.ones(construction_delay), np.ones(len(years_optim)))) +bounds = [(1., 1.)] + [(1., 1.)] * construction_delay + (len(years_optim)) * [(1./10., 10.)] # Use minimize to find the minimum of the function -result = minimize(fitting_renewable, x0, bounds=bounds, options={'disp': True, 'maxiter': 500, 'maxfun': 500, 'method': 'trust-constr', 'FACTR': 1.e-7}) +result = minimize(fitting_renewable, x0, bounds=bounds, method='trust-constr', + options={'disp': True, 'maxiter': 2000, 'xtol': 1e-20}) prod_df, invest_df = run_model(result.x) # Print the result print("Function value at the optimum:", result.fun) -print("initial production", result.x[0]) -print("invest before year start", result.x[1:1+construction_delay]) -print("invest at the optimum", result.x[1+construction_delay:]) +print("initial production", result.x[0] * initial_production) +print("invest before year start", result.x[1:1+construction_delay] * invest_year_start) +print("invest at the optimum", result.x[1+construction_delay:] * invest_year_start) new_chart = TwoAxesInstanciatedChart('years', 'hydropower production (TWh)', @@ -152,7 +149,7 @@ def fitting_renewable(x: list): new_chart = TwoAxesInstanciatedChart('years', 'hydropower invest (G$)', chart_name='investments') -serie = InstanciatedSeries(list(years_optim), list(result.x)[1+construction_delay:], 'invests_at_poles', 'lines+markers') +serie = InstanciatedSeries(list(years_optim), list(result.x[1+construction_delay:] * invest_year_start), 'invests_at_poles', 'lines+markers') new_chart.series.append(serie) serie = InstanciatedSeries(list(years), list(invest_df[GlossaryEnergy.InvestValue]), 'invests', 'lines') new_chart.series.append(serie) diff --git a/data_energy/fitting/windpower.py b/data_energy/fitting/windpower.py index 2d3e59aa..6382b2e2 100644 --- a/data_energy/fitting/windpower.py +++ b/data_energy/fitting/windpower.py @@ -52,7 +52,7 @@ prod_IEA_interpolated = f(years_IEA_interpolated) # increase discretization in order to smooth production between 2020 and 2030 -years_optim = years_IEA_interpolated #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) +years_optim = np.linspace(year_start, year_end, 8) #years_IEA_interpolated #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) invest_year_start = 80. #G$ From 269c007ac6d76fdbdd818b931a06aa85f28e6c95 Mon Sep 17 00:00:00 2001 From: benherry Date: Thu, 14 Nov 2024 07:59:34 +0100 Subject: [PATCH 02/13] feat: using bsplines as in sostrades-core to optimize the invests at the poles. Initialize the discipline at each run so that the invest_before_year_start is correctly updated --- data_energy/fitting/hydropower.py | 85 +++++++++++++++++-------------- 1 file changed, 48 insertions(+), 37 deletions(-) diff --git a/data_energy/fitting/hydropower.py b/data_energy/fitting/hydropower.py index 32e564ce..37c3f0eb 100644 --- a/data_energy/fitting/hydropower.py +++ b/data_energy/fitting/hydropower.py @@ -17,10 +17,13 @@ import numpy as np import pandas as pd +import pickle +from copy import deepcopy from climateeconomics.glossarycore import GlossaryCore from scipy.interpolate import interp1d from scipy.optimize import minimize from sostrades_core.execution_engine.execution_engine import ExecutionEngine +from sostrades_core.tools.bspline.bspline import BSpline from sostrades_core.tools.post_processing.charts.two_axes_instanciated_chart import ( InstanciatedSeries, TwoAxesInstanciatedChart, @@ -53,78 +56,86 @@ years_optim = np.linspace(year_start, year_end, 8) #np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #years_IEA_interpolated #sorted(list(set(years_IEA_interpolated + list(np.arange(year_start, max(year_start, 2030) + 1))))) invest_year_start = 18.957 #G$ -name = 'Test' -model_name = GlossaryEnergy.Hydropower -ee = ExecutionEngine(name) -ns_dict = {'ns_public': name, - 'ns_energy': name, - 'ns_energy_study': f'{name}', - 'ns_electricity': name, - 'ns_resource': name} -ee.ns_manager.add_ns_def(ns_dict) - -mod_path = 'energy_models.models.electricity.hydropower.hydropower_disc.HydropowerDiscipline' -builder = ee.factory.get_builder_from_module( - model_name, mod_path) - -ee.factory.set_builders_to_coupling_builder(builder) - -ee.configure() -ee.display_treeview_nodes() - +name = 'usecase_witness_optim_nze_eval' +model_name = f"WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.electricity.{GlossaryEnergy.Hydropower}" +# recover the input data of the discipline from the iea nze scenario +with open('dm_iea_nze.pkl', 'rb') as f: + dm = pickle.load(f) +f.close() +inputs_dict = deepcopy(dm) +inputs_dict.update({f'{name}.{GlossaryEnergy.CO2TaxesValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.{GlossaryEnergy.CO2TaxesValue}')}) +inputs_dict.update({f'{name}.{GlossaryEnergy.StreamsCO2EmissionsValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.{GlossaryEnergy.StreamsCO2EmissionsValue}')}) +inputs_dict.update({f'{name}.{GlossaryEnergy.StreamPricesValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.{GlossaryEnergy.StreamPricesValue}')}) +inputs_dict.update({f'{name}.{GlossaryEnergy.ResourcesPriceValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.{GlossaryEnergy.ResourcesPriceValue}')}) +inputs_dict.update({f'{name}.{GlossaryEnergy.TransportCostValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.biogas.{GlossaryEnergy.TransportCostValue}')}) def run_model(x: list, year_end: int = year_end): init_prod = x[0] * initial_production invest_before_year_start = x[1:1 + construction_delay] * invest_year_start invest_years_optim = x[1 + construction_delay:] * invest_year_start # interpolate on missing years - f = interp1d(years_optim, invest_years_optim, kind='linear') - invests = f(years) + #f = interp1d(years_optim, invest_years_optim, kind='linear') + #invests = f(years) + list_t = np.linspace(0.0, 1.0, len(years)) + bspline = BSpline(n_poles=len(years_optim)) + bspline.set_ctrl_pts(invest_years_optim) + invests, b_array = bspline.eval_list_t(list_t) invest_df = pd.DataFrame({GlossaryEnergy.Years: years, GlossaryCore.InvestValue: list(invests)}) - inputs_dict = { + ee = ExecutionEngine(name) + ns_dict = {'ns_public': name, + 'ns_energy': name, + 'ns_energy_study': f'{name}', + 'ns_electricity': name, + 'ns_resource': name} + ee.ns_manager.add_ns_def(ns_dict) + + mod_path = 'energy_models.models.electricity.hydropower.hydropower_disc.HydropowerDiscipline' + builder = ee.factory.get_builder_from_module( + model_name, mod_path) + + ee.factory.set_builders_to_coupling_builder(builder) + + ee.configure() + #ee.display_treeview_nodes() + + inputs_dict.update({ f'{name}.{GlossaryEnergy.YearStart}': year_start, f'{name}.{GlossaryEnergy.YearEnd}': year_end, f'{name}.{model_name}.{GlossaryEnergy.InvestLevelValue}': invest_df, - f'{name}.{GlossaryEnergy.CO2TaxesValue}': pd.DataFrame( - {GlossaryEnergy.Years: years, GlossaryEnergy.CO2Tax: np.linspace(0., 0., len(years))}), - f'{name}.{GlossaryEnergy.StreamsCO2EmissionsValue}': pd.DataFrame({GlossaryEnergy.Years: years}), - f'{name}.{GlossaryEnergy.StreamPricesValue}': pd.DataFrame({GlossaryEnergy.Years: years}), - f'{name}.{GlossaryEnergy.ResourcesPriceValue}': pd.DataFrame({GlossaryEnergy.Years: years}), - f'{name}.{GlossaryEnergy.TransportCostValue}': pd.DataFrame({GlossaryEnergy.Years: years, 'transport': np.zeros(len(years))}), #f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, f'{name}.{model_name}.initial_production': init_prod, f'{name}.{model_name}.{GlossaryEnergy.InvestmentBeforeYearStartValue}': pd.DataFrame( {GlossaryEnergy.Years: np.arange(year_start - construction_delay, year_start), GlossaryEnergy.InvestValue: invest_before_year_start}), - } + }) ee.load_study_from_input_dict(inputs_dict) ee.execute() prod_df = ee.dm.get_value(ee.dm.get_all_namespaces_from_var_name(GlossaryEnergy.TechnoProductionValue)[0]) #PWh - return prod_df[[GlossaryEnergy.Years, "electricity (TWh)"]], invest_df + return prod_df[[GlossaryEnergy.Years, "electricity (TWh)"]], invest_df, ee def fitting_renewable(x: list): - prod_df, invest_df = run_model(x) + prod_df, invest_df, ee = run_model(x) prod_values_model = prod_df.loc[prod_df[GlossaryEnergy.Years].isin( years_IEA_interpolated), "electricity (TWh)"].values * 1000. # TWh return (((prod_values_model - prod_IEA_interpolated) / (initial_production * np.ones_like(prod_values_model))) ** 2).mean() # Initial guess for the variables invest from year 2025 to 2100. -x0 = np.concatenate((np.array([1.]), np.ones(construction_delay), np.ones(len(years_optim)))) -bounds = [(1., 1.)] + [(1., 1.)] * construction_delay + (len(years_optim)) * [(1./10., 10.)] +x0 = np.concatenate((np.array([1.]), np.array([0.]), 80./invest_year_start * np.ones(construction_delay - 1), np.ones(len(years_optim)))) +bounds = [(1., 1.)] + [(0., 0.)] + [(80./invest_year_start/2., 80./invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1./10., 10.)] # Use minimize to find the minimum of the function -result = minimize(fitting_renewable, x0, bounds=bounds, method='trust-constr', +result = minimize(fitting_renewable, x0, bounds=bounds, #method='trust-constr', options={'disp': True, 'maxiter': 2000, 'xtol': 1e-20}) -prod_df, invest_df = run_model(result.x) +prod_df, invest_df, ee = run_model(result.x) # Print the result print("Function value at the optimum:", result.fun) @@ -149,9 +160,9 @@ def fitting_renewable(x: list): new_chart = TwoAxesInstanciatedChart('years', 'hydropower invest (G$)', chart_name='investments') -serie = InstanciatedSeries(list(years_optim), list(result.x[1+construction_delay:] * invest_year_start), 'invests_at_poles', 'lines+markers') +serie = InstanciatedSeries(list(years_optim), list(result.x[1+construction_delay:] * invest_year_start), 'invests_at_poles', 'scatter') new_chart.series.append(serie) -serie = InstanciatedSeries(list(years), list(invest_df[GlossaryEnergy.InvestValue]), 'invests', 'lines') +serie = InstanciatedSeries(list(years), list(invest_df[GlossaryEnergy.InvestValue]), 'invests_bspline', 'lines') new_chart.series.append(serie) new_chart.to_plotly().show() From bb19a999f72db26b08518b5c8cc927b33a73c252 Mon Sep 17 00:00:00 2001 From: benherry Date: Thu, 14 Nov 2024 10:47:04 +0100 Subject: [PATCH 03/13] feat: using bspline, taking invest_before_year_start(year_start - construction_delay) = 0 as workaround to bug, initial_production not a design var anymore, graph correction --- data_energy/fitting/gaseous_bioenergy.py | 71 ++++++++++++------------ data_energy/fitting/hydropower.py | 18 +++--- 2 files changed, 43 insertions(+), 46 deletions(-) diff --git a/data_energy/fitting/gaseous_bioenergy.py b/data_energy/fitting/gaseous_bioenergy.py index c1da3bf3..39715229 100644 --- a/data_energy/fitting/gaseous_bioenergy.py +++ b/data_energy/fitting/gaseous_bioenergy.py @@ -25,7 +25,7 @@ InstanciatedSeries, TwoAxesInstanciatedChart, ) - +from sostrades_core.tools.bspline.bspline import BSpline from energy_models.glossaryenergy import GlossaryEnergy from climateeconomics.glossarycore import GlossaryCore from copy import deepcopy @@ -69,15 +69,6 @@ 'ns_biogas': f'{name}', 'ns_resource': name} mod_path = 'energy_models.models.biogas.anaerobic_digestion.anaerobic_digestion_disc.AnaerobicDigestionDiscipline' -ee = ExecutionEngine(name) -ee.ns_manager.add_ns_def(ns_dict) -builder = ee.factory.get_builder_from_module( - model_name, mod_path) - -ee.factory.set_builders_to_coupling_builder(builder) - -ee.configure() -ee.display_treeview_nodes() # recover the input data of the discipline from the iea nze scenario with open('dm_iea_nze.pkl', 'rb') as f: @@ -91,23 +82,33 @@ inputs_dict.update({f'{name}.{GlossaryEnergy.TransportCostValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.biogas.{GlossaryEnergy.TransportCostValue}')}) -def run_model(x: list, inputs_dict: dict = inputs_dict, year_end: int = year_end, - ee: ExecutionEngine = ee): - init_prod = x[0] * initial_production - invest_before_year_start = x[1:1 + construction_delay] * invest_year_start - invest_years_optim = x[1 + construction_delay:] * invest_year_start - # interpolate on missing years - f = interp1d(years_optim, invest_years_optim, kind='linear') - invests = f(years) +def run_model(x: list, inputs_dict: dict = inputs_dict, year_end: int = year_end): + invest_before_year_start = x[0:construction_delay] * invest_year_start + invest_years_optim = x[construction_delay:] * invest_year_start + # interpolate on missing years using bspline as in sostrades-core + list_t = np.linspace(0.0, 1.0, len(years)) + bspline = BSpline(n_poles=len(years_optim)) + bspline.set_ctrl_pts(invest_years_optim) + invests, b_array = bspline.eval_list_t(list_t) invest_df = pd.DataFrame({GlossaryEnergy.Years: years, GlossaryCore.InvestValue: list(invests)}) + ee = ExecutionEngine(name) + ee.ns_manager.add_ns_def(ns_dict) + builder = ee.factory.get_builder_from_module( + model_name, mod_path) + + ee.factory.set_builders_to_coupling_builder(builder) + + ee.configure() + #ee.display_treeview_nodes() + inputs_dict.update({ f'{name}.{GlossaryEnergy.YearStart}': year_start, f'{name}.{GlossaryEnergy.YearEnd}': year_end, f'{name}.{model_name}.{GlossaryEnergy.InvestLevelValue}': invest_df, #f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, - f'{name}.{model_name}.initial_production': init_prod, + f'{name}.{model_name}.initial_production': initial_production, f'{name}.{model_name}.{GlossaryEnergy.InvestmentBeforeYearStartValue}': pd.DataFrame({GlossaryEnergy.Years: np.arange(year_start - construction_delay, year_start), GlossaryEnergy.InvestValue: invest_before_year_start}), }) @@ -117,49 +118,49 @@ def run_model(x: list, inputs_dict: dict = inputs_dict, year_end: int = year_end prod_df = ee.dm.get_value(ee.dm.get_all_namespaces_from_var_name(GlossaryEnergy.TechnoProductionValue)[0]) #PWh - return prod_df[[GlossaryEnergy.Years, "biogas (TWh)"]], invest_df + return prod_df[[GlossaryEnergy.Years, "biogas (TWh)"]], invest_df, ee def fitting_renewable(x: list): - prod_df, invest_df = run_model(x) + prod_df, invest_df, ee = run_model(x) prod_values_model = prod_df.loc[prod_df[GlossaryEnergy.Years].isin( years_IEA_interpolated), "biogas (TWh)"].values * 1000. # TWh return (((prod_values_model - prod_IEA_interpolated) / (invest_year_start * np.ones_like(prod_values_model))) ** 2).mean() # Initial guess for the variables invest from year 2025 to 2100. -x0 = np.concatenate((np.array([0.87]), 1/2.4 * np.ones(construction_delay), np.ones(len(years_optim)))) -bounds = [(0.87, 0.87)] + [(1./2.4, 1./2.4)] * construction_delay + (len(years_optim)) * [(1./3., 3. * 1.)] +x0 = np.concatenate((np.array([0.]), 1/2.4 * np.ones(construction_delay - 1), np.ones(len(years_optim)))) +bounds = [(0., 0.)] + [(1./2.4/3., 1./2.4 * 3.)] * (construction_delay - 1) + (len(years_optim)) * [(1./3., 3. * 1.)] # Use minimize to find the minimum of the function -result = minimize(fitting_renewable, x0, bounds=bounds, method='trust-constr', options={'disp': True, 'maxiter': 500}) #, 'maxfun': 500, 'ftol': 1.e-6, 'maxls': 50}) +result = minimize(fitting_renewable, x0, bounds=bounds, #method='trust-constr', + options={'disp': True, 'maxiter': 500}) #, 'maxfun': 500, 'ftol': 1.e-6, 'maxls': 50}) -prod_df, invest_df = run_model(result.x) +prod_df, invest_df, ee = run_model(result.x) # Print the result print("Function value at the optimum:", result.fun) -print("initial production", result.x[0] * initial_production) -print("invest before year start", result.x[1:1+construction_delay] * invest_year_start) -print("invest at the optimum", result.x[1+construction_delay:] * invest_year_start) +print("invest before year start", result.x[0:construction_delay] * invest_year_start) +print("invest at the poles at the optimum", result.x[construction_delay:] * invest_year_start) new_chart = TwoAxesInstanciatedChart('years', 'biogas production (TWh)', - chart_name='Production : model vs historic') + chart_name='Production : witness vs IEA') serie = InstanciatedSeries(list(prod_df[GlossaryEnergy.Years].values), list(prod_df["biogas (TWh)"].values * 1000.), 'model', 'lines+markers') new_chart.series.append(serie) -serie = InstanciatedSeries(years_IEA, df_prod_iea["biogas AnaerobicDigestion (TWh)"].values, 'historic', 'scatter') +serie = InstanciatedSeries(years_IEA, df_prod_iea["biogas AnaerobicDigestion (TWh)"].values, 'IEA', 'scatter') new_chart.series.append(serie) -serie = InstanciatedSeries(list(years_IEA_interpolated), list(prod_IEA_interpolated), 'historic_interpolated', 'lines+markers') +serie = InstanciatedSeries(list(years_IEA_interpolated), list(prod_IEA_interpolated), 'IEA_interpolated', 'lines+markers') new_chart.series.append(serie) new_chart.to_plotly().show() new_chart = TwoAxesInstanciatedChart('years', 'biogas invest (G$)', chart_name='investments') -serie = InstanciatedSeries(list(years_optim), list(result.x[1+construction_delay:] * invest_year_start), 'invests_at_poles', 'lines+markers') +serie = InstanciatedSeries(list(years_optim), list(result.x[construction_delay:] * invest_year_start), 'invests_at_poles', 'scatter') new_chart.series.append(serie) -serie = InstanciatedSeries(list(years), list(invest_df[GlossaryEnergy.InvestValue]), 'invests', 'lines') +serie = InstanciatedSeries(list(years), list(invest_df[GlossaryEnergy.InvestValue]), 'invests_bspline', 'lines') new_chart.series.append(serie) new_chart.to_plotly().show() @@ -178,7 +179,5 @@ def fitting_renewable(x: list): df_invest_mix['biogas.AnaerobicDigestion'] = invest_df[GlossaryCore.InvestValue] df_invest_mix.to_csv(invest_mix_csv, index=False, sep=',') # values to set in the invest_design_space_NZE.csv -f = interp1d(years, df_invest_mix['biogas.AnaerobicDigestion'].values, kind='linear') -invest_at_poles = f(np.linspace(year_start, year_end, 8)) -print(f"invest at poles={invest_at_poles}") +print(f"invest at poles={result.x[1+construction_delay:] * invest_year_start}") diff --git a/data_energy/fitting/hydropower.py b/data_energy/fitting/hydropower.py index 37c3f0eb..9d9d38aa 100644 --- a/data_energy/fitting/hydropower.py +++ b/data_energy/fitting/hydropower.py @@ -74,9 +74,7 @@ def run_model(x: list, year_end: int = year_end): init_prod = x[0] * initial_production invest_before_year_start = x[1:1 + construction_delay] * invest_year_start invest_years_optim = x[1 + construction_delay:] * invest_year_start - # interpolate on missing years - #f = interp1d(years_optim, invest_years_optim, kind='linear') - #invests = f(years) + # interpolate on missing years using bspline as in sostrades-core list_t = np.linspace(0.0, 1.0, len(years)) bspline = BSpline(n_poles=len(years_optim)) bspline.set_ctrl_pts(invest_years_optim) @@ -128,6 +126,8 @@ def fitting_renewable(x: list): # Initial guess for the variables invest from year 2025 to 2100. +# there is a bug with the invest before year start => first value must be set to 0 +# otherwise initial production at year start is not as expected x0 = np.concatenate((np.array([1.]), np.array([0.]), 80./invest_year_start * np.ones(construction_delay - 1), np.ones(len(years_optim)))) bounds = [(1., 1.)] + [(0., 0.)] + [(80./invest_year_start/2., 80./invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1./10., 10.)] @@ -141,19 +141,19 @@ def fitting_renewable(x: list): print("Function value at the optimum:", result.fun) print("initial production", result.x[0] * initial_production) print("invest before year start", result.x[1:1+construction_delay] * invest_year_start) -print("invest at the optimum", result.x[1+construction_delay:] * invest_year_start) +print("invest at the poles at the optimum", result.x[1+construction_delay:] * invest_year_start) new_chart = TwoAxesInstanciatedChart('years', 'hydropower production (TWh)', - chart_name='Production : model vs historic') + chart_name='witness vs IEA') serie = InstanciatedSeries(list(prod_df[GlossaryEnergy.Years].values), list(prod_df["electricity (TWh)"].values * 1000.), 'model', 'lines') new_chart.series.append(serie) -serie = InstanciatedSeries(years_IEA, df_prod_iea['electricity (TWh)'].values, 'historic', 'scatter') +serie = InstanciatedSeries(years_IEA, df_prod_iea['electricity (TWh)'].values, 'IEA', 'scatter') new_chart.series.append(serie) -serie = InstanciatedSeries(list(years_IEA_interpolated), list(prod_IEA_interpolated), 'historic_interpolated', 'lines+markers') +serie = InstanciatedSeries(list(years_IEA_interpolated), list(prod_IEA_interpolated), 'IEA_interpolated', 'lines+markers') new_chart.series.append(serie) new_chart.to_plotly().show() @@ -184,6 +184,4 @@ def fitting_renewable(x: list): df_invest_mix.to_csv(invest_mix_csv, index=False, sep=',') # values to set in the invest_design_space_NZE.csv -f = interp1d(years, df_invest_mix['electricity.Hydropower'].values, kind='linear') -invest_at_poles = f(np.linspace(year_start, year_end, 8)) -print(f"invest at poles={invest_at_poles}") \ No newline at end of file +print(f"invest at poles={result.x[1+construction_delay:] * invest_year_start}") \ No newline at end of file From e634f6f25974526e72d626d1404788fda22829dc Mon Sep 17 00:00:00 2001 From: benherry Date: Sun, 17 Nov 2024 09:07:39 +0100 Subject: [PATCH 04/13] fix: price variable --- data_energy/fitting/windpower.py | 179 ++++++++++++++++++++----------- 1 file changed, 115 insertions(+), 64 deletions(-) diff --git a/data_energy/fitting/windpower.py b/data_energy/fitting/windpower.py index 6382b2e2..ddc29294 100644 --- a/data_energy/fitting/windpower.py +++ b/data_energy/fitting/windpower.py @@ -15,9 +15,10 @@ ''' import os from functools import reduce - +import pickle import numpy as np import pandas as pd +from copy import deepcopy from climateeconomics.glossarycore import GlossaryCore from scipy.interpolate import interp1d from scipy.optimize import minimize @@ -26,9 +27,16 @@ InstanciatedSeries, TwoAxesInstanciatedChart, ) - +from sostrades_core.tools.bspline.bspline import BSpline +from energy_models.models.electricity.wind_onshore.wind_onshore_disc import ( + WindOnshoreDiscipline, +) +from energy_models.models.electricity.wind_offshore.wind_offshore_disc import ( + WindOffshoreDiscipline, +) from energy_models.glossaryenergy import GlossaryEnergy + """ This script is used to calibrate the windpower invest so that the electricity production matches the IEA NZE scenario production values between 2020 and 2050 @@ -39,6 +47,7 @@ years_IEA = [2020, 2025, 2030, 2035, 2040, 2045, 2050, 2100] years = np.arange(year_start, year_end + 1) + # source: IEA report NZE2021Ch02 models_path_abs = os.path.dirname(os.path.abspath(__file__)).split(os.sep + "models")[0] df_prod_iea = pd.read_csv( @@ -46,19 +55,25 @@ new_row = pd.DataFrame({'years': [2100], 'electricity (TWh)': [35000.]}) df_prod_iea = pd.concat([df_prod_iea, new_row], ignore_index=True) +df_price_iea = pd.read_csv( + os.path.join(models_path_abs, 'models', 'witness-core', 'climateeconomics', 'data', 'IEA_NZE_electricity_Technologies_Mix_prices.csv')) + # interpolate data between 2050 and 2100 years_IEA_interpolated = years f = interp1d(years_IEA, df_prod_iea['electricity (TWh)'].values, kind='linear') prod_IEA_interpolated = f(years_IEA_interpolated) -# increase discretization in order to smooth production between 2020 and 2030 +# optimization at the poles just like in witness-full study years_optim = np.linspace(year_start, year_end, 8) #years_IEA_interpolated #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) invest_year_start = 80. #G$ +construction_delay = GlossaryEnergy.TechnoConstructionDelayDict['WindOffshore'] # same construction delay for windonshore and windoffshore +if construction_delay != GlossaryEnergy.TechnoConstructionDelayDict['WindOnshore']: + raise ValueError(f"must adapt script as construction delay for windOnshore and windOffshore differ") -name = 'Test' -model_name_onshore = GlossaryEnergy.WindOnshore -model_name_offshore = GlossaryEnergy.WindOffshore +name = 'usecase_witness_optim_nze_eval' +model_name_onshore = f"WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.electricity.{GlossaryEnergy.WindOnshore}" +model_name_offshore = f"WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.electricity.{GlossaryEnergy.WindOffshore}" ns_dict = {'ns_public': name, 'ns_energy': name, 'ns_energy_study': f'{name}', @@ -67,57 +82,76 @@ mod_path_onshore = 'energy_models.models.electricity.wind_onshore.wind_onshore_disc.WindOnshoreDiscipline' mod_path_offshore = 'energy_models.models.electricity.wind_offshore.wind_offshore_disc.WindOffshoreDiscipline' -ee = ExecutionEngine(name) -ee.ns_manager.add_ns_def(ns_dict) -builder = [] -builder.append(ee.factory.get_builder_from_module( - model_name_onshore, mod_path_onshore)) -builder.append(ee.factory.get_builder_from_module( - model_name_offshore, mod_path_offshore)) -ee.factory.set_builders_to_coupling_builder(builder) - -ee.configure() -ee.display_treeview_nodes() - - - +# if want to modify the capex of both onshore and offshore +#dict_techno_dict_default = {model_name_onshore: WindOnshoreDiscipline.techno_infos_dict_default, +# model_name_offshore: WindOffshoreDiscipline.techno_infos_dict_default} +techno_info_dict_default = WindOnshoreDiscipline.techno_infos_dict_default +Capex_init0 = WindOnshoreDiscipline.techno_infos_dict_default['Capex_init'] + +# recover the input data of the discipline from the iea nze scenario +with open('dm_iea_nze.pkl', 'rb') as f: + dm = pickle.load(f) +f.close() +inputs_dict = deepcopy(dm) +inputs_dict.update({f'{name}.{GlossaryEnergy.CO2TaxesValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.{GlossaryEnergy.CO2TaxesValue}')}) +inputs_dict.update({f'{name}.{GlossaryEnergy.StreamsCO2EmissionsValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.{GlossaryEnergy.StreamsCO2EmissionsValue}')}) +inputs_dict.update({f'{name}.{GlossaryEnergy.StreamPricesValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.{GlossaryEnergy.StreamPricesValue}')}) +inputs_dict.update({f'{name}.{GlossaryEnergy.ResourcesPriceValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.{GlossaryEnergy.ResourcesPriceValue}')}) +inputs_dict.update({f'{name}.{GlossaryEnergy.TransportCostValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.electricity.{GlossaryEnergy.TransportCostValue}')}) + +# initial_production from IEA is split between windonshore and windoffshore following arbitrary ratio +init_prod_onshore_over_offshore = 1508. / 107.69 # taken from initial witness results +initial_prod = df_prod_iea.loc[df_prod_iea[GlossaryEnergy.Years] == year_start]['electricity (TWh)'].values[0] +init_prod_dict = { + model_name_onshore: initial_prod * init_prod_onshore_over_offshore / (1. + init_prod_onshore_over_offshore), + model_name_offshore: initial_prod / (1. + init_prod_onshore_over_offshore)} +ratio_invest_onshore_offshore = 3.6689 # taken from initial witness results def run_model(x: list, year_end: int = year_end): - init_age_distrib_factor = x[0] - invest_years_optim = x[1:] - # interpolate on missing years - f = interp1d(years_optim, invest_years_optim, kind='linear') - invests = f(years) + techno_info_dict_default['Capex_init'] = Capex_init0 * x[0] + invest_before_year_start = x[1:construction_delay + 1] * invest_year_start + invest_years_optim = x[construction_delay + 1:] * invest_year_start + # interpolate on missing years using bspline as in sostrades-core + list_t = np.linspace(0.0, 1.0, len(years)) + bspline = BSpline(n_poles=len(years_optim)) + bspline.set_ctrl_pts(invest_years_optim) + invests, b_array = bspline.eval_list_t(list_t) invest_df = pd.DataFrame({GlossaryEnergy.Years: years, GlossaryCore.InvestValue: list(invests)}) - + invest_before_year_start_df = pd.DataFrame({GlossaryEnergy.Years: np.arange(year_start - construction_delay, year_start), + GlossaryEnergy.InvestValue: invest_before_year_start}) # split investment between onshore and offshore - ratio_invest_onshore_offshore = 3.6689 * np.ones(len(invests)) # taken from initial witness results - invest_df[model_name_onshore] = invest_df[GlossaryCore.InvestValue] * ratio_invest_onshore_offshore / (1. + ratio_invest_onshore_offshore) - invest_df[model_name_offshore] = invest_df[GlossaryCore.InvestValue] / (1. + ratio_invest_onshore_offshore) - init_prod_onshore_over_offshore = 1508./107.69 # taken from initial witness results - initial_prod = df_prod_iea.loc[df_prod_iea[GlossaryEnergy.Years] == year_start]['electricity (TWh)'].values[0] - init_prod_dict = {model_name_onshore: initial_prod * init_prod_onshore_over_offshore / (1. + init_prod_onshore_over_offshore), - model_name_offshore: initial_prod / (1. + init_prod_onshore_over_offshore)} - - inputs_dict = { + for df in [invest_df, invest_before_year_start_df]: + df[model_name_onshore] = df[GlossaryCore.InvestValue] * ratio_invest_onshore_offshore / (1. + ratio_invest_onshore_offshore) + df[model_name_offshore] = df[GlossaryCore.InvestValue] / (1. + ratio_invest_onshore_offshore) + + + ee = ExecutionEngine(name) + ee.ns_manager.add_ns_def(ns_dict) + builder = [] + builder.append(ee.factory.get_builder_from_module( + model_name_onshore, mod_path_onshore)) + builder.append(ee.factory.get_builder_from_module( + model_name_offshore, mod_path_offshore)) + ee.factory.set_builders_to_coupling_builder(builder) + + ee.configure() + #ee.display_treeview_nodes() + + inputs_dict.update({ f'{name}.{GlossaryEnergy.YearStart}': year_start, f'{name}.{GlossaryEnergy.YearEnd}': year_end, - f'{name}.{GlossaryEnergy.CO2TaxesValue}': pd.DataFrame( - {GlossaryEnergy.Years: years, GlossaryEnergy.CO2Tax: np.linspace(0., 0., len(years))}), - f'{name}.{GlossaryEnergy.StreamsCO2EmissionsValue}': pd.DataFrame({GlossaryEnergy.Years: years}), - f'{name}.{GlossaryEnergy.StreamPricesValue}': pd.DataFrame({GlossaryEnergy.Years: years}), - f'{name}.{GlossaryEnergy.ResourcesPriceValue}': pd.DataFrame({GlossaryEnergy.Years: years}), - f'{name}.{GlossaryEnergy.TransportCostValue}': pd.DataFrame({GlossaryEnergy.Years: years, 'transport': np.zeros(len(years))}), - } + f'{name}.{model_name_onshore}.techno_infos_dict': techno_info_dict_default, + }) for model_name in [model_name_offshore, model_name_onshore]: inputs_dict.update({ - f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, + #f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, f'{name}.{model_name}.initial_production': init_prod_dict[model_name], f'{name}.{model_name}.{GlossaryEnergy.InvestLevelValue}': pd.DataFrame({GlossaryEnergy.Years: years, GlossaryCore.InvestValue: invest_df[model_name].values}), + f'{name}.{model_name}.{GlossaryEnergy.InvestmentBeforeYearStartValue}': pd.DataFrame({GlossaryEnergy.Years: np.arange(year_start - construction_delay, year_start), + GlossaryEnergy.InvestValue:invest_before_year_start_df[model_name].values}), }) - # bug: must load the study twice so that modifications are taked into accout - ee.load_study_from_input_dict(inputs_dict) + ee.load_study_from_input_dict(inputs_dict) ee.execute() @@ -130,49 +164,66 @@ def run_model(x: list, year_end: int = year_end): df_prod['electricity (TWh)'] = df_prod.drop(GlossaryEnergy.Years, axis=1).sum(axis=1) * 1000. #PWh df_prod_model = df_prod.loc[df_prod[GlossaryEnergy.Years].isin(years_IEA_interpolated)] - return df_prod, df_prod_model, invest_df + price_df = ee.dm.get_value(f"{name}.{model_name}.{GlossaryEnergy.TechnoPricesValue}") + + return df_prod, price_df, df_prod_model, invest_df, ee def fitting_renewable(x: list): - df_prod, df_prod_model, invest_df = run_model(x) - return (((df_prod_model['electricity (TWh)'].values - prod_IEA_interpolated)) ** 2).mean() + df_prod, price_df, df_prod_model, invest_df, ee = run_model(x) + price_iea_values = df_price_iea['WindOnshore'].values + years_price_iea = df_price_iea['years'].values + price_model_values = (price_df.loc[price_df[GlossaryEnergy.Years].isin(years_price_iea), f"{GlossaryEnergy.WindOnshore}_wotaxes"]).values + + return (((df_prod_model['electricity (TWh)'].values - prod_IEA_interpolated) ** 2).mean() + ((price_model_values - price_iea_values) ** 2).mean()) # Initial guess for the variables invest from year 2025 to 2100. -x0 = np.concatenate((np.array([1.0]), invest_year_start * np.ones(len(years_optim)))) -bounds = [(1., 2.)] + (len(years_optim)) * [(invest_year_start/10., 10.0 * invest_year_start)] +x0 = np.concatenate((np.array([1.]), np.array([0.]), 80./invest_year_start * np.ones(construction_delay - 1), np.ones(len(years_optim)))) +bounds = [(1., 1.)] + [(0., 0.)] + [(80./invest_year_start/2., 80./invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1./10., 10.)] # Use minimize to find the minimum of the function result = minimize(fitting_renewable, x0, bounds=bounds) -df_prod, df_prod_model, invest_df = run_model(result.x) +df_prod, price_df, df_prod_model, invest_df, ee = run_model(result.x) # Print the result print("Function value at the optimum:", result.fun) -print("init age distrib at the optimum", result.x[0]) -print("invest at the optimum", result.x[1:]) -print("prod at the optimum", df_prod_model['electricity (TWh)'].values) +print('Capex_init wind onshore:', result.x[0] * Capex_init0) +print("invest before year start", result.x[1:construction_delay + 1] * invest_year_start) +print("invest at the poles at the optimum", result.x[construction_delay + 1:] * invest_year_start) + new_chart = TwoAxesInstanciatedChart('years', 'production (TWh)', - chart_name='Windpower Production : model vs historic') + chart_name='Windpower Production : witness vs IEA') -serie = InstanciatedSeries(years_IEA_interpolated, df_prod_model['electricity (TWh)'].values, 'model', 'lines') +serie = InstanciatedSeries(list(years_IEA_interpolated), df_prod_model['electricity (TWh)'].values, 'model', 'lines') new_chart.series.append(serie) -serie = InstanciatedSeries(years_IEA, df_prod_iea['electricity (TWh)'].values, 'historic', 'scatter') +serie = InstanciatedSeries(years_IEA, df_prod_iea['electricity (TWh)'].values, 'IEA', 'scatter') new_chart.series.append(serie) -serie = InstanciatedSeries(list(years_IEA_interpolated), list(prod_IEA_interpolated), 'historic_interpolated', 'lines+markers') +serie = InstanciatedSeries(list(years_IEA_interpolated), list(prod_IEA_interpolated), 'IEA_interpolated', 'lines+markers') new_chart.series.append(serie) new_chart.to_plotly().show() new_chart = TwoAxesInstanciatedChart('years', 'invest (G$)', chart_name='Windpower investments') -serie = InstanciatedSeries(years_optim, list(result.x)[1:], 'invests_at_poles', 'lines+markers') +serie = InstanciatedSeries(list(years_optim), list(result.x[construction_delay + 1:] * invest_year_start), 'invests_at_poles', 'scatter') +new_chart.series.append(serie) +serie = InstanciatedSeries(list(years), list(invest_df[GlossaryEnergy.InvestValue]), 'invests', 'lines') +new_chart.series.append(serie) + +new_chart.to_plotly().show() + +new_chart = TwoAxesInstanciatedChart('years', 'Price ($/MWh)', + chart_name='Wind Onshore price') +serie = InstanciatedSeries(list(df_price_iea['years'].values), list(df_price_iea['WindOnshore'].values), 'IEA', 'scatter') new_chart.series.append(serie) -serie = InstanciatedSeries(years, list(invest_df[GlossaryEnergy.InvestValue]), 'invests', 'lines') +# in witness vs iea post -processing, take f"{GlossaryEnergy.WindOnshore}" but same value +serie = InstanciatedSeries(list(years), list(price_df[f"{GlossaryEnergy.WindOnshore}_wotaxes"].values), 'Witness', 'lines') new_chart.series.append(serie) new_chart.to_plotly().show() @@ -198,7 +249,7 @@ def fitting_renewable(x: list): df_invest_mix['electricity.WindOnshore'] = invest_df['WindOnshore'] df_invest_mix.to_csv(invest_mix_csv, index=False, sep=',') # values to set in the invest_design_space_NZE.csv -for techno in ['WindOffshore', 'WindOnshore']: - f = interp1d(years, df_invest_mix[f"electricity.{techno}"].values, kind='linear') - invest_at_poles = f(np.linspace(year_start, year_end, 8)) - print(f"invest at poles for {techno}={invest_at_poles}") \ No newline at end of file +print(f"invest at poles for WindOnshore={result.x[construction_delay + 1:] * invest_year_start * ratio_invest_onshore_offshore / (1. + ratio_invest_onshore_offshore)}") +print(f"invest at poles for WindOffshore={result.x[construction_delay + 1:] * invest_year_start / (1. + ratio_invest_onshore_offshore)}") +print(f"invest before year start for WindOnshore={result.x[1:construction_delay +1] * invest_year_start * ratio_invest_onshore_offshore / (1. + ratio_invest_onshore_offshore)}") +print(f"invest before year start for WindOffshore={result.x[1:construction_delay + 1] * invest_year_start / (1. + ratio_invest_onshore_offshore)}") \ No newline at end of file From 1a8715e6657e70727b3650df4049002594615edd Mon Sep 17 00:00:00 2001 From: benherry Date: Sun, 17 Nov 2024 10:13:02 +0100 Subject: [PATCH 05/13] fix: correct print invest poles --- data_energy/fitting/gaseous_bioenergy.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data_energy/fitting/gaseous_bioenergy.py b/data_energy/fitting/gaseous_bioenergy.py index 39715229..f8b1bd12 100644 --- a/data_energy/fitting/gaseous_bioenergy.py +++ b/data_energy/fitting/gaseous_bioenergy.py @@ -179,5 +179,5 @@ def fitting_renewable(x: list): df_invest_mix['biogas.AnaerobicDigestion'] = invest_df[GlossaryCore.InvestValue] df_invest_mix.to_csv(invest_mix_csv, index=False, sep=',') # values to set in the invest_design_space_NZE.csv -print(f"invest at poles={result.x[1+construction_delay:] * invest_year_start}") +print(f"invest at poles={result.x[construction_delay:] * invest_year_start}") From e5a4f3c65b679c05b2c33f6a735ea6742ea654e6 Mon Sep 17 00:00:00 2001 From: benherry Date: Sun, 17 Nov 2024 10:13:47 +0100 Subject: [PATCH 06/13] feat: normalize objective fn and change bounds capex-init to activate this desin var --- data_energy/fitting/windpower.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/data_energy/fitting/windpower.py b/data_energy/fitting/windpower.py index ddc29294..196d9e0c 100644 --- a/data_energy/fitting/windpower.py +++ b/data_energy/fitting/windpower.py @@ -175,12 +175,12 @@ def fitting_renewable(x: list): years_price_iea = df_price_iea['years'].values price_model_values = (price_df.loc[price_df[GlossaryEnergy.Years].isin(years_price_iea), f"{GlossaryEnergy.WindOnshore}_wotaxes"]).values - return (((df_prod_model['electricity (TWh)'].values - prod_IEA_interpolated) ** 2).mean() + ((price_model_values - price_iea_values) ** 2).mean()) + return ((((df_prod_model['electricity (TWh)'].values - prod_IEA_interpolated)/prod_IEA_interpolated.mean()) ** 2).mean() + (((price_model_values - price_iea_values)/price_iea_values.mean()) ** 2).mean()) # Initial guess for the variables invest from year 2025 to 2100. x0 = np.concatenate((np.array([1.]), np.array([0.]), 80./invest_year_start * np.ones(construction_delay - 1), np.ones(len(years_optim)))) -bounds = [(1., 1.)] + [(0., 0.)] + [(80./invest_year_start/2., 80./invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1./10., 10.)] +bounds = [(0.5, 1.5)] + [(0., 0.)] + [(80./invest_year_start/2., 80./invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1./10., 10.)] # Use minimize to find the minimum of the function result = minimize(fitting_renewable, x0, bounds=bounds) From 841a733bec553b8ce9bf469e68bfc658062747eb Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 22 Nov 2024 09:00:41 +0000 Subject: [PATCH 07/13] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .coveragerc | 2 +- .../issue_templates/user_story_template.md | 2 +- CREDITS.rst | 6 +- LICENSE | 2 +- LICENSES/BSD-3-Clause.txt | 2 +- LICENSES/MIT.txt | 2 +- NOTICE | 2 +- README.md | 2 +- .../fitting/clean_energy_simple_techno.py | 8 +- .../fitting/fossil_energy_simple_techno.py | 7 +- data_energy/fitting/gaseous_bioenergy.py | 34 +- data_energy/fitting/hydropower.py | 33 +- data_energy/fitting/windpower.py | 48 +- .../biomassburyingfossilization.csv | 1 - 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.../coal_gasification_disc.py | 4 +- .../coal_gasification_disc.markdown | 20 +- .../documentation/pyrolysis_disc.markdown | 8 +- .../models/syngas/pyrolysis/pyrolysis_disc.py | 2 +- .../reversed_water_gas_shift.py | 17 +- .../reversed_water_gas_shift_disc.py | 2 +- .../smr/documentation/smr_disc.markdown | 12 +- energy_models/models/syngas/smr/smr.py | 7 +- .../animal_manure/animal_manure.py | 2 +- .../wet_crop_residue/wet_crop_residues.py | 1 - .../wet_crop_residues_disc.py | 2 +- .../energy_mix_optim_sub_process/process.py | 2 - .../energy_mix_optim_sub_process/usecase.py | 6 +- .../process.py | 2 +- .../usecase.py | 1 + .../energy/MDA/energy_process_v0/process.py | 2 +- .../sos_processes/energy/MDO/__init__.py | 2 +- .../MDO/energy_mix_optim_process/__init__.py | 2 +- .../MDO/energy_mix_optim_process/process.py | 1 + .../usecase_with_utilization_ratio.py | 1 + .../__init__.py | 2 +- .../process.py | 1 + .../techno_mix/biodiesel_mix/usecase.py | 4 +- .../energy/techno_mix/biogas_mix/usecase.py | 4 +- .../techno_mix/biomass_dry_mix/usecase.py | 2 +- .../usecase_coarse.py | 2 +- .../techno_mix/carbon_capture_mix/usecase.py | 2 +- .../usecase_coarse.py | 2 +- .../techno_mix/carbon_storage_mix/usecase.py | 4 +- .../techno_mix/clean_energy_mix/__init__.py | 2 +- .../techno_mix/electricity_mix/usecase.py | 5 +- .../energy/techno_mix/ethanol_mix/usecase.py | 2 +- .../gaseous_hydrogen_mix/usecase.py | 2 +- .../hightemperatureheat_mix/usecase.py | 2 +- .../hydrotreated_oil_fuel_mix/usecase.py | 2 +- .../techno_mix/liquid_fuel_mix/usecase.py | 4 +- .../lowtemperatureheat_mix/usecase.py | 2 +- .../mediumtemperatureheat_mix/usecase.py | 2 +- .../energy/techno_mix/methane_mix/usecase.py | 2 +- .../energy/techno_mix/methanol_mix/usecase.py | 4 +- .../energy/techno_mix/syngas_mix/usecase.py | 2 +- .../post_proc_technology_mix.py | 2 +- ...t_2024-07-14 Jul00_24technos_8streams.json | 130 +- ..._2024-07-14 Jul01_24technos_12streams.json | 170 +- ..._2024-07-14 Jul02_29technos_11streams.json | 170 +- ...ct_2024-07-14 Jul52_5technos_5streams.json | 62 +- ..._2024-07-14 Jul59_24technos_11streams.json | 160 +- ...ct_2024-07-15 Jul36_5technos_5streams.json | 62 +- ...ct_2024-07-15 Jul39_5technos_5streams.json | 62 +- .../techno_dict/data/techno_dict_test.json | 182 +- .../techno_dict/data/techno_dicts.py | 3 + .../techno_dict/techno_dict_builder.py | 28 +- .../sos_processes/techno_dict/visualistion.py | 11 +- .../witness_sub_process_builder.py | 1 + .../_l0_test_base_stream_invest_limit.py | 4 +- .../_l0_test_compute_el_nuclear_databases.py | 8 +- .../tests/_l2_test_run_optim_usecases.py | 2 +- .../tests/data_tests/data_nuclear_test.json | 4759 ++++++++++++++++- .../tests/l0_test_compute_CaKOH_DAC_price.py | 1 - .../l0_test_compute_amine_scrubbing_price.py | 1 - .../l0_test_compute_anaerobic_digestion.py | 1 - ...l0_test_compute_biomass_dry_crop_energy.py | 1 - ...st_compute_biomass_dry_managed_wood_mix.py | 1 - ..._compute_biomass_dry_unmanaged_wood_mix.py | 1 - .../l0_test_compute_biomass_fermentation.py | 1 - .../tests/l0_test_compute_ccus_disc.py | 2 +- .../l0_test_compute_coal_extraction_price.py | 1 - ...ompute_cs_biomass_burying_fertilization.py | 1 - ...0_test_compute_cs_carbon_storage_techno.py | 1 - ...l0_test_compute_cs_deep_ocean_injection.py | 1 - ...0_test_compute_cs_deep_saline_formation.py | 1 - ..._test_compute_cs_depleted_oil_gaz_price.py | 1 - ...0_test_compute_cs_enhanced_oil_recovery.py | 1 - ...test_compute_cs_geologic_mineralization.py | 1 - ...st_compute_cs_pure_carbon_solid_storage.py | 1 - .../tests/l0_test_compute_cs_reforestation.py | 2 +- .../l0_test_compute_el_biogas_fired_price.py | 1 - .../l0_test_compute_el_biomass_fired_price.py | 1 - .../tests/l0_test_compute_el_ccgast_price.py | 1 - .../l0_test_compute_el_coal_gen_price.py | 2 - .../l0_test_compute_el_gas_turbine_price.py | 1 - .../tests/l0_test_compute_el_geothermal.py | 1 - .../tests/l0_test_compute_el_nuclear.py | 1 - .../tests/l0_test_compute_el_oil_gen_price.py | 1 - .../l0_test_compute_el_solar_pv_price.py | 4 +- .../l0_test_compute_el_solar_thermal_price.py | 2 - .../tests/l0_test_compute_el_wind_offshore.py | 1 - .../tests/l0_test_compute_el_wind_onshore.py | 1 - .../tests/l0_test_compute_energy_mix.py | 9 +- .../l0_test_compute_fg_calcium_looping.py | 1 - ...test_compute_fg_chilled_ammonia_process.py | 1 - .../tests/l0_test_compute_fg_co2_membranes.py | 1 - .../l0_test_compute_fg_flue_gas_techno.py | 1 - .../l0_test_compute_fg_monoethanolamine.py | 1 - .../l0_test_compute_fg_piperazine_process.py | 1 - ...st_compute_fg_pressure_swing_adsorption.py | 1 - ...0_test_compute_fsl_fossil_simple_techno.py | 1 - energy_models/tests/l0_test_compute_hefa.py | 1 - .../tests/l0_test_compute_high_heat_chp.py | 1 - ..._test_compute_high_heat_electric_boiler.py | 4 +- .../l0_test_compute_high_heat_geothermal.py | 1 - .../l0_test_compute_high_heat_natural_gas.py | 1 - .../tests/l0_test_compute_high_heatpump.py | 1 - .../tests/l0_test_compute_hydrogen_price.py | 6 +- .../tests/l0_test_compute_hydrogen_wgs.py | 3 +- .../l0_test_compute_kero_fossil_price.py | 3 +- ...0_test_compute_lh_hydrogen_liquefaction.py | 2 - .../tests/l0_test_compute_low_heat_chp.py | 1 - ...0_test_compute_low_heat_electric_boiler.py | 1 - .../l0_test_compute_low_heat_geothermal.py | 1 - .../l0_test_compute_low_heat_natural_gas.py | 1 - .../tests/l0_test_compute_low_heatpump.py | 1 - .../tests/l0_test_compute_medium_heat_chp.py | 1 - ...est_compute_medium_heat_electric_boiler.py | 1 - .../l0_test_compute_medium_heat_geothermal.py | 1 - ...l0_test_compute_medium_heat_natural_gas.py | 1 - .../tests/l0_test_compute_medium_heatpump.py | 1 - .../l0_test_compute_methane_fossil_gas.py | 1 - ..._test_compute_methane_methanation_price.py | 1 - ...0_test_compute_methane_upgrading_biogas.py | 1 - ...test_compute_methanol_co2_hydrogenation.py | 1 - .../tests/l0_test_compute_pellets_price.py | 1 - ...est_compute_syngas_biomass_gasification.py | 1 - ...0_test_compute_syngas_coal_gasification.py | 3 +- .../tests/l0_test_compute_syngas_rwgs.py | 1 - .../tests/l0_test_compute_syngas_smr.py | 1 - .../l0_test_compute_transesterification.py | 4 +- energy_models/tests/l0_test_energy_invest.py | 3 - energy_models/tests/l0_test_header.py | 10 +- .../tests/l0_test_independent_invest.py | 2 +- .../l0_test_investments_profile_builder.py | 6 +- energy_models/tests/l0_test_one_invest.py | 1 + .../tests/l1_test_base_stream_electricity.py | 6 +- .../tests/l1_test_energy_global_values.py | 14 +- .../tests/l1_test_gradient_biogas.py | 1 - .../tests/l1_test_gradient_carbon_capture.py | 12 +- .../tests/l1_test_gradient_carbon_storage.py | 3 +- ...est_gradient_clean_energy_simple_techno.py | 1 - .../tests/l1_test_gradient_electricity.py | 1 - .../l1_test_gradient_energy_mix_for_coarse.py | 4 +- .../tests/l1_test_gradient_ethanol.py | 4 +- .../tests/l1_test_gradient_flue_gas.py | 1 - .../l1_test_gradient_fossil_simple_techno.py | 2 - .../tests/l1_test_gradient_hydrogen.py | 3 +- .../l1_test_gradient_hydrotreated_oil_fuel.py | 2 +- .../tests/l1_test_gradient_liquid_fuel.py | 4 +- .../tests/l1_test_gradient_liquid_hydrogen.py | 4 - .../tests/l1_test_gradient_methane.py | 4 +- .../tests/l1_test_gradient_methanol.py | 2 +- .../tests/l1_test_gradient_one_invest.py | 2 - energy_models/tests/l1_test_gradient_ratio.py | 1 - .../tests/l1_test_gradient_solid_fuel.py | 2 - energy_models/tests/l2_test_simple.py | 4 +- .../performances/energy_models_perfos.csv | 2 - .../tests/to_fix/CalciumLoopingDiscipline.py | 3 +- .../to_fix/MonoEthanolAmineDiscipline.py | 3 +- .../to_fix/PiperazineProcessDiscipline.py | 3 +- headers_ignore_config.json | 60 +- parameters_glossary.csv | 6 +- platform_version_required.txt | 2 +- pytest.ini | 2 +- 453 files changed, 6321 insertions(+), 1867 deletions(-) diff --git a/.coveragerc b/.coveragerc index cc0bafcf..df8d576b 100644 --- a/.coveragerc +++ b/.coveragerc @@ -1,7 +1,7 @@ [run] relative_files = True branch = True -omit = +omit = */tests/* */__init__.py docs/ diff --git a/.gitlab/issue_templates/user_story_template.md b/.gitlab/issue_templates/user_story_template.md index 4fbf43fb..3693f927 100644 --- a/.gitlab/issue_templates/user_story_template.md +++ b/.gitlab/issue_templates/user_story_template.md @@ -1,4 +1,4 @@ -__As a__ type_of_user +__As a__ type_of_user __I want to__ some_goal __So that__ some_reason diff --git a/CREDITS.rst b/CREDITS.rst index b719402e..a929d5c5 100644 --- a/CREDITS.rst +++ b/CREDITS.rst @@ -8,12 +8,12 @@ witness-energy depends on software with compatible licenses that are listed belo `Pandas `_ BSD 3-Clause - + `scipy `_ BSD 3-Clause `nose2 `_ BSD 3-Clause - + `plotly `_ - MIT \ No newline at end of file + MIT diff --git a/LICENSE b/LICENSE index 7a4a3ea2..d6456956 100644 --- a/LICENSE +++ b/LICENSE @@ -199,4 +199,4 @@ distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and - limitations under the License. \ No newline at end of file + limitations under the License. diff --git a/LICENSES/BSD-3-Clause.txt b/LICENSES/BSD-3-Clause.txt index 78eb69c2..dc10ebd7 100644 --- a/LICENSES/BSD-3-Clause.txt +++ b/LICENSES/BSD-3-Clause.txt @@ -8,4 +8,4 @@ Redistribution and use in source and binary forms, with or without modification, 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/LICENSES/MIT.txt b/LICENSES/MIT.txt index c6514b2e..a2874160 100644 --- a/LICENSES/MIT.txt +++ b/LICENSES/MIT.txt @@ -16,4 +16,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file +SOFTWARE. diff --git a/NOTICE b/NOTICE index 573281d5..fb3ad55e 100644 --- a/NOTICE +++ b/NOTICE @@ -4,6 +4,6 @@ Copyright 2022 The Apache Software Foundation. This product includes software developed at The Apache Software Foundation (http://www.apache.org/). -This software contains code originally developed +This software contains code originally developed at Airbus SAS. (https://www.airbus.com/) Copyright 2020 - 2021 Airbus SAS. All Rights Reserved. diff --git a/README.md b/README.md index 6e85cbea..9d2fc90b 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ This package contains the following main disciplines categories: For more information, please look at the documentation associated. -The technologies are in the models folder. Most of the technology models logic is generic and is thus implemented in the +The technologies are in the models folder. Most of the technology models logic is generic and is thus implemented in the mother classes *techno type* and *techno disc*. The streams are in the core/stream_type folder. They similarly rely on mother class. diff --git a/data_energy/fitting/clean_energy_simple_techno.py b/data_energy/fitting/clean_energy_simple_techno.py index 651e6975..6509b459 100644 --- a/data_energy/fitting/clean_energy_simple_techno.py +++ b/data_energy/fitting/clean_energy_simple_techno.py @@ -33,7 +33,7 @@ df_invest_historic = DatabaseWitnessEnergy.get_techno_invest_df(techno_name=GlossaryEnergy.CleanEnergySimpleTechno) df_prod_historic = DatabaseWitnessEnergy.get_techno_prod(techno_name=GlossaryEnergy.CleanEnergySimpleTechno, year=2020)[1].value -ref_price_2023 = 70.76 # $/MWh +ref_price_2023 = 70.76 # $/MWh # data to run techno construction_delay = GlossaryEnergy.TechnoConstructionDelayDict[GlossaryEnergy.CleanEnergySimpleTechno] year_start_fitting = int(max(df_invest_historic['years'].min() + construction_delay, df_prod_historic['years'].min(), year_calibration)) @@ -111,7 +111,7 @@ def fitting_renewable(x: list): # Initial guess for the variables x0 = np.array([250., 1., 0.0, 0.2, 0.1]) -#x0 = np.array([743.8, 1.3, 0.06, 0.0, 0.06]) +# x0 = np.array([743.8, 1.3, 0.06, 0.0, 0.06]) bounds = [(0, 10000), (0, 1.1), (0.00, 0.), (0.001, 0.99), (0.0001, 0.3)] @@ -121,7 +121,7 @@ def fitting_renewable(x: list): prod_values_model, price_model_values = run_model(result.x) # Print the result -#print("Optimal solution:", result.x) +# print("Optimal solution:", result.x) print("Function value at the optimum:", result.fun) @@ -150,4 +150,4 @@ def fitting_renewable(x: list): graph_list = disc.get_post_processing_list(filters) for graph in graph_list: graph.to_plotly().show() - pass \ No newline at end of file + pass diff --git a/data_energy/fitting/fossil_energy_simple_techno.py b/data_energy/fitting/fossil_energy_simple_techno.py index 9588ae37..66f6cfa6 100644 --- a/data_energy/fitting/fossil_energy_simple_techno.py +++ b/data_energy/fitting/fossil_energy_simple_techno.py @@ -32,7 +32,7 @@ df_invest_historic = DatabaseWitnessEnergy.get_techno_invest_df(techno_name=GlossaryEnergy.FossilSimpleTechno) df_prod_historic = DatabaseWitnessEnergy.get_techno_prod(techno_name=GlossaryEnergy.FossilSimpleTechno, year=2020)[1].value -ref_price_2023 = 121.5 # $/MWh Source: chatgpt LCOE without tax +ref_price_2023 = 121.5 # $/MWh Source: chatgpt LCOE without tax # data to run techno construction_delay = GlossaryEnergy.TechnoConstructionDelayDict[GlossaryEnergy.FossilSimpleTechno] year_start_fitting = int(max(df_invest_historic['years'].min() + construction_delay, df_prod_historic['years'].min(), year_calibration)) @@ -68,7 +68,6 @@ ee.display_treeview_nodes() - def run_model(x: list, year_end: int = year_end_fitting): techno_dict_default["Capex_init"] = x[0] init_age_distrib_factor = x[1] @@ -158,7 +157,7 @@ def fitting_renewable(x: list): """ Results obtained: -Function value at the optimum: 16826745.79920797 +Function value at the optimum: 16826745.79920797 => less than 6% error at max between model and historic production between 2015 and 2023 => no error on the price Optimal capex_init : 222.638 @@ -167,4 +166,4 @@ def fitting_renewable(x: list): Optimal opex_percentage : 0.262 Optimal wacc : 0.058 Optimal utilization_ratio [100. 100. 100. 100. 100. 100.] -""" \ No newline at end of file +""" diff --git a/data_energy/fitting/gaseous_bioenergy.py b/data_energy/fitting/gaseous_bioenergy.py index f8b1bd12..a3ffca1d 100644 --- a/data_energy/fitting/gaseous_bioenergy.py +++ b/data_energy/fitting/gaseous_bioenergy.py @@ -14,21 +14,22 @@ limitations under the License. ''' import os +import pickle +from copy import deepcopy import numpy as np import pandas as pd -import pickle +from climateeconomics.glossarycore import GlossaryCore from scipy.interpolate import interp1d from scipy.optimize import minimize from sostrades_core.execution_engine.execution_engine import ExecutionEngine +from sostrades_core.tools.bspline.bspline import BSpline from sostrades_core.tools.post_processing.charts.two_axes_instanciated_chart import ( - InstanciatedSeries, - TwoAxesInstanciatedChart, + InstanciatedSeries, + TwoAxesInstanciatedChart, ) -from sostrades_core.tools.bspline.bspline import BSpline + from energy_models.glossaryenergy import GlossaryEnergy -from climateeconomics.glossarycore import GlossaryCore -from copy import deepcopy """ This script is used to calibrate the gaseous bioenergy invest so that the energy production matches the IEA NZE scenario @@ -51,14 +52,14 @@ initial_production = df_prod_iea.loc[df_prod_iea[GlossaryEnergy.Years] == year_start]["biogas AnaerobicDigestion (TWh)"].values[0] # interpolate data between 2050 and 2100 -years_IEA_interpolated = years #np.arange(years_IEA[0], years_IEA[-1] + 1, 5) +years_IEA_interpolated = years # np.arange(years_IEA[0], years_IEA[-1] + 1, 5) f = interp1d(years_IEA, df_prod_iea["biogas AnaerobicDigestion (TWh)"].values, kind='linear') prod_IEA_interpolated = f(years) # increase discretization in order to smooth production between 2020 and 2030 -years_optim = np.linspace(year_start, year_end, 8) #np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) +years_optim = np.linspace(year_start, year_end, 8) # np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) -invest_year_start = 3.432 #G$ +invest_year_start = 3.432 # G$ # chose the name so that it mathes the datamanager of the IEA vs NZE study name = 'usecase_witness_optim_nze_eval' @@ -101,13 +102,13 @@ def run_model(x: list, inputs_dict: dict = inputs_dict, year_end: int = year_end ee.factory.set_builders_to_coupling_builder(builder) ee.configure() - #ee.display_treeview_nodes() + # ee.display_treeview_nodes() inputs_dict.update({ f'{name}.{GlossaryEnergy.YearStart}': year_start, f'{name}.{GlossaryEnergy.YearEnd}': year_end, f'{name}.{model_name}.{GlossaryEnergy.InvestLevelValue}': invest_df, - #f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, + # f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, f'{name}.{model_name}.initial_production': initial_production, f'{name}.{model_name}.{GlossaryEnergy.InvestmentBeforeYearStartValue}': pd.DataFrame({GlossaryEnergy.Years: np.arange(year_start - construction_delay, year_start), GlossaryEnergy.InvestValue: invest_before_year_start}), }) @@ -116,7 +117,7 @@ def run_model(x: list, inputs_dict: dict = inputs_dict, year_end: int = year_end ee.execute() - prod_df = ee.dm.get_value(ee.dm.get_all_namespaces_from_var_name(GlossaryEnergy.TechnoProductionValue)[0]) #PWh + prod_df = ee.dm.get_value(ee.dm.get_all_namespaces_from_var_name(GlossaryEnergy.TechnoProductionValue)[0]) # PWh return prod_df[[GlossaryEnergy.Years, "biogas (TWh)"]], invest_df, ee @@ -129,11 +130,11 @@ def fitting_renewable(x: list): # Initial guess for the variables invest from year 2025 to 2100. -x0 = np.concatenate((np.array([0.]), 1/2.4 * np.ones(construction_delay - 1), np.ones(len(years_optim)))) -bounds = [(0., 0.)] + [(1./2.4/3., 1./2.4 * 3.)] * (construction_delay - 1) + (len(years_optim)) * [(1./3., 3. * 1.)] +x0 = np.concatenate((np.array([0.]), 1 / 2.4 * np.ones(construction_delay - 1), np.ones(len(years_optim)))) +bounds = [(0., 0.)] + [(1. / 2.4 / 3., 1. / 2.4 * 3.)] * (construction_delay - 1) + (len(years_optim)) * [(1. / 3., 3. * 1.)] # Use minimize to find the minimum of the function -result = minimize(fitting_renewable, x0, bounds=bounds, #method='trust-constr', - options={'disp': True, 'maxiter': 500}) #, 'maxfun': 500, 'ftol': 1.e-6, 'maxls': 50}) +result = minimize(fitting_renewable, x0, bounds=bounds, # method='trust-constr', + options={'disp': True, 'maxiter': 500}) # , 'maxfun': 500, 'ftol': 1.e-6, 'maxls': 50}) prod_df, invest_df, ee = run_model(result.x) # Print the result @@ -180,4 +181,3 @@ def fitting_renewable(x: list): df_invest_mix.to_csv(invest_mix_csv, index=False, sep=',') # values to set in the invest_design_space_NZE.csv print(f"invest at poles={result.x[construction_delay:] * invest_year_start}") - diff --git a/data_energy/fitting/hydropower.py b/data_energy/fitting/hydropower.py index 9d9d38aa..36431ec7 100644 --- a/data_energy/fitting/hydropower.py +++ b/data_energy/fitting/hydropower.py @@ -14,19 +14,19 @@ limitations under the License. ''' import os +import pickle +from copy import deepcopy import numpy as np import pandas as pd -import pickle -from copy import deepcopy from climateeconomics.glossarycore import GlossaryCore from scipy.interpolate import interp1d from scipy.optimize import minimize from sostrades_core.execution_engine.execution_engine import ExecutionEngine from sostrades_core.tools.bspline.bspline import BSpline from sostrades_core.tools.post_processing.charts.two_axes_instanciated_chart import ( - InstanciatedSeries, - TwoAxesInstanciatedChart, + InstanciatedSeries, + TwoAxesInstanciatedChart, ) from energy_models.glossaryenergy import GlossaryEnergy @@ -53,8 +53,8 @@ prod_IEA_interpolated = f(years_IEA_interpolated) # increase discretization in order to smooth production between 2020 and 2030 -years_optim = np.linspace(year_start, year_end, 8) #np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #years_IEA_interpolated #sorted(list(set(years_IEA_interpolated + list(np.arange(year_start, max(year_start, 2030) + 1))))) -invest_year_start = 18.957 #G$ +years_optim = np.linspace(year_start, year_end, 8) # np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #years_IEA_interpolated #sorted(list(set(years_IEA_interpolated + list(np.arange(year_start, max(year_start, 2030) + 1))))) +invest_year_start = 18.957 # G$ name = 'usecase_witness_optim_nze_eval' model_name = f"WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.electricity.{GlossaryEnergy.Hydropower}" @@ -70,6 +70,7 @@ inputs_dict.update({f'{name}.{GlossaryEnergy.ResourcesPriceValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.{GlossaryEnergy.ResourcesPriceValue}')}) inputs_dict.update({f'{name}.{GlossaryEnergy.TransportCostValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.biogas.{GlossaryEnergy.TransportCostValue}')}) + def run_model(x: list, year_end: int = year_end): init_prod = x[0] * initial_production invest_before_year_start = x[1:1 + construction_delay] * invest_year_start @@ -97,13 +98,13 @@ def run_model(x: list, year_end: int = year_end): ee.factory.set_builders_to_coupling_builder(builder) ee.configure() - #ee.display_treeview_nodes() + # ee.display_treeview_nodes() inputs_dict.update({ f'{name}.{GlossaryEnergy.YearStart}': year_start, f'{name}.{GlossaryEnergy.YearEnd}': year_end, f'{name}.{model_name}.{GlossaryEnergy.InvestLevelValue}': invest_df, - #f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, + # f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, f'{name}.{model_name}.initial_production': init_prod, f'{name}.{model_name}.{GlossaryEnergy.InvestmentBeforeYearStartValue}': pd.DataFrame( {GlossaryEnergy.Years: np.arange(year_start - construction_delay, year_start), @@ -113,7 +114,7 @@ def run_model(x: list, year_end: int = year_end): ee.execute() - prod_df = ee.dm.get_value(ee.dm.get_all_namespaces_from_var_name(GlossaryEnergy.TechnoProductionValue)[0]) #PWh + prod_df = ee.dm.get_value(ee.dm.get_all_namespaces_from_var_name(GlossaryEnergy.TechnoProductionValue)[0]) # PWh return prod_df[[GlossaryEnergy.Years, "electricity (TWh)"]], invest_df, ee @@ -128,11 +129,11 @@ def fitting_renewable(x: list): # Initial guess for the variables invest from year 2025 to 2100. # there is a bug with the invest before year start => first value must be set to 0 # otherwise initial production at year start is not as expected -x0 = np.concatenate((np.array([1.]), np.array([0.]), 80./invest_year_start * np.ones(construction_delay - 1), np.ones(len(years_optim)))) -bounds = [(1., 1.)] + [(0., 0.)] + [(80./invest_year_start/2., 80./invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1./10., 10.)] +x0 = np.concatenate((np.array([1.]), np.array([0.]), 80. / invest_year_start * np.ones(construction_delay - 1), np.ones(len(years_optim)))) +bounds = [(1., 1.)] + [(0., 0.)] + [(80. / invest_year_start / 2., 80. / invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1. / 10., 10.)] # Use minimize to find the minimum of the function -result = minimize(fitting_renewable, x0, bounds=bounds, #method='trust-constr', +result = minimize(fitting_renewable, x0, bounds=bounds, # method='trust-constr', options={'disp': True, 'maxiter': 2000, 'xtol': 1e-20}) prod_df, invest_df, ee = run_model(result.x) @@ -140,8 +141,8 @@ def fitting_renewable(x: list): # Print the result print("Function value at the optimum:", result.fun) print("initial production", result.x[0] * initial_production) -print("invest before year start", result.x[1:1+construction_delay] * invest_year_start) -print("invest at the poles at the optimum", result.x[1+construction_delay:] * invest_year_start) +print("invest before year start", result.x[1:1 + construction_delay] * invest_year_start) +print("invest at the poles at the optimum", result.x[1 + construction_delay:] * invest_year_start) new_chart = TwoAxesInstanciatedChart('years', 'hydropower production (TWh)', @@ -160,7 +161,7 @@ def fitting_renewable(x: list): new_chart = TwoAxesInstanciatedChart('years', 'hydropower invest (G$)', chart_name='investments') -serie = InstanciatedSeries(list(years_optim), list(result.x[1+construction_delay:] * invest_year_start), 'invests_at_poles', 'scatter') +serie = InstanciatedSeries(list(years_optim), list(result.x[1 + construction_delay:] * invest_year_start), 'invests_at_poles', 'scatter') new_chart.series.append(serie) serie = InstanciatedSeries(list(years), list(invest_df[GlossaryEnergy.InvestValue]), 'invests_bspline', 'lines') new_chart.series.append(serie) @@ -184,4 +185,4 @@ def fitting_renewable(x: list): df_invest_mix.to_csv(invest_mix_csv, index=False, sep=',') # values to set in the invest_design_space_NZE.csv -print(f"invest at poles={result.x[1+construction_delay:] * invest_year_start}") \ No newline at end of file +print(f"invest at poles={result.x[1 + construction_delay:] * invest_year_start}") diff --git a/data_energy/fitting/windpower.py b/data_energy/fitting/windpower.py index 196d9e0c..953bbab5 100644 --- a/data_energy/fitting/windpower.py +++ b/data_energy/fitting/windpower.py @@ -14,28 +14,26 @@ limitations under the License. ''' import os -from functools import reduce import pickle +from copy import deepcopy +from functools import reduce + import numpy as np import pandas as pd -from copy import deepcopy from climateeconomics.glossarycore import GlossaryCore from scipy.interpolate import interp1d from scipy.optimize import minimize from sostrades_core.execution_engine.execution_engine import ExecutionEngine +from sostrades_core.tools.bspline.bspline import BSpline from sostrades_core.tools.post_processing.charts.two_axes_instanciated_chart import ( InstanciatedSeries, TwoAxesInstanciatedChart, ) -from sostrades_core.tools.bspline.bspline import BSpline + +from energy_models.glossaryenergy import GlossaryEnergy from energy_models.models.electricity.wind_onshore.wind_onshore_disc import ( WindOnshoreDiscipline, ) -from energy_models.models.electricity.wind_offshore.wind_offshore_disc import ( - WindOffshoreDiscipline, -) -from energy_models.glossaryenergy import GlossaryEnergy - """ This script is used to calibrate the windpower invest so that the electricity production matches the IEA NZE scenario @@ -64,12 +62,12 @@ prod_IEA_interpolated = f(years_IEA_interpolated) # optimization at the poles just like in witness-full study -years_optim = np.linspace(year_start, year_end, 8) #years_IEA_interpolated #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) +years_optim = np.linspace(year_start, year_end, 8) # years_IEA_interpolated #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) -invest_year_start = 80. #G$ -construction_delay = GlossaryEnergy.TechnoConstructionDelayDict['WindOffshore'] # same construction delay for windonshore and windoffshore +invest_year_start = 80. # G$ +construction_delay = GlossaryEnergy.TechnoConstructionDelayDict['WindOffshore'] # same construction delay for windonshore and windoffshore if construction_delay != GlossaryEnergy.TechnoConstructionDelayDict['WindOnshore']: - raise ValueError(f"must adapt script as construction delay for windOnshore and windOffshore differ") + raise ValueError("must adapt script as construction delay for windOnshore and windOffshore differ") name = 'usecase_witness_optim_nze_eval' model_name_onshore = f"WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.electricity.{GlossaryEnergy.WindOnshore}" @@ -84,7 +82,7 @@ mod_path_offshore = 'energy_models.models.electricity.wind_offshore.wind_offshore_disc.WindOffshoreDiscipline' # if want to modify the capex of both onshore and offshore -#dict_techno_dict_default = {model_name_onshore: WindOnshoreDiscipline.techno_infos_dict_default, +# dict_techno_dict_default = {model_name_onshore: WindOnshoreDiscipline.techno_infos_dict_default, # model_name_offshore: WindOffshoreDiscipline.techno_infos_dict_default} techno_info_dict_default = WindOnshoreDiscipline.techno_infos_dict_default Capex_init0 = WindOnshoreDiscipline.techno_infos_dict_default['Capex_init'] @@ -107,6 +105,8 @@ model_name_onshore: initial_prod * init_prod_onshore_over_offshore / (1. + init_prod_onshore_over_offshore), model_name_offshore: initial_prod / (1. + init_prod_onshore_over_offshore)} ratio_invest_onshore_offshore = 3.6689 # taken from initial witness results + + def run_model(x: list, year_end: int = year_end): techno_info_dict_default['Capex_init'] = Capex_init0 * x[0] invest_before_year_start = x[1:construction_delay + 1] * invest_year_start @@ -125,7 +125,6 @@ def run_model(x: list, year_end: int = year_end): df[model_name_onshore] = df[GlossaryCore.InvestValue] * ratio_invest_onshore_offshore / (1. + ratio_invest_onshore_offshore) df[model_name_offshore] = df[GlossaryCore.InvestValue] / (1. + ratio_invest_onshore_offshore) - ee = ExecutionEngine(name) ee.ns_manager.add_ns_def(ns_dict) builder = [] @@ -136,7 +135,7 @@ def run_model(x: list, year_end: int = year_end): ee.factory.set_builders_to_coupling_builder(builder) ee.configure() - #ee.display_treeview_nodes() + # ee.display_treeview_nodes() inputs_dict.update({ f'{name}.{GlossaryEnergy.YearStart}': year_start, @@ -145,11 +144,11 @@ def run_model(x: list, year_end: int = year_end): }) for model_name in [model_name_offshore, model_name_onshore]: inputs_dict.update({ - #f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, + # f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, f'{name}.{model_name}.initial_production': init_prod_dict[model_name], f'{name}.{model_name}.{GlossaryEnergy.InvestLevelValue}': pd.DataFrame({GlossaryEnergy.Years: years, GlossaryCore.InvestValue: invest_df[model_name].values}), f'{name}.{model_name}.{GlossaryEnergy.InvestmentBeforeYearStartValue}': pd.DataFrame({GlossaryEnergy.Years: np.arange(year_start - construction_delay, year_start), - GlossaryEnergy.InvestValue:invest_before_year_start_df[model_name].values}), + GlossaryEnergy.InvestValue: invest_before_year_start_df[model_name].values}), }) ee.load_study_from_input_dict(inputs_dict) @@ -158,10 +157,10 @@ def run_model(x: list, year_end: int = year_end): # put electricity production for both wind techno energies in a single dataframe df_prod_names = ee.dm.get_all_namespaces_from_var_name(GlossaryEnergy.TechnoProductionValue) - df_prod_list = [ee.dm.get_value(df_prod_names[i]).rename(columns={"electricity (TWh)": df_prod_names[i]}) for i in range(len(df_prod_names))] #PWh + df_prod_list = [ee.dm.get_value(df_prod_names[i]).rename(columns={"electricity (TWh)": df_prod_names[i]}) for i in range(len(df_prod_names))] # PWh df_prod = reduce(lambda left, right: pd.merge(left, right, on=GlossaryEnergy.Years), df_prod_list) # compute the sum of onshore and offshore technos: - df_prod['electricity (TWh)'] = df_prod.drop(GlossaryEnergy.Years, axis=1).sum(axis=1) * 1000. #PWh + df_prod['electricity (TWh)'] = df_prod.drop(GlossaryEnergy.Years, axis=1).sum(axis=1) * 1000. # PWh df_prod_model = df_prod.loc[df_prod[GlossaryEnergy.Years].isin(years_IEA_interpolated)] price_df = ee.dm.get_value(f"{name}.{model_name}.{GlossaryEnergy.TechnoPricesValue}") @@ -175,12 +174,12 @@ def fitting_renewable(x: list): years_price_iea = df_price_iea['years'].values price_model_values = (price_df.loc[price_df[GlossaryEnergy.Years].isin(years_price_iea), f"{GlossaryEnergy.WindOnshore}_wotaxes"]).values - return ((((df_prod_model['electricity (TWh)'].values - prod_IEA_interpolated)/prod_IEA_interpolated.mean()) ** 2).mean() + (((price_model_values - price_iea_values)/price_iea_values.mean()) ** 2).mean()) + return ((((df_prod_model['electricity (TWh)'].values - prod_IEA_interpolated) / prod_IEA_interpolated.mean()) ** 2).mean() + (((price_model_values - price_iea_values) / price_iea_values.mean()) ** 2).mean()) # Initial guess for the variables invest from year 2025 to 2100. -x0 = np.concatenate((np.array([1.]), np.array([0.]), 80./invest_year_start * np.ones(construction_delay - 1), np.ones(len(years_optim)))) -bounds = [(0.5, 1.5)] + [(0., 0.)] + [(80./invest_year_start/2., 80./invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1./10., 10.)] +x0 = np.concatenate((np.array([1.]), np.array([0.]), 80. / invest_year_start * np.ones(construction_delay - 1), np.ones(len(years_optim)))) +bounds = [(0.5, 1.5)] + [(0., 0.)] + [(80. / invest_year_start / 2., 80. / invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1. / 10., 10.)] # Use minimize to find the minimum of the function result = minimize(fitting_renewable, x0, bounds=bounds) @@ -194,7 +193,6 @@ def fitting_renewable(x: list): print("invest at the poles at the optimum", result.x[construction_delay + 1:] * invest_year_start) - new_chart = TwoAxesInstanciatedChart('years', 'production (TWh)', chart_name='Windpower Production : witness vs IEA') @@ -251,5 +249,5 @@ def fitting_renewable(x: list): # values to set in the invest_design_space_NZE.csv print(f"invest at poles for WindOnshore={result.x[construction_delay + 1:] * invest_year_start * ratio_invest_onshore_offshore / (1. + ratio_invest_onshore_offshore)}") print(f"invest at poles for WindOffshore={result.x[construction_delay + 1:] * invest_year_start / (1. + ratio_invest_onshore_offshore)}") -print(f"invest before year start for WindOnshore={result.x[1:construction_delay +1] * invest_year_start * ratio_invest_onshore_offshore / (1. + ratio_invest_onshore_offshore)}") -print(f"invest before year start for WindOffshore={result.x[1:construction_delay + 1] * invest_year_start / (1. + ratio_invest_onshore_offshore)}") \ No newline at end of file +print(f"invest before year start for WindOnshore={result.x[1:construction_delay + 1] * invest_year_start * ratio_invest_onshore_offshore / (1. + ratio_invest_onshore_offshore)}") +print(f"invest before year start for WindOffshore={result.x[1:construction_delay + 1] * invest_year_start / (1. + ratio_invest_onshore_offshore)}") diff --git a/data_energy/techno_invests/biomassburyingfossilization.csv b/data_energy/techno_invests/biomassburyingfossilization.csv index ad039d4a..c81de8fa 100644 --- a/data_energy/techno_invests/biomassburyingfossilization.csv +++ b/data_energy/techno_invests/biomassburyingfossilization.csv @@ -1,2 +1 @@ years,past years,invest - diff --git a/data_energy/techno_invests/deepoceaninjection.csv b/data_energy/techno_invests/deepoceaninjection.csv index ad039d4a..c81de8fa 100644 --- a/data_energy/techno_invests/deepoceaninjection.csv +++ b/data_energy/techno_invests/deepoceaninjection.csv @@ -1,2 +1 @@ years,past years,invest - diff --git a/data_energy/techno_invests/deepsalineformation.csv b/data_energy/techno_invests/deepsalineformation.csv index ad039d4a..c81de8fa 100644 --- a/data_energy/techno_invests/deepsalineformation.csv +++ b/data_energy/techno_invests/deepsalineformation.csv @@ -1,2 +1 @@ years,past years,invest - diff --git a/data_energy/techno_invests/depletedoilgas.csv b/data_energy/techno_invests/depletedoilgas.csv index ad039d4a..c81de8fa 100644 --- a/data_energy/techno_invests/depletedoilgas.csv +++ b/data_energy/techno_invests/depletedoilgas.csv @@ -1,2 +1 @@ years,past years,invest - diff --git a/data_energy/techno_invests/enhancedoilrecovery.csv b/data_energy/techno_invests/enhancedoilrecovery.csv index ad039d4a..c81de8fa 100644 --- a/data_energy/techno_invests/enhancedoilrecovery.csv +++ b/data_energy/techno_invests/enhancedoilrecovery.csv @@ -1,2 +1 @@ years,past years,invest - diff --git a/data_energy/techno_invests/geologicmineralization.csv b/data_energy/techno_invests/geologicmineralization.csv index ad039d4a..c81de8fa 100644 --- a/data_energy/techno_invests/geologicmineralization.csv +++ b/data_energy/techno_invests/geologicmineralization.csv @@ -1,2 +1 @@ years,past years,invest - diff --git a/data_energy/techno_invests/geothermal.csv b/data_energy/techno_invests/geothermal.csv index ff7396f5..f452ebce 100644 --- a/data_energy/techno_invests/geothermal.csv +++ b/data_energy/techno_invests/geothermal.csv @@ -5,4 +5,4 @@ years,invest 2016,2.7 2017,2.4 2018,2.5 -2019,1.2 \ No newline at end of file +2019,1.2 diff --git a/data_energy/techno_invests/global-investment-in-clean-energy-and-fossil-fuels-2015-2024.csv b/data_energy/techno_invests/global-investment-in-clean-energy-and-fossil-fuels-2015-2024.csv index e9fb810d..91882dff 100644 --- a/data_energy/techno_invests/global-investment-in-clean-energy-and-fossil-fuels-2015-2024.csv +++ b/data_energy/techno_invests/global-investment-in-clean-energy-and-fossil-fuels-2015-2024.csv @@ -8,4 +8,4 @@ Renewable power;Grids and storage;Energy efficiency and end-use;Nuclear and othe 0;470;330;562;58;11 0;605;365;655;65;17 0;735;416;646;67;20 -0;771;452;669;80;31 \ No newline at end of file +0;771;452;669;80;31 diff --git a/data_energy/techno_invests/purecarbonsolidstorage.csv b/data_energy/techno_invests/purecarbonsolidstorage.csv index ad039d4a..c81de8fa 100644 --- a/data_energy/techno_invests/purecarbonsolidstorage.csv +++ b/data_energy/techno_invests/purecarbonsolidstorage.csv @@ -1,2 +1 @@ years,past years,invest - diff --git a/data_energy/techno_invests/sources.txt b/data_energy/techno_invests/sources.txt index e1c22539..3fe173bc 100644 --- a/data_energy/techno_invests/sources.txt +++ b/data_energy/techno_invests/sources.txt @@ -8,4 +8,4 @@ CarbonStorageTechno : https://www.iea.org/energy-system/carbon-capture-utilisati DirectAirCaptureTechno : https://www.iea.org/energy-system/carbon-capture-utilisation-and-storage/direct-air-capture SolarPV: [https://www.iea.org/energy-system/renewables/solar-pv,] WindOnShore: https://about.bnef.com/blog/offshore-wind-investment-hit-all-time-high-in-2023/#:~:text=Offshore%20wind%20investment%20surged%20to,%2476.7%20billion%2C%20jumping%2079%25. -WindOffShore: https://about.bnef.com/blog/offshore-wind-investment-hit-all-time-high-in-2023/#:~:text=Offshore%20wind%20investment%20surged%20to,%2476.7%20billion%2C%20jumping%2079%25. \ No newline at end of file +WindOffShore: https://about.bnef.com/blog/offshore-wind-investment-hit-all-time-high-in-2023/#:~:text=Offshore%20wind%20investment%20surged%20to,%2476.7%20billion%2C%20jumping%2079%25. diff --git a/data_energy/techno_invests/windoffshore.csv b/data_energy/techno_invests/windoffshore.csv index 13862d2d..8d2075a2 100644 --- a/data_energy/techno_invests/windoffshore.csv +++ b/data_energy/techno_invests/windoffshore.csv @@ -5,4 +5,4 @@ years,past years,invest 2020,,57 2021,,34 2022,,43 -2023,,78 \ No newline at end of file +2023,,78 diff --git a/data_energy/techno_production_historic/combinedcyclegasturbine.csv b/data_energy/techno_production_historic/combinedcyclegasturbine.csv index 2f26048c..def1aadb 100644 --- a/data_energy/techno_production_historic/combinedcyclegasturbine.csv +++ b/data_energy/techno_production_historic/combinedcyclegasturbine.csv @@ -10,4 +10,4 @@ years,production,unit 2020,4749.16,TWh 2021,4869.71,TWh 2022,4936.04,TWh -2023,4976.33,TWh \ No newline at end of file +2023,4976.33,TWh diff --git a/data_energy/techno_production_historic/fossilgas.csv b/data_energy/techno_production_historic/fossilgas.csv index 2b69043c..0524830c 100644 --- a/data_energy/techno_production_historic/fossilgas.csv +++ b/data_energy/techno_production_historic/fossilgas.csv @@ -57,4 +57,4 @@ years,production,unit 2020,38714.09,TWh 2021,40239.016,TWh 2022,40086.875,TWh -2023,40101.74,TWh \ No newline at end of file +2023,40101.74,TWh diff --git a/data_energy/techno_production_historic/hydropower.csv b/data_energy/techno_production_historic/hydropower.csv index 00983e36..5a95a9f8 100644 --- a/data_energy/techno_production_historic/hydropower.csv +++ b/data_energy/techno_production_historic/hydropower.csv @@ -10,4 +10,4 @@ years,production,unit 2020,4343.51,TWh 2021,4275.47,TWh 2022,4298.82,TWh -2023,4197.92,TWh \ No newline at end of file +2023,4197.92,TWh diff --git a/data_energy/techno_production_historic/oilgen.csv b/data_energy/techno_production_historic/oilgen.csv index d08261f5..d3694491 100644 --- a/data_energy/techno_production_historic/oilgen.csv +++ b/data_energy/techno_production_historic/oilgen.csv @@ -37,4 +37,4 @@ years,production,unit 2020,773.0,TWh 2021,830.76,TWh 2022,849.26,TWh -2023,788.55,TWh \ No newline at end of file +2023,788.55,TWh diff --git a/data_energy/techno_production_historic/sources.txt b/data_energy/techno_production_historic/sources.txt index ac146b10..4d5859aa 100644 --- a/data_energy/techno_production_historic/sources.txt +++ b/data_energy/techno_production_historic/sources.txt @@ -11,4 +11,4 @@ CombinedGasCycleTurbine : 75% of Natural gas electricity generation https://www. Geothermal : https://geothermal-energy-journal.springeropen.com/articles/10.1186/s40517-024-00290-w CropEnergy : 1972, by reading this graph https://www.iea.org/reports/bioenergy-2#overview we get 3966Twh in 2022 (2361 from convetional crop and 1605 from short rotation) -Clean energy simple techno : \ No newline at end of file +Clean energy simple techno : diff --git a/default_process_rights.yaml b/default_process_rights.yaml index f92f4b5a..ad00a6e0 100644 --- a/default_process_rights.yaml +++ b/default_process_rights.yaml @@ -13,4 +13,4 @@ # limitations under the License. group-name: - - All users \ No newline at end of file + - All users diff --git a/energy_models/core/ccus/ccus.py b/energy_models/core/ccus/ccus.py index 9e47254c..5c2a87d4 100644 --- a/energy_models/core/ccus/ccus.py +++ b/energy_models/core/ccus/ccus.py @@ -45,7 +45,7 @@ class CCUS: def __init__(self, name): ''' - Constructor + Constructor ''' self.year_start = None self.year_end = None @@ -67,7 +67,7 @@ def configure_parameters(self, inputs_dict): self.ccs_list = [GlossaryEnergy.carbon_capture, GlossaryEnergy.carbon_storage] def compute_carbon_storage_capacity(self): - total_carbon_storage_by_invest_mt = self.inputs_dict[f"{GlossaryEnergy.carbon_storage}.{GlossaryEnergy.EnergyProductionValue}"][ GlossaryEnergy.carbon_storage].values * self.inputs_dict['scaling_factor_energy_production'] + total_carbon_storage_by_invest_mt = self.inputs_dict[f"{GlossaryEnergy.carbon_storage}.{GlossaryEnergy.EnergyProductionValue}"][GlossaryEnergy.carbon_storage].values * self.inputs_dict['scaling_factor_energy_production'] self.outputs_dict['carbon_storage_capacity (Gt)'] = pd.DataFrame({ GlossaryEnergy.Years: self.years, @@ -100,7 +100,7 @@ def compute_co2_emissions(self): GlossaryEnergy.Years: self.years, f'{GlossaryEnergy.carbon_storage} ({GlossaryEnergy.mass_unit})': carbon_storage_gt * 1e3, f'{GlossaryEnergy.carbon_capture} to be stored (Mt)': carbon_capture_to_be_stored_gt * 1e3, - f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit}) from CC technos': carbon_capture_from_cc_technos_gt * 1e3, + f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit}) from CC technos': carbon_capture_from_cc_technos_gt * 1e3, f'{GlossaryEnergy.carbon_storage} Limited by capture (Mt)': carbon_storage_limited_by_capture_gt * 1e3, }) @@ -115,7 +115,7 @@ def compute(self): def compute_CCS_price(self): ''' - Compute CCS_price + Compute CCS_price ''' ccs_price = self.inputs_dict[f'{GlossaryEnergy.carbon_capture}.{GlossaryEnergy.StreamPricesValue}'][GlossaryEnergy.carbon_capture].values +\ self.inputs_dict[f'{GlossaryEnergy.carbon_storage}.{GlossaryEnergy.StreamPricesValue}'][GlossaryEnergy.carbon_storage].values @@ -200,4 +200,4 @@ def compute_carbon_storage_limited_by_capture_gt_der( jac_carbon_capture_from_energy_mix = jacobian(lambda *args: compute_carbon_storage_limited_by_capture_gt(*args)[1], 2) jac_co2_emissions_needed_by_energy_mix = jacobian(lambda *args: compute_carbon_storage_limited_by_capture_gt(*args)[1], 3) - return jac_carbon_capture_from_cc_prod(*args), jac_carbon_capture_from_cs_prod(*args), jac_carbon_capture_from_energy_mix(*args), jac_co2_emissions_needed_by_energy_mix(*args) \ No newline at end of file + return jac_carbon_capture_from_cc_prod(*args), jac_carbon_capture_from_cs_prod(*args), jac_carbon_capture_from_energy_mix(*args), jac_co2_emissions_needed_by_energy_mix(*args) diff --git a/energy_models/core/ccus/ccus_disc.py b/energy_models/core/ccus/ccus_disc.py index 15f305a7..24050b35 100644 --- a/energy_models/core/ccus/ccus_disc.py +++ b/energy_models/core/ccus/ccus_disc.py @@ -52,7 +52,7 @@ class CCUS_Discipline(SoSWrapp): DESC_IN = { GlossaryEnergy.YearStart: ClimateEcoDiscipline.YEAR_START_DESC_IN, GlossaryEnergy.YearEnd: {'type': 'int', - 'unit': 'year', 'visibility': 'Shared', 'namespace': 'ns_public', 'range': [2000,2300]}, + 'unit': 'year', 'visibility': 'Shared', 'namespace': 'ns_public', 'range': [2000, 2300]}, 'co2_emissions_needed_by_energy_mix': {'type': 'dataframe', 'unit': 'Gt', 'visibility': SoSWrapp.SHARED_VISIBILITY, 'namespace': 'ns_energy', 'dataframe_descriptor': {GlossaryEnergy.Years: ('float', None, True), @@ -62,7 +62,7 @@ class CCUS_Discipline(SoSWrapp): 'carbon_capture_from_energy_mix': {'type': 'dataframe', 'unit': 'Gt', 'visibility': SoSWrapp.SHARED_VISIBILITY, 'namespace': 'ns_energy', 'dataframe_descriptor': {GlossaryEnergy.Years: ('float', None, True), - 'carbon_capture from energy mix (Gt)': ('float', None, True), },}, + 'carbon_capture from energy mix (Gt)': ('float', None, True), }, }, 'co2_for_food': { 'type': 'dataframe', 'unit': 'Mt', 'visibility': SoSWrapp.SHARED_VISIBILITY, 'namespace': 'ns_energy', @@ -304,7 +304,7 @@ def get_chart_co2_limited_storage(self): def get_chart_co2_emissions_sources(self): ''' - Plot all CO2 emissions sources + Plot all CO2 emissions sources ''' chart_name = 'CO2 emissions sources' co2_emissions = self.get_sosdisc_outputs('co2_emissions') @@ -341,4 +341,3 @@ def get_chart_co2_emissions_sources(self): new_chart.add_series(serie) return new_chart - diff --git a/energy_models/core/ccus/documentation/ccus_disc.md b/energy_models/core/ccus/documentation/ccus_disc.md index a280cd27..b26b18c2 100644 --- a/energy_models/core/ccus/documentation/ccus_disc.md +++ b/energy_models/core/ccus/documentation/ccus_disc.md @@ -1,9 +1,9 @@ -# Carbon Capture and Storage model +# Carbon Capture and Storage model The Carbon Capture and Storage model in the energy mix model computes the carbon capture stored limited by the CO2 to capture. -Carbon emissions stored by carbon storage technologies are limited by the amount of CO2 captured. Gaseous CO2 storage and solid carbon storage are separated and they are both limited each by the amount of CO2 and solid carbon ready to store. +Carbon emissions stored by carbon storage technologies are limited by the amount of CO2 captured. Gaseous CO2 storage and solid carbon storage are separated and they are both limited each by the amount of CO2 and solid carbon ready to store. ![](carbon_stored.PNG) @@ -11,12 +11,10 @@ The Solid carbon ready to store is for now the one created by plasma cracking te ![](Carbon_captured_to_be_stored.PNG) -If the CO2 captured to be stored is lower than zero that means that we need more carbon capture for technos than provided. A ratio of carbon captured available is then computed as : +If the CO2 captured to be stored is lower than zero that means that we need more carbon capture for technos than provided. A ratio of carbon captured available is then computed as : $$ratio_{cc\_available} = min(1.0,\frac{cc_{provided}}{cc_{needed}})$$ -This ratio is sent to technology models that needs carbon capture and their production is consequently impacted : +This ratio is sent to technology models that needs carbon capture and their production is consequently impacted : $$production = production*ratio_{cc\_available}$$ - - diff --git a/energy_models/core/consumption_CO2_emissions/consumption_CO2_emissions.py b/energy_models/core/consumption_CO2_emissions/consumption_CO2_emissions.py index b363e770..afef539c 100644 --- a/energy_models/core/consumption_CO2_emissions/consumption_CO2_emissions.py +++ b/energy_models/core/consumption_CO2_emissions/consumption_CO2_emissions.py @@ -40,7 +40,7 @@ class ConsumptionCO2Emissions(BaseStream): def __init__(self, name): ''' - Constructor + Constructor ''' super(ConsumptionCO2Emissions, self).__init__(name) self.energy_list = None @@ -61,7 +61,7 @@ def __init__(self, name): def configure(self, inputs_dict): ''' - Configure method + Configure method ''' self.configure_parameters(inputs_dict) self.configure_parameters_update(inputs_dict) @@ -134,9 +134,9 @@ def compute_CO2_emissions(self): for col, production in self.sub_production_dict[energy].items(): if col in self.CO2_list: self.CO2_production[f'{energy} {col}'] = production.values - ''' CO2 from energy mix - CO2 expelled by energy mix technologies during the process - i.e. for machinery or tractors + ''' CO2 from energy mix + CO2 expelled by energy mix technologies during the process + i.e. for machinery or tractors ''' energy_producing_co2 = self.CO2_production[[ col for col in self.CO2_production if col.endswith(f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit})')]] @@ -167,7 +167,7 @@ def compute_CO2_emissions(self): f'{GlossaryEnergy.carbon_capture} from energy mix (Mt)'] = 0.0 ''' CO2 removed by energy mix - CO2 removed by energy mix technologies during the process + CO2 removed by energy mix technologies during the process i.e. biomass processes as managed wood or crop energy ''' energy_removing_co2 = self.CO2_consumption[[ @@ -181,13 +181,13 @@ def compute_CO2_emissions(self): self.CO2_sinks[ f'{GlossaryEnergy.carbon_capture} removed by energy mix (Mt)'] = 0.0 - '''Total CO2 by use + '''Total CO2 by use which is the sum of all CO2 emissions emitted by use of net energy production ''' self.CO2_sources['Total CO2 by use (Mt)'] = self.CO2_production[[ col for col in self.CO2_production if col.endswith('CO2 by use (Mt)')]].sum(axis=1) - ''' Total C02 from Flue gas + ''' Total C02 from Flue gas sum of all production of flue gas it could be equal to carbon capture from CC technos if enough investment but not sure ''' @@ -279,9 +279,9 @@ def compute_grad_CO2_emissions_sources(self, net_production): # self.total_co2_emissions[ # f'{GlossaryEnergy.carbon_capture} from energy mix (Mt)'] = 0.0 - ''' CO2 from energy mix - CO2 expelled by energy mix technologies during the process - i.e. for machinery or tractors + ''' CO2 from energy mix + CO2 expelled by energy mix technologies during the process + i.e. for machinery or tractors ''' energy_producing_co2 = co2_production[[ col for col in co2_production if col.endswith(f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit})')]] @@ -298,8 +298,8 @@ def compute_grad_CO2_emissions_sources(self, net_production): # self.total_co2_emissions[ # f'{GlossaryEnergy.carbon_capture} from energy mix (Mt)'] = 0.0 - ''' CO2 removed by energy mix - CO2 removed by energy mix technologies during the process + ''' CO2 removed by energy mix + CO2 removed by energy mix technologies during the process i.e. biomass processes as managed wood or crop energy ''' energy_removing_co2 = co2_consumption[[ @@ -317,7 +317,7 @@ def compute_grad_CO2_emissions_sources(self, net_production): # f'{GlossaryEnergy.carbon_capture} removed energy mix (Mt)'] = 0.0 ''' Total C02 from Flue gas - sum of all production of flue gas + sum of all production of flue gas it could be equal to carbon capture from CC technos if enough investment but not sure ''' # self.total_co2_emissions[f'Total {CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = self.co2_production[[ @@ -361,8 +361,8 @@ def compute_grad_CO2_emissions_sinks(self): # Compute the CO2 emitted during the use of the net energy # If net energy is negative, CO2 by use is equals to zero - ''' CO2 removed by energy mix - CO2 removed by energy mix technologies during the process + ''' CO2 removed by energy mix + CO2 removed by energy mix technologies during the process i.e. biomass processes as managed wood or crop energy ''' energy_removing_co2 = co2_consumption[[ diff --git a/energy_models/core/demand/documentation/energy_demand_disc.markdown b/energy_models/core/demand/documentation/energy_demand_disc.markdown index 63a9f690..5b9565ed 100644 --- a/energy_models/core/demand/documentation/energy_demand_disc.markdown +++ b/energy_models/core/demand/documentation/energy_demand_disc.markdown @@ -1,15 +1,15 @@ -# Documentation +# Documentation -The energy demand model gathers demand models for energies and computes a constraint for the optimization process. The constraint is simply to check that the energy production is higher than its demand. +The energy demand model gathers demand models for energies and computes a constraint for the optimization process. The constraint is simply to check that the energy production is higher than its demand. -## Electricity demand model +## Electricity demand model The electricity demand model computes the demand in electricity varying in time with the population number and an electrical machine efficiency $$electricity\_demand = init\_elec\_demand*\frac{population}{population[2020]}*\frac{EM\_efficiency[2020]}{EM\_efficiency}$$ -with the init_elec_demand = 22847.66 TWh by default. +with the init_elec_demand = 22847.66 TWh by default. The EM_efficiency is computed with a sigmoid function calibrated to be equals to 0.95 in 2020, 0.98 in 2025 and 0.985 in the distant future (see post-processing). @@ -17,4 +17,4 @@ The electricity demand_constraint is finally computed : $$electricity\_demand\_constraint = -\frac{electricity\_prod - electricity\_demand}{ref \Delta t}$$ -with a reference defined in input parameters. \ No newline at end of file +with a reference defined in input parameters. diff --git a/energy_models/core/demand/energy_demand.py b/energy_models/core/demand/energy_demand.py index 5e161b59..cab5cdae 100644 --- a/energy_models/core/demand/energy_demand.py +++ b/energy_models/core/demand/energy_demand.py @@ -109,7 +109,7 @@ def compute(self): def compute_elec_demand_constraint(self): ''' - The constraint is the difference between the prod of electricity computed by the energy mix and the actual demand computed in this model + The constraint is the difference between the prod of electricity computed by the energy mix and the actual demand computed in this model ''' self.elec_demand['elec_demand (TWh)'] = self.compute_elec_demand_with_efficiency( ) @@ -135,10 +135,10 @@ def compute_elec_demand_with_efficiency(self): def compute_improved_efficiency_factor(self): ''' - Compute the effect of efficiency improvement based on a S-curve + Compute the effect of efficiency improvement based on a S-curve Electrical machine efficiency started at y_min =0.7 and long term efficiency is planned to be 0.985 - coeff and x0 have been tuned to fit y[2020]=0.95 and y[2025]=0.98 + coeff and x0 have been tuned to fit y[2020]=0.95 and y[2025]=0.98 ''' elec_machine_efficiency = self.electrical_machine_efficiency( diff --git a/energy_models/core/demand/energy_demand_disc.py b/energy_models/core/demand/energy_demand_disc.py index 985b6126..987cd40d 100644 --- a/energy_models/core/demand/energy_demand_disc.py +++ b/energy_models/core/demand/energy_demand_disc.py @@ -58,13 +58,13 @@ class EnergyDemandDiscipline(SoSWrapp): # old value is 20900TWh 'initial_electricity_demand': {'type': 'float', 'default': 18000., 'unit': 'TWh'}, 'long_term_elec_machine_efficiency': {'type': 'float', 'default': 0.985, 'unit': '-'}, - 'electricity_demand_constraint_ref': {'type': 'float', 'default': 2500.0, 'unit': 'TWh',}, + 'electricity_demand_constraint_ref': {'type': 'float', 'default': 2500.0, 'unit': 'TWh', }, GlossaryEnergy.PopulationDf['var_name']: GlossaryEnergy.PopulationDf, GlossaryEnergy.TransportDemandValue: {'type': 'dataframe', 'dataframe_descriptor': { GlossaryEnergy.Years: ('int', [1900, GlossaryEnergy.YearEndDefaultCore], False), GlossaryEnergy.TransportDemandValue: ('float', None, True)}, 'dataframe_edition_locked': False, 'unit': 'TWh'}, - 'transport_demand_constraint_ref': {'type': 'float', 'default': 6000.0, 'unit': 'TWh',}, + 'transport_demand_constraint_ref': {'type': 'float', 'default': 6000.0, 'unit': 'TWh', }, 'additional_demand_transport': {'type': 'float', 'default': 10., 'unit': '%'}} DESC_OUT = { diff --git a/energy_models/core/energy_ghg_emissions/documentation/energy_ghg_emissions_disc.md b/energy_models/core/energy_ghg_emissions/documentation/energy_ghg_emissions_disc.md index 1b2520c0..bd918461 100644 --- a/energy_models/core/energy_ghg_emissions/documentation/energy_ghg_emissions_disc.md +++ b/energy_models/core/energy_ghg_emissions/documentation/energy_ghg_emissions_disc.md @@ -1,31 +1,27 @@ -# Energy Green House Gases Emissions +# Energy Green House Gases Emissions The objective of the model is to aggregate the information of GHG emissions coming from each energy. -## CO2 emissions model +## CO2 emissions model The CO2 emissions model is particular because it takes into account CO2 used or captured by CCUS technologies. -Four main sources are taken into account : +Four main sources are taken into account : - The CO2 in the flue gas expelled from plants (like coal generation plants) - The CO2 emitted by the use of each net energy production (energy burned) - The CO2 emitted by technos that cannot be stored, from machinery which uses fuels (tractors for biomass, coal extractors ...) - The CO2 which is captured by technologies as Upgrading biogas for example - -Other technologies acts in the favor of the removal of carbon emissions and CO2 fluxes are divided in three categories : + +Other technologies acts in the favor of the removal of carbon emissions and CO2 fluxes are divided in three categories : - The CO2 fluxes stored by carbon storage technologies (i.e. injected in oceans) - The CO2 removed by technologies (i.e. managed wood technology removes CO2 thanks to tree carbon cycle) - The CO2 needed by the chemical reaction of a technology (i.e. Fischer Tropsch plants may needs CO2 to enrich syngas in CO for Fischer Tropsch synthesis) - + ![](co2_emissions_model.PNG) -## Other GHG emissions model +## Other GHG emissions model -The other models are taking into account two sources of GHG emissions: +The other models are taking into account two sources of GHG emissions: - The GHG emissions coming from technologies (CH4 leakage in coal mines, N2O leakage when oil is burned in oil-fired electricity plants) - The GHG emissions coming from the use (burning) of fossil fuel are biomass dry net production. - - - - diff --git a/energy_models/core/energy_ghg_emissions/energy_ghg_emissions.py b/energy_models/core/energy_ghg_emissions/energy_ghg_emissions.py index 8a8a12b5..7a7cae91 100644 --- a/energy_models/core/energy_ghg_emissions/energy_ghg_emissions.py +++ b/energy_models/core/energy_ghg_emissions/energy_ghg_emissions.py @@ -162,7 +162,7 @@ def compute_ghg_emissions(self): self.compute_gwp() def sum_ghg_emissions_by_use(self): - '''Total CO2 by use + '''Total CO2 by use which is the sum of all CO2 emissions emitted by use of net energy production ''' for ghg in self.GHG_TYPE_LIST: @@ -171,9 +171,9 @@ def sum_ghg_emissions_by_use(self): col.endswith(f'{ghg} by use {self.ghg_input_unit}')]].sum(axis=1) def compute_other_co2_emissions(self): - ''' CO2 from energy mix - CO2 expelled by energy mix technologies during the process - i.e. for machinery or tractors + ''' CO2 from energy mix + CO2 expelled by energy mix technologies during the process + i.e. for machinery or tractors ''' energy_producing_co2 = self.ghg_production_dict[GlossaryEnergy.CO2][[ col for col in self.ghg_production_dict[GlossaryEnergy.CO2] if col.endswith(f'{GlossaryEnergy.carbon_capture} {self.ghg_input_unit}')]] @@ -206,7 +206,7 @@ def compute_other_co2_emissions(self): f'{GlossaryEnergy.carbon_capture} from energy mix {self.ghg_input_unit}'] = 0.0 ''' CO2 removed by energy mix - CO2 removed by energy mix technologies during the process + CO2 removed by energy mix technologies during the process i.e. biomass processes as managed wood or crop energy ''' energy_removing_co2 = self.CO2_consumption[[ @@ -220,7 +220,7 @@ def compute_other_co2_emissions(self): self.CO2_sinks[ f'{GlossaryEnergy.carbon_capture} removed by energy mix {self.ghg_input_unit}'] = 0.0 - ''' Total C02 from Flue gas + ''' Total C02 from Flue gas sum of all production of flue gas it could be equal to carbon capture from CC technos if enough investment but not sure ''' @@ -405,9 +405,9 @@ def compute_grad_CO2_emissions_sources(self, net_production): # self.total_co2_emissions[ # f'{GlossaryEnergy.carbon_capture} from energy mix {self.ghg_input_unit}'] = 0.0 - ''' CO2 from energy mix - CO2 expelled by energy mix technologies during the process - i.e. for machinery or tractors + ''' CO2 from energy mix + CO2 expelled by energy mix technologies during the process + i.e. for machinery or tractors ''' energy_producing_co2 = co2_production[[ col for col in co2_production if col.endswith(f'{GlossaryEnergy.carbon_capture} {self.ghg_input_unit}')]] @@ -425,8 +425,8 @@ def compute_grad_CO2_emissions_sources(self, net_production): # self.total_co2_emissions[ # f'{GlossaryEnergy.carbon_capture} from energy mix {self.ghg_input_unit}'] = 0.0 - ''' CO2 removed by energy mix - CO2 removed by energy mix technologies during the process + ''' CO2 removed by energy mix + CO2 removed by energy mix technologies during the process i.e. biomass processes as managed wood or crop energy ''' energy_removing_co2 = co2_consumption[[ @@ -445,7 +445,7 @@ def compute_grad_CO2_emissions_sources(self, net_production): # f'{GlossaryEnergy.carbon_capture} removed energy mix {self.ghg_input_unit}'] = 0.0 ''' Total C02 from Flue gas - sum of all production of flue gas + sum of all production of flue gas it could be equal to carbon capture from CC technos if enough investment but not sure ''' # self.total_co2_emissions[f'Total {CarbonCapture.flue_gas_name} {self.ghg_input_unit}'] = self.co2_production[[ @@ -490,8 +490,8 @@ def compute_grad_CO2_emissions_sinks(self): # Compute the CO2 emitted during the use of the net energy # If net energy is negative, CO2 by use is equals to zero - ''' CO2 removed by energy mix - CO2 removed by energy mix technologies during the process + ''' CO2 removed by energy mix + CO2 removed by energy mix technologies during the process i.e. biomass processes as managed wood or crop energy ''' energy_removing_co2 = co2_consumption[[ diff --git a/energy_models/core/energy_ghg_emissions/energy_ghg_emissions_disc.py b/energy_models/core/energy_ghg_emissions/energy_ghg_emissions_disc.py index 7a2767ef..27934f83 100644 --- a/energy_models/core/energy_ghg_emissions/energy_ghg_emissions_disc.py +++ b/energy_models/core/energy_ghg_emissions/energy_ghg_emissions_disc.py @@ -159,7 +159,7 @@ def setup_sos_disciplines(self): 'visibility': SoSWrapp.SHARED_VISIBILITY, 'namespace': 'ns_energy', 'dataframe_descriptor': {GlossaryEnergy.Years: ('float', None, True), - GlossaryEnergy.GhGPerUse.format(ghg): ('float', None, True),} + GlossaryEnergy.GhGPerUse.format(ghg): ('float', None, True), } } dynamic_inputs[f'{energy}.{GlossaryEnergy.StreamConsumptionValue}'] = { 'type': 'dataframe', 'unit': 'PWh', diff --git a/energy_models/core/energy_mix/documentation/energy_mix_disc.md b/energy_models/core/energy_mix/documentation/energy_mix_disc.md index ab996256..8fffafd6 100644 --- a/energy_models/core/energy_mix/documentation/energy_mix_disc.md +++ b/energy_models/core/energy_mix/documentation/energy_mix_disc.md @@ -1,4 +1,3 @@ -# Energy Mix model - -The energy mix discipline acts at the top of energies. The goal is to aggregate the information arriving from each energy, calculate the net production and the CO2 emissions per kwh of energy. These informations are then send back to technologies to compute technolo production and prices. +# Energy Mix model +The energy mix discipline acts at the top of energies. The goal is to aggregate the information arriving from each energy, calculate the net production and the CO2 emissions per kwh of energy. These informations are then send back to technologies to compute technolo production and prices. diff --git a/energy_models/core/energy_mix_study_manager.py b/energy_models/core/energy_mix_study_manager.py index 28b39f1b..93edc37c 100644 --- a/energy_models/core/energy_mix_study_manager.py +++ b/energy_models/core/energy_mix_study_manager.py @@ -49,7 +49,7 @@ def setup_process(self): def configure_ds_boundaries(self, lower_bound_techno=1.0, upper_bound_techno=100.): """ - Configure design space boundaries + Configure design space boundaries """ self.lower_bound_techno = lower_bound_techno self.upper_bound_techno = upper_bound_techno diff --git a/energy_models/core/energy_process_builder.py b/energy_models/core/energy_process_builder.py index fb9c22e7..de894c89 100644 --- a/energy_models/core/energy_process_builder.py +++ b/energy_models/core/energy_process_builder.py @@ -34,8 +34,8 @@ def __init__(self, ee, invest_discipline: str = INVEST_DISCIPLINE_DEFAULT): self.techno_list = None self.invest_discipline = invest_discipline self.associate_namespace = False - #self.energy_name = filename.split('\\')[-2].replace("_mix", '') - #self.techno_list = current_techno_dict[self.energy_name] + # self.energy_name = filename.split('\\')[-2].replace("_mix", '') + # self.techno_list = current_techno_dict[self.energy_name] def setup_process(self, techno_list, invest_discipline=INVEST_DISCIPLINE_DEFAULT, associate_namespace=False): self.techno_list = techno_list diff --git a/energy_models/core/investments/convex_combination_model.py b/energy_models/core/investments/convex_combination_model.py index d6071419..6d22ebc3 100644 --- a/energy_models/core/investments/convex_combination_model.py +++ b/energy_models/core/investments/convex_combination_model.py @@ -24,7 +24,7 @@ def __init__(self): self.convex_coefficients: dict[str: float] = {} self.dataframes: list[pd.DataFrame] = [] self.convex_combination_df: pd.DataFrame = None - self.coeffs_sum : float = 0. + self.coeffs_sum: float = 0. def store_inputs(self, positive_coefficients: dict[str: float], diff --git a/energy_models/core/investments/disciplines/documentation/energy_invest_disc.md b/energy_models/core/investments/disciplines/documentation/energy_invest_disc.md index 2c2ae181..ab5dae99 100644 --- a/energy_models/core/investments/disciplines/documentation/energy_invest_disc.md +++ b/energy_models/core/investments/disciplines/documentation/energy_invest_disc.md @@ -1,6 +1,5 @@ # Investments Distribution -The distribution of investments is made according to the investments level mix dataframe in input. Each coefficient for each energy/technology over the years is normalized by the sum of coefficients for one year and multiplied by the total investments level : +The distribution of investments is made according to the investments level mix dataframe in input. Each coefficient for each energy/technology over the years is normalized by the sum of coefficients for one year and multiplied by the total investments level : $$energy\_investment = total\_investment * \frac{energy\_mix\_coefficient}{\sum energy\_mix\_coefficient}$$ - diff --git a/energy_models/core/investments/disciplines/documentation/energy_or_ccs_invest_disc.md b/energy_models/core/investments/disciplines/documentation/energy_or_ccs_invest_disc.md index 5e54ce6d..92d85aca 100644 --- a/energy_models/core/investments/disciplines/documentation/energy_or_ccs_invest_disc.md +++ b/energy_models/core/investments/disciplines/documentation/energy_or_ccs_invest_disc.md @@ -1,8 +1,7 @@ # Investments Distribution between CCS and Energy conversion -The distribution of global investments into CCS and energy conversion is made following a percentage of CCS investment compared to global investment. +The distribution of global investments into CCS and energy conversion is made following a percentage of CCS investment compared to global investment. $$ccs\_investment = global\_investment * \frac{ccs\_mix\_percentage}{100}$$ $$energy\_conversion\_investment = global\_investment *(1.0- \frac{ccs\_mix\_percentage}{100})$$ - diff --git a/energy_models/core/investments/disciplines/documentation/independent_invest_disc.md b/energy_models/core/investments/disciplines/documentation/independent_invest_disc.md index 5587e5d1..a209b029 100644 --- a/energy_models/core/investments/disciplines/documentation/independent_invest_disc.md +++ b/energy_models/core/investments/disciplines/documentation/independent_invest_disc.md @@ -3,4 +3,3 @@ The distribution of investments is made according to the investments coming from the design space. A constraint is computed in order to obtain a sum of investments lower than the investment dedicated for energy production coming from the macroeconomics model. $$investment\_constraint = energy\_investment > \sum technos\_investments$$ - diff --git a/energy_models/core/investments/disciplines/documentation/investments_profile_builder_disc.md b/energy_models/core/investments/disciplines/documentation/investments_profile_builder_disc.md index 34f20ae3..7bf3fe8f 100644 --- a/energy_models/core/investments/disciplines/documentation/investments_profile_builder_disc.md +++ b/energy_models/core/investments/disciplines/documentation/investments_profile_builder_disc.md @@ -18,10 +18,8 @@ The Investments Profile Builder uses the following formulas : $$ These calculations ensure that the investments are proportionally distributed based on the given coefficients. -The output investment profile can be exported either as a dataframe 'invest_mix' where the values of the variables -are provided for each year or as a 1D array per variable (named 'variable_array_mix') where the values are provided -only for a selected number of years referred to as the poles. -Therefore, the number of poles have to be provided by the user in the second case. To activate the second case, +The output investment profile can be exported either as a dataframe 'invest_mix' where the values of the variables +are provided for each year or as a 1D array per variable (named 'variable_array_mix') where the values are provided +only for a selected number of years referred to as the poles. +Therefore, the number of poles have to be provided by the user in the second case. To activate the second case, the user must set to True the input variable 'export_invest_profiles_at_poles' - - diff --git a/energy_models/core/investments/disciplines/documentation/investments_redistribution_disc.md b/energy_models/core/investments/disciplines/documentation/investments_redistribution_disc.md index 37d6d560..02a17b21 100644 --- a/energy_models/core/investments/disciplines/documentation/investments_redistribution_disc.md +++ b/energy_models/core/investments/disciplines/documentation/investments_redistribution_disc.md @@ -6,4 +6,3 @@ We then use another input that contains percentage of investments in each techno Used formula is : $$invest\_in\_techno = GDP\_net\_of\_damage * percentage\_of\_GDP\_invest\_in\_energy * percentage\_invest\_in\_techno$$ - diff --git a/energy_models/core/investments/disciplines/documentation/techno_invest_disc.md b/energy_models/core/investments/disciplines/documentation/techno_invest_disc.md index 2c2ae181..ab5dae99 100644 --- a/energy_models/core/investments/disciplines/documentation/techno_invest_disc.md +++ b/energy_models/core/investments/disciplines/documentation/techno_invest_disc.md @@ -1,6 +1,5 @@ # Investments Distribution -The distribution of investments is made according to the investments level mix dataframe in input. Each coefficient for each energy/technology over the years is normalized by the sum of coefficients for one year and multiplied by the total investments level : +The distribution of investments is made according to the investments level mix dataframe in input. Each coefficient for each energy/technology over the years is normalized by the sum of coefficients for one year and multiplied by the total investments level : $$energy\_investment = total\_investment * \frac{energy\_mix\_coefficient}{\sum energy\_mix\_coefficient}$$ - diff --git a/energy_models/core/investments/disciplines/independent_invest_disc.py b/energy_models/core/investments/disciplines/independent_invest_disc.py index e8ed5c93..1e0ec2d9 100644 --- a/energy_models/core/investments/disciplines/independent_invest_disc.py +++ b/energy_models/core/investments/disciplines/independent_invest_disc.py @@ -72,7 +72,7 @@ class IndependentInvestDiscipline(SoSWrapp): 'float', None, False)}, 'namespace': 'ns_invest', 'dataframe_edition_locked': False}, - GlossaryEnergy.MaxBudgetValue : GlossaryEnergy.MaxBudgetDf, + GlossaryEnergy.MaxBudgetValue: GlossaryEnergy.MaxBudgetDf, GlossaryEnergy.MaxBudgetConstraintRefValue: GlossaryEnergy.MaxBudgetConstraintRef } @@ -383,6 +383,4 @@ def pimp_string(val: str): instanciated_charts.insert(1, new_chart_energy_ratio) - - return instanciated_charts diff --git a/energy_models/core/investments/disciplines/investments_profile_builder_disc.py b/energy_models/core/investments/disciplines/investments_profile_builder_disc.py index 20f87412..5b7652d1 100644 --- a/energy_models/core/investments/disciplines/investments_profile_builder_disc.py +++ b/energy_models/core/investments/disciplines/investments_profile_builder_disc.py @@ -44,10 +44,10 @@ class InvestmentsProfileBuilderDisc(SoSWrapp): 'version': '', } ''' - Discipline that generates an output invest profile based on generic input invest profiles and input weights for + Discipline that generates an output invest profile based on generic input invest profiles and input weights for each of those profiles. Based on the input boolean EXPORT_PROFILES_AT_POLES, it can either export the output profile at the poles or for all years - then, the output variable is not named the same, as in the first case it becomes an input of the design_var discipline and + then, the output variable is not named the same, as in the first case it becomes an input of the design_var discipline and in the second case it is an input of the investment distribution ''' @@ -76,7 +76,6 @@ def setup_sos_disciplines(self): for i in range(n_profiles): dynamic_inputs[f'coeff_{i}'] = {'type': 'float', 'unit': '-'} - if 'column_names' in self.get_data_in(): column_names = self.get_sosdisc_inputs('column_names') if column_names is not None and n_profiles is not None: @@ -94,7 +93,6 @@ def setup_sos_disciplines(self): if export_profiles_at_poles is not None and export_profiles_at_poles: dynamic_inputs['nb_poles'] = {'type': 'int', 'unit': '-', 'user_level': 3} - if df_descriptor is not None and export_profiles_at_poles is not None: # the output invest profile can be provided either for all the years or for some limited number of poles. if not export_profiles_at_poles: @@ -109,7 +107,7 @@ def setup_sos_disciplines(self): dynamic_outputs[f'{var}_array_mix'] = { "type": "array", "unit": "G$", - "namespace": "ns_invest", # same namespace as for design_var discipline inputs as described in design_var_descriptor + "namespace": "ns_invest", # same namespace as for design_var discipline inputs as described in design_var_descriptor "visibility": "Shared", } @@ -157,7 +155,7 @@ def run(self): # type: (...) -> None df = inputs['df_0'] nb_poles = inputs['nb_poles'] years_poles, poles_index = self.compute_poles(df, nb_poles) - for col in column_names: # extract data at the poles + for col in column_names: # extract data at the poles df = self.model.convex_combination_df[[GlossaryEnergy.Years] + [col]] outputs = {col + '_array_mix': df[df.index.isin(poles_index)][col].values} self.store_sos_outputs_values(outputs) @@ -168,7 +166,7 @@ def compute_sos_jacobian(self): n_profiles = dict_in['n_profiles'] df = dict_in['df_0'] export_profiles_at_poles = dict_in[GlossaryEnergy.EXPORT_PROFILES_AT_POLES] - poles_index = None # initialize to avoid pylint error + poles_index = None # initialize to avoid pylint error if export_profiles_at_poles: nb_poles = dict_in['nb_poles'] years_poles, poles_index = self.compute_poles(df, nb_poles) @@ -182,7 +180,7 @@ def compute_sos_jacobian(self): (f'coeff_{i}',), derivative.reshape((len(derivative), 1)) ) else: - derivative_at_poles = derivative[poles_index].reshape((len(poles_index), 1)) #extract gradient at the poles only + derivative_at_poles = derivative[poles_index].reshape((len(poles_index), 1)) # extract gradient at the poles only self.set_partial_derivative(col_name + '_array_mix', f'coeff_{i}', derivative_at_poles) def get_chart_filter_list(self): @@ -207,7 +205,7 @@ def get_post_processing_list(self, filters=None): df = self.get_sosdisc_inputs('df_0') years = list(df[GlossaryEnergy.Years].values) # all profiles should have the same years export_profiles_at_poles = self.get_sosdisc_inputs(GlossaryEnergy.EXPORT_PROFILES_AT_POLES) - years_poles = None # initialize to avoid pylint error + years_poles = None # initialize to avoid pylint error if export_profiles_at_poles: nb_poles = self.get_sosdisc_inputs('nb_poles') years_poles, poles_index = self.compute_poles(df, nb_poles) @@ -217,7 +215,6 @@ def get_post_processing_list(self, filters=None): graph_poles = TwoAxesInstanciatedChart(GlossaryEnergy.Years, 'Invest array_mix [G$]', chart_name="Output profile invest at the poles") - for idx, column in enumerate(column_names): chart_name = f"Investments in {column}" @@ -242,7 +239,7 @@ def get_post_processing_list(self, filters=None): series_values = list(invest_profile_poles) serie_obj = InstanciatedSeries(list(years_poles), series_values, column + '_array_mix', display_type="scatter", marker_symbol='circle', - #marker=dict(color='LightSkyBlue', size=20, line=dict(color='MediumPurple', width=2)) + # marker=dict(color='LightSkyBlue', size=20, line=dict(color='MediumPurple', width=2)) ) graph_poles.add_series(serie_obj) @@ -251,4 +248,4 @@ def get_post_processing_list(self, filters=None): else: instanciated_charts.append(graph_poles) - return instanciated_charts \ No newline at end of file + return instanciated_charts diff --git a/energy_models/core/investments/energy_invest.py b/energy_models/core/investments/energy_invest.py index 49e53ad4..ea018dba 100644 --- a/energy_models/core/investments/energy_invest.py +++ b/energy_models/core/investments/energy_invest.py @@ -29,13 +29,13 @@ def __init__(self, name='Energy'): def set_energy_list(self, energy_list): ''' - Set the energy_list of the energy mix + Set the energy_list of the energy mix ''' self.energy_list = energy_list def set_invest_mix(self, mix_df): ''' - Set the invest mix of the energy mix + Set the invest mix of the energy mix ''' if not isinstance(self.energy_list, list): raise TypeError('energy_list must be defined as a list') diff --git a/energy_models/core/investments/energy_or_ccsinvest.py b/energy_models/core/investments/energy_or_ccsinvest.py index acc76eed..d2577b39 100644 --- a/energy_models/core/investments/energy_or_ccsinvest.py +++ b/energy_models/core/investments/energy_or_ccsinvest.py @@ -22,7 +22,7 @@ class EnergyOrCCSInvest: ''' - Model to split global investment into investment for Carbon Capture and Storage technologies and into investment for energy conversion + Model to split global investment into investment for Carbon Capture and Storage technologies and into investment for energy conversion ''' def __init__(self): @@ -44,7 +44,7 @@ def configure(self, input_dict): def compute(self): ''' - Compute the investment in to CCS and into energy_conversion + Compute the investment in to CCS and into energy_conversion ''' ccs_invest = self.global_invest[GlossaryEnergy.EnergyInvestmentsValue].values * \ self.invest_ccs_percentage['ccs_percentage'].values / 100.0 diff --git a/energy_models/core/investments/one_invest.py b/energy_models/core/investments/one_invest.py index 9f72efaa..f973eba7 100644 --- a/energy_models/core/investments/one_invest.py +++ b/energy_models/core/investments/one_invest.py @@ -23,7 +23,7 @@ class OneInvest(BaseInvest): ''' - Model to split global investment into investment for each technology + Model to split global investment into investment for each technology ''' def __init__(self, name='Invest'): @@ -60,7 +60,7 @@ def compute(self, inputs_dict): def set_invest_mix(self, mix_df): ''' - Set the invest mix of the energy mix + Set the invest mix of the energy mix ''' if not isinstance(self.distribution_list, list): raise TypeError('energy_list must be defined as a list') diff --git a/energy_models/core/process_builder_database.py b/energy_models/core/process_builder_database.py index 7d0e373c..5c8102c3 100644 --- a/energy_models/core/process_builder_database.py +++ b/energy_models/core/process_builder_database.py @@ -37,7 +37,7 @@ def process_namespace(self, ns_dict=None, get_from_database=False): Parameters: ns_dict (dict): The namespace definition to add. - associate_namespace (bool): If True, associates the namespace with builders. + associate_namespace (bool): If True, associates the namespace with builders. database_name (str): The name of the database. Returns: @@ -50,7 +50,7 @@ def process_namespace(self, ns_dict=None, get_from_database=False): return ns_ids def create_builder_list(self, mods_dict, ns_dict=None, associate_namespace=False, get_from_database=False): - ''' + ''' define a base namespace instantiate builders iterating over a list of module paths return the list of disciplines built @@ -72,7 +72,7 @@ def set_builder_specific_ns_database(self, builders_list, ns_dict=None, associat Parameters: builders_list (list): A list of builder objects. ns_dict (dict): A dictionary of namespaces. - associate_namespace (bool): If True, associates the namespace dictionary with the builder(s). + associate_namespace (bool): If True, associates the namespace dictionary with the builder(s). database_location (str): The path to the directory where the database will be saved. database_name (str): The name of the database. diff --git a/energy_models/core/stream_type/base_stream.py b/energy_models/core/stream_type/base_stream.py index 0b1b2afb..1ee6dc7b 100644 --- a/energy_models/core/stream_type/base_stream.py +++ b/energy_models/core/stream_type/base_stream.py @@ -70,7 +70,7 @@ def __init__(self, name): def reload_df(self): ''' - Reload all dataframes with new year start and year end + Reload all dataframes with new year start and year end ''' self.years = np.arange(self.year_start, self.year_end + 1) base_df = pd.DataFrame({GlossaryEnergy.Years: self.years}) @@ -131,11 +131,11 @@ def configure_parameters_update(self, inputs_dict): inputs_dict['scaling_factor_techno_consumption'] self.sub_land_use_required_dict[element] = inputs_dict[f'{element}.{GlossaryEnergy.LandUseRequiredValue}'] - #print(self.name, [list(inputs_dict[f'{element}.{GlossaryEnergy.LandUseRequiredValue}'].columns) for element in self.subelements_list]) + # print(self.name, [list(inputs_dict[f'{element}.{GlossaryEnergy.LandUseRequiredValue}'].columns) for element in self.subelements_list]) def compute(self, inputs, exp_min=True): ''' - Compute all energy variables with its own technologies + Compute all energy variables with its own technologies ''' _, self.consumption_woratio, _ = self.compute_production( @@ -150,7 +150,7 @@ def compute(self, inputs, exp_min=True): self.compute_energy_type_capital(inputs) - #print(self.name, list(self.production.columns)) + # print(self.name, list(self.production.columns)) return self.total_prices, self.production, self.consumption, self.consumption_woratio, self.mix_weights def compute_production(self, sub_production_dict, sub_consumption_dict): @@ -176,8 +176,8 @@ def compute_production(self, sub_production_dict, sub_consumption_dict): production, consumption = self.compute_byproducts_consumption_and_production( element, sub_production_dict, sub_consumption_dict, production, consumption) - #print(self.name, "&&#", self.unit) - #print(self.name, list(production_by_techno.columns)) + # print(self.name, "&&#", self.unit) + # print(self.name, list(production_by_techno.columns)) return production, consumption, production_by_techno def compute_byproducts_consumption_and_production(self, element, sub_production_dict, sub_consumption_dict, production, @@ -222,7 +222,7 @@ def compute_energy_type_capital(self, inputs): def compute_price(self, exp_min=True): ''' - Compute the price with all sub_prices and sub weights computed with total production + Compute the price with all sub_prices and sub weights computed with total production ''' self.total_prices[self.name] = 0. @@ -308,12 +308,12 @@ def compute_dprod_wcutoff(self, production_by_techno, elements_dict, min_prod): def compute_prod_with_exp_min(self, production_by_techno, elements_dict, min_prod): ''' Compute the production of each element by minimizing them with and exponential function to reach min prod - Objective is to decrease gradients when prod are very low + Objective is to decrease gradients when prod are very low Be careful the objective is to increase the total production to decrease the gradient then we have to modify the sum also - BIG WARNING : there is an issue in the handling of complex number in this function that may cause small errors - in gradient tests. So far, no solution has been found. This error can be reproduced by running the test on the + BIG WARNING : there is an issue in the handling of complex number in this function that may cause small errors + in gradient tests. So far, no solution has been found. This error can be reproduced by running the test on the gradients of liquid_hydrogen stream in the case of a production of techno HydrogenLiquefaction below min_prod. elements_dict contains {Name of the prod techno or energy: full name of the column} diff --git a/energy_models/core/stream_type/carbon_disciplines/carbon_capture_disc.py b/energy_models/core/stream_type/carbon_disciplines/carbon_capture_disc.py index a229b234..24737665 100644 --- a/energy_models/core/stream_type/carbon_disciplines/carbon_capture_disc.py +++ b/energy_models/core/stream_type/carbon_disciplines/carbon_capture_disc.py @@ -78,7 +78,7 @@ def init_execution(self): def run(self): ''' - Overwrite run to limit flue gas carbon capture + Overwrite run to limit flue gas carbon capture ''' super().run() @@ -87,7 +87,7 @@ def run(self): 'carbon_captured_type': self.energy_model.carbon_captured_type, 'carbon_captured_type_woratio': self.energy_model.carbon_captured_type_woratio, } - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_capture_disc.md b/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_capture_disc.md index 1c21f659..0dd9e46f 100644 --- a/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_capture_disc.md +++ b/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_capture_disc.md @@ -29,4 +29,4 @@ The technologies taken into account are: Flue gas capture is limited by the amount of flue gas available at the exit of CO2 emissing factories. The model limits (smoothly with an exponential to avoid zero gradients) the carbon capture via flue gas with the real amount of flue gas available. The mean CO2 concentration in the flue gas is retained to compute the economic data of each flue gas technology. -[^1]: https://en.wikipedia.org/wiki/Carbon_capture_and_storage \ No newline at end of file +[^1]: https://en.wikipedia.org/wiki/Carbon_capture_and_storage diff --git a/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_storage_disc.md b/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_storage_disc.md index 96b97d32..c75dd2da 100644 --- a/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_storage_disc.md +++ b/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_storage_disc.md @@ -5,7 +5,7 @@ Carbon Capture and Storage (CCS) is the process of capturing waste carbon dioxid There exist several paths for storage: -* Depleted Oil & Gas +* Depleted Oil & Gas * Enhanced Oil recovery * Deep saline formation * Deep ocean $CO_2$ injection @@ -15,4 +15,4 @@ There exist several paths for storage: [^1]: https://en.wikipedia.org/wiki/Carbon_capture_and_storage -[^2]: [Global CCS institute - CCS Image Library](https://www.globalccsinstitute.com/resources/ccs-image-library/) \ No newline at end of file +[^2]: [Global CCS institute - CCS Image Library](https://www.globalccsinstitute.com/resources/ccs-image-library/) diff --git a/energy_models/core/stream_type/carbon_disciplines/documentation/flue_gas_disc.markdown b/energy_models/core/stream_type/carbon_disciplines/documentation/flue_gas_disc.markdown index b4da18d0..efcb75dc 100644 --- a/energy_models/core/stream_type/carbon_disciplines/documentation/flue_gas_disc.markdown +++ b/energy_models/core/stream_type/carbon_disciplines/documentation/flue_gas_disc.markdown @@ -2,26 +2,26 @@ Flue gas is the gas exiting to the atmosphere via a flue, which is a pipe or channel for conveying exhaust gases from a fireplace, oven, furnace, boiler or steam generator. Quite often, the flue gas refers to the combustion exhaust gas produced at power plants. Its composition depends on what is being burned, but it will usually consist of mostly nitrogen (typically more than two-thirds) derived from the combustion of air, carbon dioxide (CO2), and water vapor as well as excess oxygen (also derived from the combustion air). It further contains a small percentage of a number of pollutants, such as particulate matter (like soot), carbon monoxide, nitrogen oxides, and sulfur oxides. Flue gas from London's Bankside Power Station, 1975[^1] -![](flue_gas.PNG) +![](flue_gas.PNG) Flue gas composition[^2] -![](flue_gas_composition.PNG) +![](flue_gas_composition.PNG) In the model, we focus on C02 concentration in the flue gas to calculate costs variation of CAPEX and electricity needs. The table below describes common flue gases from industries and their related concentration of CO2. CO2 concentration in different flue gases[^3] -![](co2_concentration_flue_gas.PNG) +![](co2_concentration_flue_gas.PNG) -Once each energy production flue gases specified, the model calculates average CO2 concentration in flue gas stream and applies variations. +Once each energy production flue gases specified, the model calculates average CO2 concentration in flue gas stream and applies variations. -The total flue gas production is finally computed and send to the carbon capture model to get the potential fluegas to be captured. +The total flue gas production is finally computed and send to the carbon capture model to get the potential fluegas to be captured. CO2 concentration and evolution of costs related to capture[^4] -![](co2_cost_evolution_by_concentration.PNG) +![](co2_cost_evolution_by_concentration.PNG) [^1]: Robin Webster, Bankside power station and St George the Martyr church, licensed under [CC BY-SA 2.0](https://creativecommons.org/licenses/by-sa/2.0/), https://en.wikipedia.org/wiki/Flue_gas [^2]: Constituents of flue gas, https://www.sciencedirect.com/topics/earth-and-planetary-sciences/flue-gas [^3]: Wang, X. and Song, C., 2020. Carbon Capture From Flue Gas and the Atmosphere: A Perspective. Frontiers in Energy Research, 8, p.265. Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), https://www.frontiersin.org/articles/10.3389/fenrg.2020.560849/full -[^4]: IEM capture cost estimates for representative oil sands flue gas streams according to CO2 concentration, https://www.researchgate.net/figure/IEM-capture-cost-estimates-for-representative-oil-sands-flue-gas-streams-according-to_tbl4_251711920 \ No newline at end of file +[^4]: IEM capture cost estimates for representative oil sands flue gas streams according to CO2 concentration, https://www.researchgate.net/figure/IEM-capture-cost-estimates-for-representative-oil-sands-flue-gas-streams-according-to_tbl4_251711920 diff --git a/energy_models/core/stream_type/carbon_disciplines/flue_gas_disc.py b/energy_models/core/stream_type/carbon_disciplines/flue_gas_disc.py index 0166a053..32b04229 100644 --- a/energy_models/core/stream_type/carbon_disciplines/flue_gas_disc.py +++ b/energy_models/core/stream_type/carbon_disciplines/flue_gas_disc.py @@ -188,7 +188,7 @@ def run(self): GlossaryEnergy.FlueGasMean: flue_gas_mean, 'flue_gas_production': self.energy_model.get_total_flue_gas_production(), 'flue_gas_prod_ratio': self.energy_model.get_total_flue_gas_prod_ratio()} - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/stream_type/carbon_models/carbon_capture.py b/energy_models/core/stream_type/carbon_models/carbon_capture.py index fc0f2af0..a83f1d58 100644 --- a/energy_models/core/stream_type/carbon_models/carbon_capture.py +++ b/energy_models/core/stream_type/carbon_models/carbon_capture.py @@ -88,7 +88,7 @@ def compute(self, inputs, exp_min=True): def compute_production(self, sub_production_dict, sub_consumption_dict): ''' - Specific compute energy production where we compute carbon captured from flue gas + Specific compute energy production where we compute carbon captured from flue gas ''' # Initialize dataframe out @@ -196,11 +196,11 @@ def compute_grad_element_mix_vs_prod(self, production_by_techno, elements_dict, ptot = p1 + p2 + p3 for dpi/dpi Old : dtechno_mix(p1)/dprod1 = dp1(ptot-p1)/ptot**2 - with p1 = fexp(prod1) + with p1 = fexp(prod1) dp1/dprod1 = 1.0*f'exp(prod1) dp2/dprod1 = 0.0 New : dtechno_mix(p1)/dprod1 = dp1/ptot-(dp1+dp2)p1/ptot**2 = dp1(ptot-p1)/ptot**2 -dp2p1/ptot**2 - with p1 = fexp(pbis1) + with p1 = fexp(pbis1) pbis1 = prod1*fg_perc = prod1*fexpp(fg_prod/(prod1+prod2)) dp1/dprod1 = f'exp(pbis1)*dpbis1 = f'exp(pbis1)*(fexpp(fg_ratio) + prod1*f'expp(fg_ratio)*dfg_ratio = dp1old * (fexpp(fg_ratio) + prod1*f'expp(fg_ratio)*dfg_ratio @@ -216,7 +216,7 @@ def compute_grad_element_mix_vs_prod(self, production_by_techno, elements_dict, with dp1/dprod1 = 1.0*f'exp(prod1) New : dtechno_mix(p2)/dprod1 = -p2dp1/ptot**2 -p2dp2/ptot**2 - with + with dp1/dprod1 = f'exp(pbis1)*dpbis1 = f'exp(pbis1)*(fexpp(fg_ratio) + prod1*f'expp(fg_ratio)*dfg_ratio = dp2old * (fexpp(fg_ratio) + prod1*f'expp(fg_ratio)*dfg_ratio dp2/dprod1 = f'exp(pbis2)*dpbis2 =f'exp(pbis2)*prod2*f'expp(fg_ratio)*dfg_ratio diff --git a/energy_models/core/stream_type/carbon_models/flue_gas.py b/energy_models/core/stream_type/carbon_models/flue_gas.py index 36254799..28ff464e 100644 --- a/energy_models/core/stream_type/carbon_models/flue_gas.py +++ b/energy_models/core/stream_type/carbon_models/flue_gas.py @@ -71,7 +71,7 @@ def get_total_flue_gas_production(self): def get_total_flue_gas_prod_ratio(self): ''' - Return mix weights which is flue gas production ratio + Return mix weights which is flue gas production ratio ''' return self.mix_weights diff --git a/energy_models/core/stream_type/energy_disc.py b/energy_models/core/stream_type/energy_disc.py index bdd5420d..7dd14b68 100644 --- a/energy_models/core/stream_type/energy_disc.py +++ b/energy_models/core/stream_type/energy_disc.py @@ -101,7 +101,7 @@ def run(self): outputs_dict = {GlossaryEnergy.CO2EmissionsValue: CO2_emissions} outputs_dict.update(ghg_per_use) - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/bio_diesel_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/bio_diesel_disc.markdown index b47a8549..d606e123 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/bio_diesel_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/bio_diesel_disc.markdown @@ -14,16 +14,16 @@ The production process we consider uses the chemical reactions of transesterific World bio fuel production was estimated around 1 100 TWh in 2019, including bioethanol and hydrotreated vegetable oil. Production in liter for 2019 and forecast to 2025: -![](biodiesel_production.PNG) +![](biodiesel_production.PNG) (Image credit: IEA, taken from [^2]) **Price:** -Price for FAME (Fatty Acid Methyl Ester) and SME (Soy Methyl Ester) is highly versatile, since end of 2020 it became more expensive (> 1000 USD/ton). With a calorific value of 37.27 MJ/kg, it appears to be superior to 100 USD/MWh. +Price for FAME (Fatty Acid Methyl Ester) and SME (Soy Methyl Ester) is highly versatile, since end of 2020 it became more expensive (> 1000 USD/ton). With a calorific value of 37.27 MJ/kg, it appears to be superior to 100 USD/MWh. - ![](biodiesel_price.PNG) + ![](biodiesel_price.PNG) (Image credit: NESTE, taken from [^3]) [^1]: Biodiesel, Wikipedia, https://en.wikipedia.org/wiki/Biodiesel [^2]: IEA 2022; Global Biofuel production, https://www.iea.org/data-and-statistics/charts/global-biofuel-production-in-2019-and-forecast-to-2025, License: CC BY 4.0. -[^3]: Biodiesel price, neste.com, https://www.neste.com/investors/market-data/biodiesel-prices-sme-fame \ No newline at end of file +[^3]: Biodiesel price, neste.com, https://www.neste.com/investors/market-data/biodiesel-prices-sme-fame diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/biomass_dry_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/biomass_dry_disc.markdown index 7169264e..0ca84952 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/biomass_dry_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/biomass_dry_disc.markdown @@ -8,7 +8,7 @@ Biomass sources for energy include: * Wood and wood processing wastes—firewood, wood pellets, and wood chips, lumber and furniture mill sawdust and waste, and black liquor from pulp and paper mills * Agricultural crops and waste materials—corn, soybeans, sugar cane, switchgrass, woody plants, and algae, and crop and food processing residues -* Biogenic materials in municipal solid waste—paper, cotton, and wool products, and food, yard, and wood wastes +* Biogenic materials in municipal solid waste—paper, cotton, and wool products, and food, yard, and wood wastes * Animal manure and human sewage ![](biomass_prod.PNG) [^1] diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/electricity_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/electricity_disc.markdown index c80bffc2..478eb1fb 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/electricity_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/electricity_disc.markdown @@ -22,7 +22,7 @@ World electricity production was estimated around 26 000 TWh in 2019, and the fo **Price:** -The selection of electricity production modes and their economic viability varies in accordance with demand and region. The economics vary considerably around the world, resulting in widespread residential selling prices, e.g. the price in Serbia is around 6 cents per kWh while Germany has one of the most expensive electricity price around 30 cents per kWh (€). +The selection of electricity production modes and their economic viability varies in accordance with demand and region. The economics vary considerably around the world, resulting in widespread residential selling prices, e.g. the price in Serbia is around 6 cents per kWh while Germany has one of the most expensive electricity price around 30 cents per kWh (€). **CO2 impact:** @@ -32,4 +32,4 @@ Electricity generation accounts for nearly 40% of total CO2 emissions, the large [^1]: Electricity Generation, Wikipedia, https://en.wikipedia.org/wiki/Electricity_generation [^2]: Electricity Mix, Our World in Data, https://ourworldindata.org/electricity-mix -[^3]: Electricity Map, electricitymap.org, https://www.electricitymap.org/map \ No newline at end of file +[^3]: Electricity Map, electricitymap.org, https://www.electricitymap.org/map diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/ethanol_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/ethanol_disc.markdown index c421d10a..a500671c 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/ethanol_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/ethanol_disc.markdown @@ -1,13 +1,13 @@ ## Ethanol ## Description -Bioethanol is a form of renewable energy that can be produced from agricultural feedstocks. -It can be made from very common crops such as hemp, sugarcane, potato, cassava and corn. -There has been considerable debate about how useful bioethanol is in replacing gasoline. +Bioethanol is a form of renewable energy that can be produced from agricultural feedstocks. +It can be made from very common crops such as hemp, sugarcane, potato, cassava and corn. +There has been considerable debate about how useful bioethanol is in replacing gasoline. Concerns about its production and use relate to increased food prices due to the large amount of arable land required for crops, as well as the energy and pollution balance of the whole cycle of ethanol production, especially from corn.[^1] -## Engines Fuel -Ethanol contains approximately 34% less energy per unit volume than gasoline, and therefore in theory, burning pure ethanol in a vehicle reduces range per unit measure by 34%, given the same fuel economy, compared to burning pure gasoline. +## Engines Fuel +Ethanol contains approximately 34% less energy per unit volume than gasoline, and therefore in theory, burning pure ethanol in a vehicle reduces range per unit measure by 34%, given the same fuel economy, compared to burning pure gasoline. However, since ethanol has a higher octane rating, the engine can be made more efficient by raising its compression ratio. More than 98% of U.S. gasoline contains ethanol, typically E10 (10% ethanol, 90% gasoline), to oxygenate the fuel, which reduces air pollution. @@ -22,7 +22,7 @@ Compared with conventional unleaded gasoline, ethanol is a particulate-free burn The most common production process is "Dry Milling" representing 90% of total production.[^3] ![](ethanol_world_prod_rfa.PNG) -(Image Credit: +(Image Credit: [Renewable Fuels Association](https://ethanolrfa.org/markets-and-statistics/annual-ethanol-production)) ## Sources diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/fuel_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/fuel_disc.markdown index 702e3893..498c221e 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/fuel_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/fuel_disc.markdown @@ -18,4 +18,4 @@ The general formula of fuels is CnH2n+2. Here, we consider several types of fuel [^1]: Hydrocarbons, [Wikipedia](https://en.wikipedia.org/wiki/Hydrocarbon) -[^2]: Ed Vitz, John W. Moore, Justin Shorb, Xavier Prat-Resina, Tim Wendorff, and Adam Hahn. Organic Compounds- Hydrocarbons,[Chemistry, Libretext](https://chem.libretexts.org/Bookshelves/General_Chemistry/Book%3A_ChemPRIME_(Moore_et_al.)/08%3A_Properties_of_Organic_Compounds/8.05%3A_Organic_Compounds-_Hydrocarbons) \ No newline at end of file +[^2]: Ed Vitz, John W. Moore, Justin Shorb, Xavier Prat-Resina, Tim Wendorff, and Adam Hahn. Organic Compounds- Hydrocarbons,[Chemistry, Libretext](https://chem.libretexts.org/Bookshelves/General_Chemistry/Book%3A_ChemPRIME_(Moore_et_al.)/08%3A_Properties_of_Organic_Compounds/8.05%3A_Organic_Compounds-_Hydrocarbons) diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/hydrotreated_oil_fuel_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/hydrotreated_oil_fuel_disc.markdown index 49e8da0e..b4ca9c10 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/hydrotreated_oil_fuel_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/hydrotreated_oil_fuel_disc.markdown @@ -1,3 +1,3 @@ ## What is Hydrotreated Vegetable Oil Fuel ? -Vegetable oil can be used as an alternative fuel in diesel engines and in heating oil burners. When vegetable oil is used directly as a fuel, in either modified or unmodified equipment, it is referred to as straight vegetable oil (SVO) or pure plant oil (PPO). Conventional diesel engines can be modified to help ensure that the viscosity of the vegetable oil is low enough to allow proper atomization of the fuel. This prevents incomplete combustion, which would damage the engine by causing a build-up of carbon. Straight vegetable oil can also be blended with conventional diesel or processed into biodiesel, HVO or bioliquids for use under a wider range of conditions. -(see [HVO](https://en.wikipedia.org/wiki/Vegetable_oil_fuel)) \ No newline at end of file +Vegetable oil can be used as an alternative fuel in diesel engines and in heating oil burners. When vegetable oil is used directly as a fuel, in either modified or unmodified equipment, it is referred to as straight vegetable oil (SVO) or pure plant oil (PPO). Conventional diesel engines can be modified to help ensure that the viscosity of the vegetable oil is low enough to allow proper atomization of the fuel. This prevents incomplete combustion, which would damage the engine by causing a build-up of carbon. Straight vegetable oil can also be blended with conventional diesel or processed into biodiesel, HVO or bioliquids for use under a wider range of conditions. +(see [HVO](https://en.wikipedia.org/wiki/Vegetable_oil_fuel)) diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/liquid_fuel_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/liquid_fuel_disc.markdown index b5f3cd3d..b75f8a87 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/liquid_fuel_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/liquid_fuel_disc.markdown @@ -19,4 +19,4 @@ We consider several methods for production: [^1]: Liquid Fuel, Wikipedia, https://en.wikipedia.org/wiki/Liquid_fuel [^2]: Engineering Toolbox, https://www.engineeringtoolbox.com [^3]: https://www.icao.int/environmental-protection/CarbonOffset/Documents/Methodology%20ICAO%20Carbon%20Calculator_v10-2017.pdf -[^4]: https://www.ipcc-nggip.iges.or.jp/public/gp/bgp/2_4_Water-borne_Navigation.pdf \ No newline at end of file +[^4]: https://www.ipcc-nggip.iges.or.jp/public/gp/bgp/2_4_Water-borne_Navigation.pdf diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/liquid_hydrogen_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/liquid_hydrogen_disc.markdown index 7e019372..2c223b0b 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/liquid_hydrogen_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/liquid_hydrogen_disc.markdown @@ -16,8 +16,8 @@ Significant datas for liquid hydrogen [^2]: Water is often considered harmless for the environment and it can be considered "zero emission", however, water vapor emitted in the athmosphere contribute to global warming (to a lesser extent than CO2). The energy cost to produce 1 kg of LH2 is between 10 to 12 kWh/kg for current industrial liquefaction plants, and goes down to around 6.0 kWh/kg in R&D prototypes. -The losses of H2 to produce LH2 are estimated around 1.6 % from input stream to output stream. +The losses of H2 to produce LH2 are estimated around 1.6 % from input stream to output stream. [^1]: Hydrogen Fuel Wikipedia page, https://en.wikipedia.org/wiki/Hydrogen_fuel -[^2]: Engineering Toolbox, https://www.engineeringtoolbox.com \ No newline at end of file +[^2]: Engineering Toolbox, https://www.engineeringtoolbox.com diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/methane_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/methane_disc.markdown index 7c01f536..0f7749b0 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/methane_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/methane_disc.markdown @@ -1,6 +1,6 @@ **Definition ([^1]):** -Methane is the simplest gaseous hydrocarbon, consisting of one carbon and four hydrogen atoms. It is also a powerful greenhouse gas that is found in small quantities in Earth's atmosphere. +Methane is the simplest gaseous hydrocarbon, consisting of one carbon and four hydrogen atoms. It is also a powerful greenhouse gas that is found in small quantities in Earth's atmosphere. Significant datas for methane [^2]: @@ -13,7 +13,7 @@ Significant datas for methane [^2]: * Density, Liquid at -161 °C, 422.62 kg/m^3 * Density, Gas at 15 °C, 1 atm, 0.6709 kg/m^3 * CH4 leakage at consumption : 0.08 kt/PJ [^6] - * CH4 leakage at transport,distribution : 0.195 kt/PJ [^6] + * CH4 leakage at transport,distribution : 0.195 kt/PJ [^6] * N2O after use : 0.0001 kt/PJ [^5] Methane is mostly used as a fuel, it produce carbon dioxide and water vapor when burned with oxygen: @@ -34,7 +34,7 @@ Although the concentration of methane in Earth's atmosphere is small (around 1.8 The following figure shows the balance between CH4 emissions and sinks, and the main sectors: -![](1200px-The_Global_Methane_Budget_2008–2017.PNG) +![](1200px-The_Global_Methane_Budget_2008–2017.PNG) (Image credit: The Global Carbon Project, taken from [^3]) @@ -47,4 +47,4 @@ The average price of methane around the world is 0.80 U.S. Dollar per liter. How [^3]: By The Global Carbon Project - http://www.globalcarbonatlas.org/en/CH4-emissions / https://essd.copernicus.org/articles/12/1561/2020/, CC BY 4.0, https://commons.wikimedia.org/w/index.php?curid=93292720 [^4]: Methane prices, 28 Jun 2021, GlobalPetrolPrices.com, https://www.globalpetrolprices.com/methane_prices/ [^5]: Winiwarter, W., 2005. The GAINS model for greenhouse gases-version 1.0: nitrous oxide (N2O).https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR55-GAINS-N2O.pdf -[^6]: Hoglund-Isaksson, L. and Mechler, R., 2005. The GAINS Model for Greenhouse gases-Version 1.0: Methane (CH4), IIASA Interim Report IR-05-054. International Institute for Applied Systems Analysis, Laxenburg. https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR54-GAINS-CH4.pdf \ No newline at end of file +[^6]: Hoglund-Isaksson, L. and Mechler, R., 2005. The GAINS Model for Greenhouse gases-Version 1.0: Methane (CH4), IIASA Interim Report IR-05-054. International Institute for Applied Systems Analysis, Laxenburg. https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR54-GAINS-CH4.pdf diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/methanol_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/methanol_disc.markdown index 43ddd8d1..f14c7b6f 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/methanol_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/methanol_disc.markdown @@ -1,6 +1,6 @@ **Definition:** -Methanol (CH3OH) is the simplest alcohol molecule. It has many applications and is mostly used in chemicals and +Methanol (CH3OH) is the simplest alcohol molecule. It has many applications and is mostly used in chemicals and industrial areas. But it also has potential as a fuel, through combustion reaction: $$2CH_{3}OH + 3O_{2} --> 2CO_{2} + 4H_{2}O$$ @@ -8,7 +8,7 @@ $$2CH_{3}OH + 3O_{2} --> 2CO_{2} + 4H_{2}O$$ ![](Methanol_production.PNG) (Image Credit: [^3]) -Methanol production can be separated into 2 kinds of processes [^2] [^4] : +Methanol production can be separated into 2 kinds of processes [^2] [^4] : * through syngas reforming: in(syngas), out(methanol, water) [NOT IMPLEMENTED YET] * through direct CO2 hydrogenation: in(carbon dioxide, hydrogen), out(methanol, water) @@ -25,10 +25,10 @@ Significant datas for liquid fuel [^1]: * CH4 after use : 0.0 kg/kg * N2O after use : 0.0 kg/kg * High Calorific value : 6.39 kWh/kg - + The advantages of Methanol as a fuel[^3]: - * It could be used in the auto industry without having to replace the current thermic engine automobile fleet + * It could be used in the auto industry without having to replace the current thermic engine automobile fleet * It is liquid at room temperature and thus easier to store The disadvantages of Methanol as a fuel: @@ -38,11 +38,11 @@ The disadvantages of Methanol as a fuel: [^1] [Engineering Toolbox](https://www.engineeringtoolbox.com) -[^2] [Eco-Techno-Economic Analysis of Methanol Production from Biogas and Power-to-X, +[^2] [Eco-Techno-Economic Analysis of Methanol Production from Biogas and Power-to-X, Emanuele Moioli and Tilman Schildhauer, Industrial & Engineering Chemistry Research 2022 61 (21), 7335-7348](https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.1c04682) [^3] [Schröder, J., Müller-Langer, F., Aakko-Saksa, P., Winther, K., Baumgarten, W. and Lindgren, M., 2020. Methanol as motor fuel: Summary Report.](https://www.iea-amf.org/content/fuel_information/methanol#general) [^4] [Nyári, J., 2018. Techno-economic feasibility study of a methanol plant using carbon dioxide and hydrogen.](http://kth.diva-portal.org/smash/get/diva2:1290829/FULLTEXT01.pdf) -[^5] [IRENA, 2021, Innovation Outlook : Renewable Methanol](https://www.irena.org/publications/2021/Jan/Innovation-Outlook-Renewable-Methanol) \ No newline at end of file +[^5] [IRENA, 2021, Innovation Outlook : Renewable Methanol](https://www.irena.org/publications/2021/Jan/Innovation-Outlook-Renewable-Methanol) diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/solid_fuel_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/solid_fuel_disc.markdown index 5e04fbc5..3912a182 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/solid_fuel_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/solid_fuel_disc.markdown @@ -10,8 +10,8 @@ Significant datas for liquid fuel [^2]: * Molecular Weight : 170.0 g/mol * Density: 1300 kg/m^3 - * CO2 after use : 2.42 kgCO2/kg - * N2O after use : 0.0014 kt/PJ [^4] + * CO2 after use : 2.42 kgCO2/kg + * N2O after use : 0.0014 kt/PJ [^4] * Calorific value : 4.86 kWh/kg[^3] @@ -25,4 +25,4 @@ The solid fuel section deals with the coal extraction part, for the burning part [^1]: Solid Fuel Wikipedia page, https://en.wikipedia.org/wiki/Solid_fuel [^2]: Engineering Toolbox, https://www.engineeringtoolbox.com [^3]: Lee, J.S., 2015. Calorific value of wood pellets (Doctoral dissertation, University of British Columbia). -[^4]: Winiwarter, W., 2005. The GAINS model for greenhouse gases-version 1.0: nitrous oxide (N2O).https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR55-GAINS-N2O.pdf \ No newline at end of file +[^4]: Winiwarter, W., 2005. The GAINS model for greenhouse gases-version 1.0: nitrous oxide (N2O).https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR55-GAINS-N2O.pdf diff --git a/energy_models/core/stream_type/energy_disciplines/electricity_disc.py b/energy_models/core/stream_type/energy_disciplines/electricity_disc.py index 284525b8..ba6e319c 100644 --- a/energy_models/core/stream_type/energy_disciplines/electricity_disc.py +++ b/energy_models/core/stream_type/energy_disciplines/electricity_disc.py @@ -53,11 +53,11 @@ class ElectricityDiscipline(EnergyDiscipline): # 4400TWh is total production, # we use a 50% higher value 'unit': 'Twh', - 'user_level': 2,}, + 'user_level': 2, }, 'hydropower_constraint_ref': {'type': 'float', 'default': 1000., 'unit': 'Twh', - 'user_level': 2,}, + 'user_level': 2, }, 'data_fuel_dict': {'type': 'dict', 'visibility': EnergyDiscipline.SHARED_VISIBILITY, 'namespace': 'ns_electricity', @@ -103,7 +103,7 @@ def run(self): } else: outputs_dict = {} - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/stream_type/energy_disciplines/syngas_disc.py b/energy_models/core/stream_type/energy_disciplines/syngas_disc.py index 2e0324e9..0d062d5a 100644 --- a/energy_models/core/stream_type/energy_disciplines/syngas_disc.py +++ b/energy_models/core/stream_type/energy_disciplines/syngas_disc.py @@ -113,7 +113,7 @@ def run(self): 'syngas_ratio': syngas_ratio, 'syngas_ratio_technos': self.energy_model.syngas_ratio} outputs_dict.update(ghg_per_use_dict) - + self.store_sos_outputs_values(outputs_dict) def compute_data_energy_dict(self): diff --git a/energy_models/core/stream_type/energy_disciplines/wet_biomass_disc.py b/energy_models/core/stream_type/energy_disciplines/wet_biomass_disc.py index 311d17aa..0b892bec 100644 --- a/energy_models/core/stream_type/energy_disciplines/wet_biomass_disc.py +++ b/energy_models/core/stream_type/energy_disciplines/wet_biomass_disc.py @@ -73,5 +73,5 @@ def run(self): GlossaryEnergy.EnergyProductionValue: production / inputs_dict[ 'scaling_factor_energy_production'], 'techno_mix': techno_mix} - + self.store_sos_outputs_values(outputs_dict) diff --git a/energy_models/core/stream_type/energy_models/electricity.py b/energy_models/core/stream_type/energy_models/electricity.py index bf71823f..0fd84658 100644 --- a/energy_models/core/stream_type/energy_models/electricity.py +++ b/energy_models/core/stream_type/energy_models/electricity.py @@ -26,7 +26,7 @@ class Electricity(EnergyType): default_techno_list = ['WindOffshore', GlossaryEnergy.WindOnshore, GlossaryEnergy.SolarPv, 'SolarThermal', GlossaryEnergy.Hydropower, GlossaryEnergy.CoalGen, 'OilGen', 'Nuclear', 'CombinedCycleGasTurbine', GlossaryEnergy.GasTurbine, 'BiogasFired', 'BiomassFired', - #è'Geothermal' + # è'Geothermal' ] def __init__(self, name): @@ -45,7 +45,7 @@ def configure_parameters(self, inputs_dict): def compute_hydropower_constraint(self): ''' - Compute hydropower production constraint so that + Compute hydropower production constraint so that ''' self.hydropower_constraint = pd.DataFrame( {GlossaryEnergy.Years: self.production[GlossaryEnergy.Years]}) diff --git a/energy_models/core/stream_type/energy_models/fossil.py b/energy_models/core/stream_type/energy_models/fossil.py index a7629e29..3ac2cadf 100644 --- a/energy_models/core/stream_type/energy_models/fossil.py +++ b/energy_models/core/stream_type/energy_models/fossil.py @@ -92,12 +92,12 @@ def compute_ghg_per_use(self, ghg_type): """ def compute_ghg_per_use(self, data_energy_dict): ''' - Specific computation for the CO2 per use taking into account the use of fossil in - petrochemical plants (plastic and textile), construction, cement and steel + Specific computation for the CO2 per use taking into account the use of fossil in + petrochemical plants (plastic and textile), construction, cement and steel - We only take into account energy emissions and not industrial emissions in energy mix - --> CO2 per use petrochemical and construction will be used in an industrial co2 emissions model + We only take into account energy emissions and not industrial emissions in energy mix + --> CO2 per use petrochemical and construction will be used in an industrial co2 emissions model This CO2_per_use is used in CO2 emitted by net energy ''' diff --git a/energy_models/core/stream_type/energy_models/liquid_fuel.py b/energy_models/core/stream_type/energy_models/liquid_fuel.py index 3385c5c0..e536a6c5 100644 --- a/energy_models/core/stream_type/energy_models/liquid_fuel.py +++ b/energy_models/core/stream_type/energy_models/liquid_fuel.py @@ -81,8 +81,8 @@ def compute_ghg_per_use(self, ghg_type): Specific computation for the CO2 per use taking into account the use of oil in petrochemical plants (plastic and textile) and construction - We only take into account energy emissions and not industrial emissions in energy mix - --> CO2 per use petrochemical and construction will be used in an industrial co2 emissions model + We only take into account energy emissions and not industrial emissions in energy mix + --> CO2 per use petrochemical and construction will be used in an industrial co2 emissions model ''' # kgcoal_per_kgsteel = 1 / 1.7 diff --git a/energy_models/core/stream_type/energy_models/solid_fuel.py b/energy_models/core/stream_type/energy_models/solid_fuel.py index 01ed2a61..06d2020d 100644 --- a/energy_models/core/stream_type/energy_models/solid_fuel.py +++ b/energy_models/core/stream_type/energy_models/solid_fuel.py @@ -71,7 +71,7 @@ class SolidFuel(EnergyType): def compute_ghg_per_use(self, ghg_type): ''' - Specific computation for the CO2 per use taking into account the use of coal for cement and steel + Specific computation for the CO2 per use taking into account the use of coal for cement and steel 1t of steel --> 1.852 t of CO2 @@ -79,8 +79,8 @@ def compute_ghg_per_use(self, ghg_type): 1T of cement --> 0.9 t of CO2 1T of cement --> 0.25 t of coal - We only take into account energy emissions and not industrial emissions in energy mix - --> CO2 per use for iron and steel and cement will be used in an industrial co2 emissions model + We only take into account energy emissions and not industrial emissions in energy mix + --> CO2 per use for iron and steel and cement will be used in an industrial co2 emissions model ''' # Source: IEA 2022, Iron and Steel technology roadmap, diff --git a/energy_models/core/stream_type/energy_models/syngas.py b/energy_models/core/stream_type/energy_models/syngas.py index 45d2fadf..00448004 100644 --- a/energy_models/core/stream_type/energy_models/syngas.py +++ b/energy_models/core/stream_type/energy_models/syngas.py @@ -70,7 +70,7 @@ def configure_parameters_update(self, inputs_dict): def compute_syngas_ratio(self): """ - Method to compute syngas ratio using production by + Method to compute syngas ratio using production by """ self.syngas_ratio_mean = np.zeros(len(self.years)) for techno in self.subelements_list: @@ -83,8 +83,8 @@ def compute_syngas_ratio(self): def compute_molar_mass(syngas_ratio): ''' - syngas ratio is the molar ratio of CO over H2 - We compute the molar mass following this ratio + syngas ratio is the molar ratio of CO over H2 + We compute the molar mass following this ratio if ratio is equal to zero syngas is h2 syngas_ratio must be between 0 and 1 (not in %) ''' @@ -95,8 +95,8 @@ def compute_molar_mass(syngas_ratio): def compute_calorific_value(syngas_ratio): ''' - syngas ratio is the molar ratio of CO over H2 - We compute the calorific_value following this ratio + syngas ratio is the molar ratio of CO over H2 + We compute the calorific_value following this ratio Ratio is on mol not kg !! So we need molar_mass ratio in the computation if ratio is equal to zero syngas is h2 syngas_ratio must be between 0 and 1 (not in %) @@ -112,8 +112,8 @@ def compute_calorific_value(syngas_ratio): def compute_high_calorific_value(syngas_ratio): ''' - syngas ratio is the molar ratio of CO over H2 - We compute the calorific_value following this ratio + syngas ratio is the molar ratio of CO over H2 + We compute the calorific_value following this ratio Ratio is on mol not kg !! So we need molar_mass ratio in the computation if ratio is equal to zero syngas is h2 syngas_ratio must be between 0 and 1 (not in %) @@ -147,8 +147,8 @@ def compute_dcal_val_dsyngas_ratio(syngas_ratio, type_cal='calorific_value'): def compute_density(syngas_ratio): ''' - syngas ratio is the molar ratio of CO over H2 - We compute the density following this ratio + syngas ratio is the molar ratio of CO over H2 + We compute the density following this ratio Ratio is on mol not kg !! So we need molar_mass ratio in the computation if ratio is equal to zero syngas is h2 ''' diff --git a/energy_models/core/stream_type/energy_type.py b/energy_models/core/stream_type/energy_type.py index 3edd6dcf..6e21d9de 100644 --- a/energy_models/core/stream_type/energy_type.py +++ b/energy_models/core/stream_type/energy_type.py @@ -89,6 +89,6 @@ def compute_ghg_per_use(self, ghg_type): elif self.data_energy_dict_input[f'{ghg_type}_per_use_unit'] == 'kg/kWh' or self.data_energy_dict_input[ f'{ghg_type}_per_use_unit'] == 'Mt/TWh': ghg_type_per_use = self.data_energy_dict_input[f'{ghg_type}_per_use'] - else : + else: raise Exception("ghg per use unit is not handled") return ghg_type_per_use diff --git a/energy_models/core/stream_type/resources_data_disc.py b/energy_models/core/stream_type/resources_data_disc.py index 83b5a374..1711ce09 100644 --- a/energy_models/core/stream_type/resources_data_disc.py +++ b/energy_models/core/stream_type/resources_data_disc.py @@ -85,7 +85,7 @@ class ResourcesDisc(SoSWrapp): df_desc_resource = { GlossaryEnergy.Years: ('int', [1900, GlossaryEnergy.YearEndDefaultCore], False), - **{resource : ('float', None, True) for resource in GlossaryEnergy.ResourcesList} + **{resource: ('float', None, True) for resource in GlossaryEnergy.ResourcesList} } DESC_IN = {GlossaryEnergy.YearStart: ClimateEcoDiscipline.YEAR_START_DESC_IN, GlossaryEnergy.YearEnd: GlossaryEnergy.YearEndVar, @@ -136,7 +136,7 @@ def run(self): outputs_dict = {GlossaryEnergy.ResourcesPriceValue: resources_price, GlossaryEnergy.RessourcesCO2EmissionsValue: co2_emissions} - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/stream_type/resources_models/resource_glossary.py b/energy_models/core/stream_type/resources_models/resource_glossary.py index 09d09513..7b871010 100644 --- a/energy_models/core/stream_type/resources_models/resource_glossary.py +++ b/energy_models/core/stream_type/resources_models/resource_glossary.py @@ -126,7 +126,7 @@ class ResourceGlossary: SolidCarbon = {'name': GlossaryEnergy.SolidCarbon, GlossaryEnergy.CO2EmissionsValue: 0.0, - 'price': 1180.} # https://www.made-in-china.com/price/solid-carbon-price.html + 'price': 1180.} # https://www.made-in-china.com/price/solid-carbon-price.html GlossaryDict = { 'Uranium': Uranium, 'Water': Water, 'SeaWater': SeaWater, GlossaryEnergy.CO2: CO2, 'BiomassDry': BiomassDry, diff --git a/energy_models/core/stream_type/stream_disc.py b/energy_models/core/stream_type/stream_disc.py index f11ae537..810b4a42 100644 --- a/energy_models/core/stream_type/stream_disc.py +++ b/energy_models/core/stream_type/stream_disc.py @@ -129,8 +129,7 @@ def run(self): self.energy_model.configure(inputs_dict) # -- compute informations cost_details, production, consumption, consumption_woratio, techno_mix = self.energy_model.compute(inputs_dict, - exp_min= - inputs_dict[ + exp_min=inputs_dict[ 'exp_min']) cost_details_technos = self.energy_model.sub_prices @@ -154,7 +153,7 @@ def run(self): GlossaryEnergy.LandUseRequiredValue: self.energy_model.land_use_required, GlossaryEnergy.EnergyTypeCapitalDfValue: self.energy_model.energy_type_capital } - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/techno_type/base_techno_models/biodiesel_techno.py b/energy_models/core/techno_type/base_techno_models/biodiesel_techno.py index 62b72401..2f9bb711 100644 --- a/energy_models/core/techno_type/base_techno_models/biodiesel_techno.py +++ b/energy_models/core/techno_type/base_techno_models/biodiesel_techno.py @@ -23,5 +23,3 @@ class BioDieselTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) self.energy_name = BioDiesel.name - - diff --git a/energy_models/core/techno_type/base_techno_models/biogas_techno.py b/energy_models/core/techno_type/base_techno_models/biogas_techno.py index 7faf5190..34c083ff 100644 --- a/energy_models/core/techno_type/base_techno_models/biogas_techno.py +++ b/energy_models/core/techno_type/base_techno_models/biogas_techno.py @@ -24,5 +24,3 @@ class BioGasTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) - - diff --git a/energy_models/core/techno_type/base_techno_models/biomass_dry_techno.py b/energy_models/core/techno_type/base_techno_models/biomass_dry_techno.py index 873d64d7..57ba5d08 100644 --- a/energy_models/core/techno_type/base_techno_models/biomass_dry_techno.py +++ b/energy_models/core/techno_type/base_techno_models/biomass_dry_techno.py @@ -42,5 +42,3 @@ def compute_land_use(self): if 'recyle_part' in self.techno_infos_dict: self.land_use[f'{self.name} (Gha)'] *= ( 1 - self.techno_infos_dict['recyle_part']) - - diff --git a/energy_models/core/techno_type/base_techno_models/carbon_capture_techno.py b/energy_models/core/techno_type/base_techno_models/carbon_capture_techno.py index f908aeb7..b6dac86e 100644 --- a/energy_models/core/techno_type/base_techno_models/carbon_capture_techno.py +++ b/energy_models/core/techno_type/base_techno_models/carbon_capture_techno.py @@ -64,8 +64,6 @@ def check_energy_demand_unit(self, energy_demand_unit, energy_demand): return energy_demand - - @staticmethod def compute_capex_variation_from_fg_ratio(fg_mean_ratio, fg_ratio_effect): @@ -201,7 +199,7 @@ def compute_dnon_usecapital_dfluegas(self, dcapex_dfluegas, dprod_dfluegas): (len(self.years), 1))) dnon_usecapital_dfluegas = dtechnocapital_dfluegas * ( - 1.0 - self.applied_ratio['applied_ratio'].values * self.utilisation_ratio/ 100.).reshape((len(self.years), 1)) + 1.0 - self.applied_ratio['applied_ratio'].values * self.utilisation_ratio / 100.).reshape((len(self.years), 1)) # we do not divide by / self.scaling_factor_invest_level because invest # and non_use_capital are in G$ diff --git a/energy_models/core/techno_type/base_techno_models/electricity_techno.py b/energy_models/core/techno_type/base_techno_models/electricity_techno.py index 70736154..1a52d94b 100644 --- a/energy_models/core/techno_type/base_techno_models/electricity_techno.py +++ b/energy_models/core/techno_type/base_techno_models/electricity_techno.py @@ -29,5 +29,3 @@ def compute_transport(self): self.transport_margin[GlossaryEnergy.MarginValue].values / 100.0 return transport_cost - - diff --git a/energy_models/core/techno_type/base_techno_models/ethanol_techno.py b/energy_models/core/techno_type/base_techno_models/ethanol_techno.py index 20b2a86d..2e2fb77b 100644 --- a/energy_models/core/techno_type/base_techno_models/ethanol_techno.py +++ b/energy_models/core/techno_type/base_techno_models/ethanol_techno.py @@ -23,5 +23,3 @@ class EthanolTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) self.energy_name = Ethanol.name - - diff --git a/energy_models/core/techno_type/base_techno_models/fossil_techno.py b/energy_models/core/techno_type/base_techno_models/fossil_techno.py index 8e3dc174..4356b544 100644 --- a/energy_models/core/techno_type/base_techno_models/fossil_techno.py +++ b/energy_models/core/techno_type/base_techno_models/fossil_techno.py @@ -30,5 +30,3 @@ def compute_transport(self): self.transport_margin[GlossaryEnergy.MarginValue].values / 100.0 return transport_cost - - diff --git a/energy_models/core/techno_type/base_techno_models/gaseous_hydrogen_techno.py b/energy_models/core/techno_type/base_techno_models/gaseous_hydrogen_techno.py index 8e68d835..62a59c0a 100644 --- a/energy_models/core/techno_type/base_techno_models/gaseous_hydrogen_techno.py +++ b/energy_models/core/techno_type/base_techno_models/gaseous_hydrogen_techno.py @@ -26,5 +26,3 @@ class GaseousHydrogenTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) - - diff --git a/energy_models/core/techno_type/base_techno_models/high_heat_techno.py b/energy_models/core/techno_type/base_techno_models/high_heat_techno.py index a76f013d..56ed04ea 100644 --- a/energy_models/core/techno_type/base_techno_models/high_heat_techno.py +++ b/energy_models/core/techno_type/base_techno_models/high_heat_techno.py @@ -29,6 +29,3 @@ def compute_transport(self): self.transport_margin[GlossaryEnergy.MarginValue].values / 100.0 return transport_cost - - - diff --git a/energy_models/core/techno_type/base_techno_models/hydrotreated_oil_fuel_techno.py b/energy_models/core/techno_type/base_techno_models/hydrotreated_oil_fuel_techno.py index e18fb450..a5902e7d 100644 --- a/energy_models/core/techno_type/base_techno_models/hydrotreated_oil_fuel_techno.py +++ b/energy_models/core/techno_type/base_techno_models/hydrotreated_oil_fuel_techno.py @@ -25,5 +25,3 @@ class HydrotreatedOilFuelTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) self.energy_name = HydrotreatedOilFuel.name - - diff --git a/energy_models/core/techno_type/base_techno_models/kerosene_techno.py b/energy_models/core/techno_type/base_techno_models/kerosene_techno.py index b5d19691..3abb986a 100644 --- a/energy_models/core/techno_type/base_techno_models/kerosene_techno.py +++ b/energy_models/core/techno_type/base_techno_models/kerosene_techno.py @@ -24,5 +24,3 @@ class KeroseneTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) - - diff --git a/energy_models/core/techno_type/base_techno_models/liquid_fuel_techno.py b/energy_models/core/techno_type/base_techno_models/liquid_fuel_techno.py index 9ce4765b..a83672ab 100644 --- a/energy_models/core/techno_type/base_techno_models/liquid_fuel_techno.py +++ b/energy_models/core/techno_type/base_techno_models/liquid_fuel_techno.py @@ -24,5 +24,3 @@ class LiquidFuelTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) - - diff --git a/energy_models/core/techno_type/base_techno_models/liquid_hydrogen_techno.py b/energy_models/core/techno_type/base_techno_models/liquid_hydrogen_techno.py index df79193e..f4fcd512 100644 --- a/energy_models/core/techno_type/base_techno_models/liquid_hydrogen_techno.py +++ b/energy_models/core/techno_type/base_techno_models/liquid_hydrogen_techno.py @@ -24,5 +24,3 @@ class LiquidHydrogenTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) - - diff --git a/energy_models/core/techno_type/base_techno_models/low_heat_techno.py b/energy_models/core/techno_type/base_techno_models/low_heat_techno.py index 7070f952..fb215280 100644 --- a/energy_models/core/techno_type/base_techno_models/low_heat_techno.py +++ b/energy_models/core/techno_type/base_techno_models/low_heat_techno.py @@ -29,6 +29,3 @@ def compute_transport(self): self.transport_margin[GlossaryEnergy.MarginValue].values / 100.0 return transport_cost - - - diff --git a/energy_models/core/techno_type/base_techno_models/medium_heat_techno.py b/energy_models/core/techno_type/base_techno_models/medium_heat_techno.py index c7a62a80..1845981e 100644 --- a/energy_models/core/techno_type/base_techno_models/medium_heat_techno.py +++ b/energy_models/core/techno_type/base_techno_models/medium_heat_techno.py @@ -29,5 +29,3 @@ def compute_transport(self): self.transport_margin[GlossaryEnergy.MarginValue].values / 100.0 return transport_cost - - diff --git a/energy_models/core/techno_type/base_techno_models/methane_techno.py b/energy_models/core/techno_type/base_techno_models/methane_techno.py index b8a249b5..1a7d69ae 100644 --- a/energy_models/core/techno_type/base_techno_models/methane_techno.py +++ b/energy_models/core/techno_type/base_techno_models/methane_techno.py @@ -24,5 +24,3 @@ class MethaneTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) - - diff --git a/energy_models/core/techno_type/base_techno_models/methanol_techno.py b/energy_models/core/techno_type/base_techno_models/methanol_techno.py index 3c480b49..177acaca 100644 --- a/energy_models/core/techno_type/base_techno_models/methanol_techno.py +++ b/energy_models/core/techno_type/base_techno_models/methanol_techno.py @@ -23,5 +23,3 @@ class MethanolTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) self.energy_name = Methanol.name - - diff --git a/energy_models/core/techno_type/base_techno_models/renewable_techno.py b/energy_models/core/techno_type/base_techno_models/renewable_techno.py index 6021d840..1ac56a81 100644 --- a/energy_models/core/techno_type/base_techno_models/renewable_techno.py +++ b/energy_models/core/techno_type/base_techno_models/renewable_techno.py @@ -30,6 +30,3 @@ def compute_transport(self): self.transport_margin[GlossaryEnergy.MarginValue].values / 100.0 return transport_cost - - - diff --git a/energy_models/core/techno_type/base_techno_models/solid_fuel_techno.py b/energy_models/core/techno_type/base_techno_models/solid_fuel_techno.py index d58b8db4..d57fea77 100644 --- a/energy_models/core/techno_type/base_techno_models/solid_fuel_techno.py +++ b/energy_models/core/techno_type/base_techno_models/solid_fuel_techno.py @@ -24,5 +24,3 @@ class SolidFuelTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) - - diff --git a/energy_models/core/techno_type/base_techno_models/syngas_techno.py b/energy_models/core/techno_type/base_techno_models/syngas_techno.py index 48107d7a..6b6d4a1a 100644 --- a/energy_models/core/techno_type/base_techno_models/syngas_techno.py +++ b/energy_models/core/techno_type/base_techno_models/syngas_techno.py @@ -42,8 +42,6 @@ def configure_energy_data(self, inputs_dict): self.data_energy_dict['high_calorific_value'] = calorific_value self.data_energy_dict['density'] = density - - def compute_transport(self): # Electricity has no Calorific value overload # Warning transport cost unit must $/kWh diff --git a/energy_models/core/techno_type/base_techno_models/wet_biomass_techno.py b/energy_models/core/techno_type/base_techno_models/wet_biomass_techno.py index 42c96716..2a2abffe 100644 --- a/energy_models/core/techno_type/base_techno_models/wet_biomass_techno.py +++ b/energy_models/core/techno_type/base_techno_models/wet_biomass_techno.py @@ -24,5 +24,3 @@ class WetBiomassTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) - - diff --git a/energy_models/core/techno_type/disciplines/carbon_capture_techno_disc.py b/energy_models/core/techno_type/disciplines/carbon_capture_techno_disc.py index c845ecbb..79bae2ec 100644 --- a/energy_models/core/techno_type/disciplines/carbon_capture_techno_disc.py +++ b/energy_models/core/techno_type/disciplines/carbon_capture_techno_disc.py @@ -115,7 +115,7 @@ def set_partial_derivatives_flue_gas(self, energy_name=GlossaryEnergy.electricit self.set_partial_derivative_for_other_types( (GlossaryEnergy.TechnoProductionValue, f'{self.energy_name} ({self.techno_model.product_unit})'), ( GlossaryEnergy.FlueGasMean, GlossaryEnergy.FlueGasMean), - dprod_dfluegas * (self.techno_model.applied_ratio['applied_ratio'].values * utilisation_ratio/ 100.)[:, + dprod_dfluegas * (self.techno_model.applied_ratio['applied_ratio'].values * utilisation_ratio / 100.)[:, np.newaxis] * scaling_factor_invest_level / scaling_factor_techno_production) production, consumption = self.get_sosdisc_outputs( @@ -132,7 +132,7 @@ def set_partial_derivatives_flue_gas(self, energy_name=GlossaryEnergy.electricit self.set_partial_derivative_for_other_types( (GlossaryEnergy.TechnoConsumptionValue, column), (GlossaryEnergy.FlueGasMean, GlossaryEnergy.FlueGasMean), - dprod_column_dfluegas * (self.techno_model.applied_ratio['applied_ratio'].values * utilisation_ratio/ 100.)[:, + dprod_column_dfluegas * (self.techno_model.applied_ratio['applied_ratio'].values * utilisation_ratio / 100.)[:, np.newaxis] * scaling_factor_invest_level / scaling_factor_techno_production) self.set_partial_derivative_for_other_types( (GlossaryEnergy.TechnoConsumptionWithoutRatioValue, diff --git a/energy_models/core/techno_type/disciplines/syngas_techno_disc.py b/energy_models/core/techno_type/disciplines/syngas_techno_disc.py index cbbdb317..7c4e60f2 100644 --- a/energy_models/core/techno_type/disciplines/syngas_techno_disc.py +++ b/energy_models/core/techno_type/disciplines/syngas_techno_disc.py @@ -71,7 +71,7 @@ def run(self): super().run() outputs_dict = {'syngas_ratio': np.array([self.syngas_ratio])} - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/techno_type/techno_disc.py b/energy_models/core/techno_type/techno_disc.py index ccec5346..b12605ea 100644 --- a/energy_models/core/techno_type/techno_disc.py +++ b/energy_models/core/techno_type/techno_disc.py @@ -74,7 +74,7 @@ class TechnoDiscipline(SoSWrapp): 'visibility': SoSWrapp.SHARED_VISIBILITY, 'namespace': 'ns_public', 'user_level': 2}, 'smooth_type': {'type': 'string', 'default': 'smooth_max', - 'possible_values': ['smooth_max', 'soft_max', ], # 'cons_smooth_max' : deactivated cause gradients are wrong when all ratios are 1. (no limiting stream) + 'possible_values': ['smooth_max', 'soft_max', ], # 'cons_smooth_max' : deactivated cause gradients are wrong when all ratios are 1. (no limiting stream) 'user_level': 2, 'structuring': False, 'visibility': SoSWrapp.SHARED_VISIBILITY, 'namespace': 'ns_public'}, GlossaryEnergy.BoolApplyRatio: {'type': 'bool', 'default': True, 'user_level': 2, 'structuring': True, @@ -216,7 +216,7 @@ def setup_sos_disciplines(self): energy_name=self.energy_name, byproducts_list=GlossaryEnergy.techno_byproducts[self.techno_name]), GlossaryEnergy.LandUseRequiredValue: GlossaryEnergy.get_land_use_df(techno_name=self.techno_name), - 'age_distrib_production': GlossaryEnergy.get_age_distrib_prod_df(energy_name=self.energy_name), # todo: not coupled, rename cols and move to DESC_OUT + 'age_distrib_production': GlossaryEnergy.get_age_distrib_prod_df(energy_name=self.energy_name), # todo: not coupled, rename cols and move to DESC_OUT GlossaryEnergy.TechnoDetailedPricesValue: GlossaryEnergy.get_techno_detailed_price_df(techno_name=self.techno_name), }) self.add_inputs(dynamic_inputs) @@ -303,7 +303,7 @@ def update_default_values(self): def run(self): ''' - Generic run for all technologies + Generic run for all technologies ''' # -- get inputs inputs_dict = self.get_sosdisc_inputs() @@ -524,7 +524,7 @@ def compute_sos_jacobian(self): var_cons = (consumption[column] / installed_power['new_power_production']).fillna(0) self.dcons_column_dinvest = self.dpower_dinvest.copy() else: - var_cons = (consumption[column] /production[f'{self.energy_name} ({self.techno_model.product_unit})']).fillna(0) + var_cons = (consumption[column] / production[f'{self.energy_name} ({self.techno_model.product_unit})']).fillna(0) self.dcons_column_dinvest = self.dprod_dinvest.copy() for line in range(len(years)): @@ -1398,7 +1398,7 @@ def get_chart_capex(self): years = cost_details[GlossaryEnergy.Years] capex = cost_details[f'Capex_{self.techno_name}'] new_chart = TwoAxesInstanciatedChart(GlossaryEnergy.Years, '$/MWh', chart_name=chart_name) - serie = InstanciatedSeries( years, capex, '','lines') + serie = InstanciatedSeries(years, capex, '', 'lines') new_chart.series.append(serie) return new_chart diff --git a/energy_models/core/techno_type/techno_type.py b/energy_models/core/techno_type/techno_type.py index f770ddb1..aa4d5dd1 100644 --- a/energy_models/core/techno_type/techno_type.py +++ b/energy_models/core/techno_type/techno_type.py @@ -201,7 +201,6 @@ def configure_parameters_update(self, inputs_dict): <= self.year_end] self.stream_prices = inputs_dict[GlossaryEnergy.StreamPricesValue] - self.invest_level = inputs_dict[GlossaryEnergy.InvestLevelValue].loc[ inputs_dict[GlossaryEnergy.InvestLevelValue][GlossaryEnergy.Years] <= self.year_end] @@ -280,7 +279,7 @@ def select_resources_ratios(self): def apply_resources_ratios(self, apply_ressources_ratio: bool = True): """! Select the most constraining ratio and apply it to production and consumption. - To avoid clipping effects, the applied ratio is not the minimum value between all the ratios, + To avoid clipping effects, the applied ratio is not the minimum value between all the ratios, but the smoothed minimum value between all the ratio (see func_manager documentation for more). A model variables is set in this method: -self.applied_ratio: the effective ratio applied for each year @@ -351,11 +350,11 @@ def compute_capital(self): ''' Compute Capital & loss of capital because of the unusability of the technology. When the applied ratio is below 1, the technology does not produce all the energy possible. - Investments on this technology is consequently non_use. - This method computes the non_use of capital + Investments on this technology is consequently non_use. + This method computes the non_use of capital Capex is in $/MWh - Prod in TWh + Prod in TWh then capex*prod_wo_ratio is in $/MWh*(1e6MWh)= M$ We divide by scaling_factor_invest_level to put non_use_capital in G$ @@ -470,7 +469,7 @@ def compute_cost_of_resources_usage(self): """ Cost of resource R = need of resource R x price of resource R """ - cost_of_resource_usage = { + cost_of_resource_usage = { GlossaryEnergy.Years: self.years, } for resource in self.resources_used_for_production: @@ -616,7 +615,7 @@ def check_capex_unity(self, data_tocheck): """ Put all capex in $/MWh """ - capex_init = None # intialize capex init variable + capex_init = None # intialize capex init variable if data_tocheck['Capex_init_unit'] == 'euro': # it is a total capital requirement TCR , need to be divided by # full_load_hours available power and capacity factor @@ -742,7 +741,6 @@ def get_electricity_needs(self): return elec_need - def check_energy_demand_unit(self, energy_demand_unit, energy_demand): """ Compute energy demand in kWh/kWh or MWh/MWh (equivalent) @@ -787,7 +785,7 @@ def compute_efficiency(self): self.cost_details['efficiency'] = efficiency return efficiency - + def sigmoid_function(self, x, eff_max, eff_ini, x_shift, slope): x = x - x_shift # Logistic function @@ -882,7 +880,7 @@ def compute_co2_tax(self): @abstractmethod def get_theoretical_co2_prod(self, unit='kg/kWh'): - ''' + ''' Get the theoretical CO2 production for a given technology, Need to be overloaded in each technology model (example in SMR) ''' @@ -891,7 +889,7 @@ def get_theoretical_co2_prod(self, unit='kg/kWh'): def compute_primary_energy_production(self): ''' Compute the primary energy production for each technology - (primary energy is H2 for H2 techno , Kero for Kero techno ...etc) + (primary energy is H2 for H2 techno , Kero for Kero techno ...etc) ''' # First compute the initial aging distribution with the initial # production found in the techno discipline @@ -918,7 +916,6 @@ def compute_primary_energy_production(self): def compute_primary_installed_power(self): - if 'full_load_hours' in self.techno_infos_dict: full_load_hours = self.techno_infos_dict['full_load_hours'] else: @@ -948,9 +945,9 @@ def compute_primary_installed_power(self): def compute_aging_distribution_production(self): ''' Compute the aging distribution production of primary energy for years of study - Start with the initial distribution and add a year on the age each year + Start with the initial distribution and add a year on the age each year Add also the yearly production regarding the investment - All productions older than the lifetime are removed from the dataframe + All productions older than the lifetime are removed from the dataframe ''' # To break the object link with initial distrib aging_distrib_year_df = pd.DataFrame( @@ -1210,7 +1207,6 @@ def d_non_use_capital_d_invest_level(self, d_capital_d_invest_level): utilisation_ratio = self.utilisation_ratio return np.diag(applied_ratio * utilisation_ratio / 100.) * d_capital_d_invest_level - def compute_dlanduse_dinvest(self): """ compute grad d_land_use / d_invest @@ -1552,7 +1548,6 @@ def compute_dcapex_dinvest(self, invest_list, data_config): return (1.0 - maximum_learning_capex_ratio) * dcapex_calc_list_dinvest_list * dinvest_func.reshape( len(invest_list)) - "---------END OF GRADIENTS---------" def compute_initial_age_distribution(self): @@ -1576,4 +1571,4 @@ def compute_initial_plants_historical_prod(self): }) self.initial_plants_historical_prod.sort_values(GlossaryEnergy.Years, inplace=True) - self.initial_plants_historical_prod[f'cum energy ({self.product_unit})'] = self.initial_plants_historical_prod[f'energy ({self.product_unit})'].cumsum() \ No newline at end of file + self.initial_plants_historical_prod[f'cum energy ({self.product_unit})'] = self.initial_plants_historical_prod[f'energy ({self.product_unit})'].cumsum() diff --git a/energy_models/database_witness_energy.py b/energy_models/database_witness_energy.py index 851b01ec..8d75c4cc 100644 --- a/energy_models/database_witness_energy.py +++ b/energy_models/database_witness_energy.py @@ -148,8 +148,8 @@ def get_techno_invest_before_year_start(cls, techno_name: str, year_start: int, out_df = heavy_collected_data.get_between_years(year_start=year_start - construction_delay, year_end=year_start - 1) return out_df, heavy_collected_data - techno_production_historic_folder = join(Path(__file__).parents[1], "data_energy", "techno_production_historic") + @classmethod def get_techno_prod(cls, techno_name: str, year: int, is_available_at_year: bool = False): name_formatted = techno_name.replace(".", "_") @@ -172,7 +172,6 @@ def get_techno_prod(cls, techno_name: str, year: int, is_available_at_year: bool out = heavy_collected_data.get_value_at_year(year=year) return out, heavy_collected_data - techno_age_distrib_folder = join(Path(__file__).parents[1], "data_energy", "techno_factories_age") @classmethod @@ -195,4 +194,3 @@ def get_techno_age_distrib_factor(cls, techno_name: str, year: int, is_available out = heavy_collected_data.get_value_at_year(year=year) return out, heavy_collected_data - diff --git a/energy_models/datasets_database/datasets/readme.txt b/energy_models/datasets_database/datasets/readme.txt index ec39174a..d2adfefa 100644 --- a/energy_models/datasets_database/datasets/readme.txt +++ b/energy_models/datasets_database/datasets/readme.txt @@ -1,4 +1,4 @@ -The dataset database (the folder "datasets_database\datasets") have to contain the parameters you want to update in your usecase. +The dataset database (the folder "datasets_database\datasets") have to contain the parameters you want to update in your usecase. To create a new dataset: @@ -17,4 +17,4 @@ Example of descriptor.json file content: "y_array":"@array@y_array.csv", "z_list":[1.0,2.0,3.0], "d":"@dataframe@d.csv" -} \ No newline at end of file +} diff --git a/energy_models/datasets_database/mappings/readme.txt b/energy_models/datasets_database/mappings/readme.txt index 0c94152d..bda58bc9 100644 --- a/energy_models/datasets_database/mappings/readme.txt +++ b/energy_models/datasets_database/mappings/readme.txt @@ -1,6 +1,6 @@ To describe wich dataset goes with wich data in a specific usecase, the usecase will need a dataset_mapping file. -This file can be created next to the usecase.py file For testing purpose, or in the "datasets_database\mappings" folder if the mapping is official and where everyone can retrieve it. +This file can be created next to the usecase.py file For testing purpose, or in the "datasets_database\mappings" folder if the mapping is official and where everyone can retrieve it. The dataset mapping file has the following structure: @@ -31,4 +31,3 @@ A dataset information has the following format: - dataset_disc1 → name of the dataset - |* → means that it is for all parameters of this dataset. For now you can't specify a single parameter but it will be possible in the future. If several dataset are specified for one namespace, if a parameter is present in several datasets, the value of the parameter will be the value of the last dataset to have the parameter value. - diff --git a/energy_models/glossaryenergy.py b/energy_models/glossaryenergy.py index 8b0feaa9..06b13b42 100644 --- a/energy_models/glossaryenergy.py +++ b/energy_models/glossaryenergy.py @@ -1072,25 +1072,25 @@ class GlossaryEnergy(GlossaryWitnessCore): Transesterification: [electricity], # heat -> low, no electricity AnaerobicDigestion: [electricity], # produce heat -> low, dont consume electricity, consume biomass_dry and wet ManagedWood: [electricity], # consume fuel and electricity .. les tronçonneuses et les camions (donc transport fuel) - UnmanagedWood: [electricity], # consume fuel and electricity .. les tronçonneuses et les camions (donc transport fuel) + UnmanagedWood: [electricity], # consume fuel and electricity .. les tronçonneuses et les camions (donc transport fuel) f"{direct_air_capture}.{AmineScrubbing}": [electricity, methane], # put heat instead of methane - f"{direct_air_capture}.{CalciumPotassiumScrubbing}": [electricity, methane], # put heat instead of methane - f"{direct_air_capture}.{DirectAirCaptureTechno}": [GlossaryWitnessCore.clean_energy, fossil], # dont touch + f"{direct_air_capture}.{CalciumPotassiumScrubbing}": [electricity, methane], # put heat instead of methane + f"{direct_air_capture}.{DirectAirCaptureTechno}": [GlossaryWitnessCore.clean_energy, fossil], # dont touch # in flue gas techno: heat comes directly from plant so just electricity - f"{flue_gas_capture}.{CalciumLooping}": [electricity], # heat and electricity - f"{flue_gas_capture}.{ChilledAmmoniaProcess}": [electricity], # heat and electricity - f"{flue_gas_capture}.{CO2Membranes}": [electricity], # heat and electricity - f"{flue_gas_capture}.{FlueGasTechno}": [GlossaryWitnessCore.clean_energy], # heat and electricity -> not heat because - f"{flue_gas_capture}.{MonoEthanolAmine}": [electricity], # heat and electricity - f"{flue_gas_capture}.{PiperazineProcess}": [electricity], # heat and electricity - f"{flue_gas_capture}.{PressureSwingAdsorption}": [electricity], # heat and electricity + f"{flue_gas_capture}.{CalciumLooping}": [electricity], # heat and electricity + f"{flue_gas_capture}.{ChilledAmmoniaProcess}": [electricity], # heat and electricity + f"{flue_gas_capture}.{CO2Membranes}": [electricity], # heat and electricity + f"{flue_gas_capture}.{FlueGasTechno}": [GlossaryWitnessCore.clean_energy], # heat and electricity -> not heat because + f"{flue_gas_capture}.{MonoEthanolAmine}": [electricity], # heat and electricity + f"{flue_gas_capture}.{PiperazineProcess}": [electricity], # heat and electricity + f"{flue_gas_capture}.{PressureSwingAdsorption}": [electricity], # heat and electricity BiomassFired: [biomass_dry], CoalGen: [solid_fuel], GasTurbine: [methane], CombinedCycleGasTurbine: [methane], BiogasFired: [biogas], OilGen: [f"{fuel}.{liquid_fuel}"], - BiomassFermentation: [biomass_dry, electricity], # heat instead of electricity + BiomassFermentation: [biomass_dry, electricity], # heat instead of electricity ElectrolysisAWE: [electricity], ElectrolysisPEM: [electricity], ElectrolysisSOEC: [electricity], @@ -1116,9 +1116,9 @@ class GlossaryEnergy(GlossaryWitnessCore): HefaDeoxygenation: [f"{hydrogen}.{gaseous_hydrogen}", electricity], # heat instead of electricity, use resource natural_oil (trygliceride) Refinery: [f"{hydrogen}.{gaseous_hydrogen}", electricity], # idea : creer une techno puit de pétrole (CrudeOil) HydrogenLiquefaction: [f"{hydrogen}.{gaseous_hydrogen}", electricity], # might need some heat ? produced or consumed, not clear - FossilGas: [electricity], # "transport fuel" + FossilGas: [electricity], # "transport fuel" Methanation: [f"{hydrogen}.{gaseous_hydrogen}", carbon_capture], - UpgradingBiogas: [electricity, biogas], # heat not electricity + UpgradingBiogas: [electricity, biogas], # heat not electricity CO2Hydrogenation: [ f"{hydrogen}.{gaseous_hydrogen}", electricity, @@ -1126,14 +1126,14 @@ class GlossaryEnergy(GlossaryWitnessCore): ], CoalExtraction: [electricity], # transport fuel instead of electricity Pelletizing: [electricity, biomass_dry], # might be heat instead of electricity - AutothermalReforming: [methane, carbon_capture], # add heat + AutothermalReforming: [methane, carbon_capture], # add heat BiomassGasification: [electricity, biomass_dry], # heat instead of electricity, produce syngas CoElectrolysis: [electricity, carbon_capture], CoalGasification: [solid_fuel], # add heat - RWGS: [electricity, syngas], # heat instead of electricity, CO2 instead of carbon_capture + RWGS: [electricity, syngas], # heat instead of electricity, CO2 instead of carbon_capture SMR: [electricity, methane], # heat instead of elec AnimalManure: [electricity], # transport fuel in stead of elec - WetCropResidues: [electricity], # transport fuel in stead of elec + WetCropResidues: [electricity], # transport fuel in stead of elec Geothermal: [f"{heat}.{mediumtemperatureheat}"], # just electricity BiomassBuryingFossilization: [biomass_dry], # add transport fuel DeepOceanInjection: [], # add transport fuel @@ -1183,7 +1183,7 @@ class GlossaryEnergy(GlossaryWitnessCore): PureCarbonSolidStorage: [SolidCarbon], # note : could be a stream but we prefered to let it as a resource for the moment } - #TechnoResourceUsedDict[FischerTropsch] = TechnoResourceUsedDict[ReversedWaterGasShift] + TechnoResourceUsedDict[WaterGasShift] + # TechnoResourceUsedDict[FischerTropsch] = TechnoResourceUsedDict[ReversedWaterGasShift] + TechnoResourceUsedDict[WaterGasShift] TechnoBuildingResourceDict = { CoalGen: [CopperResource], @@ -1273,7 +1273,7 @@ class GlossaryEnergy(GlossaryWitnessCore): GeothermalLowHeat: f"{heat}.{lowtemperatureheat}", ElectricBoilerMediumHeat: f"{heat}.{mediumtemperatureheat}", GeothermalMediumHeat: f"{heat}.{mediumtemperatureheat}", - #CO2Hydrogenation: f'{fuel}.{methanol}', + # CO2Hydrogenation: f'{fuel}.{methanol}', NaturalGasBoilerMediumHeat: f"{heat}.{mediumtemperatureheat}", HeatPumpLowHeat: f"{heat}.{lowtemperatureheat}", CHPLowHeat: f"{heat}.{lowtemperatureheat}", @@ -1286,8 +1286,8 @@ class GlossaryEnergy(GlossaryWitnessCore): RWGS: 2, UpgradingBiogas: 2, Methanation: 2, # Thema, M., Bauer, F. and Sterner, M., 2019. Power-to-Gas: Electrolysis and methanation status review. Renewable and Sustainable Energy Reviews, 112, pp.775-787. the average time needed for planning and constructing was about 1.5years from Thema2019 - WaterGasShift: 2, # Giuliano, A., Freda, C. and Catizzone, E., 2020. Techno-economic assessment of bio-syngas production for methanol synthesis: A focus on the water gas shift and carbon capture sections. Bioengineering, 7(3), p.70. - ElectrolysisSOEC: 1, # Haldor Topsoe, 2021 Haldor Topsoe to build large-scale SOEC electrolyzer manufacturing facility to meet customer needs for green hydrogen production https://blog.topsoe.com/haldor-topsoe-to-build-large-scale-soec-electrolyzer-manufacturing-facility-to-meet-customer-needs-for-green-hydrogen-production Construction will start in 2022 and will ends in 2023 + WaterGasShift: 2, # Giuliano, A., Freda, C. and Catizzone, E., 2020. Techno-economic assessment of bio-syngas production for methanol synthesis: A focus on the water gas shift and carbon capture sections. Bioengineering, 7(3), p.70. + ElectrolysisSOEC: 1, # Haldor Topsoe, 2021 Haldor Topsoe to build large-scale SOEC electrolyzer manufacturing facility to meet customer needs for green hydrogen production https://blog.topsoe.com/haldor-topsoe-to-build-large-scale-soec-electrolyzer-manufacturing-facility-to-meet-customer-needs-for-green-hydrogen-production Construction will start in 2022 and will ends in 2023 ElectrolysisPEM: 2, ElectrolysisAWE: 1, PlasmaCracking: 2, @@ -1310,11 +1310,11 @@ class GlossaryEnergy(GlossaryWitnessCore): WindOffshore: 3, # ATB NREL 2020 WindOnshore: 3, # ATB NREL 2020 SolarPv: 1, - SolarThermal: 3, # JRC, ATB NREL, database https://solarpaces.nrel.gov/ + SolarThermal: 3, # JRC, ATB NREL, database https://solarpaces.nrel.gov/ Hydropower: 3, - Nuclear: 6, # Timilsina, G.R., 2020. Demystifying the Costs of Electricity Generation # Technologies., average + Nuclear: 6, # Timilsina, G.R., 2020. Demystifying the Costs of Electricity Generation # Technologies., average CombinedCycleGasTurbine: 2, - GasTurbine: 2, #Lazard + GasTurbine: 2, # Lazard BiogasFired: 2, CoalGen: 5, # For 1000MW hypercritical in Korea OilGen: 5, # For 1000MW hypercritical in Korea @@ -1346,104 +1346,104 @@ class GlossaryEnergy(GlossaryWitnessCore): ElectricBoilerHighHeat: 2, GeothermalHighHeat: 1, ElectricBoilerLowHeat: 2, - CHPHighHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University - NaturalGasBoilerHighHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University + CHPHighHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University + NaturalGasBoilerHighHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University NaturalGasBoilerLowHeat: 2, GeothermalLowHeat: 1, HeatPumpLowHeat: 1, GeothermalMediumHeat: 1, HeatPumpMediumHeat: 1, - CHPLowHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University + CHPLowHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University FossilSimpleTechno: 3, - NaturalGasBoilerMediumHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University + NaturalGasBoilerMediumHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University CO2Hydrogenation: 3, - GlossaryWitnessCore.CleanEnergySimpleTechno: 3, # Timilsina, G.R., 2020. Demystifying the Costs of Electricity Generation # Technologies., average + GlossaryWitnessCore.CleanEnergySimpleTechno: 3, # Timilsina, G.R., 2020. Demystifying the Costs of Electricity Generation # Technologies., average CHPMediumHeat: 2, ElectricBoilerMediumHeat: 2, } TechnoLifetimeDict = { - RWGS: 40, # for now constant in time but should increase with time - FossilGas: 23, # for now constant in time but should increase with time - UpgradingBiogas: 20, # for now constant in time but should increase with time - Methanation: 15, # for now constant in time but should increase with time - WaterGasShift: 20, # Giuliano2020 amortized on 20 years # for now constant in time but should increase with time - ElectrolysisSOEC: 8, # Around 60000hours - ElectrolysisPEM: 11, # Around 90000 operating hours with 8000 hours a year - ElectrolysisAWE: 25, # David, M., Ocampo-Martinez, C. and Sanchez-Pena, R., 2019. Advances in alkaline water electrolyzers: A review. Journal of Energy Storage, 23, pp.392-403. Around 20 and 30 years + RWGS: 40, # for now constant in time but should increase with time + FossilGas: 23, # for now constant in time but should increase with time + UpgradingBiogas: 20, # for now constant in time but should increase with time + Methanation: 15, # for now constant in time but should increase with time + WaterGasShift: 20, # Giuliano2020 amortized on 20 years # for now constant in time but should increase with time + ElectrolysisSOEC: 8, # Around 60000hours + ElectrolysisPEM: 11, # Around 90000 operating hours with 8000 hours a year + ElectrolysisAWE: 25, # David, M., Ocampo-Martinez, C. and Sanchez-Pena, R., 2019. Advances in alkaline water electrolyzers: A review. Journal of Energy Storage, 23, pp.392-403. Around 20 and 30 years PlasmaCracking: 25, HydrogenLiquefaction: 39, AnaerobicDigestion: 20, - BiomassGasification: 25, # Wang2019 Rosenfeld2020 says 20 # for now constant in time but should increase with time + BiomassGasification: 25, # Wang2019 Rosenfeld2020 says 20 # for now constant in time but should increase with time SMR: 25, CoalGasification: 20, Pyrolysis: 20, - AutothermalReforming: 15, # for now constant in time but should increase with time + AutothermalReforming: 15, # for now constant in time but should increase with time CoElectrolysis: 40, - Refinery: 35, # should be modified - FischerTropsch: 30, # for now constant in time but should increase with time - HefaDecarboxylation: 30, # https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3/tables/2 # for now constant in time but should increase with time - HefaDeoxygenation: 30,# Tao, L., Milbrandt, A., Zhang, Y. and Wang, W.C., 2017. Techno-economic and resource analysis of hydroprocessed renewable jet fuel. # Biotechnology for biofuels, 10(1), pp.1-16.# https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3/tables/2 - Transesterification: 15, # for now constant in time but should increase with time - BiomassFermentation: 45, # http://www.ethanolproducer.com/articles/2005/time-testing#:~:text=Most%20experts%20suggest%20dry%2Dmill,of%20%22useful%22%20life%20expectancy. - CoalExtraction: 35, # should be modified - Pelletizing: 25, # Wang2019 Rosenfeld2020 says 20 # for now constant in time but should increase with time - WindOffshore: 30, # ATB NREL 2020 - WindOnshore: 30, # ATB NREL 2020 - SolarPv: 25, # IRENA, EOLES model - SolarThermal: 25, # JRC, IRENA, SolarPACES - Hydropower: 50, # should be modified + Refinery: 35, # should be modified + FischerTropsch: 30, # for now constant in time but should increase with time + HefaDecarboxylation: 30, # https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3/tables/2 # for now constant in time but should increase with time + HefaDeoxygenation: 30, # Tao, L., Milbrandt, A., Zhang, Y. and Wang, W.C., 2017. Techno-economic and resource analysis of hydroprocessed renewable jet fuel. # Biotechnology for biofuels, 10(1), pp.1-16.# https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3/tables/2 + Transesterification: 15, # for now constant in time but should increase with time + BiomassFermentation: 45, # http://www.ethanolproducer.com/articles/2005/time-testing#:~:text=Most%20experts%20suggest%20dry%2Dmill,of%20%22useful%22%20life%20expectancy. + CoalExtraction: 35, # should be modified + Pelletizing: 25, # Wang2019 Rosenfeld2020 says 20 # for now constant in time but should increase with time + WindOffshore: 30, # ATB NREL 2020 + WindOnshore: 30, # ATB NREL 2020 + SolarPv: 25, # IRENA, EOLES model + SolarThermal: 25, # JRC, IRENA, SolarPACES + Hydropower: 50, # should be modified Nuclear: 60, # Cole, W.J., Gates, N., Mai, T.T., Greer, D. and Das, P., 2020. 2019 standard scenarios report: a US electric sector outlook (No. NREL/PR-6A20-75798). National Renewable Energy Lab.(NREL), Golden, CO (United States). - CombinedCycleGasTurbine: 30, # # Source: U.S. Energy Information Administration 2020, for now constant in time but should increase with time - GasTurbine: 30, # Source U.S. Energy Information Administration 2020, # for now constant in time but should increase with time - BiogasFired: 20, # Value for CHP units - CoalGen: 46, # Source: Cui, R.Y., Hultman, N., Edwards, M.R., He, L., Sen, A., Surana, K., McJeon, H., Iyer, G., Patel, P., Yu, S. and Nace, T., 2019. Quantifying operational lifetimes for coal power plants under the Paris goals. Nature communications, 10(1), pp.1-9. - OilGen: 46, # Source: Cui, R.Y., Hultman, N., Edwards, M.R., He, L., Sen, A., Surana, K., McJeon, H., Iyer, G., Patel, P., Yu, S. and Nace, T., 2019. Quantifying operational lifetimes for coal power plants under the Paris goals. Nature communications, 10(1), pp.1-9. - BiomassFired: 25, # Value for CHP units - f"{direct_air_capture}.{AmineScrubbing}": 35, # should be modified - f"{direct_air_capture}.{CalciumPotassiumScrubbing}": 35, # should be modified + CombinedCycleGasTurbine: 30, # # Source: U.S. Energy Information Administration 2020, for now constant in time but should increase with time + GasTurbine: 30, # Source U.S. Energy Information Administration 2020, # for now constant in time but should increase with time + BiogasFired: 20, # Value for CHP units + CoalGen: 46, # Source: Cui, R.Y., Hultman, N., Edwards, M.R., He, L., Sen, A., Surana, K., McJeon, H., Iyer, G., Patel, P., Yu, S. and Nace, T., 2019. Quantifying operational lifetimes for coal power plants under the Paris goals. Nature communications, 10(1), pp.1-9. + OilGen: 46, # Source: Cui, R.Y., Hultman, N., Edwards, M.R., He, L., Sen, A., Surana, K., McJeon, H., Iyer, G., Patel, P., Yu, S. and Nace, T., 2019. Quantifying operational lifetimes for coal power plants under the Paris goals. Nature communications, 10(1), pp.1-9. + BiomassFired: 25, # Value for CHP units + f"{direct_air_capture}.{AmineScrubbing}": 35, # should be modified + f"{direct_air_capture}.{CalciumPotassiumScrubbing}": 35, # should be modified f"{flue_gas_capture}.{CalciumLooping}": 25, # SAEECCT Coal USC plant lifetime - f"{flue_gas_capture}.{ChilledAmmoniaProcess}": 25, # SAEECCT Coal USC plant lifetime - f"{flue_gas_capture}.{CO2Membranes}": 25, # SAEECCT Coal USC plant lifetime - f"{flue_gas_capture}.{MonoEthanolAmine}": 25, # SAEECCT Coal USC plant lifetime - f"{flue_gas_capture}.{PiperazineProcess}": 25, # SAEECCT Coal USC plant lifetime - f"{flue_gas_capture}.{PressureSwingAdsorption}": 25, # SAEECCT Coal USC plant lifetime - BiomassBuryingFossilization: 35, # should be modified - DeepOceanInjection: 35, # should be modified - DeepSalineFormation: 35, # should be modified - DepletedOilGas: 35, # should be modified - EnhancedOilRecovery: 35, # should be modified - GeologicMineralization: 35, # should be modified - PureCarbonSolidStorage: 35, # should be modified - ManagedWood: 150, # for now constant in time but should increase with time - UnmanagedWood: 150, # for now constant in time but should increase with time - CropEnergy: 50, # for now constant in time but should increase with time + f"{flue_gas_capture}.{ChilledAmmoniaProcess}": 25, # SAEECCT Coal USC plant lifetime + f"{flue_gas_capture}.{CO2Membranes}": 25, # SAEECCT Coal USC plant lifetime + f"{flue_gas_capture}.{MonoEthanolAmine}": 25, # SAEECCT Coal USC plant lifetime + f"{flue_gas_capture}.{PiperazineProcess}": 25, # SAEECCT Coal USC plant lifetime + f"{flue_gas_capture}.{PressureSwingAdsorption}": 25, # SAEECCT Coal USC plant lifetime + BiomassBuryingFossilization: 35, # should be modified + DeepOceanInjection: 35, # should be modified + DeepSalineFormation: 35, # should be modified + DepletedOilGas: 35, # should be modified + EnhancedOilRecovery: 35, # should be modified + GeologicMineralization: 35, # should be modified + PureCarbonSolidStorage: 35, # should be modified + ManagedWood: 150, # for now constant in time but should increase with time + UnmanagedWood: 150, # for now constant in time but should increase with time + CropEnergy: 50, # for now constant in time but should increase with time FossilSimpleTechno: 25, - NaturalGasBoilerHighHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. - HeatPumpHighHeat: 25, # years # https://www.energy.gov/energysaver/heat-pump-systems - GeothermalHighHeat: 25, # in years # https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power - CHPHighHeat: 45, # Heat Producer [Online] # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. - NaturalGasBoilerLowHeat: 45, # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 + NaturalGasBoilerHighHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. + HeatPumpHighHeat: 25, # years # https://www.energy.gov/energysaver/heat-pump-systems + GeothermalHighHeat: 25, # in years # https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power + CHPHighHeat: 45, # Heat Producer [Online] # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. + NaturalGasBoilerLowHeat: 45, # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 ElectricBoilerLowHeat: 45, HeatPumpLowHeat: 25, - GeothermalLowHeat: 25, # in years # https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power - CHPLowHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. - NaturalGasBoilerMediumHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. - ElectricBoilerMediumHeat: 45, # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 - HeatPumpMediumHeat: 25, # years # https://www.energy.gov/energysaver/heat-pump-systems - GeothermalMediumHeat: 25, # in years # https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power - CHPMediumHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. + GeothermalLowHeat: 25, # in years # https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power + CHPLowHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. + NaturalGasBoilerMediumHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. + ElectricBoilerMediumHeat: 45, # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 + HeatPumpMediumHeat: 25, # years # https://www.energy.gov/energysaver/heat-pump-systems + GeothermalMediumHeat: 25, # in years # https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power + CHPMediumHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. CO2Hydrogenation: 20, - GlossaryWitnessCore.CleanEnergySimpleTechno: 30, # Cole, W.J., Gates, N., Mai, T.T., Greer, D. and Das, P., 2020. 2019 standard scenarios report: a US electric sector outlook (No. NREL/PR-6A20-75798). National Renewable Energy Lab.(NREL), Golden, CO (United States). - f"{ElectricBoilerHighHeat}": 45, # Heat Producer [Online] # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 + GlossaryWitnessCore.CleanEnergySimpleTechno: 30, # Cole, W.J., Gates, N., Mai, T.T., Greer, D. and Das, P., 2020. 2019 standard scenarios report: a US electric sector outlook (No. NREL/PR-6A20-75798). National Renewable Energy Lab.(NREL), Golden, CO (United States). + f"{ElectricBoilerHighHeat}": 45, # Heat Producer [Online] # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 f"{CarbonStorageTechno}": 35, f"{direct_air_capture}.{DirectAirCaptureTechno}": 35, f"{flue_gas_capture}.{FlueGasTechno}": 25, Reforestation: 150, # for now constant in time but should increase with time, - Geothermal: 30, # Tsiropoulos, I., Tarvydas, D. and Zucker, A., 2018. Cost development of low carbon energy technologies-Scenario-based cost trajectories to 2050, 2017 Edition. Publications Office of the European Union, Luxemburgo. - AnimalManure: 25, # for now constant in time but should increase with time + Geothermal: 30, # Tsiropoulos, I., Tarvydas, D. and Zucker, A., 2018. Cost development of low carbon energy technologies-Scenario-based cost trajectories to 2050, 2017 Edition. Publications Office of the European Union, Luxemburgo. + AnimalManure: 25, # for now constant in time but should increase with time WetCropResidues: 25, # for now constant in time but should increase with time - CO2Membranes: 25, # SAEECCT Coal USC plant lifetime + CO2Membranes: 25, # SAEECCT Coal USC plant lifetime } @classmethod diff --git a/energy_models/models/biodiesel/transesterification/documentation/transesterification_disc.markdown b/energy_models/models/biodiesel/transesterification/documentation/transesterification_disc.markdown index 8e545112..f7f38779 100644 --- a/energy_models/models/biodiesel/transesterification/documentation/transesterification_disc.markdown +++ b/energy_models/models/biodiesel/transesterification/documentation/transesterification_disc.markdown @@ -84,4 +84,4 @@ Technico-economic information (Capex, Opex) are coming from [Biodiesel productio [^1]: Biodiesel Handbook (2010), AOCS Press [^2]: https://www.sciencedirect.com/topics/chemical-engineering/transesterification -[^3]: https://www.sciencedirect.com/topics/engineering/transesterification#:~:text=Transesterification%20is%20the%20conversion%20of,presence%20of%20an%20acid%20catalyst. \ No newline at end of file +[^3]: https://www.sciencedirect.com/topics/engineering/transesterification#:~:text=Transesterification%20is%20the%20conversion%20of,presence%20of%20an%20acid%20catalyst. diff --git a/energy_models/models/biodiesel/transesterification/transesterification.py b/energy_models/models/biodiesel/transesterification/transesterification.py index 76322bd9..b709e3c7 100644 --- a/energy_models/models/biodiesel/transesterification/transesterification.py +++ b/energy_models/models/biodiesel/transesterification/transesterification.py @@ -44,7 +44,7 @@ def compute_resources_needs(self): # need in kg/kwh biodiesel self.cost_details[f'{Water.name}_needs'] = self.get_theoretical_water_needs() / self.cost_details['efficiency'] # need in kWh/kwh biodiesel - + def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] diff --git a/energy_models/models/biogas/anaerobic_digestion/documentation/anaerobic_digestion_disc.markdown b/energy_models/models/biogas/anaerobic_digestion/documentation/anaerobic_digestion_disc.markdown index 421ba720..52539217 100644 --- a/energy_models/models/biogas/anaerobic_digestion/documentation/anaerobic_digestion_disc.markdown +++ b/energy_models/models/biogas/anaerobic_digestion/documentation/anaerobic_digestion_disc.markdown @@ -5,21 +5,21 @@ A mixture of methane and carbon dioxide (CO2), biogas can be produced from organ **How Does an Anaerobic Digester Work? [^6]** -Anaerobic digestion, or methanization, uses the process of fermentation to break down organic matter from animals. The biomass is heated to approximately 37°C to 38°C (sometimes more than 50°C) and stirred continuously. After at least 20 days and a series of bacteria-induced chemical transformations, the fermented biomass produces biogas . +Anaerobic digestion, or methanization, uses the process of fermentation to break down organic matter from animals. The biomass is heated to approximately 37°C to 38°C (sometimes more than 50°C) and stirred continuously. After at least 20 days and a series of bacteria-induced chemical transformations, the fermented biomass produces biogas . The composition of biogas depends on the type of feedstock and the production pathway. The methane content of biogas typically ranges from 45% to 75% and the carbon dioxyde content from 25% to 55% by volume. -Biogas and biomethane production pathways -![](Biogasbiomethaneinfographic.png) +Biogas and biomethane production pathways +![](Biogasbiomethaneinfographic.png) (Image Credit: IEA [^5]) -## Data -Most of the data used for this model is extracted from the paper of Rajendran & al [^1] which is a review of all papers on anaerobic digestion. +## Data +Most of the data used for this model is extracted from the paper of Rajendran & al [^1] which is a review of all papers on anaerobic digestion. World initial production is extracted from the IEA site [^5]. -Typical prices of Biogas from anaerobic digestion can be found on IRENA site[^2] : "between USD 0.22 and USD 0.39 per cubic meter of methane for manure-based biogas production, and USD 0.11 to USD 0.50 per cubic meter of methane for industrial waste-based biogas production". This corresponds to a biogas price between 0.02 and 0.078 $/kWh. +Typical prices of Biogas from anaerobic digestion can be found on IRENA site[^2] : "between USD 0.22 and USD 0.39 per cubic meter of methane for manure-based biogas production, and USD 0.11 to USD 0.50 per cubic meter of methane for industrial waste-based biogas production". This corresponds to a biogas price between 0.02 and 0.078 $/kWh. The efficiency of the anaerobic digester is mentionned in Carlini & al [^3]. The age distribution of biogas plants from anaerobic digestion has been computed with the plant list in [^4]. diff --git a/energy_models/models/biomass_dry/crop_energy/crop_energy.py b/energy_models/models/biomass_dry/crop_energy/crop_energy.py index 84c98a6d..bcbc9069 100644 --- a/energy_models/models/biomass_dry/crop_energy/crop_energy.py +++ b/energy_models/models/biomass_dry/crop_energy/crop_energy.py @@ -188,4 +188,4 @@ def compute_dlanduse_dinvest(self): return dlanduse_dinvest def compute_resources_needs(self): - self.cost_details[f'{GlossaryEnergy.carbon_capture}_needs'] = -self.techno_infos_dict['CO2_from_production'] / self.data_energy_dict['high_calorific_value'] \ No newline at end of file + self.cost_details[f'{GlossaryEnergy.carbon_capture}_needs'] = -self.techno_infos_dict['CO2_from_production'] / self.data_energy_dict['high_calorific_value'] diff --git a/energy_models/models/biomass_dry/crop_energy/crop_energy_disc.py b/energy_models/models/biomass_dry/crop_energy/crop_energy_disc.py index 8e6e6f90..bb5b5675 100644 --- a/energy_models/models/biomass_dry/crop_energy/crop_energy_disc.py +++ b/energy_models/models/biomass_dry/crop_energy/crop_energy_disc.py @@ -50,7 +50,6 @@ class CropEnergyDiscipline(BiomassDryTechnoDiscipline): } techno_name = GlossaryEnergy.CropEnergy - # mdpi: according to the NASU recommendations, # a fixed value of 0.25 is applied to all crops # 50% of crops are left on the field, @@ -144,7 +143,7 @@ def init_execution(self): def run(self): ''' - specific run for crops + specific run for crops ''' super().run() self.specific_run() @@ -155,7 +154,7 @@ def specific_run(self): ''' outputs_dict = {'mix_detailed_prices': self.techno_model.price_mix, 'mix_detailed_production': self.techno_model.production_mix} - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): @@ -235,7 +234,7 @@ def get_post_processing_list(self, filters=None): def get_production_chart(self): ''' - Create chart with production details for industry/energy + Create chart with production details for industry/energy ''' production_mix_df = self.get_sosdisc_outputs('mix_detailed_production') diff --git a/energy_models/models/biomass_dry/crop_energy/documentation/crop_energy_disc.markdown b/energy_models/models/biomass_dry/crop_energy/documentation/crop_energy_disc.markdown index 9768132f..c799ed49 100644 --- a/energy_models/models/biomass_dry/crop_energy/documentation/crop_energy_disc.markdown +++ b/energy_models/models/biomass_dry/crop_energy/documentation/crop_energy_disc.markdown @@ -77,7 +77,7 @@ The computed land-use amount of hectares is the agricultural area for energy cro $$NumberOfHa=\frac{CropProductionForEnergy}{density\_per\_ha*calorific\_value}$$ -With: +With: - CropProductionForEnergy, the production of crop and residue for energy sector computed by this model **Costs** @@ -88,7 +88,7 @@ For CAPEX computation: For OPEX computation: - crop harvest and processing: 87.74 €/ha (264.4$/acre)[^7] - residue harvest (22$/t) + fertilizing (23$/t): 37.54 €/ha[^8] - + The computed price is the mixed price of crop and residue. Details in the composition of prices of crop and residue is shown in the graphics named "Detailed Price of energy crop technology over the years". Prices are computed with the input parameter crop_residue_price_percent_dif. ## Other Data @@ -102,4 +102,4 @@ Information regarding the age distribution of agricultural lands comes from Our [^5]: Bioenergy Europe, Biomass for energy: agricultural residues and energy crops, https://bioenergyeurope.org/component/attachments/attachments.html?id=561&task=download [^6]: The world bank, Cereal yield kg per hectare, https://data.worldbank.org/indicator/AG.YLD.CREL.KG [^7]: Manitoba, Crops production costs - 2021, gov.mb.ca/agriculture/farm-management/production-economics/pubs/cop-crop-production.pdf -[^8]: United States Department of Agriculture, 2016, Harvesting Crop Residue: What’s it worth?, https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcseprd1298023.pdf \ No newline at end of file +[^8]: United States Department of Agriculture, 2016, Harvesting Crop Residue: What’s it worth?, https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcseprd1298023.pdf diff --git a/energy_models/models/biomass_dry/managed_wood/documentation/managed_wood_disc.markdown b/energy_models/models/biomass_dry/managed_wood/documentation/managed_wood_disc.markdown index 0c0aefb2..051b46b0 100644 --- a/energy_models/models/biomass_dry/managed_wood/documentation/managed_wood_disc.markdown +++ b/energy_models/models/biomass_dry/managed_wood/documentation/managed_wood_disc.markdown @@ -84,7 +84,7 @@ The computed land-use amount of hectares is the global amount of managed forest $$NumberOfHa=\frac{WoodProductionForEnergy+WoodProductionForNonEnergy}{mean\_density\_per\_ha * mean\_calorific\_value} \\ * years\_between\_harvest * (1 - recycle\_part)$$ -With: +With: - WoodProductionForEnergy, the production of Managed wood and residue computed by this model - WoodProductionForNonEnergy, the computed amount of Managed Wood used for production using the inputs data wood_percentage_for_energy and wood_percentage_for_energy. @@ -97,7 +97,7 @@ For CAPEX computation: For OPEX computation: - planting (5%), manual cleaning: 269 $/ha[^5] - cutting, chipping, off_road transportation : 8 $/Mwh[^6] - + The computed price is the mixed price of wood and residue. Details in the composition of prices of wood and residue is shown in the graphics named "Detailed Price of Unmanaged wood technology over the years". Prices are computed with the input parameter wood_residue_price_percent_dif. ## Other Data @@ -110,4 +110,4 @@ Information regarding the age distribution of planted forests comes from Our Wor [^4]: European Biomass Industry Association, Recovery of forest residues, found online at https://www.eubia.org/cms/wiki-biomass/biomass-resources/challenges-related-to-biomass/recovery-of-forest-residues/ [^5]: Agriculture And Food Developement Authority, Reforestation, https://www.teagasc.ie/crops/forestry/advice/establishment/reforestation/ [^6]: Eubia, Recovery of forests residues, https://www.eubia.org/cms/wiki-biomass/biomass-resources/challenges-related-to-biomass/recovery-of-forest-residues/ -[^7]: OurworldInData, Primary vs. planted forest, https://ourworldindata.org/forest-area#primary-vs-planted-forest \ No newline at end of file +[^7]: OurworldInData, Primary vs. planted forest, https://ourworldindata.org/forest-area#primary-vs-planted-forest diff --git a/energy_models/models/biomass_dry/managed_wood/managed_wood.py b/energy_models/models/biomass_dry/managed_wood/managed_wood.py index 633a764c..0856a6c7 100644 --- a/energy_models/models/biomass_dry/managed_wood/managed_wood.py +++ b/energy_models/models/biomass_dry/managed_wood/managed_wood.py @@ -32,6 +32,7 @@ def __init__(self, name): self.production_mix = None self.price_mix = None self.mean_age_df = None + def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() diff --git a/energy_models/models/biomass_dry/managed_wood/managed_wood_disc.py b/energy_models/models/biomass_dry/managed_wood/managed_wood_disc.py index f6a12515..c6b0c9c1 100644 --- a/energy_models/models/biomass_dry/managed_wood/managed_wood_disc.py +++ b/energy_models/models/biomass_dry/managed_wood/managed_wood_disc.py @@ -49,7 +49,6 @@ class ManagedWoodDiscipline(BiomassDryTechnoDiscipline): techno_name = GlossaryEnergy.ManagedWood - # available planted forests in 2020: 294 Mha (worldbioenergy.org) # reference: @@ -153,7 +152,6 @@ class ManagedWoodDiscipline(BiomassDryTechnoDiscipline): years_between_harvest / (1 - recycle_part) # in Twh # distrib computed, for planted forests since 150 years - # distrib computed, for planted forests since 1980 (40years) # 'distrib': [3.25, 3.26, 3.27, 3.27, 3.27, 3.24, 3.21, 3.17, 3.14, 3.1, @@ -172,7 +170,7 @@ class ManagedWoodDiscipline(BiomassDryTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'define in dict'}, - + } # -- add specific techno inputs to this DESC_IN.update(BiomassDryTechnoDiscipline.DESC_IN) @@ -197,7 +195,7 @@ def specific_run(self): outputs_dict = {'mix_detailed_prices': self.techno_model.price_mix, 'mix_detailed_production': self.techno_model.production_mix} - + self.store_sos_outputs_values(outputs_dict) def get_post_processing_list(self, filters=None): diff --git a/energy_models/models/biomass_dry/unmanaged_wood/documentation/unmanaged_wood_disc.markdown b/energy_models/models/biomass_dry/unmanaged_wood/documentation/unmanaged_wood_disc.markdown index 205c9f93..4f968838 100644 --- a/energy_models/models/biomass_dry/unmanaged_wood/documentation/unmanaged_wood_disc.markdown +++ b/energy_models/models/biomass_dry/unmanaged_wood/documentation/unmanaged_wood_disc.markdown @@ -84,7 +84,7 @@ The computed land-use amount of hectares is the global amount of managed forest $$NumberOfHa=\frac{WoodProductionForEnergy+WoodProductionForNonEnergy}{mean\_density\_per\_ha * mean\_calorific\_value} \\ * years\_between\_harvest * (1 - recycle\_part)$$ -With: +With: - WoodProductionForEnergy, the production of Managed wood and residue computed by this model - WoodProductionForNonEnergy, the computed amount of Managed Wood used for production using the inputs data wood_percentage_for_energy and wood_percentage_for_energy. @@ -95,7 +95,7 @@ For CAPEX computation: For OPEX computation: - cutting, chipping, off_road transportation : 8 $/Mwh[^5] - + The computed price is the mixed price of wood and residue. Details in the composition of prices of wood and residue is shown in the graphics named "Detailed Price of Unmanaged wood technology over the years". Prices are computed with the input parameter wood_residue_price_percent_dif. ## Other Data @@ -107,4 +107,4 @@ Information regarding the age distribution of planted forests comes from Our Wor [^3]: SCA, We use the entire tree, https://www.sca.com/en/about-us/sustainability/sustainable-development/Efficient-use-of-resources/we-use-the-entire-tree/ [^4]: European Biomass Industry Association, Recovery of forest residues, found online at https://www.eubia.org/cms/wiki-biomass/biomass-resources/challenges-related-to-biomass/recovery-of-forest-residues/ [^5]: Eubia, Recovery of forests residues, https://www.eubia.org/cms/wiki-biomass/biomass-resources/challenges-related-to-biomass/recovery-of-forest-residues/ -[^6]: OurworldInData, Primary vs. planted forest, https://ourworldindata.org/forest-area#primary-vs-planted-forest \ No newline at end of file +[^6]: OurworldInData, Primary vs. planted forest, https://ourworldindata.org/forest-area#primary-vs-planted-forest diff --git a/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood.py b/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood.py index fe681840..ee36e7e4 100644 --- a/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood.py +++ b/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood.py @@ -36,7 +36,6 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() - def grad_production_invest(self, capex, production, production_mix): dcapex_dinvest = self.compute_dcapex_dinvest(self.invest_level.loc[self.invest_level[GlossaryEnergy.Years] diff --git a/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood_disc.py b/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood_disc.py index fc004812..b28e2431 100644 --- a/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood_disc.py +++ b/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood_disc.py @@ -50,7 +50,6 @@ class UnmanagedWoodDiscipline(BiomassDryTechnoDiscipline): techno_name = GlossaryEnergy.UnmanagedWood - # reference: # https://qtimber.daf.qld.gov.au/guides/wood-density-and-hardness wood_density = 600.0 # kg/m3 @@ -149,7 +148,7 @@ class UnmanagedWoodDiscipline(BiomassDryTechnoDiscipline): wood_density * 3.36) / years_between_harvest / (1 - recycle_part) # in Twh # distrib computed, for planted forests since 150 years - + # distrib computed, for planted forests since 1980 (40years) # 'distrib': [3.25, 3.26, 3.27, 3.27, 3.27, 3.24, 3.21, 3.17, 3.14, 3.1, # 3.04, 2.99, 2.94, 2.89, 2.83, 2.77, 2.71, 2.66, 2.57, 2.51, @@ -167,7 +166,7 @@ class UnmanagedWoodDiscipline(BiomassDryTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'define in dict'}, - + } # -- add specific techno inputs to this DESC_IN.update(BiomassDryTechnoDiscipline.DESC_IN) @@ -192,7 +191,7 @@ def specific_run(self): outputs_dict = {'mix_detailed_prices': self.techno_model.price_mix, 'mix_detailed_production': self.techno_model.production_mix} - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/models/carbon_capture/direct_air_capture/amine_scrubbing/documentation/amine_scrubbing_disc.markdown b/energy_models/models/carbon_capture/direct_air_capture/amine_scrubbing/documentation/amine_scrubbing_disc.markdown index 4218ceb9..de5240e5 100644 --- a/energy_models/models/carbon_capture/direct_air_capture/amine_scrubbing/documentation/amine_scrubbing_disc.markdown +++ b/energy_models/models/carbon_capture/direct_air_capture/amine_scrubbing/documentation/amine_scrubbing_disc.markdown @@ -32,4 +32,4 @@ Plus, detailed about the sharing of Capex and Opex are given [^2]: Buijs, W. and De Flart, S., 2017. Direct air capture of CO2 with an amine resin: A molecular modeling study of the CO2 capturing process. Industrial & engineering chemistry research, 56(43), pp.12297-12304. [^3]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. [^4]: Keith, D.W., Holmes, G., Angelo, D.S. and Heidel, K., 2018. A process for capturing CO2 from the atmosphere. Joule, 2(8), pp.1573-1594. -[^5]: Vo, T.T., Wall, D.M., Ring, D., Rajendran, K. and Murphy, J.D., 2018. Techno-economic analysis of biogas upgrading via amine scrubber, carbon capture and ex-situ methanation. Applied energy, 212, pp.1191-1202. \ No newline at end of file +[^5]: Vo, T.T., Wall, D.M., Ring, D., Rajendran, K. and Murphy, J.D., 2018. Techno-economic analysis of biogas upgrading via amine scrubber, carbon capture and ex-situ methanation. Applied energy, 212, pp.1191-1202. diff --git a/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/calcium_potassium_scrubbing.py b/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/calcium_potassium_scrubbing.py index d9364a90..01d51187 100644 --- a/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/calcium_potassium_scrubbing.py +++ b/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/calcium_potassium_scrubbing.py @@ -55,7 +55,6 @@ def compute_other_streams_needs(self): def compute_byproducts_production(self): - self.production_detailed[f'{CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = self.cost_details[ f'{Methane.name}_needs'] * \ self.production_detailed[ diff --git a/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/documentation/calcium_potassium_scrubbing_disc.markdown b/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/documentation/calcium_potassium_scrubbing_disc.markdown index ee8170b0..c8231ba8 100644 --- a/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/documentation/calcium_potassium_scrubbing_disc.markdown +++ b/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/documentation/calcium_potassium_scrubbing_disc.markdown @@ -30,4 +30,4 @@ Technical data was found in [^1], [^2], [^3] and [^4]. [^1]: https://www.cell.com/joule/pdfExtended/S2542-4351(18)30225-3 [^2]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. [^3]: Keith, D.W., Holmes, G., Angelo, D.S. and Heidel, K., 2018. A process for capturing CO2 from the atmosphere. Joule, 2(8), pp.1573-1594. -[^4]: Vo, T.T., Wall, D.M., Ring, D., Rajendran, K. and Murphy, J.D., 2018. Techno-economic analysis of biogas upgrading via amine scrubber, carbon capture and ex-situ methanation. Applied energy, 212, pp.1191-1202. \ No newline at end of file +[^4]: Vo, T.T., Wall, D.M., Ring, D., Rajendran, K. and Murphy, J.D., 2018. Techno-economic analysis of biogas upgrading via amine scrubber, carbon capture and ex-situ methanation. Applied energy, 212, pp.1191-1202. diff --git a/energy_models/models/carbon_capture/direct_air_capture/direct_air_capture_techno/direct_air_capture_techno.py b/energy_models/models/carbon_capture/direct_air_capture/direct_air_capture_techno/direct_air_capture_techno.py index c4976590..423371b0 100644 --- a/energy_models/models/carbon_capture/direct_air_capture/direct_air_capture_techno/direct_air_capture_techno.py +++ b/energy_models/models/carbon_capture/direct_air_capture/direct_air_capture_techno/direct_air_capture_techno.py @@ -49,4 +49,4 @@ def compute_byproducts_production(self): self.production_detailed[f'{CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = \ self.cost_details[f'{Fossil.name}_needs'] * self.production_detailed[f'{CCTechno.energy_name} ({self.product_unit})'] * \ Fossil.data_energy_dict[GlossaryEnergy.CO2PerUse] / Fossil.data_energy_dict[ - 'calorific_value'] \ No newline at end of file + 'calorific_value'] diff --git a/energy_models/models/carbon_capture/flue_gas_capture/calcium_looping/calcium_looping_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/calcium_looping/calcium_looping_disc.py index 41a7ce79..5c148bfa 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/calcium_looping/calcium_looping_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/calcium_looping/calcium_looping_disc.py @@ -43,7 +43,6 @@ class CalciumLoopingDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.CalciumLooping}' - # Most of the data from this model come from : # Guandalini, G., Romano, M.C., Ho, M., Wiley, D., Rubin, E.S. and Abanades, J.C., 2019. # A sequential approach for the economic evaluation of new CO2 capture technologies for power plants. @@ -99,7 +98,7 @@ class CalciumLoopingDiscipline(CCTechnoDiscipline): initial_capture = 5 # Mt # We assume 0.5 MT increase per year, with a capex ~ 40$/ton - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, GlossaryEnergy.FlueGasMean: {'type': 'dataframe', 'namespace': 'ns_flue_gas', diff --git a/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/chilled_ammonia_process_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/chilled_ammonia_process_disc.py index 29453ca3..a5796b21 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/chilled_ammonia_process_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/chilled_ammonia_process_disc.py @@ -43,7 +43,6 @@ class ChilledAmmoniaProcessDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.ChilledAmmoniaProcess}' - # Most of the data from this model come from : # Guandalini, G., Romano, M.C., Ho, M., Wiley, D., Rubin, E.S. and Abanades, J.C., 2019. # A sequential approach for the economic evaluation of new CO2 capture technologies for power plants. @@ -99,7 +98,7 @@ class ChilledAmmoniaProcessDiscipline(CCTechnoDiscipline): initial_capture = 5 # Mt # We assume 0.5 MT increase per year, with a capex ~ 40$/ton - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, GlossaryEnergy.FlueGasMean: {'type': 'dataframe', 'namespace': 'ns_flue_gas', diff --git a/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/documentation/chilled_ammonia_process_disc.markdown b/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/documentation/chilled_ammonia_process_disc.markdown index 2d2c6bb1..4de99b42 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/documentation/chilled_ammonia_process_disc.markdown +++ b/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/documentation/chilled_ammonia_process_disc.markdown @@ -1,6 +1,6 @@ **Definition[^1] :** -The Chilled Ammonia Process is based on the chemistry of the NH3-CO2-H2O system and the ability of the ammoniated solution to absorb CO2 at low temperature and to release the CO2 at moderately elevated temperature. +The Chilled Ammonia Process is based on the chemistry of the NH3-CO2-H2O system and the ability of the ammoniated solution to absorb CO2 at low temperature and to release the CO2 at moderately elevated temperature. Fundamentals of Chilled ammonia process[^1] ![](cap.PNG) diff --git a/energy_models/models/carbon_capture/flue_gas_capture/co2_membranes/co2_membranes_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/co2_membranes/co2_membranes_disc.py index 9ddcfacc..b5467d06 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/co2_membranes/co2_membranes_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/co2_membranes/co2_membranes_disc.py @@ -43,7 +43,6 @@ class CO2MembranesDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.CO2Membranes}' - # Most of the data from this model come from : # Guandalini, G., Romano, M.C., Ho, M., Wiley, D., Rubin, E.S. and Abanades, J.C., 2019. # A sequential approach for the economic evaluation of new CO2 capture technologies for power plants. @@ -94,7 +93,7 @@ class CO2MembranesDiscipline(CCTechnoDiscipline): initial_capture = 5 # Mt # We assume 0.5 MT increase per year, with a capex ~ 40$/ton - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, GlossaryEnergy.FlueGasMean: {'type': 'dataframe', 'namespace': 'ns_flue_gas', diff --git a/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/documentation/flue_gas_disc.md b/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/documentation/flue_gas_disc.md index 250f8afa..b004c27d 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/documentation/flue_gas_disc.md +++ b/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/documentation/flue_gas_disc.md @@ -2,4 +2,4 @@ ** Simplified Flue Gas technology, used in the WITNESS Full Coarse process to demonstrate the Energy Mix** -** This technology uses the energy produced in the Renewable stream as a consumption ** \ No newline at end of file +** This technology uses the energy produced in the Renewable stream as a consumption ** diff --git a/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/flue_gas_techno_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/flue_gas_techno_disc.py index f56b72c8..93de742b 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/flue_gas_techno_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/flue_gas_techno_disc.py @@ -45,7 +45,6 @@ class FlueGasTechnoDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.FlueGasTechno}' - heat_to_power_lost = 0.243 heat_duty = 18 elec_demand_capture = 338 diff --git a/energy_models/models/carbon_capture/flue_gas_capture/generic_flue_gas_techno_model.py b/energy_models/models/carbon_capture/flue_gas_capture/generic_flue_gas_techno_model.py index 58afa5fc..f3731d32 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/generic_flue_gas_techno_model.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/generic_flue_gas_techno_model.py @@ -53,4 +53,3 @@ def compute_streams_consumption(self): self.consumption_detailed[f'{GlossaryEnergy.electricity} ({self.energy_unit})'] = self.cost_details[f'{GlossaryEnergy.electricity}_needs'] * \ self.production_detailed[f'{CCTechno.energy_name} ({self.product_unit})'] / self.compute_electricity_variation_from_fg_ratio( self.flue_gas_ratio[GlossaryEnergy.FlueGasMean].values, self.fg_ratio_effect) - diff --git a/energy_models/models/carbon_capture/flue_gas_capture/mono_ethanol_amine/mono_ethanol_amine_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/mono_ethanol_amine/mono_ethanol_amine_disc.py index 509c80ac..d93a094e 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/mono_ethanol_amine/mono_ethanol_amine_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/mono_ethanol_amine/mono_ethanol_amine_disc.py @@ -44,7 +44,6 @@ class MonoEthanolAmineDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.MonoEthanolAmine}' - # Most of the data from this model come from : # Guandalini, G., Romano, M.C., Ho, M., Wiley, D., Rubin, E.S. and Abanades, J.C., 2019. # A sequential approach for the economic evaluation of new CO2 capture technologies for power plants. @@ -100,7 +99,7 @@ class MonoEthanolAmineDiscipline(CCTechnoDiscipline): initial_capture = 15 # Mt # We assume 0.5 MT increase per year, with a capex ~ 40$/ton - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, GlossaryEnergy.FlueGasMean: {'type': 'dataframe', 'namespace': 'ns_flue_gas', diff --git a/energy_models/models/carbon_capture/flue_gas_capture/piperazine_process/piperazine_process_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/piperazine_process/piperazine_process_disc.py index 90e0ff1c..abaa031b 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/piperazine_process/piperazine_process_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/piperazine_process/piperazine_process_disc.py @@ -43,7 +43,6 @@ class PiperazineProcessDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.PiperazineProcess}' - # Most of the data from this model come from : # Guandalini, G., Romano, M.C., Ho, M., Wiley, D., Rubin, E.S. and Abanades, J.C., 2019. # A sequential approach for the economic evaluation of new CO2 capture technologies for power plants. @@ -99,7 +98,7 @@ class PiperazineProcessDiscipline(CCTechnoDiscipline): initial_capture = 5 # Mt # We assume 0.5 MT increase per year, with a capex ~ 40$/ton - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, GlossaryEnergy.FlueGasMean: {'type': 'dataframe', 'namespace': 'ns_flue_gas', diff --git a/energy_models/models/carbon_capture/flue_gas_capture/pressure_swing_adsorption/pressure_swing_adsorption_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/pressure_swing_adsorption/pressure_swing_adsorption_disc.py index d8c23ce8..b83143c9 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/pressure_swing_adsorption/pressure_swing_adsorption_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/pressure_swing_adsorption/pressure_swing_adsorption_disc.py @@ -43,7 +43,6 @@ class PressureSwingAdsorptionDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.PressureSwingAdsorption}' - # Most of the data from this model come from : # Guandalini, G., Romano, M.C., Ho, M., Wiley, D., Rubin, E.S. and Abanades, J.C., 2019. # A sequential approach for the economic evaluation of new CO2 capture technologies for power plants. @@ -94,7 +93,7 @@ class PressureSwingAdsorptionDiscipline(CCTechnoDiscipline): initial_capture = 5 # Mt # We assume 0.5 MT increase per year, with a capex ~ 40$/ton - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, GlossaryEnergy.FlueGasMean: {'type': 'dataframe', 'namespace': 'ns_flue_gas', diff --git a/energy_models/models/carbon_storage/biomass_burying_fossilization/biomass_burying_fossilization_disc.py b/energy_models/models/carbon_storage/biomass_burying_fossilization/biomass_burying_fossilization_disc.py index 5e2e6293..d6e8bc42 100644 --- a/energy_models/models/carbon_storage/biomass_burying_fossilization/biomass_burying_fossilization_disc.py +++ b/energy_models/models/carbon_storage/biomass_burying_fossilization/biomass_burying_fossilization_disc.py @@ -65,7 +65,7 @@ class BiomassBuryingFossilizationDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 # in kg at year_start - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/biomass_burying_fossilization/documentation/biomass_burying_fossilization_disc.markdown b/energy_models/models/carbon_storage/biomass_burying_fossilization/documentation/biomass_burying_fossilization_disc.markdown index fd9be6d3..1db239d1 100644 --- a/energy_models/models/carbon_storage/biomass_burying_fossilization/documentation/biomass_burying_fossilization_disc.markdown +++ b/energy_models/models/carbon_storage/biomass_burying_fossilization/documentation/biomass_burying_fossilization_disc.markdown @@ -11,4 +11,4 @@ According to [^3], dry biomass is composed of 42.5% of carbon and using molar ma [^1]: The East Africa Sisal Company Ltd, http://www.eastafricasisal.com/peatland-restoration/ [^2]: Carbon sequestration via wood burial, BMC,https://cbmjournal.biomedcentral.com/articles/10.1186/1750-0680-3-1 -[^3]: INTA, http://repo-desa.inta.gob.ar/xmlui/bitstream/handle/20.500.12123/1303/INTA_CRPatagoniaNorte-EEABariloche_WarrenRaffa_D_How_does_crop_residue_removal_affect_soil.pdf?sequence=3 \ No newline at end of file +[^3]: INTA, http://repo-desa.inta.gob.ar/xmlui/bitstream/handle/20.500.12123/1303/INTA_CRPatagoniaNorte-EEABariloche_WarrenRaffa_D_How_does_crop_residue_removal_affect_soil.pdf?sequence=3 diff --git a/energy_models/models/carbon_storage/carbon_storage_techno/carbon_storage_techno_disc.py b/energy_models/models/carbon_storage/carbon_storage_techno/carbon_storage_techno_disc.py index 8adea9b3..e3c5a6b8 100644 --- a/energy_models/models/carbon_storage/carbon_storage_techno/carbon_storage_techno_disc.py +++ b/energy_models/models/carbon_storage/carbon_storage_techno/carbon_storage_techno_disc.py @@ -67,7 +67,7 @@ class CarbonStorageTechnoDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 # in kg at year_start - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/carbon_storage_techno/documentation/carbon_storage_techno_disc.markdown b/energy_models/models/carbon_storage/carbon_storage_techno/documentation/carbon_storage_techno_disc.markdown index c555ce8a..44824bc6 100644 --- a/energy_models/models/carbon_storage/carbon_storage_techno/documentation/carbon_storage_techno_disc.markdown +++ b/energy_models/models/carbon_storage/carbon_storage_techno/documentation/carbon_storage_techno_disc.markdown @@ -1,3 +1,3 @@ **Definition :** -A simplified techno used to demonstrate EnergyMix \ No newline at end of file +A simplified techno used to demonstrate EnergyMix diff --git a/energy_models/models/carbon_storage/deep_ocean_injection/deep_ocean_injection_disc.py b/energy_models/models/carbon_storage/deep_ocean_injection/deep_ocean_injection_disc.py index 89109eef..5b4f8ac4 100644 --- a/energy_models/models/carbon_storage/deep_ocean_injection/deep_ocean_injection_disc.py +++ b/energy_models/models/carbon_storage/deep_ocean_injection/deep_ocean_injection_disc.py @@ -70,7 +70,7 @@ class DeepOceanInjectionDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/deep_ocean_injection/documentation/deep_ocean_injection_disc.markdown b/energy_models/models/carbon_storage/deep_ocean_injection/documentation/deep_ocean_injection_disc.markdown index 01e80c67..4d7f0b38 100644 --- a/energy_models/models/carbon_storage/deep_ocean_injection/documentation/deep_ocean_injection_disc.markdown +++ b/energy_models/models/carbon_storage/deep_ocean_injection/documentation/deep_ocean_injection_disc.markdown @@ -10,4 +10,4 @@ The overall cost varies between 2.2$ / tCO2 for direct injection and up to 15.7$ [^1]: IPCC, https://www.ipcc.ch/site/assets/uploads/2018/03/srccs_chapter6-1.pdf [^2]: Ocean Storage of CO2, The Liquid Grid, http://theliquidgrid.com/2018/07/22/ocean-storage-of-co2 -[^3]: Ocean Storage of CO2, The Liquid Grid, https://www.ipcc.ch/site/assets/uploads/2018/03/srccs_chapter6-1.pdf \ No newline at end of file +[^3]: Ocean Storage of CO2, The Liquid Grid, https://www.ipcc.ch/site/assets/uploads/2018/03/srccs_chapter6-1.pdf diff --git a/energy_models/models/carbon_storage/deep_saline_formation/deep_saline_formation_disc.py b/energy_models/models/carbon_storage/deep_saline_formation/deep_saline_formation_disc.py index 1ef902c0..465b0542 100644 --- a/energy_models/models/carbon_storage/deep_saline_formation/deep_saline_formation_disc.py +++ b/energy_models/models/carbon_storage/deep_saline_formation/deep_saline_formation_disc.py @@ -70,7 +70,7 @@ class DeepSalineFormationDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 # in kg at year_start - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/deep_saline_formation/documentation/deep_saline_formation_disc.markdown b/energy_models/models/carbon_storage/deep_saline_formation/documentation/deep_saline_formation_disc.markdown index 333bce90..bcd6c538 100644 --- a/energy_models/models/carbon_storage/deep_saline_formation/documentation/deep_saline_formation_disc.markdown +++ b/energy_models/models/carbon_storage/deep_saline_formation/documentation/deep_saline_formation_disc.markdown @@ -15,4 +15,4 @@ Technical data was found in [^3]. [^1]: Carbon Storage, Equinor.com, https://www.equinor.com/en/what-we-do/carbon-capture-and-storage.html [^2]: Carbon Storage, Global CCS Institute, https://www.globalccsinstitute.com/archive/hub/publications/119816/costs-co2-storage-post-demonstration-ccs-eu.pdf -[^3]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. \ No newline at end of file +[^3]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. diff --git a/energy_models/models/carbon_storage/depleted_oil_gas/depleted_oil_gas_disc.py b/energy_models/models/carbon_storage/depleted_oil_gas/depleted_oil_gas_disc.py index dfa1cb89..fc36c7a7 100644 --- a/energy_models/models/carbon_storage/depleted_oil_gas/depleted_oil_gas_disc.py +++ b/energy_models/models/carbon_storage/depleted_oil_gas/depleted_oil_gas_disc.py @@ -72,7 +72,7 @@ class DepletedOilGasDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/depleted_oil_gas/documentation/depleted_oil_gas_disc.markdown b/energy_models/models/carbon_storage/depleted_oil_gas/documentation/depleted_oil_gas_disc.markdown index f8a8dc4d..e24434d0 100644 --- a/energy_models/models/carbon_storage/depleted_oil_gas/documentation/depleted_oil_gas_disc.markdown +++ b/energy_models/models/carbon_storage/depleted_oil_gas/documentation/depleted_oil_gas_disc.markdown @@ -13,4 +13,4 @@ Technical data was found in [^3]. [^1]: Scottish Centre for Carbon Storage, 2012, https://www.researchgate.net/figure/Injection-of-carbon-dioxide-into-depleted-oil-and-gas-reservoir-Image-source-Scottish_fig13_327750644 [^2]: Global CCS institute, https://www.globalccsinstitute.com/archive/hub/publications/119816/costs-co2-storage-post-demonstration-ccs-eu.pdf -[^3]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. \ No newline at end of file +[^3]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. diff --git a/energy_models/models/carbon_storage/enhanced_oil_recovery/documentation/enhanced_oil_recovery_disc.markdown b/energy_models/models/carbon_storage/enhanced_oil_recovery/documentation/enhanced_oil_recovery_disc.markdown index 121785fb..05d54735 100644 --- a/energy_models/models/carbon_storage/enhanced_oil_recovery/documentation/enhanced_oil_recovery_disc.markdown +++ b/energy_models/models/carbon_storage/enhanced_oil_recovery/documentation/enhanced_oil_recovery_disc.markdown @@ -8,4 +8,4 @@ Oil & Gas companies such as Oxy or Total invest in CO2 capture and underground i -[^1]: Energy.gov, https://www.energy.gov/fe/science-innovation/oil-gas-research/enhanced-oil-recoveryupdate_of_financial_data_for_coal_fired_chp_plants_may17_july17.pdf \ No newline at end of file +[^1]: Energy.gov, https://www.energy.gov/fe/science-innovation/oil-gas-research/enhanced-oil-recoveryupdate_of_financial_data_for_coal_fired_chp_plants_may17_july17.pdf diff --git a/energy_models/models/carbon_storage/enhanced_oil_recovery/enhanced_oil_recovery_disc.py b/energy_models/models/carbon_storage/enhanced_oil_recovery/enhanced_oil_recovery_disc.py index 100925a5..28fc4507 100644 --- a/energy_models/models/carbon_storage/enhanced_oil_recovery/enhanced_oil_recovery_disc.py +++ b/energy_models/models/carbon_storage/enhanced_oil_recovery/enhanced_oil_recovery_disc.py @@ -70,7 +70,7 @@ class EnhancedOilRecoveryDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/geologic_mineralization/documentation/geologic_mineralization_disc.markdown b/energy_models/models/carbon_storage/geologic_mineralization/documentation/geologic_mineralization_disc.markdown index b065da8b..c089f3fa 100644 --- a/energy_models/models/carbon_storage/geologic_mineralization/documentation/geologic_mineralization_disc.markdown +++ b/energy_models/models/carbon_storage/geologic_mineralization/documentation/geologic_mineralization_disc.markdown @@ -13,4 +13,4 @@ The researchers report that approximately 80% of the carbon becomes embedded in Technical data was found in [^2]. [^1]: Carbfix Website, https://www.carbfix.com/ -[^2]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. \ No newline at end of file +[^2]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. diff --git a/energy_models/models/carbon_storage/geologic_mineralization/geologic_mineralization_disc.py b/energy_models/models/carbon_storage/geologic_mineralization/geologic_mineralization_disc.py index 706788c4..8db35bfb 100644 --- a/energy_models/models/carbon_storage/geologic_mineralization/geologic_mineralization_disc.py +++ b/energy_models/models/carbon_storage/geologic_mineralization/geologic_mineralization_disc.py @@ -70,7 +70,7 @@ class GeologicMineralizationDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/pure_carbon_solid_storage/documentation/pure_carbon_solid_storage_disc.markdown b/energy_models/models/carbon_storage/pure_carbon_solid_storage/documentation/pure_carbon_solid_storage_disc.markdown index 799990ea..4e6646b1 100644 --- a/energy_models/models/carbon_storage/pure_carbon_solid_storage/documentation/pure_carbon_solid_storage_disc.markdown +++ b/energy_models/models/carbon_storage/pure_carbon_solid_storage/documentation/pure_carbon_solid_storage_disc.markdown @@ -15,4 +15,4 @@ Using molar masses we know that in order to obtain 1 kg of carbon we need 3,67 k **Datas :** Technical data was found in [^1]. -[^1]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. \ No newline at end of file +[^1]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. diff --git a/energy_models/models/carbon_storage/pure_carbon_solid_storage/pure_carbon_solid_storage_disc.py b/energy_models/models/carbon_storage/pure_carbon_solid_storage/pure_carbon_solid_storage_disc.py index 05ecfcf2..ed59465c 100644 --- a/energy_models/models/carbon_storage/pure_carbon_solid_storage/pure_carbon_solid_storage_disc.py +++ b/energy_models/models/carbon_storage/pure_carbon_solid_storage/pure_carbon_solid_storage_disc.py @@ -78,13 +78,13 @@ class PureCarbonSolidStorageDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 - + carbon_zero_quantity_to_be_stored = pd.DataFrame( {GlossaryEnergy.Years: range(GlossaryEnergy.YearStartDefault, GlossaryEnergy.YearEndDefault + 1), GlossaryEnergy.carbon_storage: 0.}) DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'carbon_quantity_to_be_stored': {'type': 'dataframe', 'unit': 'Mt', 'default': carbon_zero_quantity_to_be_stored, 'namespace': 'ns_carb', 'visibility': 'Shared', 'structuring': True, @@ -129,7 +129,7 @@ def init_execution(self): def run(self): ''' - specific run for carbon storage + specific run for carbon storage ''' # -- get inputs CSTechnoDiscipline.run(self) diff --git a/energy_models/models/carbon_storage/reforestation/documentation/reforestation_disc.markdown b/energy_models/models/carbon_storage/reforestation/documentation/reforestation_disc.markdown index 7ab26b0d..2b5fe400 100644 --- a/energy_models/models/carbon_storage/reforestation/documentation/reforestation_disc.markdown +++ b/energy_models/models/carbon_storage/reforestation/documentation/reforestation_disc.markdown @@ -29,7 +29,7 @@ The worldwide gain in forest cover totalled 5.2 million hectares per year, the a ## Carbon storage -The intensity of the metabolic processes in trees depends not only on their age but also on certain environmental factors, such as density, temperature, humidity, availability of nutrients, and presence of weather anomalies. There are so many variables that any estimates as to the amount of CO2 absorbed by forests are beset with uncertainty. Consequently, there is a risk that important political decisions regarding climate change mitigation will be based on uncertain predictions. +The intensity of the metabolic processes in trees depends not only on their age but also on certain environmental factors, such as density, temperature, humidity, availability of nutrients, and presence of weather anomalies. There are so many variables that any estimates as to the amount of CO2 absorbed by forests are beset with uncertainty. Consequently, there is a risk that important political decisions regarding climate change mitigation will be based on uncertain predictions. A tree absorbs 30 kgCO2 per year in average [^2]. The world mean density of trees per hectare is 225.40[3]. @@ -45,4 +45,4 @@ Investments are taken to corresponds to actual trend of 1.6Mha new planted fores [^1]: Food and Agriculture Organisation of the United Nations, http://www.fao.org/documents/card/en/c/ca8642en [^2]: ecotree, https://ecotree.green/en/how-much-co2-does-a-tree-absorb [^3]: OurworldInData, Number of trees per km2, 2014, https://ourworldindata.org/grapher/number-of-trees-per-km?tab=chart&country=World+%28mean%29~FRA~RUS~USA -[^4]: OurworldInData, Primary vs. planted forest, https://ourworldindata.org/forest-area#primary-vs-planted-forest \ No newline at end of file +[^4]: OurworldInData, Primary vs. planted forest, https://ourworldindata.org/forest-area#primary-vs-planted-forest diff --git a/energy_models/models/carbon_storage/reforestation/reforestation_disc.py b/energy_models/models/carbon_storage/reforestation/reforestation_disc.py index 98099ce6..95f9361a 100644 --- a/energy_models/models/carbon_storage/reforestation/reforestation_disc.py +++ b/energy_models/models/carbon_storage/reforestation/reforestation_disc.py @@ -96,7 +96,7 @@ class ReforestationDiscipline(CSTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default}, - + } # -- add specific techno inputs to this DESC_IN.update(CSTechnoDiscipline.DESC_IN) diff --git a/energy_models/models/clean_energy/clean_energy_simple_techno/documentation/renewable_simple_techno_disc.markdown b/energy_models/models/clean_energy/clean_energy_simple_techno/documentation/renewable_simple_techno_disc.markdown index 9abeadb4..5ef54454 100644 --- a/energy_models/models/clean_energy/clean_energy_simple_techno/documentation/renewable_simple_techno_disc.markdown +++ b/energy_models/models/clean_energy/clean_energy_simple_techno/documentation/renewable_simple_techno_disc.markdown @@ -3,6 +3,3 @@ ** Simplified techno that produces energy cleanly, used to demonstrate EnergyMix.** ** The energy produced is used in simplified carbon capture techno ** - - - diff --git a/energy_models/models/electricity/biomass_fired/biomass_fired.py b/energy_models/models/electricity/biomass_fired/biomass_fired.py index 73f10fe5..9e90dcea 100644 --- a/energy_models/models/electricity/biomass_fired/biomass_fired.py +++ b/energy_models/models/electricity/biomass_fired/biomass_fired.py @@ -38,7 +38,6 @@ def compute_byproducts_production(self): self.consumption_detailed[f'{BiomassDry.name} ({self.product_unit})'] - \ self.production_detailed[f'{ElectricityTechno.energy_name} ({self.product_unit})'] # TWh - def get_theoretical_co2_prod(self, unit='kg/kWh'): ''' Get co2 needs in kg co2 /kWh diff --git a/energy_models/models/electricity/biomass_fired/biomass_fired_disc.py b/energy_models/models/electricity/biomass_fired/biomass_fired_disc.py index d58594ee..c1b1697b 100644 --- a/energy_models/models/electricity/biomass_fired/biomass_fired_disc.py +++ b/energy_models/models/electricity/biomass_fired/biomass_fired_disc.py @@ -45,7 +45,6 @@ class BiomassFiredDiscipline(ElectricityTechnoDiscipline): techno_name = GlossaryEnergy.BiomassFired - # Source for Initial prod in TWh (2019): # IEA 2022, Data Tables # https://www.iea.org/data-and-statistics/data-tables?country=WORLD&energy=Renewables%20%26%20waste&year=2019, @@ -81,7 +80,7 @@ class BiomassFiredDiscipline(ElectricityTechnoDiscipline): 'efficiency': 1, 'techno_evo_eff': 'no', # yes or no 'full_load_hours': 8760, - f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9 #No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW, + f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9 # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW, # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } diff --git a/energy_models/models/electricity/biomass_fired/documentation/biomass_fired_disc.markdown b/energy_models/models/electricity/biomass_fired/documentation/biomass_fired_disc.markdown index 2b8fcf1d..f91c8bc3 100644 --- a/energy_models/models/electricity/biomass_fired/documentation/biomass_fired_disc.markdown +++ b/energy_models/models/electricity/biomass_fired/documentation/biomass_fired_disc.markdown @@ -1,15 +1,15 @@ # Biomass Fired Electricity -Most biopower plants use direct-fired combustion systems. They burn biomass directly to produce high-pressure steam +Most biopower plants use direct-fired combustion systems. They burn biomass directly to produce high-pressure steam that drives a turbine generator to make electricity. Direct combustion systems feed a biomass feedstock into a combustor or furnace, where the biomass is burned with excess air to heat water in a boiler to create steam. Instead of direct combustion, some developing technologies gasify -the biomass to produce a combustible gas, and others produce pyrolysis oils that can be used to replace liquid fuels. -Boiler fuel can include wood chips, pellets, sawdust, or bio-oil. Steam from the boiler is then expanded through a +the biomass to produce a combustible gas, and others produce pyrolysis oils that can be used to replace liquid fuels. +Boiler fuel can include wood chips, pellets, sawdust, or bio-oil. Steam from the boiler is then expanded through a steam turbine, which spins to run a generator and produce electricity. -Drying the biomass before combusting or gasifying it improves the overall process efficiency, +Drying the biomass before combusting or gasifying it improves the overall process efficiency, but may not be economically viable in many cases (see: Biogas Fired technology).[^1] Production of high heat is calculated in TWh. Where, consumption of biomass dry(TWh) is more than production of electricity(TWh). @@ -23,4 +23,4 @@ The data used for this model is extracted from the IEA Data & statistics[^2] and [^3][IRENA Power Generation Costs, 2019, (p110-119)](https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2020/Jun/IRENA_Power_Generation_Costs_2019.pdf) -[^4]https://en.wikipedia.org/wiki/Biomass_heating_system \ No newline at end of file +[^4]https://en.wikipedia.org/wiki/Biomass_heating_system diff --git a/energy_models/models/electricity/coal_gen/coal_gen_disc.py b/energy_models/models/electricity/coal_gen/coal_gen_disc.py index 7dccde66..827504ed 100644 --- a/energy_models/models/electricity/coal_gen/coal_gen_disc.py +++ b/energy_models/models/electricity/coal_gen/coal_gen_disc.py @@ -114,7 +114,7 @@ class CoalGenDiscipline(ElectricityTechnoDiscipline): 'efficiency': 0.41, 'efficiency_max': 0.48, 'efficiency evolution slope': 0.5, - f"{GlossaryEnergy.CopperResource}_needs": 1150 /1e9 #According to the IEA, Coal powered stations need 1150 kg of copper for each MW implemented. Computing the need in Mt/MW., + f"{GlossaryEnergy.CopperResource}_needs": 1150 / 1e9 # According to the IEA, Coal powered stations need 1150 kg of copper for each MW implemented. Computing the need in Mt/MW., # IEA Executive summary - Role of critical minerals in clean energy transitions 2022 } @@ -124,7 +124,7 @@ class CoalGenDiscipline(ElectricityTechnoDiscipline): # License: CC BY 4.0. initial_production = 9914.45 # in TWh at year_start # Invest before year start in $ - + FLUE_GAS_RATIO = np.array([0.13]) DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, diff --git a/energy_models/models/electricity/coal_gen/documentation/coal_gen_disc.markdown b/energy_models/models/electricity/coal_gen/documentation/coal_gen_disc.markdown index 01ea319e..1a7e66f2 100644 --- a/energy_models/models/electricity/coal_gen/documentation/coal_gen_disc.markdown +++ b/energy_models/models/electricity/coal_gen/documentation/coal_gen_disc.markdown @@ -1,5 +1,5 @@ **Definition (extracted from [^1]):** -A coal-fired power station is a thermal power station that burns coal to generate electricity. Thermal energy produced by coal combustion converts boiler water into steam. This steam is then used to spin turbines and in turn activate generators. Coal-generated electricity represents about 38% of global electricity and produces over 10 billion tonnes of carbon dioxide every year. Coal-fired power plant technology development has been focused on two targets: +A coal-fired power station is a thermal power station that burns coal to generate electricity. Thermal energy produced by coal combustion converts boiler water into steam. This steam is then used to spin turbines and in turn activate generators. Coal-generated electricity represents about 38% of global electricity and produces over 10 billion tonnes of carbon dioxide every year. Coal-fired power plant technology development has been focused on two targets: -Improving the average efficiency from 38% to 48% by increasing the pressure and temperature of the process (up to 650 C and 330 bar for advanced ultra-supercritical or retrofitted subcritical power plants) which allows a drop from 900 to 670 kg/MWh in CO2 emissions. @@ -7,29 +7,29 @@ A coal-fired power station is a thermal power station that burns coal to generat Production of high heat is calculated in TWh. Where, consumption of solid fuel(TWh) is more than production of electricity(TWh). -![](Coal_fired_power_plant_diagram.png) +![](Coal_fired_power_plant_diagram.png) (Image Credit: Tennessee Valley Authority, taken from [^1]) -## Data +## Data According to [^2] and based on 227 power stations, median water consumption is 2220 L/MWh. -![](water_consumption_coalgen.jpg) +![](water_consumption_coalgen.jpg) (Image Credit: [^2]) According to [^5], CAPEX median value is 1900 USD/kW and OPEX median value is 3.39%. -![](CAPEX_OPEX.PNG) -(Table taken from [^5], source IEA) +![](CAPEX_OPEX.PNG) +(Table taken from [^5], source IEA) Fuel and electricity average consumption on worldwide coal-fired power plant for several technologies.[^4] -![](Ressources_costs_coalgen.png) +![](Ressources_costs_coalgen.png) (Table taken from [^4], source IEA) Of the 2 080 GW existing coal fleet, 62% is 20 years old (YO) or less, 16% over 40 YO, 22% from 20 to 40 YO. [^6] -### GHG emissions +### GHG emissions The GAINS model also predicts N2O fugitive emissions from coal energy. The emission factor is equal to 0.0014 kt/PJ. [^7] @@ -50,4 +50,4 @@ The GAINS model also predicts N2O fugitive emissions from coal energy. The emiss [^8]: https://en.wikipedia.org/wiki/Coal-fired_power_station -[^9]: https://www.tva.com/energy/our-power-system/coal/how-a-coal-plant-works#:~:text=Coal%2Dfired%20plants%20produce%20electricity,to%20start%20the%20process%20over. \ No newline at end of file +[^9]: https://www.tva.com/energy/our-power-system/coal/how-a-coal-plant-works#:~:text=Coal%2Dfired%20plants%20produce%20electricity,to%20start%20the%20process%20over. diff --git a/energy_models/models/electricity/gas/biogas_fired/biogas_fired.py b/energy_models/models/electricity/gas/biogas_fired/biogas_fired.py index 39382388..9b9e754e 100644 --- a/energy_models/models/electricity/gas/biogas_fired/biogas_fired.py +++ b/energy_models/models/electricity/gas/biogas_fired/biogas_fired.py @@ -40,8 +40,8 @@ def compute_byproducts_production(self): self.production_detailed[f'{ElectricityTechno.energy_name} ({self.product_unit})'] def get_theoretical_co2_prod(self, unit='kg/kWh'): - ''' - Get co2 needs in kg co2 /kWh + ''' + Get co2 needs in kg co2 /kWh ''' biogas_data = BioGas.data_energy_dict # kg of C02 per kWh of biogas burnt diff --git a/energy_models/models/electricity/gas/biogas_fired/biogas_fired_disc.py b/energy_models/models/electricity/gas/biogas_fired/biogas_fired_disc.py index 177fcd64..7cd6bc53 100644 --- a/energy_models/models/electricity/gas/biogas_fired/biogas_fired_disc.py +++ b/energy_models/models/electricity/gas/biogas_fired/biogas_fired_disc.py @@ -45,7 +45,6 @@ class BiogasFiredDiscipline(ElectricityTechnoDiscipline): techno_name = GlossaryEnergy.BiogasFired - # IEA 2022, Data Tables, # https://www.iea.org/data-and-statistics/data-tables?country=WORLD&energy=Renewables%20%26%20waste&year=2019 # License: CC BY 4.0. @@ -71,7 +70,7 @@ class BiogasFiredDiscipline(ElectricityTechnoDiscipline): 'efficiency': 1, 'techno_evo_eff': 'no', # yes or no 'full_load_hours': 8760, - f"{GlossaryEnergy.CopperResource}_needs": 1100 /1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW + f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } diff --git a/energy_models/models/electricity/gas/biogas_fired/documentation/biogas_fired_disc.markdown b/energy_models/models/electricity/gas/biogas_fired/documentation/biogas_fired_disc.markdown index 9712693d..38ce6eb1 100644 --- a/energy_models/models/electricity/gas/biogas_fired/documentation/biogas_fired_disc.markdown +++ b/energy_models/models/electricity/gas/biogas_fired/documentation/biogas_fired_disc.markdown @@ -1,30 +1,30 @@ # Biogas Fired Electricity -Biogas can be used in different types of internal combustion engines. Other internal combustion -engines such as gas turbines are suitable for the conversion of biogas into both electricity and -heat. The digestate is the remaining inorganic matter that was not transformed into biogas. +Biogas can be used in different types of internal combustion engines. Other internal combustion +engines such as gas turbines are suitable for the conversion of biogas into both electricity and +heat. The digestate is the remaining inorganic matter that was not transformed into biogas. It can be used as an agricultural fertiliser. -Biogas can be used as the fuel in the system of producing biogas from agricultural wastes and -co-generating heat and electricity in a combined heat and power (CHP) plant. +Biogas can be used as the fuel in the system of producing biogas from agricultural wastes and +co-generating heat and electricity in a combined heat and power (CHP) plant. -Unlike the other green energy such as wind and solar, the biogas can be quickly accessed on demand. -The global warming potential can also be greatly reduced when using biogas as the fuel +Unlike the other green energy such as wind and solar, the biogas can be quickly accessed on demand. +The global warming potential can also be greatly reduced when using biogas as the fuel instead of fossil fuel.[^1] Production of high heat is calculated in TWh. Where, consumption of biogas(TWh) is more than production of electricity(TWh). ## Combined Heat and Power (CHP) -The biogas is fed into a combustion engine that turns an electrical generator producing electricity that is injected +The biogas is fed into a combustion engine that turns an electrical generator producing electricity that is injected into the electrical grid via a set of electrical protections and transformers, generating a lot of heat in the process. Typically, a biogas CHP will convert 40% of the biogas energy into electricity, and 50% into hot water. CHP have high capacity factor (about 95%) which means that they produce electricity steadily throughout the year.[^5] -## Data -The data used for this model is extracted partly from the IEA Data & statistics[^2] and survey on Biogas installed power -generation capacity, 2010-2018[^3] and partly from an IRENA Report[^4]. +## Data +The data used for this model is extracted partly from the IEA Data & statistics[^2] and survey on Biogas installed power +generation capacity, 2010-2018[^3] and partly from an IRENA Report[^4]. [^1][Wikipedia](https://en.wikipedia.org/wiki/Biogas#Biogas_generated_heat/electricity) @@ -38,4 +38,4 @@ generation capacity, 2010-2018[^3] and partly from an IRENA Report[^4]. [^6]https://www.sciencedirect.com/science/article/abs/pii/S0306261911008348 -[^7]https://en.wikipedia.org/wiki/Biogas#Biogas_generated_heat/electricity \ No newline at end of file +[^7]https://en.wikipedia.org/wiki/Biogas#Biogas_generated_heat/electricity diff --git a/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine.py b/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine.py index 023a2cc2..602ffabe 100644 --- a/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine.py +++ b/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine.py @@ -26,6 +26,7 @@ class CCGasT(ElectricityTechno): COPPER_RESOURCE_NAME = GlossaryEnergy.CopperResource + def compute_other_streams_needs(self): self.cost_details[f'{Methane.name}_needs'] = self.techno_infos_dict[f'{Methane.name}_needs'] diff --git a/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine_disc.py b/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine_disc.py index 02ee1dc3..1774b7ae 100644 --- a/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine_disc.py +++ b/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine_disc.py @@ -84,7 +84,7 @@ class CombinedCycleGasTurbineDiscipline(ElectricityTechnoDiscipline): # 'efficiency': 0.55, #https://www.ipieca.org/resources/energy-efficiency-solutions/combined-cycle-gas-turbines-2022#:~:text=The%20overall%20efficiency%20of%20an,drops%20significantly%20at%20partial%20load. 'techno_evo_eff': 'no', # yes or no 'full_load_hours': 8760, - f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW. + f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW. # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } @@ -100,7 +100,7 @@ class CombinedCycleGasTurbineDiscipline(ElectricityTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + } # -- add specific techno inputs to this DESC_IN.update(ElectricityTechnoDiscipline.DESC_IN) diff --git a/energy_models/models/electricity/gas/combined_cycle_gas_turbine/documentation/combined_cycle_gas_turbine_disc.markdown b/energy_models/models/electricity/gas/combined_cycle_gas_turbine/documentation/combined_cycle_gas_turbine_disc.markdown index c789edaa..c42dfdb7 100644 --- a/energy_models/models/electricity/gas/combined_cycle_gas_turbine/documentation/combined_cycle_gas_turbine_disc.markdown +++ b/energy_models/models/electricity/gas/combined_cycle_gas_turbine/documentation/combined_cycle_gas_turbine_disc.markdown @@ -2,42 +2,42 @@ **Definition:** -Gas power plants generate electricity by burning gas. There exists different types of gas power plant to generate electricity. All of them use a gas turbine: "natural gas is added, along with a stream of air, which combusts and expands through this turbine causing a generator to spin a magnet, making electricity."[^1]. Within this process, waste heat is generated. Some types of plant use this waste heat (see below). -Natural gas power plants are cheap and quick to build. They also have very high thermodynamic efficiencies compared to other power plants. +Gas power plants generate electricity by burning gas. There exists different types of gas power plant to generate electricity. All of them use a gas turbine: "natural gas is added, along with a stream of air, which combusts and expands through this turbine causing a generator to spin a magnet, making electricity."[^1]. Within this process, waste heat is generated. Some types of plant use this waste heat (see below). +Natural gas power plants are cheap and quick to build. They also have very high thermodynamic efficiencies compared to other power plants. There are two types of natural gas power plants: **Simple cycle gas plants** and **combined cycle gas plants**. The former consists of a gas turbine connected to a generator and the latter consists of a simple cycle plant, combined with another external combustion engine. -## Simple Cycle +## Simple Cycle "The simple cycle is simpler but less efficient than the combined cycle. However, simple cycle plants are able to dispatch faster than coal-fired power plants or nuclear plants. This means they can be turned on or off faster in order to meet societies electricity needs. Often needed on the grid with wind power and solar power, its purpose is to meet the fluctuating electricity needs of society, known as peaking power."[^1] ## Combined Cycle Gas Plant "Combined cycle plants are more efficient because it makes use of the hot exhaust gases that would otherwise be dispelled from the system. These exhaust gases are used to boil water into steam which can then spin another turbine and generate more electricity. The thermal efficiency of the combined cycle can get up to 60%. Moreover, these plants produce one third of the waste heat of a plant with a 33% efficiency (like a typical nuclear power plant or an older coal power plant). The cost of a combined cycle plants is generally higher since they cost more to build and run."[^1] - -## Data + +## Data Production of high heat is calculated in TWh. Where, consumption of methane(TWh) is more than production of electricity(TWh). The efficiency drops significantly at partial load. The typical simple-cycle efficiency is 33–43% at maximum load and drops significantly at partial load. The efficiency for a CCGT will vary with size and type of turbine selected. But tends to increase with the size of the turbine. -The data used for this model is extracted from World Bank[^2], the International Energy Agency[^3], the Energy Information Administration[^4], Lazard[^5] and Fraunhofer[^6]. -In its document[^2], the World Bank gather data from several sources to compute the Levelized Cost of Energy and compare the different results. +The data used for this model is extracted from World Bank[^2], the International Energy Agency[^3], the Energy Information Administration[^4], Lazard[^5] and Fraunhofer[^6]. +In its document[^2], the World Bank gather data from several sources to compute the Levelized Cost of Energy and compare the different results. -### GHG emissions +### GHG emissions The GAINS model predicts methane fugitive emissions from gas energy. Emission factors from gas production are adapted from IPCC guidelines and a mean value has been taken for the leakage at industrial and power plants of 0.1025 kt/PJ [^8]. The GAINS model also predict N2O fugitive emissions from gas energy. The emission factor is equal to 0.0001 kt/PJ. [^7] ### Hypotheses -For global investment and production we only have data for gas electricity without the detail for each technology. For the production, the Energy Information Agency[^3] explains that in 2017, 53% of the gas electricity was produced by Combined Cycle Gas Plant and the left 47% by gas turbine. This information was used for our assumption that 55% of global production comes from CCGT and 45% from GT. -Regarding investment, the only information we found is also from the Energy Information Agency[^3]. It states that the majority of the investment goes into CCGT plant. Our hypothesis is that 75% of investment of the 2 past years in gas plant was for CCGT plant and 25% for GT plant. +For global investment and production we only have data for gas electricity without the detail for each technology. For the production, the Energy Information Agency[^3] explains that in 2017, 53% of the gas electricity was produced by Combined Cycle Gas Plant and the left 47% by gas turbine. This information was used for our assumption that 55% of global production comes from CCGT and 45% from GT. +Regarding investment, the only information we found is also from the Energy Information Agency[^3]. It states that the majority of the investment goes into CCGT plant. Our hypothesis is that 75% of investment of the 2 past years in gas plant was for CCGT plant and 25% for GT plant. ## Some insight on gas Electricity evolution Global electricity generation by source and scenario (TWh)[^3] -![Global electricity generation by source and scenario (TWh)[^3]](electricitybysourceIEA.PNG) +![Global electricity generation by source and scenario (TWh)[^3]](electricitybysourceIEA.PNG) Global power generation capacity by source and scenario[^3] -![](byscenarioprodelecIEA.PNG) +![](byscenarioprodelecIEA.PNG) Global annual average power sector investment, historical and by scenario, 2019-2040[^3] ![](investIEA.PNG) @@ -57,4 +57,4 @@ by scenario, 2019-2040[^3] [^7]: Winiwarter, W., 2005. The GAINS model for greenhouse gases-version 1.0: nitrous oxide (N2O).https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR55-GAINS-N2O.pdf [^8]: Höglund-Isaksson, L. and Mechler, R., 2005. The GAINS Model for Greenhouse gases–Version 1.0: Methane (CH4), IIASA Interim Report IR-05-054. International Institute for Applied Systems Analysis, Laxenburg. https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR54-GAINS-CH4.pdf -[^9]: https://www.ipieca.org/resources/energy-efficiency-solutions/combined-cycle-gas-turbines-2022#:~:text=A%20combined%2Dcycle%20gas%20turbine,or%20as%20a%20mechanical%20drive \ No newline at end of file +[^9]: https://www.ipieca.org/resources/energy-efficiency-solutions/combined-cycle-gas-turbines-2022#:~:text=A%20combined%2Dcycle%20gas%20turbine,or%20as%20a%20mechanical%20drive diff --git a/energy_models/models/electricity/gas/gas_turbine/documentation/gas_turbine_disc.markdown b/energy_models/models/electricity/gas/gas_turbine/documentation/gas_turbine_disc.markdown index f2611a8c..d94b4e0e 100644 --- a/energy_models/models/electricity/gas/gas_turbine/documentation/gas_turbine_disc.markdown +++ b/energy_models/models/electricity/gas/gas_turbine/documentation/gas_turbine_disc.markdown @@ -2,11 +2,11 @@ **Definition:** -Gas power plants generate electricity by burning gas. There exists different types of gas power plant to generate electricity. All of them use a gas turbine: "natural gas is added, along with a stream of air, which combusts and expands through this turbine causing a generator to spin a magnet, making electricity."[^1]. Within this process, waste heat is generated. Some types of plant use this waste heat (see below). -Natural gas power plants are cheap and quick to build. They also have very high thermodynamic efficiencies compared to other power plants. +Gas power plants generate electricity by burning gas. There exists different types of gas power plant to generate electricity. All of them use a gas turbine: "natural gas is added, along with a stream of air, which combusts and expands through this turbine causing a generator to spin a magnet, making electricity."[^1]. Within this process, waste heat is generated. Some types of plant use this waste heat (see below). +Natural gas power plants are cheap and quick to build. They also have very high thermodynamic efficiencies compared to other power plants. There are two types of natural gas power plants: **Simple cycle gas plants** and **combined cycle gas plants**. The former consists of a gas turbine connected to a generator and the latter consists of a simple cycle plant, combined with another external combustion engine. -## Simple Cycle +## Simple Cycle "The simple cycle is simpler but less efficient than the combined cycle. However, simple cycle plants are able to dispatch faster than coal-fired power plants or nuclear plants. This means they can be turned on or off faster in order to meet societies electricity needs. Often needed on the grid with wind power and solar power, its purpose is to meet the fluctuating electricity needs of society, known as peaking power."[^1] ## Combined Cycle Gas Plant @@ -15,28 +15,28 @@ There are two types of natural gas power plants: **Simple cycle gas plants** and Production of high heat is calculated in TWh. Where, consumption of methane(TWh) is more than production of electricity(TWh). -## Data -The data used for this model is extracted from World Bank[^2], the International Energy Agency[^3], the Energy Information Administration[^4], Lazard[^5] and Fraunhofer[^6]. -In its document[^2], the World Bank gather data from several sources to compute the Levelized Cost of Energy and compare the different results. +## Data +The data used for this model is extracted from World Bank[^2], the International Energy Agency[^3], the Energy Information Administration[^4], Lazard[^5] and Fraunhofer[^6]. +In its document[^2], the World Bank gather data from several sources to compute the Levelized Cost of Energy and compare the different results. -### GHG emissions +### GHG emissions The GAINS model predicts methane fugitive emissions from gas energy. Emission factors from gas production are adapted from IPCC guidelines and a mean value has been taken for the leakage at industrial and power plants of 0.1025 kt/PJ [^8]. The GAINS model also predicts N2O fugitive emissions from gas energy. The emission factor is equal to 0.0001 kt/PJ. [^7] ### Hypotheses -For global investment and production we only have data for gas electricity without the detail for each technology. For the production, the Energy Information Agency[^3] explains that in 2017, 53% of the gas electricity was produced by Combined Cycle Gas Plant and the left 47% by gas turbine. This information was used for our assumption that 55% of global production comes from CCGT and 45% from GT. -Regarding investment, the only information we found is also from the Energy Information Agency[^3]. It states that the majority of the investment goes into CCGT plant. Our hypothesis is that 75% of investment of the 2 past years in gas plant was for CCGT plant and 25% for GT plant. +For global investment and production we only have data for gas electricity without the detail for each technology. For the production, the Energy Information Agency[^3] explains that in 2017, 53% of the gas electricity was produced by Combined Cycle Gas Plant and the left 47% by gas turbine. This information was used for our assumption that 55% of global production comes from CCGT and 45% from GT. +Regarding investment, the only information we found is also from the Energy Information Agency[^3]. It states that the majority of the investment goes into CCGT plant. Our hypothesis is that 75% of investment of the 2 past years in gas plant was for CCGT plant and 25% for GT plant. ## Some insight on gas Electricity evolution Global electricity generation by source and scenario (TWh)[^3] -![Global electricity generation by source and scenario (TWh)[^3]](electricitybysourceIEA.PNG) +![Global electricity generation by source and scenario (TWh)[^3]](electricitybysourceIEA.PNG) Global power generation capacity by source and scenario[^3] -![](byscenarioprodelecIEA.PNG) +![](byscenarioprodelecIEA.PNG) Global annual average power sector investment, historical and by scenario, 2019-2040[^3] @@ -58,4 +58,4 @@ by scenario, 2019-2040[^3] [^7]: Winiwarter, W., 2005. The GAINS model for greenhouse gases-version 1.0: nitrous oxide (N2O).https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR55-GAINS-N2O.pdf [^8]: Hoglund-Isaksson, L. and Mechler, R., 2005. The GAINS Model for Greenhouse gases–Version 1.0: Methane (CH4), IIASA Interim Report IR-05-054. International Institute for Applied Systems Analysis, Laxenburg. https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR54-GAINS-CH4.pdf -[^9]: https://www.energy.gov/fecm/how-gas-turbine-power-plants-work \ No newline at end of file +[^9]: https://www.energy.gov/fecm/how-gas-turbine-power-plants-work diff --git a/energy_models/models/electricity/gas/gas_turbine/gas_turbine_disc.py b/energy_models/models/electricity/gas/gas_turbine/gas_turbine_disc.py index c60a3f5c..1cffd578 100644 --- a/energy_models/models/electricity/gas/gas_turbine/gas_turbine_disc.py +++ b/energy_models/models/electricity/gas/gas_turbine/gas_turbine_disc.py @@ -78,7 +78,7 @@ class GasTurbineDiscipline(ElectricityTechnoDiscipline): 'efficiency': 1, 'techno_evo_eff': 'no', # yes or no 'full_load_hours': 8760, - f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9# According to the IEA, Gaz powered stations need 1100 kg of copper for each MW implemented. Computing the need in Mt/MW., + f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9 # According to the IEA, Gaz powered stations need 1100 kg of copper for each MW implemented. Computing the need in Mt/MW., # IEA Executive summary - Role of critical minerals in clean energy transitions 2022 } @@ -95,7 +95,7 @@ class GasTurbineDiscipline(ElectricityTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + } # -- add specific techno inputs to this DESC_IN.update(ElectricityTechnoDiscipline.DESC_IN) diff --git a/energy_models/models/electricity/geothermal/documentation/geothermal_disc.markdown b/energy_models/models/electricity/geothermal/documentation/geothermal_disc.markdown index 7a216117..d18838a3 100644 --- a/energy_models/models/electricity/geothermal/documentation/geothermal_disc.markdown +++ b/energy_models/models/electricity/geothermal/documentation/geothermal_disc.markdown @@ -3,7 +3,7 @@ **Definition[^1] :** Geothermal energy is a type of renewable energy which is generated within the earth and can be used directly for heating or transformed into electricity. An advantage of geothermal energy over some other renewable energy sources is that it is available year-long (whereas solar and wind energy present higher variability and intermittence) and can be found around the globe. However, for electricity generation, medium- to high-temperature resources, which are usually close to volcanically active regions, are needed. -![](geothermal_schema.PNG) +![](geothermal_schema.PNG) (Image Credit Geothermal Tomorrow 2008, [^1b]) Geothermal energy technologies are distinguished into three main subtechnologies[^2], namely flash geothermal, Organic Rankine Cycle (ORC binary) geothermal and Enhanced Geothermal System (EGS). The capital investment costs of geothermal power plants depend highly on local sites. @@ -12,17 +12,17 @@ A geothermal power plant is not producing electricity full time, in order to mod Consumption of heat is calculated in TWh for medium range temperature. It depends on production of electricity(TWh) and efficiency. -**Capacity factor[^3] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. +**Capacity factor[^3] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. + +## Data +Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. -## Data -Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. - Typical uncertainty and expenditure profiles for a geothermal project[^1] -![](geothermal_uncertainty.PNG) +![](geothermal_uncertainty.PNG) ## Some insight on Geothermal electricity evolution IEA Geothermal electricity power generation prediction[^7] -![](geothermal_forecast.PNG) +![](geothermal_forecast.PNG) [^1]: [IRENA Geothermal power (2017)](https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2017/Aug/IRENA_Geothermal_Power_2017.pdf) [^1b]: [Vision, G.T.P. and Mission, E.G.S., Geothermal Tomorrow 08](https://www.nrel.gov/docs/fy08osti/43504.pdf) diff --git a/energy_models/models/electricity/geothermal/geothermal_disc.py b/energy_models/models/electricity/geothermal/geothermal_disc.py index e2c6f6b1..792ae61d 100644 --- a/energy_models/models/electricity/geothermal/geothermal_disc.py +++ b/energy_models/models/electricity/geothermal/geothermal_disc.py @@ -49,7 +49,6 @@ class GeothermalDiscipline(ElectricityTechnoDiscipline): # Cost development of low carbon energy technologies-Scenario-based cost trajectories to 2050, 2017 Edition. # Publications Office of the European Union, Luxemburgo. - techno_infos_dict_default = {'maturity': 0, 'Opex_percentage': 0.045, # Fixed 4.0% and recurrent 0.5 % @@ -71,7 +70,7 @@ class GeothermalDiscipline(ElectricityTechnoDiscipline): # https://www.sciencedirect.com/science/article/abs/pii/S0375650513001120 'CO2_from_production': 0.0, 'CO2_from_production_unit': 'kg/kg', - f"{GlossaryEnergy.CopperResource}_needs": 1100 /1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW + f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } diff --git a/energy_models/models/electricity/hydropower/documentation/hydropower_disc.markdown b/energy_models/models/electricity/hydropower/documentation/hydropower_disc.markdown index e42f6aac..ac8b5543 100644 --- a/energy_models/models/electricity/hydropower/documentation/hydropower_disc.markdown +++ b/energy_models/models/electricity/hydropower/documentation/hydropower_disc.markdown @@ -11,7 +11,7 @@ Hydropower is mainly associated with the Hoover Dam a huge facility harnessing t Hydropower itself does not produce heat as a primary output. Instead, it generates electricity through the kinetic energy of flowing water. any heat generated in these incidental ways is relatively small compared to the electrical energy produced by the hydropower system. Overall, hydropower is considered an efficient and low-heat-producing method of electricity generation. -## Data +## Data Most of the data used for this model is extracted from International Energy Agency (IEA)[^4] and International Renewable Energy Agency (IRENA)[^5]. @@ -19,11 +19,11 @@ Data's about Hydropower plant commissioning year has been extracted from a power ![](hydropower_plant_world_age_distribution.png) -## Some insight on hydropower evolution +## Some insight on hydropower evolution IEA hydropower generation in the Sustainable Development Scenario, 2000-2030 [^7] -![](hydropower-generation-in-the-sustainable-development-scenario-2000-2030.png) +![](hydropower-generation-in-the-sustainable-development-scenario-2000-2030.png) Global weighted average total installed costs, capacity factors and LCOE for hydropower, 2010-2019 [^8] diff --git a/energy_models/models/electricity/hydropower/hydropower.py b/energy_models/models/electricity/hydropower/hydropower.py index af207a5a..44f5d517 100644 --- a/energy_models/models/electricity/hydropower/hydropower.py +++ b/energy_models/models/electricity/hydropower/hydropower.py @@ -21,4 +21,4 @@ class Hydropower(ElectricityTechno): - pass \ No newline at end of file + pass diff --git a/energy_models/models/electricity/hydropower/hydropower_disc.py b/energy_models/models/electricity/hydropower/hydropower_disc.py index ace54eea..8d73a75f 100644 --- a/energy_models/models/electricity/hydropower/hydropower_disc.py +++ b/energy_models/models/electricity/hydropower/hydropower_disc.py @@ -64,7 +64,7 @@ class HydropowerDiscipline(ElectricityTechnoDiscipline): 'efficiency': 1.0, # No need of efficiency here 'learning_rate': 0.0, 'techno_evo_eff': 'no', - f"{GlossaryEnergy.CopperResource}_needs": 1100 /1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW + f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } diff --git a/energy_models/models/electricity/nuclear/documentation/nuclear_disc.markdown b/energy_models/models/electricity/nuclear/documentation/nuclear_disc.markdown index 89c94343..185b13f8 100644 --- a/energy_models/models/electricity/nuclear/documentation/nuclear_disc.markdown +++ b/energy_models/models/electricity/nuclear/documentation/nuclear_disc.markdown @@ -3,38 +3,38 @@ **Definition[^1] :** Nuclear power is the use of nuclear reactions that release nuclear energy to generate heat, which most frequently is then used in steam turbines to produce electricity in a nuclear power plant. Nuclear power can be obtained from nuclear fission, nuclear decay and nuclear fusion reactions. Presently, the vast majority of electricity from nuclear power is produced by nuclear fission of uranium and plutonium. Nuclear decay processes are used in niche applications such as radioisotope thermoelectric generators in some space probes such as Voyager 2. Generating electricity from fusion power remains at the focus of international research. This article mostly deals with nuclear fission power for electricity generation. -![](nuclearpowerpic.jpg) +![](nuclearpowerpic.jpg) (Image Credit: TTstudio/ Shutterstock) A nuclear power plant is not producing electricity full time, in order to model yearly production a variable is set : **Capacity Factor (%)**. -**Capacity factor[^2] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. +**Capacity factor[^2] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. -## Data +## Data Most of the data used for this model is extracted from Internation Atomic Energy Agency (IAEA) [^3] -World Nuclear Association (WNA) [^5], National Renewable Energy Laboratory (NREL)[^6]. +World Nuclear Association (WNA) [^5], National Renewable Energy Laboratory (NREL)[^6]. Production of high heat in TWh is calculated as waste heat of its electricity production to the efficiency generation. Water Consumption[^7] -![](nuclear_water.png) +![](nuclear_water.png) Uranium enrichment, from uranium ore[^8] -![](uranium_enrichment.PNG) +![](uranium_enrichment.PNG) Uranium as fuel cost[^9] -![](uranium_cost.PNG) +![](uranium_cost.PNG) Nuclear power plants age distribution[^3] -![](nuclear_age_distribution.png) +![](nuclear_age_distribution.png) ## Waste disposal and Decommissioning Following an analysis from the World Nuclear Waste Report [^10], waste disposal cost is added as a levy and is part of the energy price. -Decommissioning costs are integrated to the Factory Capex and is consequently spread on its lifespan. +Decommissioning costs are integrated to the Factory Capex and is consequently spread on its lifespan. ## Some insight on Nuclear evolution IEA Nuclear power generation prediction[^4] -![](nuclear_capacity_forecast.PNG) +![](nuclear_capacity_forecast.PNG) ## Fuel comparison With a complete combustion or fission, approx. 8 kWh of heat can be generated from 1 kg of coal, approx. 12 kWh from 1 kg of mineral oil and around 24,000,000 kWh from 1 kg of uranium-235.[^13] @@ -54,4 +54,3 @@ With a complete combustion or fission, approx. 8 kWh of heat can be generated fr [^13]: Data - uranium used per kg, https://www.euronuclear.org/glossary/fuel-comparison/ [^14]: Data - efficiency and heat recovery factor, https://www.sciencedirect.com/science/article/abs/pii/S0306261921001975 [^15]: Working: https://www.eia.gov/energyexplained/nuclear/nuclear-power-plants.php#:~:text=Nuclear%20power%20comes%20from%20nuclear,magnetic%20generators%20to%20produce%20electricity. - diff --git a/energy_models/models/electricity/nuclear/nuclear.py b/energy_models/models/electricity/nuclear/nuclear.py index 0a276757..1d010c96 100644 --- a/energy_models/models/electricity/nuclear/nuclear.py +++ b/energy_models/models/electricity/nuclear/nuclear.py @@ -47,7 +47,6 @@ def compute_byproducts_production(self): # self.production[f'{ElectricityTechno.energy_name} ({self.product_unit})']) / \ # self.techno_infos_dict['efficiency'] - def get_theoretical_uranium_fuel_needs(self): """ Get Uranium fuel needs in kg Uranium fuel /kWh electricty diff --git a/energy_models/models/electricity/nuclear/nuclear_disc.py b/energy_models/models/electricity/nuclear/nuclear_disc.py index c3dcc2f8..a73a24ba 100644 --- a/energy_models/models/electricity/nuclear/nuclear_disc.py +++ b/energy_models/models/electricity/nuclear/nuclear_disc.py @@ -48,7 +48,6 @@ class NuclearDiscipline(ElectricityTechnoDiscipline): # 2019 standard scenarios report: a US electric sector outlook (No. NREL/PR-6A20-75798). # National Renewable Energy Lab.(NREL), Golden, CO (United States). - techno_infos_dict_default = {'maturity': 0, 'Opex_percentage': 0.024, # Fixed 1.9 and recurrent 0.5 % @@ -72,7 +71,7 @@ class NuclearDiscipline(ElectricityTechnoDiscipline): 'decommissioning_cost_unit': '$/kW', # World Nuclear Waste Report 2019, Chapter 6 (https://worldnuclearwastereport.org) # average of 1000 $/kW - f"{GlossaryEnergy.CopperResource}_needs": 1473/ 1e9, # According to the IEA, Nuclear power stations need 1473 kg of copper for each MW implemented. Computing the need in Mt/MW + f"{GlossaryEnergy.CopperResource}_needs": 1473 / 1e9, # According to the IEA, Nuclear power stations need 1473 kg of copper for each MW implemented. Computing the need in Mt/MW # IEA Executive summary - Role of critical minerals in clean energy transitions 2022 } diff --git a/energy_models/models/electricity/oil_gen/documentation/oil_gen_disc.markdown b/energy_models/models/electricity/oil_gen/documentation/oil_gen_disc.markdown index 37876500..b04fcd9a 100644 --- a/energy_models/models/electricity/oil_gen/documentation/oil_gen_disc.markdown +++ b/energy_models/models/electricity/oil_gen/documentation/oil_gen_disc.markdown @@ -1,13 +1,13 @@ **Definition** -Oil-fired generating plants burn oil to produce electricity. They are similar in construction and operation +Oil-fired generating plants burn oil to produce electricity. They are similar in construction and operation to coal-fired and natural gas-fired facilities.[^2] -Heavy fuel oil was once a significant source of energy for electric power generation. -After oil price increases of the 1970s, oil was displaced by coal and later natural gas. -Distillate oil is still important as the fuel source for diesel engine power plants used especially in -isolated communities not interconnected to a grid. Liquid fuels may also be used by gas turbine power plants, -especially for peaking or emergency service. Of the three fossil fuel sources, oil has the advantages of easier +Heavy fuel oil was once a significant source of energy for electric power generation. +After oil price increases of the 1970s, oil was displaced by coal and later natural gas. +Distillate oil is still important as the fuel source for diesel engine power plants used especially in +isolated communities not interconnected to a grid. Liquid fuels may also be used by gas turbine power plants, +especially for peaking or emergency service. Of the three fossil fuel sources, oil has the advantages of easier transportation and handling than solid coal, and easier on-site storage than natural gas.[^1] Production of high heat is calculated in TWh. Where, consumption of liquid fuel(TWh) is more than production of electricity(TWh). @@ -16,13 +16,13 @@ Production of high heat is calculated in TWh. Where, consumption of liquid fuel( Data are extracted from IEA data tables[^3] and balances[^4]. CO2 emissions information from RTE French data[^5]. -### GHG emissions +### GHG emissions The GAINS model also predicts N2O fugitive emissions from oil energy. The emission factor is equal to 0.008 kt/PJ for oil combustion via electricity plants. [^7] -## Sources +## Sources [^1]: [Fossil Fuel Power on Wikipedia](https://en.wikipedia.org/wiki/Fossil_fuel_power_station#Oil) @@ -38,4 +38,3 @@ The GAINS model also predicts N2O fugitive emissions from oil energy. The emissi [^7]: Winiwarter, W., 2005. The GAINS model for greenhouse gases-version 1.0: nitrous oxide (N2O).https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR55-GAINS-N2O.pdf [^8]: https://www.eia.gov/energyexplained/oil-and-petroleum-products/refining-crude-oil-the-refining-process.php - diff --git a/energy_models/models/electricity/oil_gen/oil_gen.py b/energy_models/models/electricity/oil_gen/oil_gen.py index 9b65fedd..01a7c7d4 100644 --- a/energy_models/models/electricity/oil_gen/oil_gen.py +++ b/energy_models/models/electricity/oil_gen/oil_gen.py @@ -54,14 +54,13 @@ def compute_byproducts_production(self): def grad_price_vs_stream_price(self): ''' - Compute the gradient of global price vs energy prices + Compute the gradient of global price vs energy prices Work also for total CO2_emissions vs energy CO2 emissions ''' liquid_fuel_needs = self.techno_infos_dict['fuel_demand'] efficiency = self.compute_efficiency() return {LiquidFuel.name: np.diag(liquid_fuel_needs / efficiency)} - def compute_dprod_dinvest(self, capex_list, invest_list, invest_before_year_start, techno_dict, dcapex_list_dinvest_list): dprod_dinvest = ElectricityTechno.compute_dprod_dinvest( diff --git a/energy_models/models/electricity/oil_gen/oil_gen_disc.py b/energy_models/models/electricity/oil_gen/oil_gen_disc.py index eb090c35..bc07abdb 100644 --- a/energy_models/models/electricity/oil_gen/oil_gen_disc.py +++ b/energy_models/models/electricity/oil_gen/oil_gen_disc.py @@ -115,7 +115,7 @@ class OilGenDiscipline(ElectricityTechnoDiscipline): 'transport_cost_unit': '$/kg', # check if pertinent 'techno_evo_eff': 'no', 'efficiency': 1, - f"{GlossaryEnergy.CopperResource}_needs": 1100 /1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW + f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } @@ -131,12 +131,12 @@ class OilGenDiscipline(ElectricityTechnoDiscipline): # https://www.iea.org/reports/world-energy-investment-2019/power-sector # License: CC BY 4.0. # (linear from 2016, 2017, 2018 data) - + oil_flue_gas_ratio = np.array([0.12]) DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flue_gas_co2_ratio': {'type': 'array', 'default': oil_flue_gas_ratio, 'unit': ''}, } diff --git a/energy_models/models/electricity/renewable_electricity_simple_techno/documentation/renewable_simple_techno_disc.markdown b/energy_models/models/electricity/renewable_electricity_simple_techno/documentation/renewable_simple_techno_disc.markdown index 9abeadb4..5ef54454 100644 --- a/energy_models/models/electricity/renewable_electricity_simple_techno/documentation/renewable_simple_techno_disc.markdown +++ b/energy_models/models/electricity/renewable_electricity_simple_techno/documentation/renewable_simple_techno_disc.markdown @@ -3,6 +3,3 @@ ** Simplified techno that produces energy cleanly, used to demonstrate EnergyMix.** ** The energy produced is used in simplified carbon capture techno ** - - - diff --git a/energy_models/models/electricity/renewable_electricity_simple_techno/renewable_electricity_simple_techno_disc.py b/energy_models/models/electricity/renewable_electricity_simple_techno/renewable_electricity_simple_techno_disc.py index 713f304d..25a852c9 100644 --- a/energy_models/models/electricity/renewable_electricity_simple_techno/renewable_electricity_simple_techno_disc.py +++ b/energy_models/models/electricity/renewable_electricity_simple_techno/renewable_electricity_simple_techno_disc.py @@ -53,7 +53,6 @@ class RenewableElectricitySimpleTechnoDiscipline(ElectricityTechnoDiscipline): # 2019 standard scenarios report: a US electric sector outlook (No. NREL/PR-6A20-75798). # National Renewable Energy Lab.(NREL), Golden, CO (United States). - techno_infos_dict_default = {'maturity': 0, 'Opex_percentage': 0.12, # Fixed 1.9 and recurrent 0.5 % @@ -78,7 +77,7 @@ class RenewableElectricitySimpleTechnoDiscipline(ElectricityTechnoDiscipline): # and Nuclear Power Estimates up to 2050 initial_production = 6590.0 # Invest in 2019 => 29.6 bn - + # Age distribution => IAEA OPEX Nuclear 2020 - Number of Reactors by Age # (as of 1 January 2020) DESC_IN = {'techno_infos_dict': {'type': 'dict', diff --git a/energy_models/models/electricity/solar_pv/documentation/solar_pv_disc.markdown b/energy_models/models/electricity/solar_pv/documentation/solar_pv_disc.markdown index 90f0d087..d938da61 100644 --- a/energy_models/models/electricity/solar_pv/documentation/solar_pv_disc.markdown +++ b/energy_models/models/electricity/solar_pv/documentation/solar_pv_disc.markdown @@ -1,4 +1,4 @@ -# Solar Photovoltaic +# Solar Photovoltaic **Definition[^1]:** "Solar cells, also called photovoltaic cells, convert sunlight directly into electricity. @@ -6,26 +6,26 @@ Photovoltaics (often shortened as PV) gets its name from the process of converti The maximum possible energy output of a given installation assumes its continuous operation at full nameplate capacity over the relevant period. The actual energy output during that period and the capacity factor vary greatly depending on a range of factors, for renewable energy the main factor being the weather conditions. For solar PV it is then important to take into account the $capacity factor$ defined as "the ratio of the net electricity generated, for the time considered, to the energy that could have been generated at continuous full-power operation during the same period"[^2]. -Solar photovoltaic (PV) systems primarily generate electricity, not heat. These systems convert sunlight directly into electrical energy through the photovoltaic effect, which occurs in solar cells. +Solar photovoltaic (PV) systems primarily generate electricity, not heat. These systems convert sunlight directly into electrical energy through the photovoltaic effect, which occurs in solar cells. In summary, while solar PV systems themselves do not produce heat, they can indirectly impact heat production by supplying electricity for heating purposes, reducing the demand for traditional heating sources, and contributing to overall energy savings. -## Data +## Data To generate the data for this model we used International Energy Agency (IEA)[^3] and International Renewable Energy Agency (IRENA)[^4] reports. ## Some insight on Solar PV evolution IEA solar PV power generation in the Sustainable Development Scenario, 2000-2030[^7] -![](IEAsolarprodsds.png) +![](IEAsolarprodsds.png) Global weighted average total installed costs, capacity factors and LCOE for PV, 2010–2019 ![](IRENAcostevol.png) - + ## Land use -Solar PV are disposed in lands and most of it on crops category of lands.[^5] +Solar PV are disposed in lands and most of it on crops category of lands.[^5] Because in developed countries, where solar PV are the most deployed, barren lands and desert are scarce (around 10% of the global barren lands surface), it will not be considered in this model. -Moreover, only 3% of the urban surface can be used for solar PV, very few rooftops are eligible to solar PV. So for this first version of land use model it will not be considered either. +Moreover, only 3% of the urban surface can be used for solar PV, very few rooftops are eligible to solar PV. So for this first version of land use model it will not be considered either. The power by hectare value has been computed on the base of 357 MWh/acre[^6], giving 315059 kWh/ha. -[^1]: Solar Photovoltaic Technology Basics. NREL.gov, www.nrel.gov/research/re-photovoltaics.html +[^1]: Solar Photovoltaic Technology Basics. NREL.gov, www.nrel.gov/research/re-photovoltaics.html [^2]: Capacity factor. NRC.gov, https://www.nrc.gov/reading-rm/basic-ref/glossary/capacity-factor-net.html [^3]: IEA 2022, World Energy Outlook 2019, IEA, Paris https://www.iea.org/reports/world-energy-outlook-2019, License: CC BY 4.0. [^4]: IRENA (2020), Renewable Power Generation Costs in 2019, @@ -33,4 +33,4 @@ International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/publicat [^5]: Scientific report (2021), https://www.nature.com/articles/s41598-021-82042-5 [^6]: greenCoast, 2019, Solar Farm Land Requirements: How Much Land Do You Need?, https://greencoast.org/solar-farm-land-requirements/ [^7]: IEA 2022, Solar PV power generation in the Sustainable Development Scenario, 2000-2030, IEA, Paris https://www.iea.org/data-and-statistics/charts/solar-pv-power-generation-in-the-sustainable-development-scenario-2000-2030, License: CC BY 4.0. -[^8]: https://www.eia.gov/energyexplained/solar/photovoltaics-and-electricity.php#:~:text=A%20photovoltaic%20(PV)%20cell%2C,convert%20artificial%20light%20into%20electricity \ No newline at end of file +[^8]: https://www.eia.gov/energyexplained/solar/photovoltaics-and-electricity.php#:~:text=A%20photovoltaic%20(PV)%20cell%2C,convert%20artificial%20light%20into%20electricity diff --git a/energy_models/models/electricity/solar_pv/solar_pv_disc.py b/energy_models/models/electricity/solar_pv/solar_pv_disc.py index ad9119d4..483b6f27 100644 --- a/energy_models/models/electricity/solar_pv/solar_pv_disc.py +++ b/energy_models/models/electricity/solar_pv/solar_pv_disc.py @@ -77,14 +77,14 @@ class SolarPvDiscipline(ElectricityTechnoDiscipline): 'transport_cost_unit': '$/kg', # check if pertient 'techno_evo_eff': 'no', GlossaryEnergy.EnergyEfficiency: 1.0, - f"{GlossaryEnergy.CopperResource}_needs": 2822 / 1e9 # According to the IEA, Solar PV panels need 2822 kg of copper for each MW implemented. Computing the need in Mt/MW, + f"{GlossaryEnergy.CopperResource}_needs": 2822 / 1e9 # According to the IEA, Solar PV panels need 2822 kg of copper for each MW implemented. Computing the need in Mt/MW, # IEA Executive summary - Role of critical minerals in clean energy transitions 2022 } techno_info_dict = techno_infos_dict_default initial_production = 700 # in TWh at year_start source IEA 2019 # Invest before year start in $ source IEA 2019 - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/electricity/solar_thermal/documentation/solar_thermal_disc.markdown b/energy_models/models/electricity/solar_thermal/documentation/solar_thermal_disc.markdown index 7fc2d8fe..6eb8cdc3 100644 --- a/energy_models/models/electricity/solar_thermal/documentation/solar_thermal_disc.markdown +++ b/energy_models/models/electricity/solar_thermal/documentation/solar_thermal_disc.markdown @@ -18,20 +18,20 @@ Production of heat for high temperature is calculated in TWh. It depends on prod ![](solar_thermal_type.png) (Image Credit: IEA [^1b]) -## Data +## Data Most of the data used for this model is extracted from Greenpeace International, SolarPACES and ESTELA report[^2], National Renewable Energy Laboratory (NREL) [^3], Joint Research Center [^1] and International Renewable Energy Agency (IRENA)[^4]. ## Some insight on SolarThermal evolution IEA solar thermal power generation in the Sustainable Development Scenario, 2000-2030 -![](concentrating-solar-power-generation-in-the-sustainable-development-scenario-2000-2030.png) +![](concentrating-solar-power-generation-in-the-sustainable-development-scenario-2000-2030.png) Global weighted average total installed costs, capacity factors and LCOE for CSP, 2010-2019 ![](irena_csp.png) - + ## Land use -Solar thermal are disposed in lands and most of it on crops category of lands.[^5] +Solar thermal are disposed in lands and most of it on crops category of lands.[^5] Because in developed countries, where solar thermal are the most deployed, barren lands and desert are scarce (around 10% of the global barren lands surface), it will not be considered in this model. -Moreover, only 3% of the urban surface can be used for solar thermal, very few rooftops are eligible to solar panels. So for this first version of land use model it will not be considered either. +Moreover, only 3% of the urban surface can be used for solar thermal, very few rooftops are eligible to solar panels. So for this first version of land use model it will not be considered either. The power by hectare value has been computed on the base of 357 MWh/acre[^6] for photovoltaic panels, and solar thermal uses 10% less space than Solar photovoltaic, giving 346564,9 kWh/ha. @@ -45,4 +45,4 @@ International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/publicat [^5]: Scientific report (2021), https://www.nature.com/articles/s41598-021-82042-5 [^6]: greenCoast, 2019, Solar Farm Land Requirements: How Much Land Do You Need?, https://greencoast.org/solar-farm-land-requirements/ [^7]: Working principle: https://www.bmwk.de/Redaktion/EN/Artikel/Energy/research-priorities-solar-thermal-power-plants.html#:~:text=In%20solar%20thermal%20power%20plants,transforms%20this%20energy%20into%20electricity. -[^8]: Efficiency: https://www.volker-quaschning.de/articles/fundamentals2/index.php#:~:text=The%20efficiency%20of%20a%20solar,losses%20are%20usually%20below%2010%25. \ No newline at end of file +[^8]: Efficiency: https://www.volker-quaschning.de/articles/fundamentals2/index.php#:~:text=The%20efficiency%20of%20a%20solar,losses%20are%20usually%20below%2010%25. diff --git a/energy_models/models/electricity/solar_thermal/solar_thermal_disc.py b/energy_models/models/electricity/solar_thermal/solar_thermal_disc.py index 207b585b..a8a014c1 100644 --- a/energy_models/models/electricity/solar_thermal/solar_thermal_disc.py +++ b/energy_models/models/electricity/solar_thermal/solar_thermal_disc.py @@ -71,7 +71,7 @@ class SolarThermalDiscipline(ElectricityTechnoDiscipline): 'density_per_ha_unit': 'kWh/ha', 'CO2_from_production': 0.0, 'CO2_from_production_unit': 'kg/kg', - f"{GlossaryEnergy.CopperResource}_needs": 1100 /1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW + f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } @@ -80,7 +80,7 @@ class SolarThermalDiscipline(ElectricityTechnoDiscipline): # Invest before year start # from # https://www.irena.org/Statistics/View-Data-by-Topic/Finance-and-Investment/Investment-Trends - + # from database https://solarpaces.nrel.gov/ # Nb plants 'Operational' and not pilot/demo/proto # only commercial or production diff --git a/energy_models/models/electricity/wind_offshore/documentation/wind_offshore_disc.markdown b/energy_models/models/electricity/wind_offshore/documentation/wind_offshore_disc.markdown index 72daa95d..ee4a6619 100644 --- a/energy_models/models/electricity/wind_offshore/documentation/wind_offshore_disc.markdown +++ b/energy_models/models/electricity/wind_offshore/documentation/wind_offshore_disc.markdown @@ -8,24 +8,24 @@ Offshore electricity production, through wind farms can be split in two contruct - offshore floating Fixed bottom foundations and floating offshore concepts[^2] -![](Fixed-bottom-foundations-and-floating-offshore-concepts-9.png) - +![](Fixed-bottom-foundations-and-floating-offshore-concepts-9.png) + Wind is split into 14 class (speed class m/s). A wind farm is not producing electricity full time, in order to model yearly production a variable is set : **Capacity Factor (%)**. -**Capacity factor[^3] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. +**Capacity factor[^3] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. -Wind offshore energy production, like wind onshore energy production, does not directly produce heat in the form of thermal energy. Instead, it generates electricity from the kinetic energy of moving air (wind). -## Data -Most of the data used for this model is extracted from International Energy Agency (IEA)[^4], -International Renewable Energy Agency (IRENA)[^5], Global Wind Energy Council (GWEC)[^6] and National Renewable Energy Laboratory (NREL)[^7]. +Wind offshore energy production, like wind onshore energy production, does not directly produce heat in the form of thermal energy. Instead, it generates electricity from the kinetic energy of moving air (wind). +## Data +Most of the data used for this model is extracted from International Energy Agency (IEA)[^4], +International Renewable Energy Agency (IRENA)[^5], Global Wind Energy Council (GWEC)[^6] and National Renewable Energy Laboratory (NREL)[^7]. Some IRENA data -![](irena-offshore.png) +![](irena-offshore.png) ## Some insight on Wind offshore evolution IEA Offshore power generation prediction -![](IEA-offshore-prediction.PNG) +![](IEA-offshore-prediction.PNG) [^1]: https://en.wikipedia.org/wiki/Offshore_wind_power [^2]: IOPscience, 2016, Wind turbines: current status, obstacles, trends and technologies, https://iopscience.iop.org/article/10.1088/1757-899X/161/1/012079 @@ -35,4 +35,4 @@ IEA Offshore power generation prediction [^6]: Global Wind Energy Council (GWEC), Global wind report 2019, https://gwec.net/global-wind-report-2019/ [^7]: National Renewable Energy Laboratory USA (NREL), Annual Technology Baseline 2020, https://atb.nrel.gov/electricity/2020/data.php [^8]: https://www.sciencedirect.com/topics/engineering/offshore-wind-energy -[^9]: https://en.wikipedia.org/wiki/Offshore_wind_power \ No newline at end of file +[^9]: https://en.wikipedia.org/wiki/Offshore_wind_power diff --git a/energy_models/models/electricity/wind_offshore/wind_offshore_disc.py b/energy_models/models/electricity/wind_offshore/wind_offshore_disc.py index 9ef3fa76..f08d715d 100644 --- a/energy_models/models/electricity/wind_offshore/wind_offshore_disc.py +++ b/energy_models/models/electricity/wind_offshore/wind_offshore_disc.py @@ -71,7 +71,7 @@ class WindOffshoreDiscipline(ElectricityTechnoDiscipline): # techno_infos_dict_default['capacity_factor'] initial_production = 89 # IEA in 2019 # Invest in 2019 => 29.6 bn - + # Age distribution => GWEC Annual-Wind-Report_2019_digital_final_2r DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, diff --git a/energy_models/models/electricity/wind_onshore/documentation/wind_onshore_disc.markdown b/energy_models/models/electricity/wind_onshore/documentation/wind_onshore_disc.markdown index 36dc8b53..2c75f0bb 100644 --- a/energy_models/models/electricity/wind_onshore/documentation/wind_onshore_disc.markdown +++ b/energy_models/models/electricity/wind_onshore/documentation/wind_onshore_disc.markdown @@ -4,25 +4,25 @@ Wind farms consist of many individual wind turbines, which are connected to the electric power transmission network. Onshore wind is an inexpensive source of electric power, competitive with or in many places cheaper than coal or gas plants. Onshore wind farms have a greater visual impact on the landscape than other power stations, as they need to be spread over more land and need to be built away from dense population Growth of wind energy, AIP[^2] -![](growthofwind.jpg) +![](growthofwind.jpg) Wind is split into 10 class (speed class m/s). A wind farm is not producing electricity full time, in order to model yearly production a variable is set : **Capacity Factor (%)**. -**Capacity factor[^3] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. +**Capacity factor[^3] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. The mechanical energy generated by the rotating blades is then transferred to a generator inside the turbine. The generator converts this mechanical energy into electrical energy. This electricity is then typically fed into the electrical grid. While wind onshore energy production doesn't directly produce heat, it can indirectly impact heat production and consumption in a region. -## Data -Most of the data used for this model is extracted from International Energy Agency (IEA)[^4], -International Renewable Energy Agency (IRENA)[^5], Global Wind Energy Council (GWEC)[^6] and National Renewable Energy Laboratory (NREL)[^7]. +## Data +Most of the data used for this model is extracted from International Energy Agency (IEA)[^4], +International Renewable Energy Agency (IRENA)[^5], Global Wind Energy Council (GWEC)[^6] and National Renewable Energy Laboratory (NREL)[^7]. Some IRENA data -![](irena-onshore.png) +![](irena-onshore.png) ## Some insight on Wind onshore evolution IEA Onshore power generation prediction -![](IEA-onshore-prediction.PNG) +![](IEA-onshore-prediction.PNG) [^1]: https://en.wikipedia.org/wiki/Wind_power [^2]: American Institute of Physics, Growth of wind energy points to future challenges, promise, https://techxplore.com/news/2019-08-growth-energy-future.html @@ -31,4 +31,4 @@ IEA Onshore power generation prediction [^5]: IRENA (2020), Future of wind 2019, International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Oct/IRENA_Future_of_wind_2019.pdf [^6]: Global Wind Energy Council (GWEC), Global wind report 2019, https://gwec.net/global-wind-report-2019/ [^7]: National Renewable Energy Laboratory USA (NREL), Annual Technology Baseline 2020, https://atb.nrel.gov/electricity/2020/data.php -[^8]: https://www.iberdrola.com/sustainability/renewables-energy-wind-power#:~:text=Onshore%20wind%20energy%20is%20responsible,it%20to%20the%20distribution%20network. \ No newline at end of file +[^8]: https://www.iberdrola.com/sustainability/renewables-energy-wind-power#:~:text=Onshore%20wind%20energy%20is%20responsible,it%20to%20the%20distribution%20network. diff --git a/energy_models/models/electricity/wind_onshore/wind_onshore_disc.py b/energy_models/models/electricity/wind_onshore/wind_onshore_disc.py index afb1ae35..ff1f8b4c 100644 --- a/energy_models/models/electricity/wind_onshore/wind_onshore_disc.py +++ b/energy_models/models/electricity/wind_onshore/wind_onshore_disc.py @@ -59,7 +59,7 @@ class WindOnshoreDiscipline(ElectricityTechnoDiscipline): 'efficiency': 1.0, 'CO2_from_production': 0.0, 'CO2_from_production_unit': 'kg/kg', - f"{GlossaryEnergy.CopperResource}_needs": 2900 / 1e9 # According to the IEA, Onshore Wind turbines need 2900 kg of copper for each MW implemented. Computing the need in Mt/MW, + f"{GlossaryEnergy.CopperResource}_needs": 2900 / 1e9 # According to the IEA, Onshore Wind turbines need 2900 kg of copper for each MW implemented. Computing the need in Mt/MW, # IEA Executive summary - Role of critical minerals in clean energy transitions 2022 } @@ -70,7 +70,7 @@ class WindOnshoreDiscipline(ElectricityTechnoDiscipline): # techno_infos_dict_default['capacity_factor'] initial_production = 1323 # IEA in 2019 # Invest in 2019 => 138.2 bn less 29.6 bn offshore => 108.6 bn - + # Age distribution => GWEC Annual-Wind-Report_2019_digital_final_2r DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, diff --git a/energy_models/models/ethanol/biomass_fermentation/biomass_fermentation.py b/energy_models/models/ethanol/biomass_fermentation/biomass_fermentation.py index b1f4179b..df1f2626 100644 --- a/energy_models/models/ethanol/biomass_fermentation/biomass_fermentation.py +++ b/energy_models/models/ethanol/biomass_fermentation/biomass_fermentation.py @@ -42,7 +42,6 @@ def compute_other_streams_needs(self): self.cost_details[f'{BiomassDry.name}_needs'] = self.get_theoretical_biomass_needs() / self.cost_details['efficiency'] self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] - def compute_byproducts_production(self): carbon_production_factor = self.get_theoretical_co2_prod() self.production_detailed[f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit})'] = carbon_production_factor * \ diff --git a/energy_models/models/ethanol/biomass_fermentation/documentation/biomass_fermentation_disc.markdown b/energy_models/models/ethanol/biomass_fermentation/documentation/biomass_fermentation_disc.markdown index 110120d1..30d49f13 100644 --- a/energy_models/models/ethanol/biomass_fermentation/documentation/biomass_fermentation_disc.markdown +++ b/energy_models/models/ethanol/biomass_fermentation/documentation/biomass_fermentation_disc.markdown @@ -1,11 +1,11 @@ ## Definition -Bioethanol is a form of renewable energy that can be produced from agricultural feedstocks. -It can be made from very common crops such as hemp, sugarcane, potato, cassava and corn. -There has been considerable debate about how useful bioethanol is in replacing gasoline. +Bioethanol is a form of renewable energy that can be produced from agricultural feedstocks. +It can be made from very common crops such as hemp, sugarcane, potato, cassava and corn. +There has been considerable debate about how useful bioethanol is in replacing gasoline. Concerns about its production and use relate to increased food prices due to the large amount of arable land required for crops, as well as the energy and pollution balance of the whole cycle of ethanol production, especially from corn.[^1] -What does fermentation of biomass produce? +What does fermentation of biomass produce? Fermentation is an anaerobic process that breaks down the glucose within organic materials. It is a series of chemical reactions that convert sugars to alcohol or acid. Yeast or bacteria are added to the biomass material, which feed on the sugars to produce ethanol and carbon dioxide. ## Dry Milling Process @@ -13,14 +13,14 @@ Fermentation is an anaerobic process that breaks down the glucose within organic Over 90 percent of the grain ethanol produced today comes from the dry milling process, with the remaining coming from wet mills. The main difference between the two is in the initial treatment of the grain. ![](drymill.jpg) -(Image Credit: +(Image Credit: [Renewable Fuels Association, How is Ethanol Made ?](https://ethanolrfa.org/ethanol-101/how-is-ethanol-made)) In dry milling, the entire grain kernel is first ground into “meal,” then slurried with water to form a “mash.” -Enzymes are added to the mash to convert starch to sugar. The mash is cooked, then cooled and transferred to fermenters. Yeast is added and the conversion of sugar to alcohol begins. After fermentation, +Enzymes are added to the mash to convert starch to sugar. The mash is cooked, then cooled and transferred to fermenters. Yeast is added and the conversion of sugar to alcohol begins. After fermentation, the resulting “beer” is separated from the remaining “stillage.” The ethanol is then distilled and dehydrated, then blended with about 2% denaturant (such as gasoline) to render it undrinkable. It is then ready for shipment. -The stillage is sent through a centrifuge that separates the solids from the solubles. +The stillage is sent through a centrifuge that separates the solids from the solubles. These co-products eventually become distillers grains, as well as corn distillers oil.[^2] ## Sources @@ -29,4 +29,4 @@ These co-products eventually become distillers grains, as well as corn distiller [^2] [Renewable Fuels Association, How is Ethanol Made ?](https://ethanolrfa.org/ethanol-101/how-is-ethanol-made) -[^3] https://www.eia.gov/energyexplained/biomass/ \ No newline at end of file +[^3] https://www.eia.gov/energyexplained/biomass/ diff --git a/energy_models/models/fossil/fossil_simple_techno/documentation/fossil_simple_techno_disc.markdown b/energy_models/models/fossil/fossil_simple_techno/documentation/fossil_simple_techno_disc.markdown index 120ec36b..9169ef07 100644 --- a/energy_models/models/fossil/fossil_simple_techno/documentation/fossil_simple_techno_disc.markdown +++ b/energy_models/models/fossil/fossil_simple_techno/documentation/fossil_simple_techno_disc.markdown @@ -1,7 +1,3 @@ # Fossil Techno ** Simplified techno that produces fossil energy , used to demonstrate EnergyMix.** - - - - diff --git a/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno.py b/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno.py index 9c8dd8a2..6225a766 100644 --- a/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno.py +++ b/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno.py @@ -25,7 +25,6 @@ class FossilSimpleTechno(FossilTechno): - def compute_specifif_costs_of_technos(self): self.specific_costs = pd.DataFrame({ GlossaryEnergy.Years: self.years, diff --git a/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno_disc.py b/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno_disc.py index 44cbfca0..cb6ea67f 100644 --- a/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno_disc.py +++ b/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno_disc.py @@ -53,7 +53,6 @@ class FossilSimpleTechnoDiscipline(FossilTechnoDiscipline): } techno_name = GlossaryEnergy.FossilSimpleTechno - prod_solid_fuel = 45000. # TWh prod_liquid_fuel = 53000. # TWh prod_methane = 39106.77 # TWh @@ -90,7 +89,6 @@ class FossilSimpleTechnoDiscipline(FossilTechnoDiscipline): # net production = 90717.76 TWh initial_production = 136917.16 # TWh - FLUE_GAS_RATIO = np.array([0.12]) DESC_IN = {'techno_infos_dict': {'type': 'dict', diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/awe/documentation/electrolysis_awe_disc.markdown b/energy_models/models/gaseous_hydrogen/electrolysis/awe/documentation/electrolysis_awe_disc.markdown index 81dfd89f..a908ebbc 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/awe/documentation/electrolysis_awe_disc.markdown +++ b/energy_models/models/gaseous_hydrogen/electrolysis/awe/documentation/electrolysis_awe_disc.markdown @@ -4,9 +4,9 @@ AWE is the most established technology for electrolysis with relatively low capi -At the cathode side, two molecules of water reacts with electrons to form the hydrogen and hydroxyl ions ($OH^-$) .Hydroxyl ions transfer through the porous diaphragm to the anode,resulting in half molecule of oxygen (O2) and one molecule of water (H2O). +At the cathode side, two molecules of water reacts with electrons to form the hydrogen and hydroxyl ions ($OH^-$) .Hydroxyl ions transfer through the porous diaphragm to the anode,resulting in half molecule of oxygen (O2) and one molecule of water (H2O). -![](alkaline_electrolysis.PNG) +![](alkaline_electrolysis.PNG) (Image Credit: S.Shiva Kumar & V.Himabindu [^2]) @@ -30,7 +30,7 @@ The lifetime of an alkaline electrolyser is around 25 years and the construction -As alkaline electrolysers are the most mature electrolysis technology, they dominate the market, especially for large-scale projects. In 2020, the global electrolysis capacity is 200 MW [^6]. Assuming 8000 yearly load hours, the global production of electrolysis is around 1.6 TWh. The PEM hydrogen production is of 0.24TWh per year (see PEM documentation) and others electrolysis technologies are not yet commercialized. Then we assume that AWE world production in 2020 is 0.76 TWh. +As alkaline electrolysers are the most mature electrolysis technology, they dominate the market, especially for large-scale projects. In 2020, the global electrolysis capacity is 200 MW [^6]. Assuming 8000 yearly load hours, the global production of electrolysis is around 1.6 TWh. The PEM hydrogen production is of 0.24TWh per year (see PEM documentation) and others electrolysis technologies are not yet commercialized. Then we assume that AWE world production in 2020 is 0.76 TWh. Alkakine electrolysers is the lowest efficient electrolysis technology we assume that no investments have been made during the past years. diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/awe/electrolysis_awe.py b/energy_models/models/gaseous_hydrogen/electrolysis/awe/electrolysis_awe.py index 2661d746..c768c10a 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/awe/electrolysis_awe.py +++ b/energy_models/models/gaseous_hydrogen/electrolysis/awe/electrolysis_awe.py @@ -31,14 +31,14 @@ class ElectrolysisAWE(GaseousHydrogenTechno): def compute_resources_needs(self): # Cost of water for 1 kWH of H2 self.cost_details[f"{GlossaryEnergy.WaterResource}_needs"] = self.get_water_needs() + def compute_other_streams_needs(self): # Efficiency ifor electrolysis means electric efficiency and is here to # compute the elec needs in kWh/kWh 1/efficiency self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = 1.0 / self.cost_details['efficiency'] - def get_water_needs(self): - ''' + ''' Get water needs in kg Water /kWh H2 1 mol of H20 for 1 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -54,7 +54,7 @@ def get_water_needs(self): return water_needs def get_oxygen_produced(self): - ''' + ''' Get oxygen needs in kg O2 /kWh H2 1 mol of O2 for 2 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -79,4 +79,3 @@ def compute_byproducts_production(self): # self.production[f'{lowheattechno.energy_name} ({self.product_unit})'] = \ # self.consumption[f'{GlossaryEnergy.electricity} ({self.product_unit})'] \ # - self.production[f'{GaseousHydrogenTechno.energy_name} ({self.product_unit})'] # in TWH - diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/pem/documentation/electrolysis_pem_disc.markdown b/energy_models/models/gaseous_hydrogen/electrolysis/pem/documentation/electrolysis_pem_disc.markdown index 6354f697..9fa0f61a 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/pem/documentation/electrolysis_pem_disc.markdown +++ b/energy_models/models/gaseous_hydrogen/electrolysis/pem/documentation/electrolysis_pem_disc.markdown @@ -2,7 +2,7 @@ PEM electrolysers use a proton exchange membrane to separate the protons (H+) from water and oxygen. Operating conditions range between 20-100 C and up to 100 bar. -![](PEM_electrolysis.PNG) +![](PEM_electrolysis.PNG) (Image Credit: S.Shiva Kumar & V.Himabindu [^2]) @@ -26,10 +26,10 @@ PEM electrolyser OPEX costs are 2 - 3 % of CAPEX excluding electricity [^4]. It is worth to note, that electrolysers are still built in small volumes for niche markets, which puts the large expected costs reductions into context. Significant cost reductions are expected to come from technology innovations, improved supply chains and bigger production volumes, which enable more efficient manufacturing techniques [^4]. -PEM electrolysers need expensive noble metals (platinum, iridium) which makes them more expensive and less efficient than alkaline electrolysers [^2]. +PEM electrolysers need expensive noble metals (platinum, iridium) which makes them more expensive and less efficient than alkaline electrolysers [^2]. -PEM electrolysers are not yet fully developped but Buttler [^6] reported around 6MW of PEM electrolysers nominal power around the world in 2017. With new project emerging, the nominal power of PEM is around 50MW in 2021 (a new 10MW hydrogen electrolysis plant, the largest of its kind in Europe operates in 2020[^7] and another 20 MW, the largest of its kind in the world, will start operating in 2021 in Quebec (Canada) [^10] ) leading to a global hydrogen production of 0.4TWh per year. +PEM electrolysers are not yet fully developped but Buttler [^6] reported around 6MW of PEM electrolysers nominal power around the world in 2017. With new project emerging, the nominal power of PEM is around 50MW in 2021 (a new 10MW hydrogen electrolysis plant, the largest of its kind in Europe operates in 2020[^7] and another 20 MW, the largest of its kind in the world, will start operating in 2021 in Quebec (Canada) [^10] ) leading to a global hydrogen production of 0.4TWh per year. Public investment in Europe for electrolysers is handled by the Fuel Cell and Hydrogen Joint Undertaking (FCH-JU) organism [^8].In 2019, european investments was around 156 MEUR or around 190 MDollars. We assume half of it is dedicated tor PEM. Around 36% of PEM electrolysers is financed by European union worldwide [^9]. Consequently the hypothesis investment in 2019 for PEM is around : $$\frac{190*100}{2*36}= 263.88 \ MDollars$$ @@ -55,4 +55,4 @@ In Electrolysis, heat production assumed the net difference between total electr [^9]:https://www.euractiv.com/section/energy/news/europe-china-battle-for-global-supremacy-on-electrolyser-manufacturing/ -[^10]: https://www.cummins.com/news/releases/2021/01/26/cummins-hydrogen-technology-powers-largest-proton-exchange-membrane-pemCUMMINS \ No newline at end of file +[^10]: https://www.cummins.com/news/releases/2021/01/26/cummins-hydrogen-technology-powers-largest-proton-exchange-membrane-pemCUMMINS diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/pem/electrolysis_pem.py b/energy_models/models/gaseous_hydrogen/electrolysis/pem/electrolysis_pem.py index 7d50187e..115ae829 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/pem/electrolysis_pem.py +++ b/energy_models/models/gaseous_hydrogen/electrolysis/pem/electrolysis_pem.py @@ -38,7 +38,7 @@ def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = 1.0 / self.cost_details['efficiency'] def get_water_needs(self): - ''' + ''' Get water needs in kg Water /kWh H2 1 mol of H20 for 1 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -54,7 +54,7 @@ def get_water_needs(self): return water_needs def get_oxygen_produced(self): - ''' + ''' Get oxygen needs in kg O2 /kWh H2 1 mol of O2 for 2 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -72,7 +72,7 @@ def get_oxygen_produced(self): def get_theoretical_platinum_needs(self): """ Get platinum needs in kg platinum /kWh H2 - + https://www.energy.gov/sites/prod/files/2016/03/f30/At_A_GLANCE%20%28FCTO%29.pdf According to the Fuel Cell Technologies Office, 1g of platinum enables the production of 8K of H2 diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/soec/documentation/electrolysis_soec_disc.markdown b/energy_models/models/gaseous_hydrogen/electrolysis/soec/documentation/electrolysis_soec_disc.markdown index f6641467..6b9a81f7 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/soec/documentation/electrolysis_soec_disc.markdown +++ b/energy_models/models/gaseous_hydrogen/electrolysis/soec/documentation/electrolysis_soec_disc.markdown @@ -1,8 +1,8 @@ # SOEC (Solid Oxyde Electrolyser Cell) Electrolysis -The solid oxide electrolysis (SOE) first introduced by Donitz and Erdle in the 1980s. Solid oxide electrolysis has attracted an abundant deal of attention due to the electrical energy converts into the chemical energy along with producing the ultra-pure hydrogen with greater efficiency. Solid oxide electrolysis operates at high pressure and high temperatures 500-850 C and utilizes the water in the form of steam. [^1] +The solid oxide electrolysis (SOE) first introduced by Donitz and Erdle in the 1980s. Solid oxide electrolysis has attracted an abundant deal of attention due to the electrical energy converts into the chemical energy along with producing the ultra-pure hydrogen with greater efficiency. Solid oxide electrolysis operates at high pressure and high temperatures 500-850 C and utilizes the water in the form of steam. [^1] -![](SOEC.PNG) +![](SOEC.PNG) (Image Credit: S.Shiva Kumar & V.Himabindu [^1]) @@ -22,7 +22,7 @@ CAPEX estimates for SOEC electrolysers range across 2 800 to 600 USD/kW dependin SOEC is in a pre-commercial and fundamental research stage although Sunfire is already offering systems of 150 kW in a 20 ft or 40 ft container. Other companies investigating the SOEC technologies are Haldor Topsoe (incooperation with Riso DTU), Cermatec (in cooperation with the -Idaho National Laboratory), FuelCell Energy and Toshiba. The initial production of the technology is assumed negligible. +Idaho National Laboratory), FuelCell Energy and Toshiba. The initial production of the technology is assumed negligible. ## Heat In Electrolysis, heat production assumed the net difference between total electricity consumption and total hydrogen production. diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec.py b/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec.py index 7658fe1e..060b422b 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec.py +++ b/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec.py @@ -37,9 +37,8 @@ def compute_other_streams_needs(self): # compute the elec needs in kWh/kWh 1/efficiency self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = 1.0 / self.cost_details['efficiency'] - def get_water_needs(self): - ''' + ''' Get water needs in kg Water /kWh H2 1 mol of H20 for 1 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -55,7 +54,7 @@ def get_water_needs(self): return water_needs def get_oxygen_produced(self): - ''' + ''' Get oxygen needs in kg O2 /kWh H2 1 mol of O2 for 2 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -81,5 +80,3 @@ def compute_byproducts_production(self): # self.production[f'{lowheattechno.energy_name} ({self.product_unit})'] = \ # self.consumption[f'{GlossaryEnergy.electricity} ({self.product_unit})'] \ # - self.production[f'{GaseousHydrogenTechno.energy_name} ({self.product_unit})'] # in TWH - - diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec_disc.py b/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec_disc.py index bf74f7c7..4854a8a4 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec_disc.py +++ b/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec_disc.py @@ -45,7 +45,6 @@ class ElectrolysisSOECDiscipline(GaseousHydrogenTechnoDiscipline): } techno_name = GlossaryEnergy.ElectrolysisSOEC - techno_infos_dict_default = {'maturity': 5, 'Opex_percentage': 0.03, 'CO2_from_production': 0.0, diff --git a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA clean.markdown b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA clean.markdown index f17382ed..9b9270cb 100644 --- a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA clean.markdown +++ b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA clean.markdown @@ -16,14 +16,14 @@ $$X = \dfrac {H2\_revenue}{SUM\_revenues}$$ $$= \dfrac {H2\_revenue}{H2\_revenue+ Carbon\_sold\_revenue+Carbon\_storage\_revenue}$$ -if Carbon\_prod - Carbon\_demand < 0: +if Carbon\_prod - Carbon\_demand < 0: $$X = \dfrac {H2\_revenue}{H2\_revenue+ Carbon\_sold\_revenue+ 0}$$ $$= \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+ [Carbon\_prod * Carbon\_price]}$$ -if Carbon\_prod - Carbon\_demand > 0 : - +if Carbon\_prod - Carbon\_demand > 0 : +        Carbon\_storage = Carbon\_prod - Carbon\_demand        if Carbon\_storage < Carbon\_storage\_max : @@ -43,7 +43,7 @@ $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+[Carbon\_demand * Ca \ ### Gradient computation: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+ [Carbon\_prod * Carbon\_price]}$$ @@ -72,7 +72,7 @@ $$=\dfrac {[ \dfrac {\partial H2\_prod}{\partial invest} * H2\_price * Carbon\_s \ \ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand : $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ] + [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}]+ [Carbon\_demand * (Carbon\_price- \dfrac {Carbon\_mol * CO2\_credit}{CO2\_mol})]}$$ @@ -131,4 +131,4 @@ $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\ finally, the only gradient to compute is: -$$\dfrac {\partial H2\_price}{\partial energy\_prices} = (Id_{H2\_column}, Zero_{other\_column})$$ \ No newline at end of file +$$\dfrac {\partial H2\_price}{\partial energy\_prices} = (Id_{H2\_column}, Zero_{other\_column})$$ diff --git a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA_resume.markdown b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA_resume.markdown index 3be45e5f..9a66efd2 100644 --- a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA_resume.markdown +++ b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA_resume.markdown @@ -11,7 +11,7 @@ $$H2_{price}= PC_{cost} * Margin * X$$ $$\dfrac {\partial H2_{price}}{\partial energy\_prices}= Margin * X * \dfrac {\partial PC_{cost}}{\partial energy\_prices} + PC_{cost} * Margin * \dfrac {\partial X}{\partial energy\_prices}$$ - $$\dfrac {\partial H2_{price}}{\partial energy\_CO2\_emission}= Margin * X * \dfrac {\partial PC_{cost}}{\partial energy\_CO2\_emission} + $$\dfrac {\partial H2_{price}}{\partial energy\_CO2\_emission}= Margin * X * \dfrac {\partial PC_{cost}}{\partial energy\_CO2\_emission} + 0 $$ ### X computation: @@ -24,14 +24,14 @@ with: if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand        if Carbon\_storage < Carbon\_storage\_max : $$A = [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}] + [Carbon\_demand * (Carbon\_price - \dfrac {Carbon\_mol * CO2\_credit}{CO2\_mol})]$$ -       if Carbon\_storage > Carbon\_storage\_max : +       if Carbon\_storage > Carbon\_storage\_max : $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\_mol * CO2\_credit}{CO2\_mol}]$$ \ @@ -42,7 +42,7 @@ $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\ $$\dfrac {\partial X}{\partial energy\_prices} = \dfrac { - \dfrac {\partial H2\_price}{\partial energy\_prices} * H2\_prod * + \dfrac {\partial H2\_price}{\partial energy\_prices} * H2\_prod * A }{[H2\_revenue + A ]^2 @@ -53,7 +53,7 @@ with: if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -85,7 +85,7 @@ if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ $$B = Carbon\_price$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -117,4 +117,4 @@ finally, the only gradient to compute is: $$ \dfrac {\partial H2\_price}{\partial energy\_prices} = (Id_{H2\_column}, Zero_{other\_column}) -$$ \ No newline at end of file +$$ diff --git a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc.markdown b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc.markdown index 7f44f77f..936f1ffa 100644 --- a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc.markdown +++ b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc.markdown @@ -1,6 +1,6 @@ # PlasmaCracking -## Introduction +## Introduction The plasma cracking process consists in breaking the connection between the carbon and the hydrogen of the methane using high frequency microwaves. The reaction is the following: $$CH_4 --> C + 2H_2$$ @@ -13,13 +13,13 @@ This process allows to extract solid carbon out of methane and if used with biom -![](plasmacracking.png) +![](plasmacracking.png) ## Gradient computation - Summary H2_price : techno price \ X : percentage resource \ -PC_cost : Plasma Cracking cost +PC_cost : Plasma Cracking cost ### Formula: @@ -53,11 +53,11 @@ $$X = \dfrac {H2\_revenue}{H2\_revenue with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -71,7 +71,7 @@ $${\footnotesize A = [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol} $$\dfrac {\partial X}{\partial energy\_prices} = \dfrac { - \dfrac {\partial H2\_price}{\partial energy\_prices} * H2\_prod * + \dfrac {\partial H2\_price}{\partial energy\_prices} * H2\_prod * A }{[H2\_revenue + A ]^2 @@ -80,11 +80,11 @@ $$ with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -107,13 +107,13 @@ $$ with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ $$B = Carbon\_price$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -139,13 +139,13 @@ $$ with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ $$B = Carbon\_price$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -168,13 +168,13 @@ $$ with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ $$B = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -203,14 +203,14 @@ $$X = \dfrac {H2\_revenue}{SUM\_revenues}$$ $$= \dfrac {H2\_revenue}{H2\_revenue+ Carbon\_sold\_revenue+Carbon\_storage\_revenue}$$ -if Carbon\_prod - Carbon\_demand < 0: +if Carbon\_prod - Carbon\_demand < 0: $$X = \dfrac {H2\_revenue}{H2\_revenue+ Carbon\_sold\_revenue+ 0}$$ $$= \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+ [Carbon\_prod * Carbon\_price]}$$ -if Carbon\_prod > Carbon\_demand : - +if Carbon\_prod > Carbon\_demand : +        Carbon\_storage = Carbon\_prod - Carbon\_demand $${\scriptsize X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+[Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_prod -Carbon\_demand)* Carbon\_mol * CO2\_credit}{CO2\_mol}]}}$$ @@ -224,7 +224,7 @@ $$= \dfrac {H2\_revenue}{H2\_revenue + A}$$ ### Gradient computation: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+ [Carbon\_prod * Carbon\_price]}$$ @@ -257,7 +257,7 @@ $$=\dfrac {[ \dfrac {\partial H2\_prod}{\partial invest} * H2\_price * Carbon\_s -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand : $${\scriptsize X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ] + [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}]+ [Carbon\_demand * (Carbon\_price- \dfrac {Carbon\_mol * CO2\_credit}{CO2\_mol})]} }$$ diff --git a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc_v2.markdown b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc_v2.markdown index 9c1326f6..31e1f903 100644 --- a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc_v2.markdown +++ b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc_v2.markdown @@ -1,6 +1,6 @@ # PlasmaCracking -## Introduction +## Introduction The plasma cracking process consists in breaking the connection between the carbon and the hydrogen of the methane using high frequency microwaves. The reaction is the following: $$CH_4 --> C + 2H_2$$ @@ -38,11 +38,11 @@ $$X = \dfrac {H2\_revenue}{H2\_revenue with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -52,7 +52,7 @@ $$A = [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}] + [Carbon\_demand * (Carbon\_price - \dfrac {Carbon\_mol * CO2\_credit}{CO2\_mol})]$$ -       if Carbon\_storage > Carbon\_storage\_max : +       if Carbon\_storage > Carbon\_storage\_max : $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\_mol * CO2\_credit}{CO2\_mol}]$$ \ @@ -63,7 +63,7 @@ $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\ $$\dfrac {\partial X}{\partial energy\_prices} = \dfrac { - \dfrac {\partial H2\_price}{\partial energy\_prices} * H2\_prod * + \dfrac {\partial H2\_price}{\partial energy\_prices} * H2\_prod * A }{[H2\_revenue + A ]^2 @@ -72,15 +72,15 @@ $$ with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand -       if Carbon\_storage < Carbon\_storage\_max : +       if Carbon\_storage < Carbon\_storage\_max : $$A = [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}] + [Carbon\_demand * (Carbon\_price @@ -105,13 +105,13 @@ $$ with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ $$B = Carbon\_price$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -123,7 +123,7 @@ $$A = [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}] $$B = \dfrac {Carbon\_mol * CO2\_credit}{CO2\_mol}$$ -       if Carbon\_storage > Carbon\_storage\_max : +       if Carbon\_storage > Carbon\_storage\_max : $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\_mol * CO2\_credit}{CO2\_mol}]$$ @@ -149,14 +149,14 @@ $$X = \dfrac {H2\_revenue}{SUM\_revenues}$$ $$= \dfrac {H2\_revenue}{H2\_revenue+ Carbon\_sold\_revenue+Carbon\_storage\_revenue}$$ -if Carbon\_prod - Carbon\_demand < 0: +if Carbon\_prod - Carbon\_demand < 0: $$X = \dfrac {H2\_revenue}{H2\_revenue+ Carbon\_sold\_revenue+ 0}$$ $$= \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+ [Carbon\_prod * Carbon\_price]}$$ -if Carbon\_prod - Carbon\_demand > 0 : - +if Carbon\_prod - Carbon\_demand > 0 : +        Carbon\_storage = Carbon\_prod - Carbon\_demand        if Carbon\_storage < Carbon\_storage\_max : @@ -176,7 +176,7 @@ $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+[Carbon\_demand * Ca \ ### Gradient computation: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+ [Carbon\_prod * Carbon\_price]}$$ @@ -205,7 +205,7 @@ $$=\dfrac {[ \dfrac {\partial H2\_prod}{\partial invest} * H2\_price * Carbon\_s \ \ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand : $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ] + [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}]+ [Carbon\_demand * (Carbon\_price- \dfrac {Carbon\_mol * CO2\_credit}{CO2\_mol})]}$$ @@ -254,4 +254,4 @@ with: $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\_mol * CO2\_credit}{CO2\_mol}]$$ \ \ -\ \ No newline at end of file +\ diff --git a/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking.py b/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking.py index 539fbb0a..3bbef3b7 100644 --- a/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking.py +++ b/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking.py @@ -50,7 +50,6 @@ def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() self.cost_details[f'{GlossaryEnergy.methane}_needs'] = self.get_theoretical_methane_needs() / self.cost_details['efficiency'] - def compute_byproducts_production(self): C_per_h2 = self.get_theoretical_solid_carbon_production() @@ -58,7 +57,7 @@ def compute_byproducts_production(self): C_per_h2 * self.production_detailed[f'{GaseousHydrogenTechno.energy_name} ({self.product_unit})'] def get_theoretical_solid_carbon_production(self): - ''' + ''' Get methane needs in kg C /kWh H2 1 mol of C for 2 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -75,7 +74,7 @@ def get_theoretical_solid_carbon_production(self): return methane_needs def get_theoretical_methane_needs(self): - ''' + ''' Get methane needs in kWh CH4 /kWh H2 1 mol of CH4 for 2 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -92,7 +91,7 @@ def get_theoretical_methane_needs(self): return methane_needs def get_theoretical_cO2_prod(self): - ''' + ''' Need to model the fact that carbon is created but not CO2 ''' diff --git a/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking_disc.py b/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking_disc.py index 054aaed6..0340c724 100644 --- a/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking_disc.py +++ b/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking_disc.py @@ -89,7 +89,7 @@ class PlasmaCrackingDiscipline(GaseousHydrogenTechnoDiscipline): }, 'market_demand': {'type': 'dataframe', 'default': market_demand, 'unit': 'Mt/year', 'structuring': True, 'dataframe_descriptor': {GlossaryEnergy.Years: ('float', None, True), - 'carbon_demand': ('float', None, True),} + 'carbon_demand': ('float', None, True), } } } diff --git a/energy_models/models/gaseous_hydrogen/water_gas_shift/documentation/water_gas_shift_disc.markdown b/energy_models/models/gaseous_hydrogen/water_gas_shift/documentation/water_gas_shift_disc.markdown index 595b5c10..c543f390 100644 --- a/energy_models/models/gaseous_hydrogen/water_gas_shift/documentation/water_gas_shift_disc.markdown +++ b/energy_models/models/gaseous_hydrogen/water_gas_shift/documentation/water_gas_shift_disc.markdown @@ -4,20 +4,20 @@ Water Gas Shift reaction involves reaction between carbon monoxyde ($CO$) and wa The reaction can be used to enrich a syngas to obtain a given $CO$ to $H_2$ ratio for a specific technology (Fischer Tropsch reaction or methanol synthesis for example) or to produce $CO$-free hydrogen by cleaning $CO$ residues from syngas which are poisonous and deadly. -In order to achieve large-scale hydrogen production from syngas, an appropriate catalyst must be chosen to facilitate the reaction. +In order to achieve large-scale hydrogen production from syngas, an appropriate catalyst must be chosen to facilitate the reaction. -![](WGS_catalysts.PNG) +![](WGS_catalysts.PNG) (Image Credit: Pal, D. [^1]) -The figure above shows a broad classification of catalysts that have been commonly used for the WGS reaction. WGS catalysts may be divided into five categories: High-Temperature Catalysts, Low-Temperature Catalysts, Ceria and Noble Metal based Catalysts; Carbon based Catalysts and Nanostructured Catalysts. All processes to obtain the catalysts and a comparison of them can be found in [^1]. +The figure above shows a broad classification of catalysts that have been commonly used for the WGS reaction. WGS catalysts may be divided into five categories: High-Temperature Catalysts, Low-Temperature Catalysts, Ceria and Noble Metal based Catalysts; Carbon based Catalysts and Nanostructured Catalysts. All processes to obtain the catalysts and a comparison of them can be found in [^1]. -The syngas in the model is defined with a syngas ratio ($r_1$) which is the molar ratio of CO over $H_2$. The objective of the reaction is to eliminate the CO inside the syngas to obtain another syngas at a different molar ratio ($r_2$). With a zero $r_2$ ratio, the syngas is fully converted into hydrogen. +The syngas in the model is defined with a syngas ratio ($r_1$) which is the molar ratio of CO over $H_2$. The objective of the reaction is to eliminate the CO inside the syngas to obtain another syngas at a different molar ratio ($r_2$). With a zero $r_2$ ratio, the syngas is fully converted into hydrogen. -However, the reaction products carbon dioxyde ($CO_2$) which can be captured and stored with suitable technologies (see Carbon Capture and Storage technologies on flue gas). +However, the reaction products carbon dioxyde ($CO_2$) which can be captured and stored with suitable technologies (see Carbon Capture and Storage technologies on flue gas). + +The main reaction of this technology is : -The main reaction of this technology is : - $$(H_2 +r_1 CO) + cH_20 --> dCO_2 + e(H_2 +r_2CO)$$ @@ -25,7 +25,7 @@ with $r_1$ and $r_2$ syngas ratios before and after the reaction : $$r_i = \frac{mol CO}{mol H2}$$ -and with $c$,$d$ and $e$ coefficients of the reaction that can be computed with $r_1$ and $r_2$ to satisfy chemical equilibrium : +and with $c$,$d$ and $e$ coefficients of the reaction that can be computed with $r_1$ and $r_2$ to satisfy chemical equilibrium : $$c = \frac{r1-r2}{1+r2}$$ @@ -34,15 +34,15 @@ $$d = r1 - \frac{r2(1+r1)}{1+r2}$$ $$e = \frac{1+r1}{1+r2}$$ -## Data +## Data -Economic datas are computed following the work in [^2] where a techno-economic analysis is performed on a two-stage WGS combining Low-Temperature and High-Temperature catalysts. -Theoretical datas about production and consumption have been computed with coefficients above depending on $CO$ to $H_2$ ratios ($r_1$ and $r_2$). Other technical datas (i.e. construction delay, lifetime or learning rate, efficiency) can be found in [^2] or [^3]. +Economic datas are computed following the work in [^2] where a techno-economic analysis is performed on a two-stage WGS combining Low-Temperature and High-Temperature catalysts. +Theoretical datas about production and consumption have been computed with coefficients above depending on $CO$ to $H_2$ ratios ($r_1$ and $r_2$). Other technical datas (i.e. construction delay, lifetime or learning rate, efficiency) can be found in [^2] or [^3]. The initial world production has ## Heat -[^4] WGSR is the reaction of an equimolar mixture of steam and carbon monoxide and the process is moderately exothermic. +[^4] WGSR is the reaction of an equimolar mixture of steam and carbon monoxide and the process is moderately exothermic. It is an important step in the reforming process. CO + O(a) → CO2 + ∗ .... ΔH°= −283kJ/mol diff --git a/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift.py b/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift.py index c264e3c4..66fd15f2 100644 --- a/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift.py +++ b/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift.py @@ -57,7 +57,7 @@ def configure_parameters_update(self, inputs_dict): def check_capex_unity(self, data_config): ''' - Overload the check_capex_unity for this particular model + Overload the check_capex_unity for this particular model ''' capex_list = np.array(data_config['Capex_init_vs_CO_conversion']) @@ -506,7 +506,7 @@ def compute_dprice_WGS_wo_taxes_dsyngas_ratio(self): def compute_resources_needs(self): # need in kg - self.cost_details[f"{GlossaryEnergy.WaterResource}_needs"] = self.get_theoretical_water_needs()/ self.cost_details['efficiency'] + self.cost_details[f"{GlossaryEnergy.WaterResource}_needs"] = self.get_theoretical_water_needs() / self.cost_details['efficiency'] def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() @@ -552,7 +552,7 @@ def compute_byproducts_production(self): # self.production[f'{GaseousHydrogenTechno.energy_name} ({self.product_unit})'] # in TWH def get_theoretical_syngas_needs(self, syngas_ratio): - ''' + ''' (H2 +r1CO) + cH20 --> dCO2 + e(H2 +r2CO) e = (1+r1)/(1+r2) @@ -580,7 +580,7 @@ def get_theoretical_syngas_needs(self, syngas_ratio): return syngas_needs def get_theoretical_water_needs(self): - ''' + ''' (H2 +r1CO) + cH20 --> dCO2 + e(H2 +r2CO) e = (1+r1)/(1+r2) @@ -606,7 +606,7 @@ def get_theoretical_water_needs(self): return water_needs def get_theoretical_co2_prod(self, unit='kg/kWh'): - ''' + ''' Get co2 needs in kg co2 /kWh H2 1 mol of CO2 for 4 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one diff --git a/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift_disc.py b/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift_disc.py index de53890f..be17c180 100644 --- a/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift_disc.py +++ b/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift_disc.py @@ -100,7 +100,7 @@ class WaterGasShiftDiscipline(GaseousHydrogenTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'syngas_ratio': {'type': 'array', 'unit': '%', 'visibility': GaseousHydrogenTechnoDiscipline.SHARED_VISIBILITY, 'namespace': 'ns_syngas'}, diff --git a/energy_models/models/heat/high/chphighheat/chphighheat_disc.py b/energy_models/models/heat/high/chphighheat/chphighheat_disc.py index b91258a5..7ed1ce75 100644 --- a/energy_models/models/heat/high/chphighheat/chphighheat_disc.py +++ b/energy_models/models/heat/high/chphighheat/chphighheat_disc.py @@ -49,7 +49,6 @@ class CHPHighHeatDiscipline(HighHeatTechnoDiscipline): # Heat Producer [Online] # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. - techno_infos_dict_default = { 'Capex_init': 1300, # https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW @@ -91,7 +90,7 @@ class CHPHighHeatDiscipline(HighHeatTechnoDiscipline): # Renewable Methane Association [online] DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + } DESC_IN.update(HighHeatTechnoDiscipline.DESC_IN) # -- add specific techno outputs to this diff --git a/energy_models/models/heat/high/chphighheat/documentation/chphighheat_disc.markdown b/energy_models/models/heat/high/chphighheat/documentation/chphighheat_disc.markdown index c944c674..07f6b0c9 100644 --- a/energy_models/models/heat/high/chphighheat/documentation/chphighheat_disc.markdown +++ b/energy_models/models/heat/high/chphighheat/documentation/chphighheat_disc.markdown @@ -22,7 +22,7 @@ initial production for high heat temp = 39 TWh ## Working: CHP can use different fuels in the energy generation process, including fossil fuels and renewable fuels such as biofuels. -CHP is a technology that is used on a local scale. The energy produced is distributed to adjacent communities or buildings. Well insulated pipes underground, distribute the energy to local houses, and commercial buildings. +CHP is a technology that is used on a local scale. The energy produced is distributed to adjacent communities or buildings. Well insulated pipes underground, distribute the energy to local houses, and commercial buildings. CHP is only suitable where there is a year round demand. Buildings such as schools, hospitals, leisure centres, university halls, care homes and similar settings benefit from CHP. @@ -36,7 +36,7 @@ Combustion turbine, or reciprocating engine, with heat recovery unit Steam boiler with steam turbine. ![img.png](img.png) - + Combustion turbine or reciprocating engine CHP systems burn fuel (natural gas, oil, or biogas) to turn generators to produce electricity and use heat recovery devices to capture the heat from the turbine or engine. This heat is converted into useful thermal energy, usually in the form of steam or hot water. ## Data @@ -51,7 +51,7 @@ The data used for this model is extracted from the EPA, IEA Data, ScienceDirect [^4]: [CHP(Data study and comparison) - IEA(International Energy Agency)](https://iea.blob.core.windows.net/assets/d459f7d5-1ba7-49d9-ad56-915fba22f267/chp_report.pdf) -[^5]: Capex, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW +[^5]: Capex, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW [^6]: Efficiency, https://www.epa.gov/chp/chp-benefits#:~:text=By%20recovering%20and%20using%20heat,of%2065%20to%2080%20percent. @@ -61,9 +61,8 @@ The data used for this model is extracted from the EPA, IEA Data, ScienceDirect [^9]: co2 captured production, https://odr.chalmers.se/server/api/core/bitstreams/65470fdd-f00a-4607-8d0f-59152df05ea8/content -[^10]: Opex percentage, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1, 40$/kW for 1000$/kW capex +[^10]: Opex percentage, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1, 40$/kW for 1000$/kW capex [^11]: Electric demand, https://www.carboncommentary.com/blog/2007/10/01/domestic-combined-heat-and-power - -[^12]: Initial production, https://www.statista.com/statistics/678192/chp-electricity-generation-germany/ +[^12]: Initial production, https://www.statista.com/statistics/678192/chp-electricity-generation-germany/ diff --git a/energy_models/models/heat/high/electric_boiler_high_heat/documentation/electric_boiler_high_heat_disc.markdown b/energy_models/models/heat/high/electric_boiler_high_heat/documentation/electric_boiler_high_heat_disc.markdown index 36cc6961..ca9420fe 100644 --- a/energy_models/models/heat/high/electric_boiler_high_heat/documentation/electric_boiler_high_heat_disc.markdown +++ b/energy_models/models/heat/high/electric_boiler_high_heat/documentation/electric_boiler_high_heat_disc.markdown @@ -3,7 +3,7 @@ What is the Electric Boiler? An Electric Boiler is a Boiler which operates using electricity rather than a combustible fossil fuel such as gas or oil boilers. -Electrically heated boilers make use of electric current running through a heating element to create heat, to boil water into high-temperature and high-pressure steam. +Electrically heated boilers make use of electric current running through a heating element to create heat, to boil water into high-temperature and high-pressure steam. They can be classified into the automatically controlled and the manually controlled types. High temperature heat in electric boiler technology, the temperature range is above 400 degree C. @@ -28,7 +28,7 @@ The cold water is passed over the metal heating element and heat is transferred, The exact way in which the boiler works in terms of heating and storing the water depends on the type of electric boiler/heater used. ![img.png](img.png) -## Data +## Data The data used for this model is extracted from EHC, WBA, EHB Leaflet & Rio heating [^1]: [Electric boiler Working - rio heating](https://www.rioheating.com/how-do-electric-heaters-work/#:~:text=Electric%20heating%20is%20the%20process,where%20the%20heat%20is%20created.) @@ -39,7 +39,7 @@ The data used for this model is extracted from EHC, WBA, EHB Leaflet & Rio heati [^4]: [About Electric Boiler – EHB Leaflet](https://www.labour.gov.hk/eng/public/bpvd/EHB_Leaflet_2016_Eng_2%20web.pdf) -[^5]: [Electric Boiler: World heat production – WBA(World Bioenergy Association)](https://www.worldbioenergy.org/uploads/211214%20WBA%20GBS%202021.pdf) +[^5]: [Electric Boiler: World heat production – WBA(World Bioenergy Association)](https://www.worldbioenergy.org/uploads/211214%20WBA%20GBS%202021.pdf) [^6]: Capex, https://capgemini-my.sharepoint.com/personal/valentin_joncquieres_capgemini_com/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fvalentin%5Fjoncquieres%5Fcapgemini%5Fcom%2FDocuments%2FFichiers%20de%20conversation%20Microsoft%20Teams%2FPriyankaChintada%5Ffinal%5Fthesis%2Epdf&parent=%2Fpersonal%2Fvalentin%5Fjoncquieres%5Fcapgemini%5Fcom%2FDocuments%2FFichiers%20de%20conversation%20Microsoft%20Teams&ga=1 # table 5.2. @@ -49,4 +49,4 @@ The data used for this model is extracted from EHC, WBA, EHB Leaflet & Rio heati [^9]: Electricity demand, https://billswiz.com/electric-boiler-electricity-use -[^10]: Initial production, https://www.worldbioenergy.org/uploads/211214%20WBA%20GBS%202021.pdf \ No newline at end of file +[^10]: Initial production, https://www.worldbioenergy.org/uploads/211214%20WBA%20GBS%202021.pdf diff --git a/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat.py b/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat.py index 0232e784..bfd7eba6 100644 --- a/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat.py +++ b/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat.py @@ -32,7 +32,6 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] - def configure_input(self, inputs_dict): ''' Configure with inputs_dict from the discipline diff --git a/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat_disc.py b/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat_disc.py index 73fe0784..3fd29732 100644 --- a/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat_disc.py +++ b/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat_disc.py @@ -46,7 +46,6 @@ class ElectricBoilerHighHeatDiscipline(HighHeatTechnoDiscipline): # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 - techno_infos_dict_default = { 'Capex_init': 42.86, @@ -72,11 +71,11 @@ class ElectricBoilerHighHeatDiscipline(HighHeatTechnoDiscipline): initial_production = 139.67 # Renewable Association [online] - + flux_input_dict = {'land_rate': 22000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - - + + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(HighHeatTechnoDiscipline.DESC_IN) diff --git a/energy_models/models/heat/high/geothermal_high_heat/documentation/geothermal_high_heat_disc.markdown b/energy_models/models/heat/high/geothermal_high_heat/documentation/geothermal_high_heat_disc.markdown index 10fc4b83..933d6286 100644 --- a/energy_models/models/heat/high/geothermal_high_heat/documentation/geothermal_high_heat_disc.markdown +++ b/energy_models/models/heat/high/geothermal_high_heat/documentation/geothermal_high_heat_disc.markdown @@ -3,13 +3,13 @@ **Definition[2] :** Geothermal heating is the direct use of geothermal energy for some heating applications. Humans have taken advantage of geothermal heat this way since the Paleolithic era. Approximately seventy countries made direct use of a total of 270 PJ of geothermal heating in 2004. As of 2007, 28 GW of geothermal heating capacity is installed around the world, satisfying 0.07% of global primary energy consumption.[1] Thermal efficiency is high since no energy conversion is needed, but capacity factors tend to be low (around 20%) since the heat is mostly needed in the winter -![](geothermal-heat-pump.jpg) +![](geothermal-heat-pump.jpg) (Image Credit Geothermal Heating System, [^1]) Geothermal energy originates from the heat retained within the Earth since the original formation of the planet, from radioactive decay of minerals, and from solar energy absorbed at the surface.Most high temperature geothermal heat is harvested in regions close to tectonic plate boundaries where volcanic activity rises close to the surface of the Earth. In these areas, ground and groundwater can be found with temperatures higher than the target temperature of the application. However, even cold ground contains heat. Below 6 metres (20 ft), the undisturbed ground temperature is consistently at the mean annual air temperature, and this heat can be extracted with a ground source heat pump. -## Data -Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. -![](Data.PNG) +## Data +Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. +![](Data.PNG) (Image Credit Geothermal Tomorrow 2008, [^2]) @@ -30,4 +30,3 @@ Most of the data used for this model is extracted from and International Renewa [^8]: Steel needs, https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power Page:21 [^9]: Initial production, https://www.iea.org/data-and-statistics/charts/direct-use-of-geothermal-energy-world-2012-2024 - diff --git a/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat.py b/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat.py index 44a64238..743e65e2 100644 --- a/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat.py +++ b/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat.py @@ -34,7 +34,6 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] - def compute_byproducts_production(self): carbon_production_factor = self.get_theoretical_co2_prod() self.production_detailed[f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit})'] = carbon_production_factor * \ diff --git a/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat_disc.py b/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat_disc.py index af51df8b..ce148037 100644 --- a/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat_disc.py +++ b/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat_disc.py @@ -47,8 +47,6 @@ class GeothermalHighHeatDiscipline(HighHeatTechnoDiscipline): techno_name = GlossaryEnergy.GeothermalHighHeat energy_name = hightemperatureheat.name - - techno_infos_dict_default = { 'Capex_init': 3830, # https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2017/Aug/IRENA_Geothermal_Power_2017.pdf @@ -81,7 +79,7 @@ class GeothermalHighHeatDiscipline(HighHeatTechnoDiscipline): flux_input_dict = {'land_rate': 23000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } @@ -118,7 +116,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/high/heat_pump_high_heat/documentation/heat_pump_high_heat_disc.markdown b/energy_models/models/heat/high/heat_pump_high_heat/documentation/heat_pump_high_heat_disc.markdown index e4bc51ab..dc7befa5 100644 --- a/energy_models/models/heat/high/heat_pump_high_heat/documentation/heat_pump_high_heat_disc.markdown +++ b/energy_models/models/heat/high/heat_pump_high_heat/documentation/heat_pump_high_heat_disc.markdown @@ -3,7 +3,7 @@ [^2] A heat pump can be an energy-efficient alternative to fossil-fuel furnaces and water heaters that use natural gas and heating oil, which emit CO2 directly. Heat pumps also are more efficient than electric resistance heat. Switching from fossil fuel heat-generating devices, such as furnaces and boilers, to an efficient electric heat pump can be step toward a zero-carbon system. -![](HeatPumps.avif) +![](HeatPumps.avif) (Image Credit: IEA – International Energy Agency, taken from [^1]) COP (Coefficient of Performance) [^3] @@ -12,10 +12,10 @@ The Co-efficient of performance (COP) is an expression of the efficiency of a he COP is defined as the relationship between the power (kWh) that is drawn out of the heat pump as cooling or heat, and the power (kWh) that is supplied to the compressor. -## Data -![](Heat_Model_Assumptions.png) +## Data +![](Heat_Model_Assumptions.png) (Heat Model Assumptions, taken from [^4]) -## References +## References [^1]: [How a heat pump works – The Future of Heat Pumps - IEA](https://www.iea.org/reports/the-future-of-heat-pumps/how-a-heat-pump-works) [^2]: [The Important Role of Heat Pumps in a Sustainable Future](https://www.reuters.com/article/sponsored/the-important-role-of-heat-pumps-in-a-sustainable-future) @@ -33,6 +33,3 @@ COP is defined as the relationship between the power (kWh) that is drawn out of [^8]: Opex, https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx [^9]: Initial production, https://www.iea.org/reports/heat-pumps - - - diff --git a/energy_models/models/heat/high/heat_pump_high_heat/heat_pump_high_heat_disc.py b/energy_models/models/heat/high/heat_pump_high_heat/heat_pump_high_heat_disc.py index cb179340..2dfd0420 100644 --- a/energy_models/models/heat/high/heat_pump_high_heat/heat_pump_high_heat_disc.py +++ b/energy_models/models/heat/high/heat_pump_high_heat/heat_pump_high_heat_disc.py @@ -47,7 +47,6 @@ class HeatPumpHighHeatDiscipline(HighHeatTechnoDiscipline): techno_name = GlossaryEnergy.HeatPumpHighHeat energy_name = hightemperatureheat.name - # Heat pumps offer an energy-efficient alternative to furnaces and air conditioners for all climates. # Heat pump can reduce your electricity use for heating by approximately 50% compared to # electric resistance heating such as furnaces and baseboard heaters. @@ -63,7 +62,7 @@ class HeatPumpHighHeatDiscipline(HighHeatTechnoDiscipline): # 660euro/kW/(lifetime * Number of hours in year) # Source:- https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx 'Capex_init_unit': '$/kWh', 'Opex_percentage': 0.04, - ## https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx + # https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx 'efficiency': 1.0, # consumptions and productions already have efficiency included 'CO2_from_production': 0.0, 'CO2_from_production_unit': 'kg/kg', @@ -101,7 +100,7 @@ class HeatPumpHighHeatDiscipline(HighHeatTechnoDiscipline): flux_input_dict = {'land_rate': 24000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(HighHeatTechnoDiscipline.DESC_IN) @@ -137,7 +136,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/high/natural_gas_boiler_high_heat/documentation/natural_gas_boiler_high_heat_disc.markdown b/energy_models/models/heat/high/natural_gas_boiler_high_heat/documentation/natural_gas_boiler_high_heat_disc.markdown index 4df7eaa8..5c282e7e 100644 --- a/energy_models/models/heat/high/natural_gas_boiler_high_heat/documentation/natural_gas_boiler_high_heat_disc.markdown +++ b/energy_models/models/heat/high/natural_gas_boiler_high_heat/documentation/natural_gas_boiler_high_heat_disc.markdown @@ -42,14 +42,11 @@ The data used for this model is extracted from the IEA Data, ScienceDirect & MET [^4]: [Natural Gas Model - ScienceDirect](https://www.sciencedirect.com/topics/engineering/natural-gas) [^5]: Methane demand, https://www.google.com/search?q=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat&rlz=1C1UEAD_enIN1000IN1000&oq=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat+&aqs=chrome..69i57.90503j0j7&sourceid=chrome&ie=UTF-8 - + [^6]: https://www.google.com/search?q=co2+captured+production+to+produce+heat+in+natural+gas+boiler&rlz=1C1UEAD_enIN1000IN1000&oq=co2+captured+production+to+produce+heat+in+natural+gas+boiler&aqs=chrome..69i57.37619j0j7&sourceid=chrome&ie=UTF-8 - + [^7]: co2 captured production, https://www.google.com/search?q=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat&rlz=1C1UEAD_enIN1000IN1000&oq=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat+&aqs=chrome..69i57.90503j0j7&sourceid=chrome&ie=UTF-8 [^8]: Initial production, https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=Electricity%20and%20heat&indicator=HeatGenByFuel [^9]: https://www.google.com/search?q=TJ+to+TWh&rlz=1C1UEAD_enIN1000IN1000&oq=TJ+to+TWh&aqs=chrome..69i57.35591j0j7&sourceid=chrome&ie=UTF-8 - - - diff --git a/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat.py b/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat.py index c25db254..8963a51a 100644 --- a/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat.py +++ b/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat.py @@ -40,7 +40,6 @@ def compute_other_streams_needs(self): # and then we divide by efficiency self.cost_details[f'{Methane.name}_needs'] = self.get_theoretical_methane_needs() / self.cost_details['efficiency'] - def compute_byproducts_production(self): # CO2 production self.production_detailed[f'{CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = Methane.data_energy_dict[ @@ -51,7 +50,7 @@ def compute_byproducts_production(self): f'{Methane.name} ({self.product_unit})'] def get_theoretical_methane_needs(self): - # we need as output kwh/kwh + # we need as output kwh/kwh methane_demand = self.techno_infos_dict['methane_demand'] methane_needs = methane_demand diff --git a/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat_disc.py b/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat_disc.py index a64f4d15..c12beb4a 100644 --- a/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat_disc.py +++ b/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat_disc.py @@ -106,7 +106,7 @@ class NaturalGasBoilerHighHeatDiscipline(HighHeatTechnoDiscipline): # Renewable Methane Association [online] flux_input_dict = {'land_rate': 20000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(HighHeatTechnoDiscipline.DESC_IN) @@ -141,7 +141,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/low/chplowheat/chplowheat_disc.py b/energy_models/models/heat/low/chplowheat/chplowheat_disc.py index 88cc2425..77e6d98d 100644 --- a/energy_models/models/heat/low/chplowheat/chplowheat_disc.py +++ b/energy_models/models/heat/low/chplowheat/chplowheat_disc.py @@ -94,7 +94,7 @@ class CHPLowHeatDiscipline(LowHeatTechnoDiscipline): # Renewable Methane Association [online] DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + } DESC_IN.update(LowHeatTechnoDiscipline.DESC_IN) # -- add specific techno outputs to this diff --git a/energy_models/models/heat/low/chplowheat/documentation/chplowheat_disc.markdown b/energy_models/models/heat/low/chplowheat/documentation/chplowheat_disc.markdown index c1af5ef5..2aa70f74 100644 --- a/energy_models/models/heat/low/chplowheat/documentation/chplowheat_disc.markdown +++ b/energy_models/models/heat/low/chplowheat/documentation/chplowheat_disc.markdown @@ -22,7 +22,7 @@ initial production for low heat temp = 39 TWh ## Working: CHP can use different fuels in the energy generation process, including fossil fuels and renewable fuels such as biofuels. -CHP is a technology that is used on a local scale. The energy produced is distributed to adjacent communities or buildings. Well insulated pipes underground, distribute the energy to local houses, and commercial buildings. +CHP is a technology that is used on a local scale. The energy produced is distributed to adjacent communities or buildings. Well insulated pipes underground, distribute the energy to local houses, and commercial buildings. CHP is only suitable where there is a year round demand. Buildings such as schools, hospitals, leisure centres, university halls, care homes and similar settings benefit from CHP. @@ -36,7 +36,7 @@ Combustion turbine, or reciprocating engine, with heat recovery unit Steam boiler with steam turbine. ![img.png](img.png) - + Combustion turbine or reciprocating engine CHP systems burn fuel (natural gas, oil, or biogas) to turn generators to produce electricity and use heat recovery devices to capture the heat from the turbine or engine. This heat is converted into useful thermal energy, usually in the form of steam or hot water. ## Data @@ -51,7 +51,7 @@ The data used for this model is extracted from the EPA, IEA Data, ScienceDirect [^4]: [CHP(Data study and comparison) - IEA(International Energy Agency)](https://iea.blob.core.windows.net/assets/d459f7d5-1ba7-49d9-ad56-915fba22f267/chp_report.pdf) -[^5]: Capex, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW +[^5]: Capex, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW [^6]: Efficiency, https://www.epa.gov/chp/chp-benefits#:~:text=By%20recovering%20and%20using%20heat,of%2065%20to%2080%20percent. @@ -61,9 +61,8 @@ The data used for this model is extracted from the EPA, IEA Data, ScienceDirect [^9]: co2 captured production, https://odr.chalmers.se/server/api/core/bitstreams/65470fdd-f00a-4607-8d0f-59152df05ea8/content -[^10]: Opex percentage, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1, 40$/kW for 1000$/kW capex +[^10]: Opex percentage, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1, 40$/kW for 1000$/kW capex [^11]: Electric demand, https://www.carboncommentary.com/blog/2007/10/01/domestic-combined-heat-and-power - -[^12]: Initial production, https://www.statista.com/statistics/678192/chp-electricity-generation-germany/ +[^12]: Initial production, https://www.statista.com/statistics/678192/chp-electricity-generation-germany/ diff --git a/energy_models/models/heat/low/electric_boiler_low_heat/documentation/electric_boiler_low_heat_disc.markdown b/energy_models/models/heat/low/electric_boiler_low_heat/documentation/electric_boiler_low_heat_disc.markdown index a630f693..d9455e09 100644 --- a/energy_models/models/heat/low/electric_boiler_low_heat/documentation/electric_boiler_low_heat_disc.markdown +++ b/energy_models/models/heat/low/electric_boiler_low_heat/documentation/electric_boiler_low_heat_disc.markdown @@ -3,7 +3,7 @@ What is the Electric Boiler? An Electric Boiler is a Boiler which operates using electricity rather than a combustible fossil fuel such as gas or oil boilers. -Electrically heated boilers make use of electric current running through a heating element to create heat, to boil water into high-temperature and high-pressure steam. +Electrically heated boilers make use of electric current running through a heating element to create heat, to boil water into high-temperature and high-pressure steam. They can be classified into the automatically controlled and the manually controlled types. Low temperature heat in electric boiler technology, the temperature range is below 100 degree C. @@ -28,7 +28,7 @@ The cold water is passed over the metal heating element and heat is transferred, The exact way in which the boiler works in terms of heating and storing the water depends on the type of electric boiler/heater used. ![img.png](img.png) -## Data +## Data The data used for this model is extracted from EHC, WBA, EHB Leaflet & Rio heating. [^1]: [Electric boiler Working - rio heating](https://www.rioheating.com/how-do-electric-heaters-work/#:~:text=Electric%20heating%20is%20the%20process,where%20the%20heat%20is%20created.) diff --git a/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat.py b/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat.py index e79ae4f4..a7f321d8 100644 --- a/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat.py +++ b/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat.py @@ -32,7 +32,6 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] - def get_theoretical_electricity_needs(self): # we need as output kwh/kwh elec_demand = self.techno_infos_dict['elec_demand'] diff --git a/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat_disc.py b/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat_disc.py index 499ee5ae..d7e983e2 100644 --- a/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat_disc.py +++ b/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat_disc.py @@ -50,8 +50,6 @@ class ElectricBoilerLowHeatDiscipline(LowHeatTechnoDiscipline): # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 - - techno_infos_dict_default = { 'Capex_init': 42.86, @@ -79,8 +77,8 @@ class ElectricBoilerLowHeatDiscipline(LowHeatTechnoDiscipline): # Renewable Association [online] flux_input_dict = {'land_rate': 21000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - - + + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(LowHeatTechnoDiscipline.DESC_IN) @@ -115,7 +113,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/low/geothermal_low_heat/documentation/geothermal_low_heat_disc.markdown b/energy_models/models/heat/low/geothermal_low_heat/documentation/geothermal_low_heat_disc.markdown index 10fc4b83..933d6286 100644 --- a/energy_models/models/heat/low/geothermal_low_heat/documentation/geothermal_low_heat_disc.markdown +++ b/energy_models/models/heat/low/geothermal_low_heat/documentation/geothermal_low_heat_disc.markdown @@ -3,13 +3,13 @@ **Definition[2] :** Geothermal heating is the direct use of geothermal energy for some heating applications. Humans have taken advantage of geothermal heat this way since the Paleolithic era. Approximately seventy countries made direct use of a total of 270 PJ of geothermal heating in 2004. As of 2007, 28 GW of geothermal heating capacity is installed around the world, satisfying 0.07% of global primary energy consumption.[1] Thermal efficiency is high since no energy conversion is needed, but capacity factors tend to be low (around 20%) since the heat is mostly needed in the winter -![](geothermal-heat-pump.jpg) +![](geothermal-heat-pump.jpg) (Image Credit Geothermal Heating System, [^1]) Geothermal energy originates from the heat retained within the Earth since the original formation of the planet, from radioactive decay of minerals, and from solar energy absorbed at the surface.Most high temperature geothermal heat is harvested in regions close to tectonic plate boundaries where volcanic activity rises close to the surface of the Earth. In these areas, ground and groundwater can be found with temperatures higher than the target temperature of the application. However, even cold ground contains heat. Below 6 metres (20 ft), the undisturbed ground temperature is consistently at the mean annual air temperature, and this heat can be extracted with a ground source heat pump. -## Data -Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. -![](Data.PNG) +## Data +Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. +![](Data.PNG) (Image Credit Geothermal Tomorrow 2008, [^2]) @@ -30,4 +30,3 @@ Most of the data used for this model is extracted from and International Renewa [^8]: Steel needs, https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power Page:21 [^9]: Initial production, https://www.iea.org/data-and-statistics/charts/direct-use-of-geothermal-energy-world-2012-2024 - diff --git a/energy_models/models/heat/low/geothermal_low_heat/geothermal_low_heat_disc.py b/energy_models/models/heat/low/geothermal_low_heat/geothermal_low_heat_disc.py index b36978e3..21dceb30 100644 --- a/energy_models/models/heat/low/geothermal_low_heat/geothermal_low_heat_disc.py +++ b/energy_models/models/heat/low/geothermal_low_heat/geothermal_low_heat_disc.py @@ -47,8 +47,6 @@ class GeothermalLowHeatDiscipline(LowHeatTechnoDiscipline): techno_name = GlossaryEnergy.GeothermalLowHeat energy_name = lowtemperatureheat.name - - techno_infos_dict_default = { 'Capex_init': 3830, # https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2017/Aug/IRENA_Geothermal_Power_2017.pdf @@ -81,7 +79,7 @@ class GeothermalLowHeatDiscipline(LowHeatTechnoDiscipline): flux_input_dict = {'land_rate': 18000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(LowHeatTechnoDiscipline.DESC_IN) @@ -117,7 +115,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/low/heat_pump_low_heat/documentation/heat_pump_low_heat_disc.markdown b/energy_models/models/heat/low/heat_pump_low_heat/documentation/heat_pump_low_heat_disc.markdown index 43eb372e..dc7befa5 100644 --- a/energy_models/models/heat/low/heat_pump_low_heat/documentation/heat_pump_low_heat_disc.markdown +++ b/energy_models/models/heat/low/heat_pump_low_heat/documentation/heat_pump_low_heat_disc.markdown @@ -3,7 +3,7 @@ [^2] A heat pump can be an energy-efficient alternative to fossil-fuel furnaces and water heaters that use natural gas and heating oil, which emit CO2 directly. Heat pumps also are more efficient than electric resistance heat. Switching from fossil fuel heat-generating devices, such as furnaces and boilers, to an efficient electric heat pump can be step toward a zero-carbon system. -![](HeatPumps.avif) +![](HeatPumps.avif) (Image Credit: IEA – International Energy Agency, taken from [^1]) COP (Coefficient of Performance) [^3] @@ -12,10 +12,10 @@ The Co-efficient of performance (COP) is an expression of the efficiency of a he COP is defined as the relationship between the power (kWh) that is drawn out of the heat pump as cooling or heat, and the power (kWh) that is supplied to the compressor. -## Data -![](Heat_Model_Assumptions.png) +## Data +![](Heat_Model_Assumptions.png) (Heat Model Assumptions, taken from [^4]) -## References +## References [^1]: [How a heat pump works – The Future of Heat Pumps - IEA](https://www.iea.org/reports/the-future-of-heat-pumps/how-a-heat-pump-works) [^2]: [The Important Role of Heat Pumps in a Sustainable Future](https://www.reuters.com/article/sponsored/the-important-role-of-heat-pumps-in-a-sustainable-future) @@ -33,4 +33,3 @@ COP is defined as the relationship between the power (kWh) that is drawn out of [^8]: Opex, https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx [^9]: Initial production, https://www.iea.org/reports/heat-pumps - diff --git a/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat.py b/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat.py index 976239e5..e098f13b 100644 --- a/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat.py +++ b/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat.py @@ -33,7 +33,6 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] - def compute_byproducts_production(self): # Production self.production_detailed[f'{lowtemperatureheat.name} ({self.product_unit})'] = \ diff --git a/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat_disc.py b/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat_disc.py index dbd8d1a0..c2b8f745 100644 --- a/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat_disc.py +++ b/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat_disc.py @@ -45,7 +45,6 @@ class HeatPumpLowHeatDiscipline(LowHeatTechnoDiscipline): techno_name = GlossaryEnergy.HeatPumpLowHeat energy_name = lowtemperatureheat.name - # https://www.energy.gov/energysaver/heat-pump-systems # Heat pumps offer an energy-efficient alternative to furnaces and air conditioners for all climates. # Heat pump can reduce your electricity use for heating by approximately 50% compared to @@ -62,7 +61,7 @@ class HeatPumpLowHeatDiscipline(LowHeatTechnoDiscipline): # 660euro/kW/(lifetime * Number of hours in year) # Source:- https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx 'Capex_init_unit': '$/kWh', 'Opex_percentage': 0.04, - ## https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx + # https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx 'efficiency': 1, # consumptions and productions already have efficiency included 'CO2_from_production': 0.0, 'CO2_from_production_unit': 'kg/kg', @@ -86,7 +85,7 @@ class HeatPumpLowHeatDiscipline(LowHeatTechnoDiscipline): flux_input_dict = {'land_rate': 19000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(LowHeatTechnoDiscipline.DESC_IN) @@ -122,7 +121,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/low/natural_gas_boiler_low_heat/documentation/natural_gas_boiler_low_heat_disc.markdown b/energy_models/models/heat/low/natural_gas_boiler_low_heat/documentation/natural_gas_boiler_low_heat_disc.markdown index e0693def..a7bc7d23 100644 --- a/energy_models/models/heat/low/natural_gas_boiler_low_heat/documentation/natural_gas_boiler_low_heat_disc.markdown +++ b/energy_models/models/heat/low/natural_gas_boiler_low_heat/documentation/natural_gas_boiler_low_heat_disc.markdown @@ -11,8 +11,8 @@ price of methane is in $/kWh of heat. CO2 production is in Kg/KWh. That values from methane: CO2 per use(kg/kg) / calorific value(KWh/kg) production in 2020 that is total heat produced by Natural gas is 6236761 TJ = 1683 TWh - -initial production for low heat temp = 561 TWh + +initial production for low heat temp = 561 TWh Gas boilers work by releasing the fuel through a gas valve into a sealed combustion chamber in the boiler through small jets. With an electrical ignition, the gas is combusted to create heat. This heat is absorbed by a connected pipe carrying cold water. @@ -22,7 +22,7 @@ Hot gases are produced by burning fuel in the furnace. These hot gases are made Natural gas consists mostly methane (typical >85%) with the balance being varying amounts of ethane, propane, butane and some inert components (nitrogen, carbon dioxide and helium). ![img_1.png](img_1.png) -## Data +## Data The data used for this model is extracted from the IEA Data, ScienceDirect & MET [^1]: [How Does a Gas Boiler Work – always70wade & IEA](https://always70wade.com/b/what-is-a-boiler-how-does-it-work#:~:text=Gas%20boilers%20work%20by%20releasing,connected%20pipe%20carrying%20cold%20water) @@ -34,13 +34,11 @@ The data used for this model is extracted from the IEA Data, ScienceDirect & MET [^4]: [Natural Gas Model - ScienceDirect](https://www.sciencedirect.com/topics/engineering/natural-gas) [^5]: Methane demand, https://www.google.com/search?q=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat&rlz=1C1UEAD_enIN1000IN1000&oq=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat+&aqs=chrome..69i57.90503j0j7&sourceid=chrome&ie=UTF-8 - + [^6]: https://www.google.com/search?q=co2+captured+production+to+produce+heat+in+natural+gas+boiler&rlz=1C1UEAD_enIN1000IN1000&oq=co2+captured+production+to+produce+heat+in+natural+gas+boiler&aqs=chrome..69i57.37619j0j7&sourceid=chrome&ie=UTF-8 - + [^7]: co2 captured production, https://www.google.com/search?q=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat&rlz=1C1UEAD_enIN1000IN1000&oq=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat+&aqs=chrome..69i57.90503j0j7&sourceid=chrome&ie=UTF-8 [^8]: Initial production, https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=Electricity%20and%20heat&indicator=HeatGenByFuel [^9]: https://www.google.com/search?q=TJ+to+TWh&rlz=1C1UEAD_enIN1000IN1000&oq=TJ+to+TWh&aqs=chrome..69i57.35591j0j7&sourceid=chrome&ie=UTF-8 - - diff --git a/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat.py b/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat.py index ffcb9831..9236d818 100644 --- a/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat.py +++ b/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat.py @@ -34,7 +34,6 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{Methane.name}_needs'] = self.get_theoretical_methane_needs() / self.cost_details['efficiency'] - # methane_needs # output needed in this method is in $/kwh of heat diff --git a/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat_disc.py b/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat_disc.py index fadf01a8..9596cb17 100644 --- a/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat_disc.py +++ b/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat_disc.py @@ -106,8 +106,8 @@ class NaturalGasBoilerLowHeatDiscipline(LowHeatTechnoDiscipline): # Renewable Methane Association [online] flux_input_dict = {'land_rate': 17000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - - + + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(LowHeatTechnoDiscipline.DESC_IN) @@ -142,7 +142,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/medium/chpmediumheat/chpmediumheat.py b/energy_models/models/heat/medium/chpmediumheat/chpmediumheat.py index c64d85e3..f292f00a 100644 --- a/energy_models/models/heat/medium/chpmediumheat/chpmediumheat.py +++ b/energy_models/models/heat/medium/chpmediumheat/chpmediumheat.py @@ -32,7 +32,6 @@ class CHPMediumHeat(mediumheattechno): def compute_other_streams_needs(self): self.cost_details[f'{Methane.name}_needs'] = self.get_theoretical_methane_needs() - # methane_needs # output needed in this method is in $/kwh of heat diff --git a/energy_models/models/heat/medium/chpmediumheat/chpmediumheat_disc.py b/energy_models/models/heat/medium/chpmediumheat/chpmediumheat_disc.py index 90f3246b..e3c225c5 100644 --- a/energy_models/models/heat/medium/chpmediumheat/chpmediumheat_disc.py +++ b/energy_models/models/heat/medium/chpmediumheat/chpmediumheat_disc.py @@ -92,7 +92,7 @@ class CHPMediumHeatDiscipline(MediumHeatTechnoDiscipline): # Renewable Methane Association [online] DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + } DESC_IN.update(MediumHeatTechnoDiscipline.DESC_IN) # -- add specific techno outputs to this diff --git a/energy_models/models/heat/medium/chpmediumheat/documentation/chpmediumheat_disc.markdown b/energy_models/models/heat/medium/chpmediumheat/documentation/chpmediumheat_disc.markdown index 985eff97..62e064e2 100644 --- a/energy_models/models/heat/medium/chpmediumheat/documentation/chpmediumheat_disc.markdown +++ b/energy_models/models/heat/medium/chpmediumheat/documentation/chpmediumheat_disc.markdown @@ -22,7 +22,7 @@ initial production for medium heat temp = 39 TWh ## Working: CHP can use different fuels in the energy generation process, including fossil fuels and renewable fuels such as biofuels. -CHP is a technology that is used on a local scale. The energy produced is distributed to adjacent communities or buildings. Well insulated pipes underground, distribute the energy to local houses, and commercial buildings. +CHP is a technology that is used on a local scale. The energy produced is distributed to adjacent communities or buildings. Well insulated pipes underground, distribute the energy to local houses, and commercial buildings. CHP is only suitable where there is a year round demand. Buildings such as schools, hospitals, leisure centres, university halls, care homes and similar settings benefit from CHP. @@ -36,7 +36,7 @@ Combustion turbine, or reciprocating engine, with heat recovery unit Steam boiler with steam turbine. ![img.png](img.png) - + Combustion turbine or reciprocating engine CHP systems burn fuel (natural gas, oil, or biogas) to turn generators to produce electricity and use heat recovery devices to capture the heat from the turbine or engine. This heat is converted into useful thermal energy, usually in the form of steam or hot water. ## Data @@ -51,7 +51,7 @@ The data used for this model is extracted from the EPA, IEA Data, ScienceDirect [^4]: [CHP(Data study and comparison) - IEA(International Energy Agency)](https://iea.blob.core.windows.net/assets/d459f7d5-1ba7-49d9-ad56-915fba22f267/chp_report.pdf) -[^5]: Capex, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW +[^5]: Capex, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW [^6]: Efficiency, https://www.epa.gov/chp/chp-benefits#:~:text=By%20recovering%20and%20using%20heat,of%2065%20to%2080%20percent. @@ -61,9 +61,8 @@ The data used for this model is extracted from the EPA, IEA Data, ScienceDirect [^9]: co2 captured production, https://odr.chalmers.se/server/api/core/bitstreams/65470fdd-f00a-4607-8d0f-59152df05ea8/content -[^10]: Opex percentage, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1, 40$/kW for 1000$/kW capex +[^10]: Opex percentage, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1, 40$/kW for 1000$/kW capex [^11]: Electric demand, https://www.carboncommentary.com/blog/2007/10/01/domestic-combined-heat-and-power - -[^12]: Initial production, https://www.statista.com/statistics/678192/chp-electricity-generation-germany/ +[^12]: Initial production, https://www.statista.com/statistics/678192/chp-electricity-generation-germany/ diff --git a/energy_models/models/heat/medium/electric_boiler_medium_heat/documentation/electric_boiler_medium_heat_disc.markdown b/energy_models/models/heat/medium/electric_boiler_medium_heat/documentation/electric_boiler_medium_heat_disc.markdown index 8d90b466..7869857b 100644 --- a/energy_models/models/heat/medium/electric_boiler_medium_heat/documentation/electric_boiler_medium_heat_disc.markdown +++ b/energy_models/models/heat/medium/electric_boiler_medium_heat/documentation/electric_boiler_medium_heat_disc.markdown @@ -28,7 +28,7 @@ The cold water is passed over the metal heating element and heat is transferred, The exact way in which the boiler works in terms of heating and storing the water depends on the type of electric boiler/heater used. ![img.png](img.png) -## Data +## Data The data used for this model is extracted from EHC, WBA, EHB Leaflet & Rio heating. [^1]: [Electric boiler Working - rio heating](https://www.rioheating.com/how-do-electric-heaters-work/#:~:text=Electric%20heating%20is%20the%20process,where%20the%20heat%20is%20created.) @@ -36,7 +36,7 @@ The data used for this model is extracted from EHC, WBA, EHB Leaflet & Rio heati [^2]: [Electric Boiler Overview - ehc](https://www.electric-heatingcompany.co.uk/article/electric-boiler-guide/#:~:text=An%20Electric%20Boiler%20is%20a%20device%20used%20to%20heat%20your,designed%20to%20maximise%20surface%20area.) [^3]: [Electric Boiler Process - ehc](https://www.electric-heatingcompany.co.uk/article/how-do-electric-boilers-heaters-work/) - + [^4]: [About Electric Boiler – EHB Leaflet](https://www.labour.gov.hk/eng/public/bpvd/EHB_Leaflet_2016_Eng_2%20web.pdf) [^5]: [Electric Boiler: World heat production – WBA(World Bioenergy Association)](https://www.worldbioenergy.org/uploads/211214%20WBA%20GBS%202021.pdf) diff --git a/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat.py b/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat.py index bfd17259..62529685 100644 --- a/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat.py +++ b/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat.py @@ -32,7 +32,6 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] - def get_theoretical_electricity_needs(self): # we need as output kwh/kwh elec_demand = self.techno_infos_dict['elec_demand'] diff --git a/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat_disc.py b/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat_disc.py index 23ee1454..15d88ea5 100644 --- a/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat_disc.py +++ b/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat_disc.py @@ -50,8 +50,6 @@ class ElectricBoilerMediumHeatDiscipline(MediumHeatTechnoDiscipline): # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 - - techno_infos_dict_default = { 'Capex_init': 42.86, @@ -79,7 +77,7 @@ class ElectricBoilerMediumHeatDiscipline(MediumHeatTechnoDiscipline): # Renewable Association [online] flux_input_dict = {'land_rate': 26000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(MediumHeatTechnoDiscipline.DESC_IN) @@ -115,7 +113,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/medium/geothermal_medium_heat/documentation/geothermal_medium_heat_disc.markdown b/energy_models/models/heat/medium/geothermal_medium_heat/documentation/geothermal_medium_heat_disc.markdown index 10fc4b83..933d6286 100644 --- a/energy_models/models/heat/medium/geothermal_medium_heat/documentation/geothermal_medium_heat_disc.markdown +++ b/energy_models/models/heat/medium/geothermal_medium_heat/documentation/geothermal_medium_heat_disc.markdown @@ -3,13 +3,13 @@ **Definition[2] :** Geothermal heating is the direct use of geothermal energy for some heating applications. Humans have taken advantage of geothermal heat this way since the Paleolithic era. Approximately seventy countries made direct use of a total of 270 PJ of geothermal heating in 2004. As of 2007, 28 GW of geothermal heating capacity is installed around the world, satisfying 0.07% of global primary energy consumption.[1] Thermal efficiency is high since no energy conversion is needed, but capacity factors tend to be low (around 20%) since the heat is mostly needed in the winter -![](geothermal-heat-pump.jpg) +![](geothermal-heat-pump.jpg) (Image Credit Geothermal Heating System, [^1]) Geothermal energy originates from the heat retained within the Earth since the original formation of the planet, from radioactive decay of minerals, and from solar energy absorbed at the surface.Most high temperature geothermal heat is harvested in regions close to tectonic plate boundaries where volcanic activity rises close to the surface of the Earth. In these areas, ground and groundwater can be found with temperatures higher than the target temperature of the application. However, even cold ground contains heat. Below 6 metres (20 ft), the undisturbed ground temperature is consistently at the mean annual air temperature, and this heat can be extracted with a ground source heat pump. -## Data -Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. -![](Data.PNG) +## Data +Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. +![](Data.PNG) (Image Credit Geothermal Tomorrow 2008, [^2]) @@ -30,4 +30,3 @@ Most of the data used for this model is extracted from and International Renewa [^8]: Steel needs, https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power Page:21 [^9]: Initial production, https://www.iea.org/data-and-statistics/charts/direct-use-of-geothermal-energy-world-2012-2024 - diff --git a/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat.py b/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat.py index bc05b8f6..8448d9fa 100644 --- a/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat.py +++ b/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat.py @@ -33,7 +33,7 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] - + def compute_byproducts_production(self): # Production carbon_production_factor = self.get_theoretical_co2_prod() diff --git a/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat_disc.py b/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat_disc.py index db3f1b92..00368c8a 100644 --- a/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat_disc.py +++ b/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat_disc.py @@ -47,8 +47,6 @@ class GeothermalMediumHeatDiscipline(MediumHeatTechnoDiscipline): techno_name = GlossaryEnergy.GeothermalMediumHeat energy_name = mediumtemperatureheat.name - - techno_infos_dict_default = { 'Capex_init': 3830, # https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2017/Aug/IRENA_Geothermal_Power_2017.pdf @@ -81,7 +79,7 @@ class GeothermalMediumHeatDiscipline(MediumHeatTechnoDiscipline): flux_input_dict = {'land_rate': 15000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(MediumHeatTechnoDiscipline.DESC_IN) @@ -117,7 +115,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/medium/heat_pump_medium_heat/documentation/heat_pump_medium_heat_disc.markdown b/energy_models/models/heat/medium/heat_pump_medium_heat/documentation/heat_pump_medium_heat_disc.markdown index 43eb372e..dc7befa5 100644 --- a/energy_models/models/heat/medium/heat_pump_medium_heat/documentation/heat_pump_medium_heat_disc.markdown +++ b/energy_models/models/heat/medium/heat_pump_medium_heat/documentation/heat_pump_medium_heat_disc.markdown @@ -3,7 +3,7 @@ [^2] A heat pump can be an energy-efficient alternative to fossil-fuel furnaces and water heaters that use natural gas and heating oil, which emit CO2 directly. Heat pumps also are more efficient than electric resistance heat. Switching from fossil fuel heat-generating devices, such as furnaces and boilers, to an efficient electric heat pump can be step toward a zero-carbon system. -![](HeatPumps.avif) +![](HeatPumps.avif) (Image Credit: IEA – International Energy Agency, taken from [^1]) COP (Coefficient of Performance) [^3] @@ -12,10 +12,10 @@ The Co-efficient of performance (COP) is an expression of the efficiency of a he COP is defined as the relationship between the power (kWh) that is drawn out of the heat pump as cooling or heat, and the power (kWh) that is supplied to the compressor. -## Data -![](Heat_Model_Assumptions.png) +## Data +![](Heat_Model_Assumptions.png) (Heat Model Assumptions, taken from [^4]) -## References +## References [^1]: [How a heat pump works – The Future of Heat Pumps - IEA](https://www.iea.org/reports/the-future-of-heat-pumps/how-a-heat-pump-works) [^2]: [The Important Role of Heat Pumps in a Sustainable Future](https://www.reuters.com/article/sponsored/the-important-role-of-heat-pumps-in-a-sustainable-future) @@ -33,4 +33,3 @@ COP is defined as the relationship between the power (kWh) that is drawn out of [^8]: Opex, https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx [^9]: Initial production, https://www.iea.org/reports/heat-pumps - diff --git a/energy_models/models/heat/medium/heat_pump_medium_heat/heat_pump_medium_heat_disc.py b/energy_models/models/heat/medium/heat_pump_medium_heat/heat_pump_medium_heat_disc.py index 79df2c6f..cdb21d0f 100644 --- a/energy_models/models/heat/medium/heat_pump_medium_heat/heat_pump_medium_heat_disc.py +++ b/energy_models/models/heat/medium/heat_pump_medium_heat/heat_pump_medium_heat_disc.py @@ -47,7 +47,6 @@ class HeatPumpMediumHeatDiscipline(MediumHeatTechnoDiscipline): techno_name = GlossaryEnergy.HeatPumpMediumHeat energy_name = mediumtemperatureheat.name - # Heat pumps offer an energy-efficient alternative to furnaces and air conditioners for all climates. # Heat pump can reduce your electricity use for heating by approximately 50% compared to # electric resistance heating such as furnaces and baseboard heaters. @@ -62,7 +61,7 @@ class HeatPumpMediumHeatDiscipline(MediumHeatTechnoDiscipline): # 660euro/kW/(lifetime * Number of hours in year) # Source:- https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx 'Capex_init_unit': '$/kWh', 'Opex_percentage': 0.04, - ## https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx + # https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx 'efficiency': 1, # consumptions and productions already have efficiency included 'CO2_from_production': 0.0, 'CO2_from_production_unit': 'kg/kg', @@ -94,7 +93,7 @@ class HeatPumpMediumHeatDiscipline(MediumHeatTechnoDiscipline): flux_input_dict = {'land_rate': 14000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(MediumHeatTechnoDiscipline.DESC_IN) @@ -130,7 +129,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/documentation/natural_gas_boiler_medium_heat_disc.markdown b/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/documentation/natural_gas_boiler_medium_heat_disc.markdown index 41ec6e75..677f3f0c 100644 --- a/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/documentation/natural_gas_boiler_medium_heat_disc.markdown +++ b/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/documentation/natural_gas_boiler_medium_heat_disc.markdown @@ -4,15 +4,15 @@ A gas-fired boiler provides hot water to taps throughout the home and also to ra Medium temperature heat in natural gas technology, the temperature range is 100 to 400 degree C. -In Methane demand calculation, Methane is used to produce 1kwh of heat (this information in kwh) : methane_needs is in kwh of methane / kwh of heat +In Methane demand calculation, Methane is used to produce 1kwh of heat (this information in kwh) : methane_needs is in kwh of methane / kwh of heat price of methane is in $/kWh of heat. - + CO2 production is in Kg/KWh. That values from methane: CO2 per use(kg/kg) / calorific value(KWh/kg) production in 2020 that is total heat produced by Natural gas is 6236761 TJ = 1683 TWh - -initial production for medium heat temp = 561 TWh + +initial production for medium heat temp = 561 TWh Gas boilers work by releasing the fuel through a gas valve into a sealed combustion chamber in the boiler through small jets. With an electrical ignition, the gas is combusted to create heat. This heat is absorbed by a connected pipe carrying cold water. @@ -22,7 +22,7 @@ Hot gases are produced by burning fuel in the furnace. These hot gases are made Natural gas consists mostly methane (typical >85%) with the balance being varying amounts of ethane, propane, butane and some inert components (nitrogen, carbon dioxide and helium). ![img_2.png](img_2.png) -## Data +## Data The data used for this model is extracted from the IEA Data, ScienceDirect & MET [^1]: [How Does a Gas Boiler Work – always70wade & IEA](https://always70wade.com/b/what-is-a-boiler-how-does-it-work#:~:text=Gas%20boilers%20work%20by%20releasing,connected%20pipe%20carrying%20cold%20water) @@ -34,13 +34,11 @@ The data used for this model is extracted from the IEA Data, ScienceDirect & MET [^4]: [Natural Gas Model - ScienceDirect](https://www.sciencedirect.com/topics/engineering/natural-gas) [^5]: Methane demand, https://www.google.com/search?q=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat&rlz=1C1UEAD_enIN1000IN1000&oq=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat+&aqs=chrome..69i57.90503j0j7&sourceid=chrome&ie=UTF-8 - + [^6]: https://www.google.com/search?q=co2+captured+production+to+produce+heat+in+natural+gas+boiler&rlz=1C1UEAD_enIN1000IN1000&oq=co2+captured+production+to+produce+heat+in+natural+gas+boiler&aqs=chrome..69i57.37619j0j7&sourceid=chrome&ie=UTF-8 - + [^7]: co2 captured production, https://www.google.com/search?q=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat&rlz=1C1UEAD_enIN1000IN1000&oq=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat+&aqs=chrome..69i57.90503j0j7&sourceid=chrome&ie=UTF-8 [^8]: Initial production, https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=Electricity%20and%20heat&indicator=HeatGenByFuel [^9]: https://www.google.com/search?q=TJ+to+TWh&rlz=1C1UEAD_enIN1000IN1000&oq=TJ+to+TWh&aqs=chrome..69i57.35591j0j7&sourceid=chrome&ie=UTF-8 - - diff --git a/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat.py b/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat.py index cb8d5547..5afd614c 100644 --- a/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat.py +++ b/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat.py @@ -34,7 +34,6 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{Methane.name}_needs'] = self.get_theoretical_methane_needs() / self.cost_details['efficiency'] - # methane_needs # output needed in this method is in $/kwh of heat diff --git a/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat_disc.py b/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat_disc.py index 3aa48ea5..a32c03af 100644 --- a/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat_disc.py +++ b/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat_disc.py @@ -106,8 +106,8 @@ class NaturalGasBoilerMediumHeatDiscipline(MediumHeatTechnoDiscipline): # Renewable Methane Association [online] flux_input_dict = {'land_rate': 13000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - - + + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(MediumHeatTechnoDiscipline.DESC_IN) @@ -142,7 +142,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/documentation/hefa_decarboxylation_disc.markdown b/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/documentation/hefa_decarboxylation_disc.markdown index dd5a141d..5d45675f 100644 --- a/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/documentation/hefa_decarboxylation_disc.markdown +++ b/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/documentation/hefa_decarboxylation_disc.markdown @@ -4,8 +4,8 @@ Hydroprocessing uses hydrogen to convert unsaturated compounds such as alkenes and aromatics into saturated alkanes (paraffins) and cycloalkanes, which are more stable and less reactive. The conversion is usually a two-staged process. -The first stage, the so-called hydrotreatment, takes place at reaction temperatures between 300 °C and 390 °C. -First hydrogen is added to saturate the double bonds of the unsaturated vegetable oil triglycerides. +The first stage, the so-called hydrotreatment, takes place at reaction temperatures between 300 °C and 390 °C. +First hydrogen is added to saturate the double bonds of the unsaturated vegetable oil triglycerides. Then more hydrogen is added to remove the propane backbone, hereby cleaving the saturated vegetable oil triglycerides to fatty acids. Finally, the fatty acids either undergo hydrooxygenation (by addition of more hydrogen the oxygen leaves as H2O) or decarboxylation (oxygen leaves as CO2 without further addition of hydrogen), or a combination of these two. The result is a mixture of straight chain, branched chain, and cyclic paraffinic hydrocarbons. (see [Hydrotreatment](https://www.etipbioenergy.eu/value-chains/conversion-technologies/conventional-technologies/hydrotreatment-to-hvo)) @@ -15,7 +15,7 @@ from: [ETIP Bioteenergy](https://www.etipbioenergy.eu/value-chains/conversion-te ## Decarboxylation -This technology is focused on the second reaction, producing C02 and fuel from oil and hydrogen. This reaction consumes less hydrogen but produces CO2. It is a cheaper reaction to perform, but it is likely to be less attractive than the deoxygenation reaction following environmental concerns. +This technology is focused on the second reaction, producing C02 and fuel from oil and hydrogen. This reaction consumes less hydrogen but produces CO2. It is a cheaper reaction to perform, but it is likely to be less attractive than the deoxygenation reaction following environmental concerns. ## Use in Jet Fuel @@ -33,7 +33,7 @@ Technico-economic information (Capex, Opex, Production levels, past investments, ## Heat Heat production/consumption is neglected in coal-gasification process. - + - [Tao, L., Milbrandt, A., Zhang, Y. et al. Techno-economic and resource analysis of hydroprocessed renewable jet fuel. Biotechnol Biofuels 10, 261 (2017).](https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3) - [Noah, Matthew. A Techno-Economic and Environmental Assessment of Hydroprocessed Renewable Distillate Fuels. (2011)](https://dspace.mit.edu/bitstream/handle/1721.1/65508/746766700-MIT.pdf?sequence=2&isAllowed=y) @@ -42,6 +42,3 @@ Heat production/consumption is neglected in coal-gasification process. /Sustainable Aviation Fuels (SAF) (2021)](https://www.ieabioenergy.com/wp-content/uploads/2021/06/IEA-Bioenergy-Task-39-Progress-in-the-commercialisation-of-biojet-fuels-May-2021-1.pdf), License: CC BY 4.0. - [ETIP Bioenergy. HVO/HEFA](https://www.etipbioenergy.eu/value-chains/products-end-use/products/hvo-hefa) - [De Jong, S., Antonissen, K., Hoefnagels, R. et al. Life-cycle analysis of greenhouse gas emissions from renewable jet fuel production. Biotechnol Biofuels 10, 64 (2017).](https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0739-7) - - - diff --git a/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation.py b/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation.py index 400eb19f..ea189ec2 100644 --- a/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation.py +++ b/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation.py @@ -47,10 +47,9 @@ def compute_resources_needs(self): self.cost_details[f'{NaturalOil.name}_needs'] = self.get_theoretical_natural_oil_needs() / naturaloil_data['calorific_value'] def compute_other_streams_needs(self): - self.cost_details[f'{GaseousHydrogen.name}_needs'] = self.get_theoretical_hydrogen_needs() / self.cost_details['efficiency'] + self.cost_details[f'{GaseousHydrogen.name}_needs'] = self.get_theoretical_hydrogen_needs() / self.cost_details['efficiency'] self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.elec_consumption_factor - def compute_byproducts_production(self): carbon_production_factor = self.get_theoretical_co2_prod() self.production_detailed[f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit})'] = carbon_production_factor * \ diff --git a/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation_disc.py b/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation_disc.py index 51256229..771c7beb 100644 --- a/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation_disc.py +++ b/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation_disc.py @@ -48,7 +48,6 @@ class HefaDecarboxylationDiscipline(HydrotreatedOilFuelTechnoDiscipline): # Source: # https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3/tables/2 - # conversion factors dollar_per_gallon_to_dollar_per_m3 = 264.17 gallon_to_mc = 0.00378541 diff --git a/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/documentation/hefa_deoxygenation_disc.markdown b/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/documentation/hefa_deoxygenation_disc.markdown index 1a8d20a1..79b7625b 100644 --- a/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/documentation/hefa_deoxygenation_disc.markdown +++ b/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/documentation/hefa_deoxygenation_disc.markdown @@ -4,8 +4,8 @@ ## **Hydroprocessed Esters and Fatty Acids** Hydroprocessing uses hydrogen to convert unsaturated compounds such as alkenes and aromatics into saturated alkanes (paraffins) and cycloalkanes, which are more stable and less reactive. The conversion is usually a two-staged process. -The first stage, the so-called hydrotreatment, takes place at reaction temperatures between 300 °C and 390 °C. -First hydrogen is added to saturate the double bonds of the unsaturated vegetable oil triglycerides. +The first stage, the so-called hydrotreatment, takes place at reaction temperatures between 300 °C and 390 °C. +First hydrogen is added to saturate the double bonds of the unsaturated vegetable oil triglycerides. Then more hydrogen is added to remove the propane backbone, hereby cleaving the saturated vegetable oil triglycerides to fatty acids. Finally, the fatty acids either undergo hydrooxygenation (by addition of more hydrogen the oxygen leaves as H2O) or decarboxylation (oxygen leaves as CO2 without further addition of hydrogen), or a combination of these two. The result is a mixture of straight chain, branched chain, and cyclic paraffinic hydrocarbons. (see [Hydrotreatment](https://www.etipbioenergy.eu/value-chains/conversion-technologies/conventional-technologies/hydrotreatment-to-hvo)) @@ -33,7 +33,7 @@ Technico-economic information (Capex, Opex, Production levels, past investments, ## Heat Heat production/consumption is neglected in coal-gasification process. - + - [Tao, L., Milbrandt, A., Zhang, Y. et al. Techno-economic and resource analysis of hydroprocessed renewable jet fuel. Biotechnol Biofuels 10, 261 (2017).](https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3) - [Noah, Matthew. A Techno-Economic and Environmental Assessment of Hydroprocessed Renewable Distillate Fuels. (2011)](https://dspace.mit.edu/bitstream/handle/1721.1/65508/746766700-MIT.pdf?sequence=2&isAllowed=y) @@ -42,6 +42,3 @@ Heat production/consumption is neglected in coal-gasification process. /Sustainable Aviation Fuels (SAF) (2021)](https://www.ieabioenergy.com/wp-content/uploads/2021/06/IEA-Bioenergy-Task-39-Progress-in-the-commercialisation-of-biojet-fuels-May-2021-1.pdf), License: CC BY 4.0. - [ETIP Bioenergy. HVO/HEFA](https://www.etipbioenergy.eu/value-chains/products-end-use/products/hvo-hefa) - [De Jong, S., Antonissen, K., Hoefnagels, R. et al. Life-cycle analysis of greenhouse gas emissions from renewable jet fuel production. Biotechnol Biofuels 10, 64 (2017).](https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0739-7) - - - diff --git a/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation.py b/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation.py index 8828d5fc..0146f7c2 100644 --- a/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation.py +++ b/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation.py @@ -42,17 +42,15 @@ class HefaDeoxygenation(HydrotreatedOilFuelTechno): elec_consumption_factor = .185 - def compute_resources_needs(self): naturaloil_data = NaturalOil.data_energy_dict self.cost_details[f'{NaturalOil.name}_needs'] = self.get_theoretical_natural_oil_needs( ) / naturaloil_data['calorific_value'] def compute_other_streams_needs(self): - self.cost_details[f'{GaseousHydrogen.name}_needs'] = self.get_theoretical_hydrogen_needs() / self.cost_details['efficiency'] + self.cost_details[f'{GaseousHydrogen.name}_needs'] = self.get_theoretical_hydrogen_needs() / self.cost_details['efficiency'] self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.elec_consumption_factor - def compute_byproducts_production(self): # Theoretical C02 production in kg water_calorific_value = Water.data_energy_dict['calorific_value'] diff --git a/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation_disc.py b/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation_disc.py index 6611e957..00f9344e 100644 --- a/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation_disc.py +++ b/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation_disc.py @@ -50,7 +50,6 @@ class HefaDeoxygenationDiscipline(HydrotreatedOilFuelTechnoDiscipline): # Biotechnology for biofuels, 10(1), pp.1-16. # https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3/tables/2 - # conversion factors dollar_per_gallon_to_dollar_per_m3 = 264.17 gallon_to_mc = 0.00378541 diff --git a/energy_models/models/liquid_fuel/fischer_tropsch/documentation/fischer_tropsch_disc.markdown b/energy_models/models/liquid_fuel/fischer_tropsch/documentation/fischer_tropsch_disc.markdown index 8f9baf09..752d3aab 100644 --- a/energy_models/models/liquid_fuel/fischer_tropsch/documentation/fischer_tropsch_disc.markdown +++ b/energy_models/models/liquid_fuel/fischer_tropsch/documentation/fischer_tropsch_disc.markdown @@ -9,16 +9,16 @@ $$(2n + 1) H_2 + n CO --> C_nH_{2n+2} + nH_2O$$(2) These reactions occur in the presence of metal catalysts, typically at temperatures of 150–300 °C (302–572 °F) and pressures of one to several tens of atmospheres. The process was first developed by Franz Fischer and Hans Tropsch in 1925.[^1] -For industrial use, the Fischer Tropsch reaction does not guarantee a single fuel as output of the synthesis. The output stream is called syncrude and is a mixture of different synthetic fuel such as kerosene diesel or naphtas. -For the purpose of the model, we suppose first that the Fischer Tropsch model products only kerosene and we take into account in its cost the hydrocraking process used to extract kerosene from the producted syncrude. +For industrial use, the Fischer Tropsch reaction does not guarantee a single fuel as output of the synthesis. The output stream is called syncrude and is a mixture of different synthetic fuel such as kerosene diesel or naphtas. +For the purpose of the model, we suppose first that the Fischer Tropsch model products only kerosene and we take into account in its cost the hydrocraking process used to extract kerosene from the producted syncrude. ## Usage for XtL industries -The fischer Tropsch synthesis is the main reaction to produce synthesis fuel from power or any sources of energy. The process of producing synfuels through indirect conversion is often referred to as CtL (Coal-to-Liquids Coal Gasification for syngas production) GtL (Gas-to-Liquids, SMR for syngas production) or PBtL (Power-Biomass-to-Liquids, Biomass gasification for syngas production) depending on the initial feedstock. +The fischer Tropsch synthesis is the main reaction to produce synthesis fuel from power or any sources of energy. The process of producing synfuels through indirect conversion is often referred to as CtL (Coal-to-Liquids Coal Gasification for syngas production) GtL (Gas-to-Liquids, SMR for syngas production) or PBtL (Power-Biomass-to-Liquids, Biomass gasification for syngas production) depending on the initial feedstock. The most known synthesis fuel production is the Power-to_Liquids conversion where syngas is produced through renewable electricity via Electrolysis or Co-electrolysis technologies. -### Difference of fuel synthesis pathways depending on the syngas supply [^2] +### Difference of fuel synthesis pathways depending on the syngas supply [^2] ![](Xtl.PNG) (Image Credit: [^2]) @@ -27,34 +27,34 @@ On the Figure above, all concepts are influencing the process of syngas upgradin ### Existing and projected Fischer-Tropsch production -The commercialisation of X-to-liquids processes emerges in some countries. The world's largest scale implementation of Fischer–Tropsch technology is a series of plants operated by Sasol in South Africa with a production of 201000 BPD (Barrel Per Day) based on Coal Gasification [^6] (CtL). China and its large coal production uses also coal gasification for its FT industries and produces around 112000 BPD [^7]. Qatar, is the second largest FT plant in the world based on SMR (Steam Methane Reforming) with a production of 174000 BPD (GtL). Malaysia has also a Fischer Tropsch Plant based on GtL with 12000 BPD of synthetic fuel. In the United States, India, Finland or Russia some states have invested in Fischer–Tropsch plants but not at commercial scale for now. +The commercialisation of X-to-liquids processes emerges in some countries. The world's largest scale implementation of Fischer–Tropsch technology is a series of plants operated by Sasol in South Africa with a production of 201000 BPD (Barrel Per Day) based on Coal Gasification [^6] (CtL). China and its large coal production uses also coal gasification for its FT industries and produces around 112000 BPD [^7]. Qatar, is the second largest FT plant in the world based on SMR (Steam Methane Reforming) with a production of 174000 BPD (GtL). Malaysia has also a Fischer Tropsch Plant based on GtL with 12000 BPD of synthetic fuel. In the United States, India, Finland or Russia some states have invested in Fischer–Tropsch plants but not at commercial scale for now. The first PtL (Power to Liquid with Electrolysis) demo plant at industrial scale is in construction in Norway [^3], capable of producing 10 million litres of fuel a year before scaling up the facility to commercially produce 100 million litres by 2026. The consortium has four main partners: German PtL technology provider Sunfire, Swiss-based CO2 air capture technology specialist Climeworks, Luxembourg-headquartered international engineering company Paul Worth SMS Group and Valinor, a Norwegian family-owned green investment company. ### Sunfire PtL demonstration plant in Dresden, Germany [^4] -![](Sunfire_PtL_Dresden.jpg) +![](Sunfire_PtL_Dresden.jpg) (Image Credit: Sunfire, taken from [^4]) ## Modifying the syngas ratio for the synthesis -The ratio $\frac{CO}{H_2}$ of the needed syngas (gas composed of carbon monoxyde $CO$ and hydrogen $H_2$) must be equal to : +The ratio $\frac{CO}{H_2}$ of the needed syngas (gas composed of carbon monoxyde $CO$ and hydrogen $H_2$) must be equal to : $$r_{syngas} = \frac{n}{2n+1}$$ Depending on the syngas production technology, the syngas ratio of $CO$ over $H_2$ can be different. If the ratio of input syngas is lower than $\frac{n}{2n+1}$ we need to enrich the syngas with carbon monoxyde. If the syngas ratio is higher, some CO in the syngas must be removed. -### The Reverse Water Gas Shift reaction +### The Reverse Water Gas Shift reaction The Reverse Water Gas Shift reaction is able to enrich a syngas mixture using carbon dioxyde ($CO_2$) : $$dCO_2 + e(H_2 +r_1CO) --> (H_2 +r_2 CO) + cH_20$$ -with $r_1 dCO_2 + e(H_2 +r_2CO)$$ with $r_1>r_2$ syngas ratios before and after the reaction : -and with $c$, $d$ and $e$ coefficients of the reaction that can be computed with $r_1$ and $r_2$ to satisfy chemical equilibrium : +and with $c$, $d$ and $e$ coefficients of the reaction that can be computed with $r_1$ and $r_2$ to satisfy chemical equilibrium : $$c = \frac{r1-r2}{1+r2}$$ @@ -83,12 +83,12 @@ $$e = \frac{1+r1}{1+r2}$$ In our context, we know the value of $r_2= \frac{n}{2n+1}$ with $n=12$ which is a valid assumption for kerosene jet fuel (between 10 and 16 carbon atoms by moles). -Then, we are able to determine first which technology do we need to obtain the correct syngas (WGS or RWGS) and secondly, the amount of $CO_2$ (amount of $H_2O$), the production of $H_2O$ (production of $CO_2$) and the total cost of the WGS conversion reaction (RWGS reaction respectively) which will be added to the cost of the Fischer Tropsch synthesis. Note that due to evolving investments, the syngas ratio in input of the model may be different over the years and the choice of the syngas ratio conversion could change between WGS and RWGS. +Then, we are able to determine first which technology do we need to obtain the correct syngas (WGS or RWGS) and secondly, the amount of $CO_2$ (amount of $H_2O$), the production of $H_2O$ (production of $CO_2$) and the total cost of the WGS conversion reaction (RWGS reaction respectively) which will be added to the cost of the Fischer Tropsch synthesis. Note that due to evolving investments, the syngas ratio in input of the model may be different over the years and the choice of the syngas ratio conversion could change between WGS and RWGS. -## Data +## Data -All technical and economical datas are extracted from [^5]. The paper details the techno-Economic assessment of a PtL factory using a hybrid PV-Wind power plant to provide electricity and combined to an electrolyser to produce $H_2$ and a CO2 capture plant. The $CO_2-H_2$ stream is injected into a Reverse Water Gas Shift reactor to enrich the syngas in $CO$ (considering the pure $H_2$ stream as a syngas with a zero $CO$ over $H_2$ ratio.). The economic model includes the FT and Hydrocracker part of the Figure below. +All technical and economical datas are extracted from [^5]. The paper details the techno-Economic assessment of a PtL factory using a hybrid PV-Wind power plant to provide electricity and combined to an electrolyser to produce $H_2$ and a CO2 capture plant. The $CO_2-H_2$ stream is injected into a Reverse Water Gas Shift reactor to enrich the syngas in $CO$ (considering the pure $H_2$ stream as a syngas with a zero $CO$ over $H_2$ ratio.). The economic model includes the FT and Hydrocracker part of the Figure below. ### Ptl flow diagram of Fasihi & al [^5] @@ -98,7 +98,7 @@ All technical and economical datas are extracted from [^5]. The paper details th ## Heat The Fischer-Tropsch reaction is carried out over supported cobalt-based catalysts at 20–30 bar and at temperatures below 240 °C. The FTS is a highly exothermic reaction due to a standard reaction enthalpy of −165kJ/molCO.[^8] -[^1]: [De Klerk , A. (2013) FischerTropsch Process. Kirk Othmer Encyclopedia of Chemical Technology. Weinheim: Wiley-VCH](https://onlinelibrary.wiley.com/doi/abs/10.1002/0471238961.fiscdekl.a01) +[^1]: [De Klerk , A. (2013) FischerTropsch Process. Kirk Othmer Encyclopedia of Chemical Technology. Weinheim: Wiley-VCH](https://onlinelibrary.wiley.com/doi/abs/10.1002/0471238961.fiscdekl.a01) [^2]: [Albrecht, F. (2017) A standardized methodology for the techno-economic evaluation of alternative fuels A case study, Fuel, vol 194, p511-526](https://www.sciencedirect.com/science/article/pii/S0016236116312248) diff --git a/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch.py b/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch.py index 94b57386..2a2dedb2 100644 --- a/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch.py +++ b/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch.py @@ -107,7 +107,6 @@ def select_resources_ratios(self): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() - def compute_specifif_costs_of_technos(self): nb_years = self.year_end - self.year_start + 1 sg_needs_efficiency = [self.get_theoretical_syngas_needs_for_FT( @@ -279,7 +278,7 @@ def compute_specifif_costs_of_technos(self): def grad_price_vs_stream_price(self): ''' - Compute the gradient of global price vs energy prices + Compute the gradient of global price vs energy prices Work also for total CO2_emissions vs energy CO2 emissions ''' elec_needs = self.costs_details_sg_techno[f'{GlossaryEnergy.electricity}_needs'] * \ @@ -473,7 +472,7 @@ def compute_wgs_contribution(self, sg_ratio): GlossaryEnergy.StreamsUsedForProductionValue: GlossaryEnergy.TechnoStreamsUsedDict[GlossaryEnergy.WaterGasShift], GlossaryEnergy.ConstructionDelay: GlossaryEnergy.TechnoConstructionDelayDict[GlossaryEnergy.WaterGasShift], GlossaryEnergy.LifetimeName: GlossaryEnergy.TechnoLifetimeDict[GlossaryEnergy.WaterGasShift], - GlossaryEnergy.InitialPlantsAgeDistribFactor:DatabaseWitnessEnergy.get_techno_age_distrib_factor(techno_name=WaterGasShiftDiscipline.techno_name, year=self.year_start)[0], + GlossaryEnergy.InitialPlantsAgeDistribFactor: DatabaseWitnessEnergy.get_techno_age_distrib_factor(techno_name=WaterGasShiftDiscipline.techno_name, year=self.year_start)[0], } if self.apply_stream_ratio: inputs_dict[GlossaryEnergy.AllStreamsDemandRatioValue] = self.all_streams_demand_ratio @@ -505,14 +504,12 @@ def compute_byproducts_production(self): elif self.sg_transformation_name == GlossaryEnergy.RWGS: self.production_detailed[f'{CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = 0.0 - if self.sg_transformation_name in [GlossaryEnergy.RWGS, 'WGS or RWGS']: water_prod = self.water_prod_RWGS * \ self.cost_details['syngas_needs_for_FT'] / \ self.cost_details['efficiency'] - water_prod += self.get_theoretical_water_prod_from_FT() / \ self.cost_details['efficiency'] @@ -593,7 +590,7 @@ def compute_delec_consumption_dsyngas_ratio(self, dprod_energy_dsyngas_ratio): return delec_consumption def compute_scope_2_emissions(self): - ''' + ''' Need to take into account negative CO2 from biomass_dry and CO2 from electricity (can be 0.0 or positive) ''' @@ -755,7 +752,7 @@ def compute_dco2_emissions_dsyngas_ratio(self): # GlossaryEnergy.syngas: dsyngas_emission_dsyngas_ratio} def get_theoretical_syngas_needs_for_FT(self): - ''' + ''' Get syngas needs in kWh syngas /kWh liquid_fuel H2 + n/(2n+1)CO --> 1/(2n+1) CnH_2n+1 + n/(2n+1)H20 Warning : molar mass is in g/mol but we divide and multiply by one @@ -775,7 +772,7 @@ def get_theoretical_syngas_needs_for_FT(self): return syngas_needs_for_FT def get_theoretical_water_prod_from_FT(self): - ''' + ''' Get water prod in kg H20 /kWh liquid_fuel H2 + n/(2n+1)CO --> 1/(2n+1) CnH_2n+1 + n/(2n+1)H20 Warning : molar mass is in g/mol but we divide and multiply by one diff --git a/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch_disc.py b/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch_disc.py index 9a7e6564..371a3b5c 100644 --- a/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch_disc.py +++ b/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch_disc.py @@ -99,7 +99,6 @@ class FischerTropschDiscipline(LiquidFuelTechnoDiscipline): # liquid_fuel 'carbon_number': 12} # To review - # FischerTropsch Wikipedia : # 140000+34000 BPD in Qatar GtL # 12000 BPD in Malaysia GtL @@ -114,7 +113,7 @@ class FischerTropschDiscipline(LiquidFuelTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'syngas_ratio': {'type': 'array', 'unit': '%', 'visibility': LiquidFuelTechnoDiscipline.SHARED_VISIBILITY, 'namespace': 'ns_syngas'}, diff --git a/energy_models/models/liquid_fuel/refinery/documentation/refinery_disc.markdown b/energy_models/models/liquid_fuel/refinery/documentation/refinery_disc.markdown index a58b7ed5..903060b6 100644 --- a/energy_models/models/liquid_fuel/refinery/documentation/refinery_disc.markdown +++ b/energy_models/models/liquid_fuel/refinery/documentation/refinery_disc.markdown @@ -23,7 +23,7 @@ Density = 750 kg/m^3 The average price of gasoline around the world is 4.21 USD per US gallon in 2021[^3]. However, there is substantial difference in these prices among countries. As a general rule, richer countries have higher prices while poorer countries and the countries that produce and export oil have significantly lower prices. -**Kerosene (~13%)** +**Kerosene (~13%)** Also known as paraffin, it is a combustible hydrocarbon liquid which is widely used as a fuel in aviation as well as households. @@ -82,17 +82,17 @@ We took 22000 USD per barrel and an OPEX percentage of 0.04, extracted from the **GHG emissions** -The GAINS model predicts methane fugitive emissions from crude oil extraction. Methane is emitted during each phase of oil extraction. During flaring, the waste gas out of oil facilities is burned and emits some CH4 emissions. Some other methane emissions occur during venting which consists at releasing methane directly to the atmosphere during the oil development process (wall completion, tank maintenance ...). Finally unintended leakage is also taken into account in this model for methane emissions. Emission factors from oil extraction are adapted from IEA data in the IEA Methane Tracker 2021 [^12] and compared with the Supplement data of the Hoglund Isaksson paper [^10]. +The GAINS model predicts methane fugitive emissions from crude oil extraction. Methane is emitted during each phase of oil extraction. During flaring, the waste gas out of oil facilities is burned and emits some CH4 emissions. Some other methane emissions occur during venting which consists at releasing methane directly to the atmosphere during the oil development process (wall completion, tank maintenance ...). Finally unintended leakage is also taken into account in this model for methane emissions. Emission factors from oil extraction are adapted from IEA data in the IEA Methane Tracker 2021 [^12] and compared with the Supplement data of the Hoglund Isaksson paper [^10]. ![](emissions.jpg) (source[^11]) **Hydrogen Consumption** -Refineries use hydrogen to lower the sulfur content of diesel fuel. Refinery demand for hydrogen has increased as -demand for diesel fuel has risen both domestically and internationally, and as sulfur-content regulations have become -more stringent. Oil refining is the largest consumer of hydrogen today (close to 40 Mt in 2020), and will remain so -in the short to medium term. Hydrogen used in this sector is normally produced onsite by steam methane reforming, -separated from by-product gases from petrochemical processes or sourced externally as merchant hydrogen +Refineries use hydrogen to lower the sulfur content of diesel fuel. Refinery demand for hydrogen has increased as +demand for diesel fuel has risen both domestically and internationally, and as sulfur-content regulations have become +more stringent. Oil refining is the largest consumer of hydrogen today (close to 40 Mt in 2020), and will remain so +in the short to medium term. Hydrogen used in this sector is normally produced onsite by steam methane reforming, +separated from by-product gases from petrochemical processes or sourced externally as merchant hydrogen (typically produced in dedicated plants for hydrogen production using steam methane reforming).[^9] [^1]: Oil Refinery Wikipedia page, https://en.wikipedia.org/wiki/Oil_refinery @@ -110,6 +110,6 @@ Argonne National Laboratory, https://publications.anl.gov/anlpubs/2011/01/69026. [^11]: https://capterio.com/insights/why-flare-capture-projects-make-sound-esg-investments -[^12]:IEA Methane Tracker 2021, (https://www.iea.org/articles/methane-tracker-data-explorer), License: CC BY 4.0. +[^12]:IEA Methane Tracker 2021, (https://www.iea.org/articles/methane-tracker-data-explorer), License: CC BY 4.0. -[^13]: https://www.osti.gov/servlets/purl/7261027, Page No 41 \ No newline at end of file +[^13]: https://www.osti.gov/servlets/purl/7261027, Page No 41 diff --git a/energy_models/models/liquid_fuel/refinery/refinery.py b/energy_models/models/liquid_fuel/refinery/refinery.py index e573b7c3..58c80b43 100644 --- a/energy_models/models/liquid_fuel/refinery/refinery.py +++ b/energy_models/models/liquid_fuel/refinery/refinery.py @@ -71,7 +71,6 @@ def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() / self.cost_details['efficiency'] self.cost_details[f'{GaseousHydrogen.name}_needs'] = self.techno_infos_dict['hydrogen_demand'] / self.cost_details['efficiency'] - def compute_byproducts_production(self): for energy in self.other_energy_dict: # if it s a dict, so it is a data_energy_dict diff --git a/energy_models/models/liquid_fuel/refinery/refinery_disc.py b/energy_models/models/liquid_fuel/refinery/refinery_disc.py index c22eb291..71a84d81 100644 --- a/energy_models/models/liquid_fuel/refinery/refinery_disc.py +++ b/energy_models/models/liquid_fuel/refinery/refinery_disc.py @@ -187,7 +187,7 @@ def set_partial_derivatives_techno(self, grad_dict, carbon_emissions, grad_dict_ self.set_partial_derivative_for_other_types( (GlossaryEnergy.TechnoPricesValue, self.techno_name), (GlossaryEnergy.StreamsCO2EmissionsValue, energy), grad_on_co2_tax) - + dCO2_taxes_factory = (self.techno_model.CO2_taxes[GlossaryEnergy.Years] <= self.techno_model.carbon_intensity[GlossaryEnergy.Years].max( )) * self.techno_model.carbon_intensity[self.techno_name].clip(0).values diff --git a/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/documentation/hydrogen_liquefaction_disc.markdown b/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/documentation/hydrogen_liquefaction_disc.markdown index e8b7978b..65ea9ec2 100644 --- a/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/documentation/hydrogen_liquefaction_disc.markdown +++ b/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/documentation/hydrogen_liquefaction_disc.markdown @@ -1,7 +1,7 @@ # Hydrogen liquefaction -Hydrogen liquefaction is the process of changing the state of hydrogen from gas to liquid (temperatures of around 20 K). -This is performed through thermodynamics cycles. +Hydrogen liquefaction is the process of changing the state of hydrogen from gas to liquid (temperatures of around 20 K). +This is performed through thermodynamics cycles. The data used here are taken from a model based on a four steps process: - the hydrogen feed is first pressurized and pre-cooled, - then it is cooled down with liquid nitrogen in a Claude cycle to be below the inversion point @@ -38,4 +38,3 @@ the model is based on a liquifier with a capacity of 200000 kg/day. [^5]: [Hydrogen liquefaction and liquid hydrogen storage, G Valenti (2016)](https://www.sciencedirect.com/science/article/pii/B978178242362100002X) [^6]: https://www.idealhy.eu/uploads/documents/IDEALHY_Cryogenics_2012_Precooling.pdf - \ No newline at end of file diff --git a/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction.py b/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction.py index cc1d264f..0d61970b 100644 --- a/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction.py +++ b/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction.py @@ -39,7 +39,6 @@ def compute_other_streams_needs(self): self.cost_details[f'{GaseousHydrogen.name}_needs'] = 1 / \ self.cost_details['efficiency'] - def compute_byproducts_production(self): pass # self.production[f'{lowtemperatureheat.name} ({self.product_unit})'] = (1 - self.techno_infos_dict['efficiency']) * \ diff --git a/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction_disc.py b/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction_disc.py index b0b00159..08e03728 100644 --- a/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction_disc.py +++ b/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction_disc.py @@ -71,7 +71,7 @@ class HydrogenLiquefactionDiscipline(LiquidHydrogenTechnoDiscipline): 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, 'initial_production': {'type': 'float', 'unit': 'TWh', 'default': initial_production}, - + } DESC_IN.update(LiquidHydrogenTechnoDiscipline.DESC_IN) diff --git a/energy_models/models/methane/fossil_gas/documentation/fossil_gas_disc.markdown b/energy_models/models/methane/fossil_gas/documentation/fossil_gas_disc.markdown index 1bcedb64..1670071f 100644 --- a/energy_models/models/methane/fossil_gas/documentation/fossil_gas_disc.markdown +++ b/energy_models/models/methane/fossil_gas/documentation/fossil_gas_disc.markdown @@ -6,14 +6,14 @@ It is most commonly used to produce heat or electricity for buildings or industr Where, production of high temp heat is calculated in TWh. Its production is depends on production of methane in TWh and efficiency. ![](Industry_gaz.png) -(Image Credit EIA: taken from [^2]) +(Image Credit EIA: taken from [^2]) -## GHG emissions +## GHG emissions -The GAINS model predicts methane fugitive emissions from gas energy. Emission factors from gas production are adopted from IPCC guidelines and a mean value has been taken for the world database of 0.142 kt/PJ [^4]. Losses of natural gas during its transport and final use are taken into account in the consumption model. +The GAINS model predicts methane fugitive emissions from gas energy. Emission factors from gas production are adopted from IPCC guidelines and a mean value has been taken for the world database of 0.142 kt/PJ [^4]. Losses of natural gas during its transport and final use are taken into account in the consumption model. -## Data +## Data Economic and technical datas is taken from Environmental performance indicators – 2019 data - IOGP (International Association of Oil and Gas Producers) [^1] and Sarhosis V, Jaya AA, Hosking LJ, Koj A, Thomas HR.Techno-economics for coalbed methane production in the South Wales Coalfield [^3]. @@ -29,4 +29,3 @@ Center](https://eprint.ncl.ac.uk/file_store/production/219105/A20E5895-2DAF-4D6F [^4]: https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR54-GAINS-CH4.pdf [^5]: https://www.sciencedirect.com/topics/earth-and-planetary-sciences/combustion-temperature [^6]: Efficiency, https://geospatial.blogs.com/geospatial/2010/01/energy-efficiency-of-fossil-fuel-power-generation.html#:~:text=The%20average%20efficiencies%20of%20power,up%20the%20stack%22%20as%20heat. - \ No newline at end of file diff --git a/energy_models/models/methane/fossil_gas/fossil_gas.py b/energy_models/models/methane/fossil_gas/fossil_gas.py index 03fb8ecd..3056e868 100644 --- a/energy_models/models/methane/fossil_gas/fossil_gas.py +++ b/energy_models/models/methane/fossil_gas/fossil_gas.py @@ -47,7 +47,6 @@ def compute_other_streams_needs(self): # needs in [kWh/kWh] divided by calorific value in [kWh/kg] to have # needs in [kg/kWh] - def compute_byproducts_production(self): # kg/kWh corresponds to Mt/TWh self.production_detailed[f'{CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = self.techno_infos_dict[ diff --git a/energy_models/models/methane/methanation/documentation/methanation_disc.markdown b/energy_models/models/methane/methanation/documentation/methanation_disc.markdown index 448f2a38..e5e8ea6a 100644 --- a/energy_models/models/methane/methanation/documentation/methanation_disc.markdown +++ b/energy_models/models/methane/methanation/documentation/methanation_disc.markdown @@ -1,16 +1,16 @@ # E-Methane or Sabatier process or Chemical methanation -The chemical methanation process is mostly used in Power-to-Gas installations where it is used to transform the produced hydrogen (by co-electrolysis) into methane. -The chemical methanation process is a catalytic exothermic gas reaction and therefore, the equilibrium can be influenced by increasing the pressure and shifting it to the product side. The main reaction of the methanation of carbon dioxyde is : +The chemical methanation process is mostly used in Power-to-Gas installations where it is used to transform the produced hydrogen (by co-electrolysis) into methane. +The chemical methanation process is a catalytic exothermic gas reaction and therefore, the equilibrium can be influenced by increasing the pressure and shifting it to the product side. The main reaction of the methanation of carbon dioxyde is : $$CO_2 + 4H_2 --> CH_4 + 2H_20$$ -## Data +## Data Economic and technical datas is taken from Rosenfeld & al [^1]. CAPEX values are similar in this paper and in the paper of Thema & al [^2]. -A full list of world Power-to-Gas plants is available in Thema & al [^2] and has been used to define the initial world production and the age distribution of existing methanation plants. +A full list of world Power-to-Gas plants is available in Thema & al [^2] and has been used to define the initial world production and the age distribution of existing methanation plants. [^1]: Rosenfeld, D. C., Bohm, H., Lindorfer, J. , Lehner, M. (2020). Scenario analysis of implementing a power-to-gas and biomass gasification system in an integrated steel plant: A techno-economic and environmental study. Renewable Energy, 147(2020), 1511-1524. -[^2]: Thema, M., Bauer, F. and Sterner, M. Power-to-Gas: Electrolysis and methanation status review, Renewable and Sustainable Energy Reviews vol 112, 2019, p.775-787 \ No newline at end of file +[^2]: Thema, M., Bauer, F. and Sterner, M. Power-to-Gas: Electrolysis and methanation status review, Renewable and Sustainable Energy Reviews vol 112, 2019, p.775-787 diff --git a/energy_models/models/methane/methanation/methanation.py b/energy_models/models/methane/methanation/methanation.py index 0055f4d8..f1174984 100644 --- a/energy_models/models/methane/methanation/methanation.py +++ b/energy_models/models/methane/methanation/methanation.py @@ -28,7 +28,6 @@ class Methanation(MethaneTechno): - def compute_resources_needs(self): # in kg of CO2 for kWh of CH4 self.cost_details[f'{GlossaryEnergy.carbon_capture}_needs'] = self.get_theoretical_co2_needs() / self.cost_details['efficiency'] @@ -63,7 +62,7 @@ def get_h2o_production(self): return production_for_1kg def get_theoretical_hydrogen_needs(self): - ''' + ''' Get hydrogen needs in kWhH2 /kWh CH4 4 mol of H2 for 1 mol of CH4 Warning : molar mass is in g/mol but we divide and multiply by one @@ -79,7 +78,7 @@ def get_theoretical_hydrogen_needs(self): return h2_needs def get_theoretical_co2_needs(self): - ''' + ''' Get hydrogen needs in kWhH2 /kWh CH4 4 mol of H2 for 1 mol of CH4 Warning : molar mass is in g/mol but we divide and multiply by one diff --git a/energy_models/models/methane/methanation/methanation_disc.py b/energy_models/models/methane/methanation/methanation_disc.py index 982f4dcc..94c9e481 100644 --- a/energy_models/models/methane/methanation/methanation_disc.py +++ b/energy_models/models/methane/methanation/methanation_disc.py @@ -41,7 +41,6 @@ class MethanationDiscipline(MethaneTechnoDiscipline): techno_name = GlossaryEnergy.Methanation - techno_infos_dict_default = {'reaction': 'CO2 + 4H2 = CH4 + 2 H20', 'Opex_percentage': 0.02, # Rosenfeld, D.C., Böhm, H., Lindorfer, J. and Lehner, M., 2020. diff --git a/energy_models/models/methane/upgrading_biogas/documentation/upgrading_biogas_disc.markdown b/energy_models/models/methane/upgrading_biogas/documentation/upgrading_biogas_disc.markdown index 296c4db8..d6438226 100644 --- a/energy_models/models/methane/upgrading_biogas/documentation/upgrading_biogas_disc.markdown +++ b/energy_models/models/methane/upgrading_biogas/documentation/upgrading_biogas_disc.markdown @@ -3,7 +3,7 @@ The upgrading process for biogas involves the removal of CO2. The most widely commercialized and used upgrading technologies are those that have for a long time been employed by the natural gas industry, pressure swing adsorption (PSA), chemical solvent scrubbing (using amines), and pressurized water scrubbing.[^2] Biogas Water Scrubber System Design, Greenlane Biogas [^3] -![](water_scrubbing.PNG) +![](water_scrubbing.PNG) (Image Credit: Hudde (2010), taken from [^3]) Amine water scrubbing is one traditional chemical solvent scrubbing method of upgrading that is applied due to its low methane slippage and its capability to provide a high purity renewable methane product. @@ -16,19 +16,19 @@ $$2(OH - 2CH_2 - NH_2) + CO_2 <--> (OH) - (CH_2)_2 - NHCOO- + OH - (CH_2)_2 - NH $$2(OH - (CH_2)_2 - NH_2) + H_2S <--> (HOCH_2CH_2NH_3)_2S$$ $$(HOCH_2CH_2NH_3)_2S + H_2S <-->2HOCH_2CH_2NH_3HS$$ -## Data +## Data -Economic and technical datas is taken from Vo & al [^1]. +Economic and technical datas is taken from Vo & al [^1]. Currently around 3.5 Mtoe of biomethane are produced worldwide and 92.3% are from upgrading biogas[^4]. The initial age distribution has been computed with biomethane plant lists from [^5] since most of biogas is converted into biomethane. ## Cooling [^6] Biogas upgrading process by receiving 2.393 kg/s biogas, 663.2 kW cooling energy, and 1650 kW power produces 0.5533 kg/s biomethane. - + [^1]: Vo, T. T.; Wall, D. M.; Ring, D.; Rajendran, K.; Murphy, J. D. (2018). Techno-economic analysis of biogas upgrading via amine scrubber, carbon capture and ex-situ methanation. Applied Energy, 212, pp. 1191-1202. [^2]:https://anaerobic-digestion.com/biogas-upgrading-technologies/ [^3]:https://biomass.ucdavis.edu/files/2015/10/Biogas-Cleanup-Report_FinalDraftv3_12Nov2014-2.pdf [^4]:Abanades, S., Abbaspour, H., Ahmadi, A., Das, B., Ehyaei, M.A., Esmaeilion, F., Assad, M.E.H., Hajilounezhad, T., Jamali, D.H., Hmida, A. and Ozgoli, H.A., 2021. A critical review of biogas production and usage with legislations framework across the globe. International Journal of Environmental Science and Technology, pp.1-24. [^5]:http://task37.ieabioenergy.com/plant-list.html -[^6]:https://www.sciencedirect.com/science/article/abs/pii/S0957582021002469 \ No newline at end of file +[^6]:https://www.sciencedirect.com/science/article/abs/pii/S0957582021002469 diff --git a/energy_models/models/methane/upgrading_biogas/upgrading_biogas.py b/energy_models/models/methane/upgrading_biogas/upgrading_biogas.py index 3ff05757..95089686 100644 --- a/energy_models/models/methane/upgrading_biogas/upgrading_biogas.py +++ b/energy_models/models/methane/upgrading_biogas/upgrading_biogas.py @@ -35,7 +35,6 @@ def compute_other_streams_needs(self): def compute_resources_needs(self): self.cost_details[f"{GlossaryEnergy.MonoEthanolAmineResource}_needs"] = self.get_MEA_loss() - def compute_byproducts_production(self): # kg/kWh corresponds to Mt/TWh co2_prod = self.get_theoretical_co2_prod() @@ -50,7 +49,7 @@ def compute_byproducts_production(self): def get_biogas_needs(self): ''' - COmpute theoretical biogas needs with proportion of CO2 and CH4 given in biogas energy + COmpute theoretical biogas needs with proportion of CO2 and CH4 given in biogas energy Divide by efficiency for realistic demand ''' biogas_data = BioGas.data_energy_dict @@ -72,7 +71,7 @@ def get_MEA_loss(self): return mea_loss def get_theoretical_co2_prod(self, unit='kg/kWh'): - ''' + ''' Get CO2 prod from upgrading biogas With the fraction of CO2 in biogas considered ''' diff --git a/energy_models/models/methanol/co2_hydrogenation/co2_hydrogenation_disc.py b/energy_models/models/methanol/co2_hydrogenation/co2_hydrogenation_disc.py index 9df27ac2..472b0d74 100644 --- a/energy_models/models/methanol/co2_hydrogenation/co2_hydrogenation_disc.py +++ b/energy_models/models/methanol/co2_hydrogenation/co2_hydrogenation_disc.py @@ -48,8 +48,6 @@ class CO2HydrogenationDiscipline(MethanolTechnoDiscipline): methanol_density = Methanol.data_energy_dict['density'] methanol_calorific_value = Methanol.data_energy_dict['calorific_value'] - - techno_infos_dict_default = { 'Capex_init': 35.58 / (20 * 50) / 5.54, # Total capital [M$] / (annual production * lifetime) [kt] / conversion factor [kWh/kg] = [$/kWh] diff --git a/energy_models/models/methanol/co2_hydrogenation/documentation/co2_hydrogenation_disc.markdown b/energy_models/models/methanol/co2_hydrogenation/documentation/co2_hydrogenation_disc.markdown index fe76d404..eb3b2a33 100644 --- a/energy_models/models/methanol/co2_hydrogenation/documentation/co2_hydrogenation_disc.markdown +++ b/energy_models/models/methanol/co2_hydrogenation/documentation/co2_hydrogenation_disc.markdown @@ -9,14 +9,14 @@ It can also be used as a fuel, through combustion reaction as an alternative to A CO2 stream and a dihydrogen stream are combined, and are brought together with a catalyst, to transform the mix into $$CH_{3}OH$$. -A raw methanol stream, containing impurities (less than for syngas reforming) comes out of the catalyzer and into a distillery, +A raw methanol stream, containing impurities (less than for syngas reforming) comes out of the catalyzer and into a distillery, to be cooled down and remove the impurities. ## Sources All the data used to fill the properties of this technology come from the sources below. -The details of the calculation/transformation of the data can be found on an excel sheet in the documentation +The details of the calculation/transformation of the data can be found on an excel sheet in the documentation folder of the technology in the corresponding git repository. [^1][Collodi, G., Azzaro, G., Ferrari, N. and Santos, S., 2017. Demonstrating large scale industrial CCS through CCU–a case study for methanol production. Energy Procedia, 114, pp.122-138.](https://www.sciencedirect.com/science/article/pii/S1876610217313280) @@ -27,9 +27,9 @@ folder of the technology in the corresponding git repository. [^4] [Engineering Toolbox](https://www.engineeringtoolbox.com) -[^5] [Eco-Techno-Economic Analysis of Methanol Production from Biogas and Power-to-X, +[^5] [Eco-Techno-Economic Analysis of Methanol Production from Biogas and Power-to-X, Emanuele Moioli and Tilman Schildhauer, Industrial & Engineering Chemistry Research 2022 61 (21), 7335-7348](https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.1c04682) [^6] [Schröder, J., Müller-Langer, F., Aakko-Saksa, P., Winther, K., Baumgarten, W. and Lindgren, M., 2020. Methanol as motor fuel: Summary Report.](https://www.iea-amf.org/content/fuel_information/methanol#general) -[^7] [Nyári, J., 2018. Techno-economic feasibility study of a methanol plant using carbon dioxide and hydrogen.](http://kth.diva-portal.org/smash/get/diva2:1290829/FULLTEXT01.pdf) \ No newline at end of file +[^7] [Nyári, J., 2018. Techno-economic feasibility study of a methanol plant using carbon dioxide and hydrogen.](http://kth.diva-portal.org/smash/get/diva2:1290829/FULLTEXT01.pdf) diff --git a/energy_models/models/solid_fuel/coal_extraction/coal_extraction_disc.py b/energy_models/models/solid_fuel/coal_extraction/coal_extraction_disc.py index 5dc83068..5cbb2564 100644 --- a/energy_models/models/solid_fuel/coal_extraction/coal_extraction_disc.py +++ b/energy_models/models/solid_fuel/coal_extraction/coal_extraction_disc.py @@ -45,7 +45,6 @@ class CoalExtractionDiscipline(SolidFuelTechnoDiscipline): } techno_name = GlossaryEnergy.CoalExtraction - # Most coal seams are too deep underground for opencast mining and require # underground mining, a method that currently accounts for about 60 # percent of world coal production. Wikipedia source : @@ -108,7 +107,7 @@ class CoalExtractionDiscipline(SolidFuelTechnoDiscipline): # From ourworldindata initial_production = 43752. - energy_own_use # First invest is zero to get exactly the initial production in 2020 - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/solid_fuel/coal_extraction/documentation/coal_extraction_disc.markdown b/energy_models/models/solid_fuel/coal_extraction/documentation/coal_extraction_disc.markdown index a9b12166..8d108a71 100644 --- a/energy_models/models/solid_fuel/coal_extraction/documentation/coal_extraction_disc.markdown +++ b/energy_models/models/solid_fuel/coal_extraction/documentation/coal_extraction_disc.markdown @@ -17,7 +17,7 @@ Lifetime : 35 years, Capex : 0.00081 USD/kWh, Opex : 0.2*Capex The exchange rate between AU.D and US.D is assumed as follow : 1AU.D = 0.77 US.D Additional datas can be found in [^3]. -![](mines_data.PNG) +![](mines_data.PNG) @@ -33,7 +33,7 @@ According to [^5], the full combustion of 1 short ton of coal emits 2.86 short t The emission of methane is a big issue for coal extraction mines. A lot of methane gas is immersed in coal seams and is leaking into the atmosphere via mineshafts the whole time the coal is mined. But that is not all. Methane leaks for decades into the atmosphere after mining from abandoned mines. Consequently, the global methane emissions from coal mining could continue groing even with declining coal production. [^4] -The Model for Calculating Coal Mine Methane (MC2M) developed by [^4] and used by IPCC models computed the annual CH4 emissions from coal mines with the equation : +The Model for Calculating Coal Mine Methane (MC2M) developed by [^4] and used by IPCC models computed the annual CH4 emissions from coal mines with the equation : $$CH4\_emissions (m^3) = coal\_production (t) *gas\_content(mine\_depth,coal\_type)*ef\_coefficient$$ @@ -63,4 +63,4 @@ In 2020, the MC2M model estimates that around 70 Mt of CH4 emissions are coming [^8]: Wikipedia page : https://en.wikipedia.org/wiki/Coal_mining [^9]: https://globalenergymonitor.org/wp-content/uploads/2021/03/Coal-Mine-Methane-On-the-Brink.pdf [^10]: https://www.eia.gov/energyexplained/coal/mining-and-transportation.php -[^11]: https://www.sciencedirect.com/science/article/abs/pii/S0360544221003376 \ No newline at end of file +[^11]: https://www.sciencedirect.com/science/article/abs/pii/S0360544221003376 diff --git a/energy_models/models/solid_fuel/pelletizing/documentation/pelletizing_disc.markdown b/energy_models/models/solid_fuel/pelletizing/documentation/pelletizing_disc.markdown index 8c008e02..2aabe23f 100644 --- a/energy_models/models/solid_fuel/pelletizing/documentation/pelletizing_disc.markdown +++ b/energy_models/models/solid_fuel/pelletizing/documentation/pelletizing_disc.markdown @@ -7,7 +7,7 @@ Biomass comes from organic feedstocks as wood, plants or wastes. - herbaceous biomass comes from crops residues or energy crops (providing biomass for energy purpose and not feeding). Algae can also be used as biomass source. - Wastes biomass comes from municipal or industrial wastes. -The heat generated during pelletizing can come from several sources, depending on the type of materials being pelletized and the equipment used. +The heat generated during pelletizing can come from several sources, depending on the type of materials being pelletized and the equipment used. Consumption of heat is calculated in TWh for high temperature. It depends on production of solid fuel(TWh) and efficiency value. @@ -20,7 +20,7 @@ Biomass in SosTrades has been divided following its use. ![](biomass.png) **Biomass problematic[^2] :** -The low energy density of biomass feedstocks tends to limit the transport distance from a biomass power plant. Moreover forests and landfields have to be well managed to stay as sustainable energy source and carbon neutral and therefore large local quantities are not available. This can place a limit on the scale of the biomass power plant, meaning that biomass struggles to take advantage of economies of scale in the generating plant because large quantities of low-cost feedstock are not available. +The low energy density of biomass feedstocks tends to limit the transport distance from a biomass power plant. Moreover forests and landfields have to be well managed to stay as sustainable energy source and carbon neutral and therefore large local quantities are not available. This can place a limit on the scale of the biomass power plant, meaning that biomass struggles to take advantage of economies of scale in the generating plant because large quantities of low-cost feedstock are not available. Transforming biomass into **pellets** offers a solution to this problem. Biomass is dried, crushed and pelletized to be converted into pellets that have standardized size and properties, high energy content and high density. It reduses costs of transports, storage and handling. Pellets can directly be used in firing or co-firing with coal in boilers. diff --git a/energy_models/models/syngas/autothermal_reforming/autothermal_reforming.py b/energy_models/models/syngas/autothermal_reforming/autothermal_reforming.py index ffc7b429..4a54f026 100644 --- a/energy_models/models/syngas/autothermal_reforming/autothermal_reforming.py +++ b/energy_models/models/syngas/autothermal_reforming/autothermal_reforming.py @@ -31,7 +31,6 @@ def compute_resources_needs(self): # need in kg to produce 1kwh of syngas self.cost_details[f'{GlossaryEnergy.OxygenResource}_needs'] = self.get_theoretical_O2_needs() / self.cost_details['efficiency'] - def compute_other_streams_needs(self): # need in kwh to produce 1kwh of syngas self.cost_details[f'{Methane.name}_needs'] = self.get_theoretical_CH4_needs() / self.cost_details['efficiency'] @@ -54,7 +53,7 @@ def get_theoretical_CH4_needs(self): return methane_needs def get_theoretical_CO2_needs(self): - ''' + ''' Get water needs in kg CO2 /kWh H2 1 mol of CO2 for 3 mol of CO and 3 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -70,7 +69,7 @@ def get_theoretical_CO2_needs(self): return water_needs def get_theoretical_O2_needs(self): - ''' + ''' Get water needs in kg O2 /kWh H2 1 mol of O2 for 3 mol of CO and 3 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one diff --git a/energy_models/models/syngas/autothermal_reforming/documentation/autothermal_reforming_disc.markdown b/energy_models/models/syngas/autothermal_reforming/documentation/autothermal_reforming_disc.markdown index 87f7f145..a70701fd 100644 --- a/energy_models/models/syngas/autothermal_reforming/documentation/autothermal_reforming_disc.markdown +++ b/energy_models/models/syngas/autothermal_reforming/documentation/autothermal_reforming_disc.markdown @@ -1,19 +1,19 @@ # Autothermal reforming -Autothermal reforming uses CO_2 and oxygen in a reaction with methane to form syngas. The reaction takes place in a single chamber where the methane is partially oxidized. The reaction is exothermic due to the oxidation. +Autothermal reforming uses CO_2 and oxygen in a reaction with methane to form syngas. The reaction takes place in a single chamber where the methane is partially oxidized. The reaction is exothermic due to the oxidation. $$2CH_4 + O_2 + CO_2 --> 3CO + H_2O + 3H_2$$ -The syngas produced ratio of H2:CO is 1:1. +The syngas produced ratio of H2:CO is 1:1. Economic and technical datas was taken from Ayodele et Al. [^1] & Cormos et Al. [^2]. -![](atr.png) +![](atr.png) ## Heat Heat production/consumption is neglected in coal-gasification process. - + [^1]: Freida Ozavize Ayodele , Siti Indati Mustapa , Bamidele Victor Ayodele and Norsyahida Mohammad (2020) An Overview of Economic Analysis and Environmental Impacts of Natural Gas Conversion Technologies -[^2]: Ana-Maria Cormos et Al. (2018). Economic Assessments of Hydrogen Production Processes Based on Natural Gas Reforming with Carbon Capture \ No newline at end of file +[^2]: Ana-Maria Cormos et Al. (2018). Economic Assessments of Hydrogen Production Processes Based on Natural Gas Reforming with Carbon Capture diff --git a/energy_models/models/syngas/biomass_gasification/biomass_gasification.py b/energy_models/models/syngas/biomass_gasification/biomass_gasification.py index ebf44aed..c8cff39f 100644 --- a/energy_models/models/syngas/biomass_gasification/biomass_gasification.py +++ b/energy_models/models/syngas/biomass_gasification/biomass_gasification.py @@ -34,6 +34,5 @@ def compute_other_streams_needs(self): self.cost_details[f'{BiomassDry.name}_needs'] = self.techno_infos_dict['biomass_demand'] - def compute_byproducts_production(self): self.compute_ghg_emissions(Methane.emission_name) diff --git a/energy_models/models/syngas/biomass_gasification/biomass_gasification_disc.py b/energy_models/models/syngas/biomass_gasification/biomass_gasification_disc.py index f224706e..6d69f29e 100644 --- a/energy_models/models/syngas/biomass_gasification/biomass_gasification_disc.py +++ b/energy_models/models/syngas/biomass_gasification/biomass_gasification_disc.py @@ -101,7 +101,7 @@ class BiomassGasificationDiscipline(SyngasTechnoDiscipline): 'kgH20_perkgSyngas': 157.75 / (10.99 / 0.42), } # We do not invest on biomass gasification yet - + syngas_ratio = BiomassGasification.syngas_COH2_ratio # 24 plants for liquid fuel production with global production of liquid fuel from biomass-derived syngas diff --git a/energy_models/models/syngas/biomass_gasification/documentation/biomass_gasification_disc.markdown b/energy_models/models/syngas/biomass_gasification/documentation/biomass_gasification_disc.markdown index 95aecd74..b05df88e 100644 --- a/energy_models/models/syngas/biomass_gasification/documentation/biomass_gasification_disc.markdown +++ b/energy_models/models/syngas/biomass_gasification/documentation/biomass_gasification_disc.markdown @@ -6,11 +6,11 @@ Biomass resources mainly include agriculture crops, agriculture crop waste, wood, wood waste and animal manure. A biomass analysis reveals a concentration of Carbon, Hydrogen, Oxygen and a bit of Sulfur and Nitrogen. ## Principle of work -The gasifier is the core equipment unit for the hydrogen production process. Fuels (such as coal and biomass) are used in the gasification process, and fuel is converted to syngas primarily containing CO, CO2, H2, H2O and CH4. The composition of syngas derived from gasification mainly depends on the fuel, gasifier type and gasification agent. +The gasifier is the core equipment unit for the hydrogen production process. Fuels (such as coal and biomass) are used in the gasification process, and fuel is converted to syngas primarily containing CO, CO2, H2, H2O and CH4. The composition of syngas derived from gasification mainly depends on the fuel, gasifier type and gasification agent. The selection of the gasifier type depends on the raw material. Various gasification agents, including air, oxygen, oxygen-enriched air and steam, can be employed for the gasification process. -Produced syngas followed the below formula and consist in a primarily mixture of carbon monoxide and hydrogen : +Produced syngas followed the below formula and consist in a primarily mixture of carbon monoxide and hydrogen : $$Biomass(CH_aO_bN_cS_d) + O_2 --> CO_2$$ $$Biomass(CH_aO_bN_cS_d) + CO_2 --> 2CO$$ @@ -22,11 +22,11 @@ $$Biomass(CH_aO_bN_cS_d) + O_2 + H_2O --> CO + CO_2 + H_2 + other species$$ (other species => $N_2$, $H_2S$ less than 0.3 Mole Frac(%)) -## World production +## World production Altalto, a partnership project by British Airways, Shell and Velocys, aims for the development of first commercial production of biojet at a capacity of 60 million liters/year. BioTfueL project34, driven by a group of companies from France (Axens, CEA, IFP Energies Nouvelles, -Avril, ThyssenKrupp Industrial Solutions, Total), aims for the production of 60 t/y FT liquids. +Avril, ThyssenKrupp Industrial Solutions, Total), aims for the production of 60 t/y FT liquids. The UPM Lappeenranta Biorefinery, producing wood-based renewable diesel from forestry residues, has started commercial production in January 2015 with 130000 t/y. To resume, there are 24 plants for liquid fuel production with global production of liquid fuel from biomass-derived syngas (750,000 t/year); 8 plants for gaseous fuel @@ -36,12 +36,12 @@ from biomass-derived syngas of 9000 t/year.[^7] In the beginning of 2021, Fulcrum bioenergy and Essar Oil UK announced Fulcrum NorthPoint project which aims for annual SAF production of 100 million liters at Essar Oil site in Stanlow (UK) with planned production start-up date in 2025. -## Datas +## Datas Economic datas is taken from Rosenfeld et al. [^1], technical datas from Wang et al. [^2], Albara et al. [^3], Sara et al. [^4], Mustafa et. al [^5]. -### GHG emissions +### GHG emissions -The Chapter 4 of the IPCC report predicts methane fugitive emissions from biomass gasification. Emission factor is provided based on the carbon contents of biomass used in a selection of plants and the composition of its syngas and has a value of 18.3 kgCH4/TJ [^8]. +The Chapter 4 of the IPCC report predicts methane fugitive emissions from biomass gasification. Emission factor is provided based on the carbon contents of biomass used in a selection of plants and the composition of its syngas and has a value of 18.3 kgCH4/TJ [^8]. [^1]: Rosenfeld, D. C., Bohm, H., Lindorfer, J. , Lehner, M. (2020). Scenario analysis of implementing a power-to-gas and biomass gasification system in an integrated steel plant: A techno-economic and environmental study. Renewable Energy, 147(2020), 1511-1524. @@ -57,4 +57,4 @@ The Chapter 4 of the IPCC report predicts methane fugitive emissions from biomas [^6]: https://www.etipbioenergy.eu/images/ETIP_B_Factsheet_BtL_2021.pdf [^7]: Molino, A., Larocca, V., Chianese, S. and Musmarra, D., 2018. Biofuels production by biomass gasification: A review. Energies, 11(4), p.811. -[^8]: IPCC report Chapter 4 Fugitive Emissions : https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/2_Volume2/19R_V2_4_Ch04_Fugitive_Emissions.pdf \ No newline at end of file +[^8]: IPCC report Chapter 4 Fugitive Emissions : https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/2_Volume2/19R_V2_4_Ch04_Fugitive_Emissions.pdf diff --git a/energy_models/models/syngas/co_electrolysis/co_electrolysis.py b/energy_models/models/syngas/co_electrolysis/co_electrolysis.py index a244e3eb..32b2973d 100644 --- a/energy_models/models/syngas/co_electrolysis/co_electrolysis.py +++ b/energy_models/models/syngas/co_electrolysis/co_electrolysis.py @@ -36,9 +36,8 @@ def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() - def get_theoretical_CO2_needs(self): - ''' + ''' Get water needs in kg CO2 /kWh syngas 1 mol of CO2 for 1 mol of CO and 1 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -54,7 +53,7 @@ def get_theoretical_CO2_needs(self): return co2_needs def get_theoretical_water_needs(self): - ''' + ''' Get water needs in kg water /kWh syngas 1 mol of H2O for 1 mol of CO and 1 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one diff --git a/energy_models/models/syngas/co_electrolysis/documentation/co_electrolysis_disc.markdown b/energy_models/models/syngas/co_electrolysis/documentation/co_electrolysis_disc.markdown index 97d06c18..d0e94186 100644 --- a/energy_models/models/syngas/co_electrolysis/documentation/co_electrolysis_disc.markdown +++ b/energy_models/models/syngas/co_electrolysis/documentation/co_electrolysis_disc.markdown @@ -1,23 +1,22 @@ # CoElectrolysis -High temperature co-electrolysis is a process which electro-chemically reduces H2O and CO2 using solid oxide electrolyzer cell via the reactions in the equations : +High temperature co-electrolysis is a process which electro-chemically reduces H2O and CO2 using solid oxide electrolyzer cell via the reactions in the equations : $$H_2O -> H2 + 1/2 O_2$$ $$CO_2 -> CO + 1/2 O_2$$ -Total reaction is : +Total reaction is : $$H_2O + CO_2 -> H2 + CO + O_2$$ -![](coelectrolysis.jpg) +![](coelectrolysis.jpg) Economic and technical datas was taken from Becker et Al. [^1] ## Heat Heat production/consumption is neglected in coal-gasification process. - -[^1]: Becker et Al. (2012) Production of FischereTropsch liquid fuels from high temperature solid oxide co-electrolysis units +[^1]: Becker et Al. (2012) Production of FischereTropsch liquid fuels from high temperature solid oxide co-electrolysis units diff --git a/energy_models/models/syngas/coal_gasification/coal_gasification.py b/energy_models/models/syngas/coal_gasification/coal_gasification.py index a9709ddb..fed4c3ae 100644 --- a/energy_models/models/syngas/coal_gasification/coal_gasification.py +++ b/energy_models/models/syngas/coal_gasification/coal_gasification.py @@ -42,7 +42,6 @@ def compute_other_streams_needs(self): # in kwh of fuel by kwh of syngas self.cost_details[f'{SolidFuel.name}_needs'] = self.get_fuel_needs() - def compute_byproducts_production(self): self.production_detailed[f'{CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = self.techno_infos_dict[ diff --git a/energy_models/models/syngas/coal_gasification/coal_gasification_disc.py b/energy_models/models/syngas/coal_gasification/coal_gasification_disc.py index 55129d39..0b79f76e 100644 --- a/energy_models/models/syngas/coal_gasification/coal_gasification_disc.py +++ b/energy_models/models/syngas/coal_gasification/coal_gasification_disc.py @@ -64,9 +64,9 @@ class CoalGasificationDiscipline(SyngasTechnoDiscipline): 'Capex_init_unit': '$/kWh', 'euro_dollar': 1.12, 'efficiency': 1.0, - 'techno_evo_eff': 'no',} + 'techno_evo_eff': 'no', } # We do not invest on coal gasification yet - + syngas_ratio = CoalGasification.syngas_COH2_ratio # From Future of hydrogen : Around 70 Mt of dedicated hydrogen are produced today, 76% from natural gas and diff --git a/energy_models/models/syngas/coal_gasification/documentation/coal_gasification_disc.markdown b/energy_models/models/syngas/coal_gasification/documentation/coal_gasification_disc.markdown index 3f2696b8..02298f08 100644 --- a/energy_models/models/syngas/coal_gasification/documentation/coal_gasification_disc.markdown +++ b/energy_models/models/syngas/coal_gasification/documentation/coal_gasification_disc.markdown @@ -1,37 +1,37 @@ # Syngas from Coal Gasification ## Definition -Coal gasification is the process of producing syngas from coal,water and oxygen. During gasification, the coal is blown through with oxygen and water vapor : +Coal gasification is the process of producing syngas from coal,water and oxygen. During gasification, the coal is blown through with oxygen and water vapor : $$3C(coal) + O_2 + H_2O \rightarrow H_2 + 3CO$$ -If the refiner wants to produce liquid fuels, the coal gas is collected at this state and routed to a Fischer–Tropsch reactor. If, however, hydrogen is the desired end-product, the coal gas (primarily the CO product) undergoes the water gas shift reaction. +If the refiner wants to produce liquid fuels, the coal gas is collected at this state and routed to a Fischer–Tropsch reactor. If, however, hydrogen is the desired end-product, the coal gas (primarily the CO product) undergoes the water gas shift reaction. -## World production +## World production The two main applications of coal gasification are to produce energy (hydrogen with Water Gas Shift reaction and liquid fuels with Fischer Tropsch reaction) and to reduced iron from iron ore. - Almost 23 % of Hydrogen is produced with coal gasification mostly in China. The world's largest scale implementation of Fischer–Tropsch technology in South Africa is based on coal gasification. + Almost 23 % of Hydrogen is produced with coal gasification mostly in China. The world's largest scale implementation of Fischer–Tropsch technology in South Africa is based on coal gasification. - In industry, syngas from coal gasification is used to transform iron oxides extracted from iron mines into iron ore using the Direct Reduced Iron (DRI) technology : + In industry, syngas from coal gasification is used to transform iron oxides extracted from iron mines into iron ore using the Direct Reduced Iron (DRI) technology : $$2FeO + (CO + H_2) \rightarrow 2Fe + CO_2 + H_2O$$ - IEA website reports a coal consumption of 3333 TWh for other transformation [^3] (includes Hydrogen and DRI) and 264.72 TWh for liquefaction plants. + IEA website reports a coal consumption of 3333 TWh for other transformation [^3] (includes Hydrogen and DRI) and 264.72 TWh for liquefaction plants. ## Datas Technical and economic datas were taken from Wang et Al. [^1] and ETSAP [^2] -### GHG emissions +### GHG emissions -The Chapter 4 of the IPCC report predicts methane fugitive emissions from biomass gasification. Emission factor is provided based on the volume of CH4 emissions released during the production and treatment of syngas and has a value of 6.1 kgCH4/TJ [^4]. +The Chapter 4 of the IPCC report predicts methane fugitive emissions from biomass gasification. Emission factor is provided based on the volume of CH4 emissions released during the production and treatment of syngas and has a value of 6.1 kgCH4/TJ [^4]. ## Heat Heat production/consumption is neglected in coal-gasification process. - + [^1]: Wang, Y., Li, G., Liu, Z. , Cui, P., Zhu, Z. (2019). Techno-economic analysis of biomass-to-hydrogen process in comparison with coal-to-hydrogen process. Energy, 185(2019), 1063-1075. [^2]: IEA ETSAP - Technology Brief P05 – May 2010 - www.etsap.org -[^3]: [IEA 2022, Data Tables](https://www.iea.org/data-and-statistics/data-tables?country=WORLD&energy=Balances&year=2019), License: CC BY 4.0. \ No newline at end of file +[^3]: [IEA 2022, Data Tables](https://www.iea.org/data-and-statistics/data-tables?country=WORLD&energy=Balances&year=2019), License: CC BY 4.0. diff --git a/energy_models/models/syngas/pyrolysis/documentation/pyrolysis_disc.markdown b/energy_models/models/syngas/pyrolysis/documentation/pyrolysis_disc.markdown index feb649e4..56ac3e5b 100644 --- a/energy_models/models/syngas/pyrolysis/documentation/pyrolysis_disc.markdown +++ b/energy_models/models/syngas/pyrolysis/documentation/pyrolysis_disc.markdown @@ -1,13 +1,13 @@ # Wood pyrolysis -Pyrolysis is the thermal decomposition of materials at elevated temperatures in an inert atmosphere. It can be applied to wood where it is burned in an oxygen free environment. +Pyrolysis is the thermal decomposition of materials at elevated temperatures in an inert atmosphere. It can be applied to wood where it is burned in an oxygen free environment. Several types of pyrolysis exist : Fast pyrolysis at 500-1000°C or slow pyrolysis at 500°C. The temperature will have a direct effect on the mass balance of the pyrolysis. If the bio-oil is the priority product, it was found that fast pyrolysis at 500°C maximize the yield, while fast pyrolysis at 1000 °C is prefered to maximize syngas production. Economic-data and technical one was taken from Salman et Al. [^1] & Wei et Al. [^2] -![](pyrolysis_out.png) +![](pyrolysis_out.png) ## Heat Pyrolysis is typically defined as the thermochemical decomposition of biomass feedstock at medium (300–800°C) to high temperatures (800–1300°C) in an inert atmosphere. @@ -16,7 +16,7 @@ The overall reaction of biomass feedstock is.. Biofuel + heat → liquid + syngas + solid C + H2O → CO + H2 .... ΔH°= +131 kJ/mol - + [^1]: Salman, & Salman, Chaudhary Awais. (2014). Techno-economic analysis of wood pyrolysis in Sweden. -[^2]: Wei, Lin & Pordesimo, L.O. & To, Filip & Herndon, Cary & Batchelor, William. (2009). Evaluation of Micro-Scale Syngas Production Costs through Modeling. \ No newline at end of file +[^2]: Wei, Lin & Pordesimo, L.O. & To, Filip & Herndon, Cary & Batchelor, William. (2009). Evaluation of Micro-Scale Syngas Production Costs through Modeling. diff --git a/energy_models/models/syngas/pyrolysis/pyrolysis_disc.py b/energy_models/models/syngas/pyrolysis/pyrolysis_disc.py index 11326054..264a8cad 100644 --- a/energy_models/models/syngas/pyrolysis/pyrolysis_disc.py +++ b/energy_models/models/syngas/pyrolysis/pyrolysis_disc.py @@ -79,7 +79,7 @@ class PyrolysisDiscipline(SyngasTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + } # -- add specific techno outputs to this DESC_IN.update(SyngasTechnoDiscipline.DESC_IN) diff --git a/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift.py b/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift.py index 0ebbffc1..bde1a7c8 100644 --- a/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift.py +++ b/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift.py @@ -72,7 +72,7 @@ def configure_parameters_update(self, inputs_dict): def check_capex_unity(self, data_config): ''' - Overload the check_capex_unity for this particular model + Overload the check_capex_unity for this particular model ''' capex_list = np.array(data_config['Capex_init_vs_CO_H2_ratio']) @@ -406,7 +406,6 @@ def compute_other_streams_needs(self): # Cost of methane for 1 kWH of H2 self.cost_details['syngas_needs'] = self.get_theoretical_syngas_needs(self.syngas_ratio) / self.cost_details['efficiency'] - def compute_byproducts_production(self): th_water_prod = self.get_theoretical_water_prod() @@ -417,7 +416,7 @@ def compute_byproducts_production(self): def compute_streams_consumption(self): """ Compute the consumption and the production of the technology for a given investment - Maybe add efficiency in consumption computation ? + Maybe add efficiency in consumption computation ? """ super().compute_streams_consumption() @@ -477,8 +476,8 @@ def compute_dco2_emissions_dsyngas_ratio(self): return dco2_emissions_dsyngas_ratio def get_theoretical_syngas_needs(self, syngas_ratio): - ''' - dCO2 + e(H2 +r1CO)--> (H2 +r2CO) + cH20 + ''' + dCO2 + e(H2 +r1CO)--> (H2 +r2CO) + cH20 e = (1+r2)/(1+r1) c = (r2-r1)/(1+r1) @@ -577,8 +576,8 @@ def compute_dsyngas_needs_dsyngas_ratio(self): return dsyngas_needs_dsyngas_ratio def get_theoretical_water_prod(self): - ''' - dCO2 + e(H2 +r1CO)--> (H2 +r2CO) + cH20 + ''' + dCO2 + e(H2 +r1CO)--> (H2 +r2CO) + cH20 e = (1+r2)/(1+r1) c = (r2-r1)/(1+r1) @@ -631,8 +630,8 @@ def compute_dwater_prod_dsynags_ratio(self): return dwater_needs_dsyngas_ratio def get_theoretical_co2_needs(self, unit='kg/kWh'): - ''' - dCO2 + e(H2 +r1CO)--> (H2 +r2CO) + cH20 + ''' + dCO2 + e(H2 +r1CO)--> (H2 +r2CO) + cH20 e = (1+r2)/(1+r1) c = (r2-r1)/(1+r1) diff --git a/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift_disc.py b/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift_disc.py index 8e7195fb..a28e1de7 100644 --- a/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift_disc.py +++ b/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift_disc.py @@ -87,7 +87,7 @@ class ReversedWaterGasShiftDiscipline(SyngasTechnoDiscipline): # Fake initial age distrib (not found in the litterature...) DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'syngas_ratio': {'type': 'array', 'unit': '%'}, 'needed_syngas_ratio': {'type': 'float', 'unit': '%'} } diff --git a/energy_models/models/syngas/smr/documentation/smr_disc.markdown b/energy_models/models/syngas/smr/documentation/smr_disc.markdown index 97659f02..44db58af 100644 --- a/energy_models/models/syngas/smr/documentation/smr_disc.markdown +++ b/energy_models/models/syngas/smr/documentation/smr_disc.markdown @@ -1,24 +1,24 @@ # Steam Methane Reforming (SMR) -Hydrogen production using steam methane reforming (SMR) is a two-step process. The first step is to inject water vapor at very high temperature (700 to 1000 Celsius degrees) with methane in a pressurized environment (3 to 25 bars), which is the SMR process. The reaction generates hydrogen (H2) and carbon monoxide (CO) : +Hydrogen production using steam methane reforming (SMR) is a two-step process. The first step is to inject water vapor at very high temperature (700 to 1000 Celsius degrees) with methane in a pressurized environment (3 to 25 bars), which is the SMR process. The reaction generates hydrogen (H2) and carbon monoxide (CO) : $$CH_4 + H_20 (+heat) --> CO + 3H_2$$ -The Water Gas Shift process is then used to extract hydrogen from syngas mix. +The Water Gas Shift process is then used to extract hydrogen from syngas mix. -## World production +## World production Annual global hydrogen production is around 70 million tonnes of hydrogen and around 75% comes from natural gas reforming.[^1] Syngas from natural gas is also used in liquefaction plants in the Gas to Liquid process (GtL).In Qatar, the second largest FT plant in the world converts natural gas to petroleum liquids at a rate of 140,000 barrels per day.Another plant in Qatar has a capacity of 34,000 barrels per day.[^4] -## Datas +## Datas Economic and technical datas is taken from Keipi & al [^2], Diglio & al [^3]. ## Heat -[^5] Natural gas and steam are fed to the pre-reformer where the heavies are converted to methane to +[^5] Natural gas and steam are fed to the pre-reformer where the heavies are converted to methane to prevent soot and enhance the efficiency of the process . More steam is added prior to entering the main reactor, where syngas is produced, in an equilibrium-limited endothermic reaction: CH4 + H2O → CO + 3H2 .... ΔH°= 206 kJ/mol @@ -28,4 +28,4 @@ CH4 + H2O → CO + 3H2 .... ΔH°= 206 kJ/mol [^2]: Tiina Keipi, Henrik Tolvanen, Jukka Konttinen,Economic analysis of hydrogen production by methane thermal decomposition: Comparison to competing technologies,Energy Conversion and Management,Volume 159,2018,Pages 264-273,ISSN 0196-8904 [^3]: Diglio, G., Hanak, D.P., Bareschino, P., Mancusi, E., Pepe, F., Montagnaro, F. and Manovic, V., 2017. Techno-economic analysis of sorption-enhanced steam methane reforming in a fixed bed reactor network integrated with fuel cell. Journal of Power Sources, 364, pp.41-51. [^4]: https://en.wikipedia.org/wiki/Fischer%E2%80%93Tropsch_process#Commercialization -[^5]: https://www.sciencedirect.com/science/article/pii/S2666790822001574 \ No newline at end of file +[^5]: https://www.sciencedirect.com/science/article/pii/S2666790822001574 diff --git a/energy_models/models/syngas/smr/smr.py b/energy_models/models/syngas/smr/smr.py index 83b3af8c..3eb5ce25 100644 --- a/energy_models/models/syngas/smr/smr.py +++ b/energy_models/models/syngas/smr/smr.py @@ -34,9 +34,8 @@ def compute_other_streams_needs(self): # need in kg to produce 1kwh of syngas self.cost_details[f'{Methane.name}_needs'] = self.get_theoretical_CH4_needs() / self.cost_details['efficiency'] - def get_theoretical_CH4_needs(self): - ''' + ''' Get CH4 needs in kWh CH4 /kWh syngas 1 mol of CH4 for 1 mol of CO and 1 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -52,9 +51,9 @@ def get_theoretical_CH4_needs(self): return methane_needs def get_theoretical_water_needs(self): - ''' + ''' Get water needs in kg water /kWh syngas - 1 mol of H2O for 1 mol of synags + 1 mol of H2O for 1 mol of synags Warning : molar mass is in g/mol but we divide and multiply by one ''' diff --git a/energy_models/models/wet_biomass/animal_manure/animal_manure.py b/energy_models/models/wet_biomass/animal_manure/animal_manure.py index 6c7e4989..01a84896 100644 --- a/energy_models/models/wet_biomass/animal_manure/animal_manure.py +++ b/energy_models/models/wet_biomass/animal_manure/animal_manure.py @@ -28,4 +28,4 @@ def compute_other_streams_needs(self): def compute_byproducts_production(self): self.production_detailed[f'{GlossaryEnergy.carbon_capture} (kg)'] = self.techno_infos_dict['CO2_from_production'] / \ self.data_energy_dict['calorific_value'] * \ - self.production_detailed[f'{WetBiomassTechno.energy_name} (kWh)'] \ No newline at end of file + self.production_detailed[f'{WetBiomassTechno.energy_name} (kWh)'] diff --git a/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues.py b/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues.py index 74f53e5d..2db5a602 100644 --- a/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues.py +++ b/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues.py @@ -25,7 +25,6 @@ class WetCropResidues(WetBiomassTechno): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() - def compute_byproducts_production(self): self.production_detailed[f'{GlossaryEnergy.carbon_capture} (kg)'] = self.techno_infos_dict['CO2_from_production'] / \ self.data_energy_dict['calorific_value'] * \ diff --git a/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues_disc.py b/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues_disc.py index b62745ca..76f885d5 100644 --- a/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues_disc.py +++ b/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues_disc.py @@ -89,7 +89,7 @@ class WetCropResiduesDiscipline(WetBiomassTechnoDiscipline): # Age distribution fake DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default}, - + } # -- add specific techno inputs to this DESC_IN.update(WetBiomassTechnoDiscipline.DESC_IN) diff --git a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/process.py b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/process.py index 26b4fc39..3957b83a 100644 --- a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/process.py +++ b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/process.py @@ -95,7 +95,6 @@ def get_builders(self): after_name=self.ee.study_name, clean_existing=False) - # --------------------------------------------- mods_dict = { energy_mix: 'energy_models.core.energy_mix.energy_mix_disc.Energy_Mix_Discipline', @@ -198,7 +197,6 @@ def get_builders(self): self.ee.ns_manager.add_ns_def(ns_dict) - # --------------------------------------------- # design variables builder design_var_path = 'sostrades_optimization_plugins.models.design_var.design_var_disc.DesignVarDiscipline' diff --git a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/usecase.py b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/usecase.py index 5cdbffb3..f01f7794 100644 --- a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/usecase.py +++ b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/usecase.py @@ -406,10 +406,10 @@ def make_dspace_utilisation_ratio(self) -> pd.DataFrame: def make_func_df(self): func_df = pd.DataFrame({ "variable": [GlossaryEnergy.ObjectiveEnergyNonUseCapitalByStream, GlossaryEnergy.ObjectiveEnergyNonUseCapital, GlossaryEnergy.CO2EmissionsObjectiveValue, GlossaryEnergy.TargetProductionConstraintValue, GlossaryEnergy.MaxBudgetConstraintValue,], - "parent": ["objectives", "objectives","objectives", "constraints", "constraints"], - "ftype": [FunctionManagerDisc.OBJECTIVE, FunctionManagerDisc.OBJECTIVE,FunctionManagerDisc.OBJECTIVE, FunctionManagerDisc.INEQ_CONSTRAINT, FunctionManagerDisc.INEQ_CONSTRAINT], + "parent": ["objectives", "objectives", "objectives", "constraints", "constraints"], + "ftype": [FunctionManagerDisc.OBJECTIVE, FunctionManagerDisc.OBJECTIVE, FunctionManagerDisc.OBJECTIVE, FunctionManagerDisc.INEQ_CONSTRAINT, FunctionManagerDisc.INEQ_CONSTRAINT], "weight": [0., 0.1, 1.0, 10.0, 10.0,], - FunctionManagerDisc.AGGR_TYPE: [FunctionManager.AGGR_TYPE_SUM,FunctionManager.AGGR_TYPE_SUM, FunctionManager.AGGR_TYPE_SUM, FunctionManager.INEQ_NEGATIVE_WHEN_SATIFIED_AND_SQUARE_IT, FunctionManager.INEQ_NEGATIVE_WHEN_SATIFIED_AND_SQUARE_IT,], + FunctionManagerDisc.AGGR_TYPE: [FunctionManager.AGGR_TYPE_SUM, FunctionManager.AGGR_TYPE_SUM, FunctionManager.AGGR_TYPE_SUM, FunctionManager.INEQ_NEGATIVE_WHEN_SATIFIED_AND_SQUARE_IT, FunctionManager.INEQ_NEGATIVE_WHEN_SATIFIED_AND_SQUARE_IT,], "namespace": [GlossaryEnergy.NS_FUNCTIONS] * 5 }) return func_df diff --git a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/process.py b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/process.py index b35827a4..0a44e2e2 100644 --- a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/process.py +++ b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/process.py @@ -30,4 +30,4 @@ class ProcessBuilder(EnergyMixFullProcessBuilder): def __init__(self, ee): super().__init__(ee) - self.techno_dict=GlossaryEnergy.DEFAULT_COARSE_TECHNO_DICT + self.techno_dict = GlossaryEnergy.DEFAULT_COARSE_TECHNO_DICT diff --git a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/usecase.py b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/usecase.py index dd7dd844..9ed53de5 100644 --- a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/usecase.py +++ b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/usecase.py @@ -35,6 +35,7 @@ def __init__( ) self.test_post_procs = False + if "__main__" == __name__: uc_cls = Study() uc_cls.load_data() diff --git a/energy_models/sos_processes/energy/MDA/energy_process_v0/process.py b/energy_models/sos_processes/energy/MDA/energy_process_v0/process.py index a17e8057..62ab1c81 100644 --- a/energy_models/sos_processes/energy/MDA/energy_process_v0/process.py +++ b/energy_models/sos_processes/energy/MDA/energy_process_v0/process.py @@ -47,7 +47,7 @@ class ProcessBuilder(WITNESSSubProcessBuilder): def __init__(self, ee): super(ProcessBuilder, self).__init__(ee) - self.techno_dict=GlossaryEnergy.DEFAULT_TECHNO_DICT + self.techno_dict = GlossaryEnergy.DEFAULT_TECHNO_DICT def get_builders(self): diff --git a/energy_models/sos_processes/energy/MDO/__init__.py b/energy_models/sos_processes/energy/MDO/__init__.py index 18d49eab..888f8915 100644 --- a/energy_models/sos_processes/energy/MDO/__init__.py +++ b/energy_models/sos_processes/energy/MDO/__init__.py @@ -12,4 +12,4 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -''' \ No newline at end of file +''' diff --git a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/__init__.py b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/__init__.py index 18d49eab..888f8915 100644 --- a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/__init__.py +++ b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/__init__.py @@ -12,4 +12,4 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -''' \ No newline at end of file +''' diff --git a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/process.py b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/process.py index 98472519..c7f30ec0 100644 --- a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/process.py +++ b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/process.py @@ -25,6 +25,7 @@ class ProcessBuilder(BaseProcessBuilder): 'category': '', 'version': '', } + def __init__(self, ee): super().__init__(ee) self.sub_process_repo = 'energy_models.sos_processes.energy.MDA' diff --git a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/usecase_with_utilization_ratio.py b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/usecase_with_utilization_ratio.py index f9492a2e..04445975 100644 --- a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/usecase_with_utilization_ratio.py +++ b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/usecase_with_utilization_ratio.py @@ -33,6 +33,7 @@ def __init__( ) self.test_post_procs = True + if '__main__' == __name__: uc_cls = Study(run_usecase=True) uc_cls.load_data() diff --git a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/__init__.py b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/__init__.py index 18d49eab..888f8915 100644 --- a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/__init__.py +++ b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/__init__.py @@ -12,4 +12,4 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -''' \ No newline at end of file +''' diff --git a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/process.py b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/process.py index c03ca314..2a59ca54 100644 --- a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/process.py +++ b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/process.py @@ -27,6 +27,7 @@ class ProcessBuilder(ProccesEnergyMixOptimFull): 'category': '', 'version': '', } + def __init__(self, ee): super().__init__(ee) self.sub_process_name = "energy_mix_optim_sub_process_coarse" diff --git a/energy_models/sos_processes/energy/techno_mix/biodiesel_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/biodiesel_mix/usecase.py index ce0ecaff..e008c066 100644 --- a/energy_models/sos_processes/energy/techno_mix/biodiesel_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/biodiesel_mix/usecase.py @@ -112,7 +112,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.Transesterification}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - ##f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: @@ -144,4 +144,4 @@ def setup_usecase(self, study_folder_path=None): if '__main__' == __name__: uc_cls = Study(main_study=True, technologies_list=DEFAULT_TECHNOLOGIES_LIST) - uc_cls.test() \ No newline at end of file + uc_cls.test() diff --git a/energy_models/sos_processes/energy/techno_mix/biogas_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/biogas_mix/usecase.py index e9ab1a80..aeb1bea7 100644 --- a/energy_models/sos_processes/energy/techno_mix/biogas_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/biogas_mix/usecase.py @@ -112,7 +112,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.AnaerobicDigestion}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: values_dict.update( @@ -142,4 +142,4 @@ def setup_usecase(self, study_folder_path=None): if '__main__' == __name__: uc_cls = Study(main_study=True, technologies_list=DEFAULT_TECHNOLOGIES_LIST) - uc_cls.test() \ No newline at end of file + uc_cls.test() diff --git a/energy_models/sos_processes/energy/techno_mix/biomass_dry_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/biomass_dry_mix/usecase.py index 549f83ee..1f013a13 100644 --- a/energy_models/sos_processes/energy/techno_mix/biomass_dry_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/biomass_dry_mix/usecase.py @@ -119,7 +119,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.CropEnergy}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: values_dict.update( diff --git a/energy_models/sos_processes/energy/techno_mix/carbon_capture_coarse_mix/usecase_coarse.py b/energy_models/sos_processes/energy/techno_mix/carbon_capture_coarse_mix/usecase_coarse.py index b48c157f..5ce8384b 100644 --- a/energy_models/sos_processes/energy/techno_mix/carbon_capture_coarse_mix/usecase_coarse.py +++ b/energy_models/sos_processes/energy/techno_mix/carbon_capture_coarse_mix/usecase_coarse.py @@ -119,7 +119,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{ccs_name}.{GlossaryEnergy.flue_gas_capture}.FlueGasTechno.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, + # f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, } techno_capital = pd.DataFrame({GlossaryEnergy.Years: years, GlossaryEnergy.Capital: 0.0, GlossaryEnergy.NonUseCapital: 0.}) diff --git a/energy_models/sos_processes/energy/techno_mix/carbon_capture_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/carbon_capture_mix/usecase.py index 0f13e127..665e8131 100644 --- a/energy_models/sos_processes/energy/techno_mix/carbon_capture_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/carbon_capture_mix/usecase.py @@ -196,7 +196,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{ccs_name}.{GlossaryEnergy.flue_gas_capture}.flue_gas_mean': self.flue_gas_mean, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, + # f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, f'{self.study_name}.{GlossaryEnergy.ccs_list}': [GlossaryEnergy.carbon_capture, GlossaryEnergy.carbon_storage] } diff --git a/energy_models/sos_processes/energy/techno_mix/carbon_storage_coarse_mix/usecase_coarse.py b/energy_models/sos_processes/energy/techno_mix/carbon_storage_coarse_mix/usecase_coarse.py index ee769948..eb543eb1 100644 --- a/energy_models/sos_processes/energy/techno_mix/carbon_storage_coarse_mix/usecase_coarse.py +++ b/energy_models/sos_processes/energy/techno_mix/carbon_storage_coarse_mix/usecase_coarse.py @@ -100,7 +100,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{ccs_name}.{GlossaryEnergy.CarbonStorageTechno}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, + # f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, } if self.main_study: diff --git a/energy_models/sos_processes/energy/techno_mix/carbon_storage_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/carbon_storage_mix/usecase.py index a3cc114d..fa3f2afb 100644 --- a/energy_models/sos_processes/energy/techno_mix/carbon_storage_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/carbon_storage_mix/usecase.py @@ -138,7 +138,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{ccs_name}.{GlossaryEnergy.techno_list}': self.technologies_list, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, + # f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, } techno_margin_dict = { f'{self.study_name}.{ccs_name}.{techno}.{GlossaryEnergy.MarginValue}': margin for techno in @@ -174,4 +174,4 @@ def setup_usecase(self, study_folder_path=None): if '__main__' == __name__: uc_cls = Study(main_study=True) uc_cls.load_data() - uc_cls.run() \ No newline at end of file + uc_cls.run() diff --git a/energy_models/sos_processes/energy/techno_mix/clean_energy_mix/__init__.py b/energy_models/sos_processes/energy/techno_mix/clean_energy_mix/__init__.py index f18f78a9..fa34a5ef 100644 --- a/energy_models/sos_processes/energy/techno_mix/clean_energy_mix/__init__.py +++ b/energy_models/sos_processes/energy/techno_mix/clean_energy_mix/__init__.py @@ -12,4 +12,4 @@ See the License for the specific language governing permissions and limitations under the License. -''' \ No newline at end of file +''' diff --git a/energy_models/sos_processes/energy/techno_mix/electricity_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/electricity_mix/usecase.py index 354511c9..e2a9c911 100644 --- a/energy_models/sos_processes/energy/techno_mix/electricity_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/electricity_mix/usecase.py @@ -28,14 +28,15 @@ DEFAULT_TECHNOLOGIES_LIST = ['WindOffshore', GlossaryEnergy.WindOnshore, GlossaryEnergy.SolarPv, 'SolarThermal', GlossaryEnergy.Hydropower, 'Nuclear', 'CombinedCycleGasTurbine', GlossaryEnergy.GasTurbine, 'BiogasFired', - #'Geothermal',todo : removed because was using medium heat temperature + # 'Geothermal',todo : removed because was using medium heat temperature GlossaryEnergy.CoalGen, 'OilGen', 'BiomassFired'] TECHNOLOGIES_LIST = ['WindOffshore', GlossaryEnergy.WindOnshore, GlossaryEnergy.SolarPv, 'SolarThermal', GlossaryEnergy.Hydropower, 'Nuclear', 'CombinedCycleGasTurbine', GlossaryEnergy.GasTurbine, 'BiogasFired', - #'Geothermal',todo : removed because was using medium heat temperature + # 'Geothermal',todo : removed because was using medium heat temperature GlossaryEnergy.CoalGen, 'OilGen', 'BiomassFired'] + class Study(EnergyMixStudyManager): def __init__(self, year_start=GlossaryEnergy.YearStartDefault, year_end=GlossaryEnergy.YearEndDefault, technologies_list=DEFAULT_TECHNOLOGIES_LIST, diff --git a/energy_models/sos_processes/energy/techno_mix/ethanol_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/ethanol_mix/usecase.py index 80f3d66d..c06945a5 100644 --- a/energy_models/sos_processes/energy/techno_mix/ethanol_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/ethanol_mix/usecase.py @@ -100,7 +100,7 @@ def setup_usecase(self, study_folder_path=None): values_dict = {f'{self.study_name}.{GlossaryEnergy.YearStart}': self.year_start, f'{self.study_name}.{GlossaryEnergy.YearEnd}': self.year_end, f'{self.study_name}.{energy_name}.{GlossaryEnergy.techno_list}': self.technologies_list, - #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: diff --git a/energy_models/sos_processes/energy/techno_mix/gaseous_hydrogen_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/gaseous_hydrogen_mix/usecase.py index 24466cc5..6773066e 100644 --- a/energy_models/sos_processes/energy/techno_mix/gaseous_hydrogen_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/gaseous_hydrogen_mix/usecase.py @@ -134,7 +134,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.WaterGasShift}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: values_dict.update( diff --git a/energy_models/sos_processes/energy/techno_mix/hightemperatureheat_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/hightemperatureheat_mix/usecase.py index db010e59..af256d3b 100644 --- a/energy_models/sos_processes/energy/techno_mix/hightemperatureheat_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/hightemperatureheat_mix/usecase.py @@ -118,7 +118,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.techno_list}': self.technologies_list, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.ElectricBoiler}.flux_input_dict': land_rate, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.NaturalGasBoiler}.flux_input_dict': land_rate, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.HeatPump}.flux_input_dict': land_rate, diff --git a/energy_models/sos_processes/energy/techno_mix/hydrotreated_oil_fuel_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/hydrotreated_oil_fuel_mix/usecase.py index 6505a7b5..70789412 100644 --- a/energy_models/sos_processes/energy/techno_mix/hydrotreated_oil_fuel_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/hydrotreated_oil_fuel_mix/usecase.py @@ -119,7 +119,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.HefaDeoxygenation}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: diff --git a/energy_models/sos_processes/energy/techno_mix/liquid_fuel_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/liquid_fuel_mix/usecase.py index 72740750..d4daa36c 100644 --- a/energy_models/sos_processes/energy/techno_mix/liquid_fuel_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/liquid_fuel_mix/usecase.py @@ -117,7 +117,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.FischerTropsch}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: values_dict.update({ @@ -146,4 +146,4 @@ def setup_usecase(self, study_folder_path=None): if '__main__' == __name__: uc_cls = Study(main_study=True) - uc_cls.test() \ No newline at end of file + uc_cls.test() diff --git a/energy_models/sos_processes/energy/techno_mix/lowtemperatureheat_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/lowtemperatureheat_mix/usecase.py index 3b83885a..9e36c4f7 100644 --- a/energy_models/sos_processes/energy/techno_mix/lowtemperatureheat_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/lowtemperatureheat_mix/usecase.py @@ -115,7 +115,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.techno_list}': self.technologies_list, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.ElectricBoiler}.flux_input_dict': land_rate, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.NaturalGasBoiler}.flux_input_dict': land_rate, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.HeatPump}.flux_input_dict': land_rate, diff --git a/energy_models/sos_processes/energy/techno_mix/mediumtemperatureheat_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/mediumtemperatureheat_mix/usecase.py index 9d1810ae..8fe9ead2 100644 --- a/energy_models/sos_processes/energy/techno_mix/mediumtemperatureheat_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/mediumtemperatureheat_mix/usecase.py @@ -117,7 +117,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.techno_list}': self.technologies_list, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.ElectricBoiler}.flux_input_dict': land_rate, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.NaturalGasBoiler}.flux_input_dict': land_rate, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.HeatPump}.flux_input_dict': land_rate, diff --git a/energy_models/sos_processes/energy/techno_mix/methane_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/methane_mix/usecase.py index 16d28003..0e3afadb 100644 --- a/energy_models/sos_processes/energy/techno_mix/methane_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/methane_mix/usecase.py @@ -113,7 +113,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.UpgradingBiogas}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: diff --git a/energy_models/sos_processes/energy/techno_mix/methanol_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/methanol_mix/usecase.py index 18b99ed1..4c0821e8 100644 --- a/energy_models/sos_processes/energy/techno_mix/methanol_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/methanol_mix/usecase.py @@ -99,7 +99,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.techno_list}': self.technologies_list, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: @@ -129,4 +129,4 @@ def setup_usecase(self, study_folder_path=None): if '__main__' == __name__: uc_cls = Study(main_study=True) - uc_cls.test() \ No newline at end of file + uc_cls.test() diff --git a/energy_models/sos_processes/energy/techno_mix/syngas_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/syngas_mix/usecase.py index 2ae94fb5..0120b2e7 100644 --- a/energy_models/sos_processes/energy/techno_mix/syngas_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/syngas_mix/usecase.py @@ -133,7 +133,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.CoElectrolysis}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: diff --git a/energy_models/sos_processes/post_processing/post_proc_technology_mix.py b/energy_models/sos_processes/post_processing/post_proc_technology_mix.py index 178dc81e..b5d1ca6b 100644 --- a/energy_models/sos_processes/post_processing/post_proc_technology_mix.py +++ b/energy_models/sos_processes/post_processing/post_proc_technology_mix.py @@ -80,7 +80,7 @@ def get_techno_price_filter_data(execution_engine, namespace, title, price_name, else: var_energyproduction_all_energy_df = var_energyproduction_all_energy_df.merge(var_energyproduction_df) var_energyproduction_all_energy_df.columns = var_energyproduction_all_energy_df.columns.str.replace( - energ + " ", energ + ".").str.replace(" (TWh)", "") #(r" \(.*\)", "") + energ + " ", energ + ".").str.replace(" (TWh)", "") # (r" \(.*\)", "") # FIXME: r" is raw string not regex, need to use re.sub y_incre += 1 diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul00_24technos_8streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul00_24technos_8streams.json index 44c4d412..588d459c 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul00_24technos_8streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul00_24technos_8streams.json @@ -1,66 +1,66 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.SOEC", - "Electrolysis.PEM", - "Electrolysis.AWE" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation", - "HefaDeoxygenation" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOffshore", - "WindOnshore", - "SolarPv", - "SolarThermal", - "Hydropower", - "Nuclear" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.CalciumLooping", - "flue_gas_capture.ChilledAmmoniaProcess", - "flue_gas_capture.CO2Membranes", - "flue_gas_capture.MonoEthanolAmine", - "flue_gas_capture.PiperazineProcess", - "flue_gas_capture.PressureSwingAdsorption" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "GeologicMineralization", - "PureCarbonSolidStorage", - "CarbonStorageTechno", - "Reforestation" - ] - }, - "biomass_dry": { - "type": "agriculture", - "value": [ - "CropEnergy" - ] - }, - "heat.hightemperatureheat": { - "type": "energy", - "value": [ - "GeothermalHighHeat" - ] - }, - "renewable": { - "type": "energy", - "value": [ - "RenewableSimpleTechno" - ] - } -} \ No newline at end of file + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.SOEC", + "Electrolysis.PEM", + "Electrolysis.AWE" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation", + "HefaDeoxygenation" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOffshore", + "WindOnshore", + "SolarPv", + "SolarThermal", + "Hydropower", + "Nuclear" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.CalciumLooping", + "flue_gas_capture.ChilledAmmoniaProcess", + "flue_gas_capture.CO2Membranes", + "flue_gas_capture.MonoEthanolAmine", + "flue_gas_capture.PiperazineProcess", + "flue_gas_capture.PressureSwingAdsorption" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "GeologicMineralization", + "PureCarbonSolidStorage", + "CarbonStorageTechno", + "Reforestation" + ] + }, + "biomass_dry": { + "type": "agriculture", + "value": [ + "CropEnergy" + ] + }, + "heat.hightemperatureheat": { + "type": "energy", + "value": [ + "GeothermalHighHeat" + ] + }, + "renewable": { + "type": "energy", + "value": [ + "RenewableSimpleTechno" + ] + } +} diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul01_24technos_12streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul01_24technos_12streams.json index 64309c83..823d7e3c 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul01_24technos_12streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul01_24technos_12streams.json @@ -1,86 +1,86 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.SOEC" - ] - }, - "hydrogen.liquid_hydrogen": { - "type": "energy", - "value": [ - "HydrogenLiquefaction" - ] - }, - "syngas": { - "type": "energy", - "value": [ - "CoElectrolysis" - ] - }, - "fuel.liquid_fuel": { - "type": "energy", - "value": [ - "Refinery", - "FischerTropsch" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation", - "HefaDeoxygenation" - ] - }, - "fuel.biodiesel": { - "type": "energy", - "value": [ - "Transesterification" - ] - }, - "solid_fuel": { - "type": "energy", - "value": [ - "Pelletizing" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOffshore", - "WindOnshore", - "SolarPv", - "SolarThermal", - "Hydropower", - "Nuclear" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.CalciumLooping", - "flue_gas_capture.ChilledAmmoniaProcess", - "flue_gas_capture.CO2Membranes", - "flue_gas_capture.MonoEthanolAmine", - "flue_gas_capture.PiperazineProcess", - "flue_gas_capture.PressureSwingAdsorption" - ] - }, - "biomass_dry": { - "type": "agriculture", - "value": [ - "ManagedWood" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "CarbonStorageTechno" - ] - }, - "heat.hightemperatureheat": { - "type": "energy", - "value": [ - "ElectricBoilerHighHeat" - ] - } -} \ No newline at end of file + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.SOEC" + ] + }, + "hydrogen.liquid_hydrogen": { + "type": "energy", + "value": [ + "HydrogenLiquefaction" + ] + }, + "syngas": { + "type": "energy", + "value": [ + "CoElectrolysis" + ] + }, + "fuel.liquid_fuel": { + "type": "energy", + "value": [ + "Refinery", + "FischerTropsch" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation", + "HefaDeoxygenation" + ] + }, + "fuel.biodiesel": { + "type": "energy", + "value": [ + "Transesterification" + ] + }, + "solid_fuel": { + "type": "energy", + "value": [ + "Pelletizing" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOffshore", + "WindOnshore", + "SolarPv", + "SolarThermal", + "Hydropower", + "Nuclear" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.CalciumLooping", + "flue_gas_capture.ChilledAmmoniaProcess", + "flue_gas_capture.CO2Membranes", + "flue_gas_capture.MonoEthanolAmine", + "flue_gas_capture.PiperazineProcess", + "flue_gas_capture.PressureSwingAdsorption" + ] + }, + "biomass_dry": { + "type": "agriculture", + "value": [ + "ManagedWood" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "CarbonStorageTechno" + ] + }, + "heat.hightemperatureheat": { + "type": "energy", + "value": [ + "ElectricBoilerHighHeat" + ] + } +} diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul02_29technos_11streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul02_29technos_11streams.json index 7c27c9c7..9e8d396f 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul02_29technos_11streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul02_29technos_11streams.json @@ -1,86 +1,86 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.SOEC" - ] - }, - "hydrogen.liquid_hydrogen": { - "type": "energy", - "value": [ - "HydrogenLiquefaction" - ] - }, - "syngas": { - "type": "energy", - "value": [ - "CoElectrolysis" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation", - "HefaDeoxygenation" - ] - }, - "fuel.biodiesel": { - "type": "energy", - "value": [ - "Transesterification" - ] - }, - "solid_fuel": { - "type": "energy", - "value": [ - "Pelletizing" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOffshore", - "WindOnshore", - "SolarPv", - "SolarThermal", - "Hydropower", - "Nuclear" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.CalciumLooping", - "flue_gas_capture.ChilledAmmoniaProcess", - "flue_gas_capture.MonoEthanolAmine", - "flue_gas_capture.PiperazineProcess", - "flue_gas_capture.PressureSwingAdsorption" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "BiomassBuryingFossilization", - "DeepOceanInjection", - "DeepSalineFormation", - "DepletedOilGas", - "EnhancedOilRecovery", - "GeologicMineralization", - "PureCarbonSolidStorage", - "CarbonStorageTechno", - "Reforestation" - ] - }, - "biomass_dry": { - "type": "agriculture", - "value": [ - "CropEnergy" - ] - }, - "heat.hightemperatureheat": { - "type": "energy", - "value": [ - "HeatPumpHighHeat" - ] - } -} \ No newline at end of file + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.SOEC" + ] + }, + "hydrogen.liquid_hydrogen": { + "type": "energy", + "value": [ + "HydrogenLiquefaction" + ] + }, + "syngas": { + "type": "energy", + "value": [ + "CoElectrolysis" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation", + "HefaDeoxygenation" + ] + }, + "fuel.biodiesel": { + "type": "energy", + "value": [ + "Transesterification" + ] + }, + "solid_fuel": { + "type": "energy", + "value": [ + "Pelletizing" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOffshore", + "WindOnshore", + "SolarPv", + "SolarThermal", + "Hydropower", + "Nuclear" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.CalciumLooping", + "flue_gas_capture.ChilledAmmoniaProcess", + "flue_gas_capture.MonoEthanolAmine", + "flue_gas_capture.PiperazineProcess", + "flue_gas_capture.PressureSwingAdsorption" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "BiomassBuryingFossilization", + "DeepOceanInjection", + "DeepSalineFormation", + "DepletedOilGas", + "EnhancedOilRecovery", + "GeologicMineralization", + "PureCarbonSolidStorage", + "CarbonStorageTechno", + "Reforestation" + ] + }, + "biomass_dry": { + "type": "agriculture", + "value": [ + "CropEnergy" + ] + }, + "heat.hightemperatureheat": { + "type": "energy", + "value": [ + "HeatPumpHighHeat" + ] + } +} diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul52_5technos_5streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul52_5technos_5streams.json index 6e5116ea..4dd7124d 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul52_5technos_5streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul52_5technos_5streams.json @@ -1,32 +1,32 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.SOEC" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOnshore" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.PressureSwingAdsorption" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "CarbonStorageTechno" - ] - } -} \ No newline at end of file + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.SOEC" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOnshore" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.PressureSwingAdsorption" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "CarbonStorageTechno" + ] + } +} diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul59_24technos_11streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul59_24technos_11streams.json index 0d692400..5d2a0f15 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul59_24technos_11streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul59_24technos_11streams.json @@ -1,81 +1,81 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.SOEC", - "Electrolysis.PEM", - "Electrolysis.AWE" - ] - }, - "hydrogen.liquid_hydrogen": { - "type": "energy", - "value": [ - "HydrogenLiquefaction" - ] - }, - "syngas": { - "type": "energy", - "value": [ - "CoElectrolysis" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation", - "HefaDeoxygenation" - ] - }, - "fuel.biodiesel": { - "type": "energy", - "value": [ - "Transesterification" - ] - }, - "solid_fuel": { - "type": "energy", - "value": [ - "Pelletizing" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOffshore", - "WindOnshore", - "SolarPv", - "SolarThermal", - "Hydropower", - "Nuclear" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.CalciumLooping", - "flue_gas_capture.ChilledAmmoniaProcess", - "flue_gas_capture.CO2Membranes", - "flue_gas_capture.MonoEthanolAmine", - "flue_gas_capture.PiperazineProcess", - "flue_gas_capture.PressureSwingAdsorption" - ] - }, - "biomass_dry": { - "type": "agriculture", - "value": [ - "CropEnergy" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "CarbonStorageTechno" - ] - }, - "heat.hightemperatureheat": { - "type": "energy", - "value": [ - "GeothermalHighHeat" - ] - } -} \ No newline at end of file + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.SOEC", + "Electrolysis.PEM", + "Electrolysis.AWE" + ] + }, + "hydrogen.liquid_hydrogen": { + "type": "energy", + "value": [ + "HydrogenLiquefaction" + ] + }, + "syngas": { + "type": "energy", + "value": [ + "CoElectrolysis" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation", + "HefaDeoxygenation" + ] + }, + "fuel.biodiesel": { + "type": "energy", + "value": [ + "Transesterification" + ] + }, + "solid_fuel": { + "type": "energy", + "value": [ + "Pelletizing" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOffshore", + "WindOnshore", + "SolarPv", + "SolarThermal", + "Hydropower", + "Nuclear" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.CalciumLooping", + "flue_gas_capture.ChilledAmmoniaProcess", + "flue_gas_capture.CO2Membranes", + "flue_gas_capture.MonoEthanolAmine", + "flue_gas_capture.PiperazineProcess", + "flue_gas_capture.PressureSwingAdsorption" + ] + }, + "biomass_dry": { + "type": "agriculture", + "value": [ + "CropEnergy" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "CarbonStorageTechno" + ] + }, + "heat.hightemperatureheat": { + "type": "energy", + "value": [ + "GeothermalHighHeat" + ] + } +} diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul36_5technos_5streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul36_5technos_5streams.json index b1baebf3..c3c8b2d7 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul36_5technos_5streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul36_5technos_5streams.json @@ -1,32 +1,32 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.AWE" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOffshore" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.PiperazineProcess" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "CarbonStorageTechno" - ] - } -} \ No newline at end of file + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.AWE" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOffshore" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.PiperazineProcess" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "CarbonStorageTechno" + ] + } +} diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul39_5technos_5streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul39_5technos_5streams.json index d5a24ad0..2b930e76 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul39_5technos_5streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul39_5technos_5streams.json @@ -1,32 +1,32 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.AWE" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "Hydropower" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.PressureSwingAdsorption" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "CarbonStorageTechno" - ] - } -} \ No newline at end of file + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.AWE" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "Hydropower" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.PressureSwingAdsorption" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "CarbonStorageTechno" + ] + } +} diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_test.json b/energy_models/sos_processes/techno_dict/data/techno_dict_test.json index 487acd3b..88fde8bb 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_test.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_test.json @@ -1,92 +1,92 @@ { - "methane": { - "type": "energy", - "value": [ - "FossilGas" - ] - }, - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.SOEC", - "Electrolysis.PEM", - "Electrolysis.AWE", - "PlasmaCracking", - "WaterGasShift" - ] - }, - "hydrogen.liquid_hydrogen": { - "type": "energy", - "value": [ - "HydrogenLiquefaction" - ] - }, - "biogas": { - "type": "energy", - "value": [ - "AnaerobicDigestion" - ] - }, - "syngas": { - "type": "energy", - "value": [ - "Pyrolysis" - ] - }, - "fuel.liquid_fuel": { - "type": "energy", - "value": [ - "Refinery", - "FischerTropsch" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation", - "HefaDeoxygenation" - ] - }, - "fuel.biodiesel": { - "type": "energy", - "value": [ - "Transesterification" - ] - }, - "solid_fuel": { - "type": "energy", - "value": [ - "CoalExtraction" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOffshore", - "WindOnshore", - "SolarPv", - "SolarThermal", - "Hydropower", - "Nuclear", - "CombinedCycleGasTurbine", - "BiogasFired", - "CoalGen", - "OilGen" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "direct_air_capture.AmineScrubbing", - "direct_air_capture.CalciumPotassiumScrubbing" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "DeepOceanInjection", - "DeepSalineFormation", - "GeologicMineralization" - ] - } -} \ No newline at end of file + "methane": { + "type": "energy", + "value": [ + "FossilGas" + ] + }, + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.SOEC", + "Electrolysis.PEM", + "Electrolysis.AWE", + "PlasmaCracking", + "WaterGasShift" + ] + }, + "hydrogen.liquid_hydrogen": { + "type": "energy", + "value": [ + "HydrogenLiquefaction" + ] + }, + "biogas": { + "type": "energy", + "value": [ + "AnaerobicDigestion" + ] + }, + "syngas": { + "type": "energy", + "value": [ + "Pyrolysis" + ] + }, + "fuel.liquid_fuel": { + "type": "energy", + "value": [ + "Refinery", + "FischerTropsch" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation", + "HefaDeoxygenation" + ] + }, + "fuel.biodiesel": { + "type": "energy", + "value": [ + "Transesterification" + ] + }, + "solid_fuel": { + "type": "energy", + "value": [ + "CoalExtraction" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOffshore", + "WindOnshore", + "SolarPv", + "SolarThermal", + "Hydropower", + "Nuclear", + "CombinedCycleGasTurbine", + "BiogasFired", + "CoalGen", + "OilGen" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "direct_air_capture.AmineScrubbing", + "direct_air_capture.CalciumPotassiumScrubbing" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "DeepOceanInjection", + "DeepSalineFormation", + "GeologicMineralization" + ] + } +} diff --git a/energy_models/sos_processes/techno_dict/data/techno_dicts.py b/energy_models/sos_processes/techno_dict/data/techno_dicts.py index 0bb0bc6a..54678d61 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dicts.py +++ b/energy_models/sos_processes/techno_dict/data/techno_dicts.py @@ -20,10 +20,13 @@ filename = "techno_dict_2024-07-14 Jul01_24technos_12streams.json" filename = "techno_dict_test.json" + + def load_dict(filename: str): filepath = join(techno_dict_folder, filename) with open(filepath, 'r') as json_file: loaded_dict = json.load(json_file) return loaded_dict + techno_dict_midway = load_dict(filename) diff --git a/energy_models/sos_processes/techno_dict/techno_dict_builder.py b/energy_models/sos_processes/techno_dict/techno_dict_builder.py index eb3a9846..6a572bb3 100644 --- a/energy_models/sos_processes/techno_dict/techno_dict_builder.py +++ b/energy_models/sos_processes/techno_dict/techno_dict_builder.py @@ -76,11 +76,8 @@ def techno_dict_builder(techno_infos: dict, initial_selection: list[str], if stream not in energy_to_producing_technos: energy_to_producing_technos[stream] = [] - - # Print the list of unique energy values - # Create the problem prob = pulp.LpProblem("Minimal_Technology_Selection", pulp.LpMinimize) @@ -138,14 +135,12 @@ def techno_dict_builder(techno_infos: dict, initial_selection: list[str], raise ValueError(f"There is a total of {len(techno_infos)} technos available, please lower the minimal_techno_number constraint value") prob += pulp.lpSum([tech_vars[t] for t in techno_infos]) >= minimal_techno_number - # Objective: Minimize the number of additional technologies selected prob += pulp.lpSum([tech_vars[t] for t in techno_infos]) # Solve the problem prob.solve() - def show_infos(): print("Energy to Technologies Dictionary:", energy_to_producing_technos) @@ -162,13 +157,12 @@ def show_infos(): rhs_value = constraint.constant print(f"{name}: LHS = {lhs_value}, RHS = {rhs_value}, Status = {lhs_value == rhs_value}") - # Print the selected technologies selected_technologies = [t for t in techno_infos if pulp.value(tech_vars[t]) == 1] selected_streams = [s for s in all_streams if pulp.value(bool_stream_produced_vars[s]) == 1] - print('='*100) print('=' * 100) - print('='*100) + print('=' * 100) + print('=' * 100) print(f"\n\nInitially Selected Technologies ({len(initial_selection)}):".upper(), initial_selection) for selected_techno in initial_selection: print(selected_techno, techno_infos[selected_techno]) @@ -193,7 +187,7 @@ def show_infos(): return techno_dict_for_witness, n_technos, n_streams -#techno_dict_builder(technologies_test) +# techno_dict_builder(technologies_test) def build_techno_infos(stream_used_by_technos: dict, stream_produced_by_techno: dict): out = {} @@ -215,15 +209,15 @@ def build_techno_infos(stream_used_by_technos: dict, stream_produced_by_techno: inital_selection = [ GlossaryEnergy.HefaDecarboxylation, GlossaryEnergy.FischerTropsch, - #f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.FlueGasTechno}", - #f"{GlossaryEnergy.direct_air_capture}.{GlossaryEnergy.DirectAirCaptureTechno}", + # f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.FlueGasTechno}", + # f"{GlossaryEnergy.direct_air_capture}.{GlossaryEnergy.DirectAirCaptureTechno}", ] technos_to_avoid = [ GlossaryEnergy.BiomassFermentation, -f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.MonoEthanolAmine}", # remove -f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.ChilledAmmoniaProcess}", # remove -f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.CO2Membranes}", # remove +f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.MonoEthanolAmine}", # remove +f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.ChilledAmmoniaProcess}", # remove +f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.CO2Membranes}", # remove f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.PressureSwingAdsorption}", # remove f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.CalciumLooping}", # remove f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.PiperazineProcess}", # remove @@ -233,15 +227,15 @@ def build_techno_infos(stream_used_by_technos: dict, stream_produced_by_techno: GlossaryEnergy.CleanEnergySimpleTechno, GlossaryEnergy.RWGS, -#GlossaryEnergy.FischerTropsch +# GlossaryEnergy.FischerTropsch ] streams_to_avoid = [ GlossaryEnergy.hightemperatureheat_energyname, GlossaryEnergy.mediumtemperatureheat_energyname, GlossaryEnergy.lowtemperatureheat_energyname, GlossaryEnergy.biomass_dry, -#GlossaryEnergy.syngas, -#f'{GlossaryEnergy.fuel}.{GlossaryEnergy.liquid_fuel}', +# GlossaryEnergy.syngas, +# f'{GlossaryEnergy.fuel}.{GlossaryEnergy.liquid_fuel}', ] streams_to_have = [ GlossaryEnergy.carbon_capture, diff --git a/energy_models/sos_processes/techno_dict/visualistion.py b/energy_models/sos_processes/techno_dict/visualistion.py index 64385eaf..9fac7e69 100644 --- a/energy_models/sos_processes/techno_dict/visualistion.py +++ b/energy_models/sos_processes/techno_dict/visualistion.py @@ -27,7 +27,6 @@ } - def generate_color_mapping(strings): # Get a list of all available colors colors = list(mcolors.CSS4_COLORS.keys()) @@ -53,15 +52,17 @@ def generate_color_mapping(strings): return color_mapping + def visualize(technologies): # Initialize the directed graph import re import networkx as nx + def improve_string(ss): - ss = ss.replace('.','\n').replace('_',' ') + ss = ss.replace('.', '\n').replace('_', ' ') ss = re.sub(r'(? remove the self.override_dump_jacobian in both tests + ''' # FIXME: address this --> remove the self.override_dump_jacobian in both tests os.remove(path_pickle) def test_02_energy_mix_co2_tax(self): diff --git a/energy_models/tests/l1_test_gradient_ethanol.py b/energy_models/tests/l1_test_gradient_ethanol.py index 55b717e6..47fbb52f 100644 --- a/energy_models/tests/l1_test_gradient_ethanol.py +++ b/energy_models/tests/l1_test_gradient_ethanol.py @@ -69,7 +69,7 @@ def setUp(self): self.invest_level = pd.DataFrame({GlossaryEnergy.Years: years, GlossaryEnergy.InvestValue: np.linspace(0.001, 0.0008, len(years)) }) - + self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: years, GlossaryEnergy.CO2Tax: np.linspace(15., 40., len(years))}) self.margin = pd.DataFrame( @@ -118,7 +118,7 @@ def test_01_biomass_fermentation_discipline_analytic_grad(self): np.arange(GlossaryEnergy.YearStartDefault, self.year_end + 1)), f'{self.name}.{GlossaryEnergy.ResourcesPriceValue}': get_default_resources_prices( np.arange(GlossaryEnergy.YearStartDefault, self.year_end + 1)), - f'{self.name}.techno_infos_dict': techno_infos_dict,} + f'{self.name}.techno_infos_dict': techno_infos_dict, } self.ee.load_study_from_input_dict(inputs_dict) diff --git a/energy_models/tests/l1_test_gradient_flue_gas.py b/energy_models/tests/l1_test_gradient_flue_gas.py index debf8c46..fb9eec81 100644 --- a/energy_models/tests/l1_test_gradient_flue_gas.py +++ b/energy_models/tests/l1_test_gradient_flue_gas.py @@ -90,7 +90,6 @@ def setUp(self): self.invest_level = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: np.linspace(22., 31., len(self.years))}) - self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) diff --git a/energy_models/tests/l1_test_gradient_fossil_simple_techno.py b/energy_models/tests/l1_test_gradient_fossil_simple_techno.py index 84ffa18a..0684d931 100644 --- a/energy_models/tests/l1_test_gradient_fossil_simple_techno.py +++ b/energy_models/tests/l1_test_gradient_fossil_simple_techno.py @@ -61,7 +61,6 @@ def setUp(self): self.invest_level = pd.DataFrame( {GlossaryEnergy.Years: years, GlossaryEnergy.InvestValue: 33.0 * 1.10 ** (years - GlossaryEnergy.YearStartDefault)}) - self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: years, GlossaryEnergy.CO2Tax: np.linspace(15., 40., len(years))}) @@ -102,7 +101,6 @@ def test_01_discipline_analytic_grad(self): self.ee.configure() self.ee.display_treeview_nodes() - invest_before_ystart = pd.DataFrame( {'past years': np.arange(-3, 0), GlossaryEnergy.InvestValue: [0.0, 1483.79, 1489.95]}) diff --git a/energy_models/tests/l1_test_gradient_hydrogen.py b/energy_models/tests/l1_test_gradient_hydrogen.py index 565b01a7..cc13988e 100644 --- a/energy_models/tests/l1_test_gradient_hydrogen.py +++ b/energy_models/tests/l1_test_gradient_hydrogen.py @@ -72,7 +72,7 @@ def setUp(self): self.plasmacracking_techno_prices = pd.DataFrame({GlossaryEnergy.Years: years, GlossaryEnergy.PlasmaCracking: np.linspace(63., 32., len(years)), - 'PlasmaCracking_wotaxes' :np.linspace(63., 32., len(years)) + 'PlasmaCracking_wotaxes': np.linspace(63., 32., len(years)) }) self.smr_consumption = pd.DataFrame({GlossaryEnergy.Years: self.years, @@ -134,7 +134,6 @@ def setUp(self): self.invest_level_negative = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: np.linspace(5000, -5000, len(self.years))}) - self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) diff --git a/energy_models/tests/l1_test_gradient_hydrotreated_oil_fuel.py b/energy_models/tests/l1_test_gradient_hydrotreated_oil_fuel.py index 00cce3eb..057076f1 100644 --- a/energy_models/tests/l1_test_gradient_hydrotreated_oil_fuel.py +++ b/energy_models/tests/l1_test_gradient_hydrotreated_oil_fuel.py @@ -69,7 +69,7 @@ def setUp(self): self.invest_level = pd.DataFrame({GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: np.linspace(0.001, 0.0008, len(self.years)) }) - + self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) self.margin = pd.DataFrame( diff --git a/energy_models/tests/l1_test_gradient_liquid_fuel.py b/energy_models/tests/l1_test_gradient_liquid_fuel.py index 4ee0a9bd..92cf003f 100644 --- a/energy_models/tests/l1_test_gradient_liquid_fuel.py +++ b/energy_models/tests/l1_test_gradient_liquid_fuel.py @@ -92,7 +92,7 @@ def setUp(self): self.invest_level_negative2 = pd.DataFrame({GlossaryEnergy.Years: years, GlossaryEnergy.InvestValue: np.linspace(4435750000.0, 5093000000.0, len(self.years)) * 1.0e-9}) - + self.invest_level = pd.DataFrame( {GlossaryEnergy.Years: years, GlossaryEnergy.InvestValue: np.linspace(4435750000.0, 5093000000.0, len(self.years)) * 1.0e-9}) @@ -289,7 +289,7 @@ def test_03_transesterification_discipline_analytic_grad_negative_invest(self): self.ee.execute() disc_techno = self.ee.root_process.proxy_disciplines[0].mdo_discipline_wrapp.mdo_discipline - self.override_dump_jacobian = True # Test seems KO in server, regenerate pickle at each test + self.override_dump_jacobian = True # Test seems KO in server, regenerate pickle at each test self.check_jacobian(location=dirname(__file__), filename=f'jacobian_{self.energy_name}_{self.model_name}_negative.pkl', discipline=disc_techno, step=1.0e-16, derr_approx='complex_step', diff --git a/energy_models/tests/l1_test_gradient_liquid_hydrogen.py b/energy_models/tests/l1_test_gradient_liquid_hydrogen.py index 841916cc..f7ca6e0c 100644 --- a/energy_models/tests/l1_test_gradient_liquid_hydrogen.py +++ b/energy_models/tests/l1_test_gradient_liquid_hydrogen.py @@ -80,8 +80,6 @@ def setUp(self): self.invest_level = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: 0.1715}) - - self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) self.margin = pd.DataFrame( @@ -233,8 +231,6 @@ def test_02_liquid_hydrogen_discipline_jacobian(self): inputs_dict[f'{namespace}.{self.energy_name}.{GlossaryEnergy.HydrogenLiquefaction}.{GlossaryEnergy.TechnoProductionValue}'][ f'{GlossaryEnergy.hydrogen}.{GlossaryEnergy.liquid_hydrogen} ({GlossaryEnergy.energy_unit})'] *= np.linspace(5.0, 5.0, len(self.years)) - - self.ee.load_study_from_input_dict(inputs_dict) self.ee.execute() diff --git a/energy_models/tests/l1_test_gradient_methane.py b/energy_models/tests/l1_test_gradient_methane.py index f1e63eaa..c1149fab 100644 --- a/energy_models/tests/l1_test_gradient_methane.py +++ b/energy_models/tests/l1_test_gradient_methane.py @@ -63,7 +63,7 @@ def setUp(self): self.stream_co2_emissions = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.electricity: 0.0, - GlossaryEnergy.carbon_capture: -2,f'{GlossaryEnergy.hydrogen}.{GlossaryEnergy.gaseous_hydrogen}': 0.0, GlossaryEnergy.biogas: -0.51}) + GlossaryEnergy.carbon_capture: -2, f'{GlossaryEnergy.hydrogen}.{GlossaryEnergy.gaseous_hydrogen}': 0.0, GlossaryEnergy.biogas: -0.51}) # Use the same inest as SMR techno self.invest_level_methanation = pd.DataFrame({GlossaryEnergy.Years: self.years, @@ -71,7 +71,6 @@ def setUp(self): self.invest_level = pd.DataFrame({GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: np.linspace(4., 5.0, len(self.years))}) - self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) @@ -332,7 +331,6 @@ def test_04_methane_discipline_jacobian(self): if mda_data_output_dict[self.energy_name][key]['is_coupling']: coupled_outputs += [f'{namespace}.{self.energy_name}.{key}'] - self.ee.load_study_from_input_dict(inputs_dict) self.ee.execute() diff --git a/energy_models/tests/l1_test_gradient_methanol.py b/energy_models/tests/l1_test_gradient_methanol.py index 0947ad68..d0185984 100644 --- a/energy_models/tests/l1_test_gradient_methanol.py +++ b/energy_models/tests/l1_test_gradient_methanol.py @@ -81,7 +81,7 @@ def setUp(self): self.invest_level = pd.DataFrame({GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: np.linspace(0.001, 0.0008, len(self.years)) }) - + self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) self.margin = pd.DataFrame( diff --git a/energy_models/tests/l1_test_gradient_one_invest.py b/energy_models/tests/l1_test_gradient_one_invest.py index da224f8b..c82be3d5 100644 --- a/energy_models/tests/l1_test_gradient_one_invest.py +++ b/energy_models/tests/l1_test_gradient_one_invest.py @@ -136,7 +136,5 @@ def test_01_one_invest_analytic_grad(self): ) - - if '__main__' == __name__: pass diff --git a/energy_models/tests/l1_test_gradient_ratio.py b/energy_models/tests/l1_test_gradient_ratio.py index dba7c279..f314d0f1 100644 --- a/energy_models/tests/l1_test_gradient_ratio.py +++ b/energy_models/tests/l1_test_gradient_ratio.py @@ -610,7 +610,6 @@ def test_08_gaseous_hydrogen_discipline_jacobian(self): if mda_data_output_dict[self.energy_name][key]['is_coupling']: coupled_outputs += [f'{namespace}.{self.energy_name}.{key}'] - # Overwrite values for ratios with values from setup inputs_dict[f'{namespace}.{GlossaryEnergy.YearEnd}'] = self.year_end inputs_dict[f'{namespace}.is_apply_ratio'] = self.is_apply_ratio diff --git a/energy_models/tests/l1_test_gradient_solid_fuel.py b/energy_models/tests/l1_test_gradient_solid_fuel.py index b0ff7701..21794b02 100644 --- a/energy_models/tests/l1_test_gradient_solid_fuel.py +++ b/energy_models/tests/l1_test_gradient_solid_fuel.py @@ -70,7 +70,6 @@ def setUp(self): self.invest_level = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: np.linspace(0.001, 0.0008, len(self.years))}) - self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) @@ -280,7 +279,6 @@ def test_03_solid_fuel_discipline_jacobian(self): for techno in technos: coupled_inputs.append(f"{self.name}.{self.energy_name}.{techno}.{GlossaryEnergy.TechnoCapitalValue}") - self.ee.load_study_from_input_dict(inputs_dict) self.ee.execute() diff --git a/energy_models/tests/l2_test_simple.py b/energy_models/tests/l2_test_simple.py index 53171602..0ab9cf72 100644 --- a/energy_models/tests/l2_test_simple.py +++ b/energy_models/tests/l2_test_simple.py @@ -21,8 +21,8 @@ class SimpleTest(AbstractJacobianUnittest): - ''' - Very simple (and quick) test to setup the jenkins jobs for l2 test + ''' + Very simple (and quick) test to setup the jenkins jobs for l2 test and the launch at stable merge ''' diff --git a/energy_models/tests/performances/energy_models_perfos.csv b/energy_models/tests/performances/energy_models_perfos.csv index 0d5a708b..5d155d3c 100644 --- a/energy_models/tests/performances/energy_models_perfos.csv +++ b/energy_models/tests/performances/energy_models_perfos.csv @@ -999,5 +999,3 @@ ncalls,tottime,percall,cumtime,percall,filename:lineno(function) 2890,0.012,0.000,0.037,0.000,C:\Users\NG92D02\Anaconda3\lib\site-packages\pandas\core\array_algos\putmask.py:29(putmask_inplace) 53622,0.037,0.000,0.037,0.000,{built-in method pandas._libs.missing.checknull} 36429,0.037,0.000,0.037,0.000,C:\Users\NG92D02\Anaconda3\lib\site-packages\numpy\core\numerictypes.py:651() - - diff --git a/energy_models/tests/to_fix/CalciumLoopingDiscipline.py b/energy_models/tests/to_fix/CalciumLoopingDiscipline.py index 4eee6d1f..4c1cf48f 100644 --- a/energy_models/tests/to_fix/CalciumLoopingDiscipline.py +++ b/energy_models/tests/to_fix/CalciumLoopingDiscipline.py @@ -57,7 +57,6 @@ def test_execute(self): disc_techno = self.ee.root_process.proxy_disciplines[0].mdo_discipline_wrapp.mdo_discipline self.check_jacobian(location=dirname(__file__), filename='jacobianIsolatedDiscTest_MDO_MDA_CCUS_carbon_capture_flue_gas_capture_CalciumLooping.pkl', - discipline=disc_techno, step=1e-15, derr_approx='complex_step', local_data = disc_techno.local_data, + discipline=disc_techno, step=1e-15, derr_approx='complex_step', local_data=disc_techno.local_data, inputs=coupling_inputs, outputs=coupling_ouputs) - \ No newline at end of file diff --git a/energy_models/tests/to_fix/MonoEthanolAmineDiscipline.py b/energy_models/tests/to_fix/MonoEthanolAmineDiscipline.py index c022ed88..c64002cb 100644 --- a/energy_models/tests/to_fix/MonoEthanolAmineDiscipline.py +++ b/energy_models/tests/to_fix/MonoEthanolAmineDiscipline.py @@ -57,7 +57,6 @@ def test_execute(self): disc_techno = self.ee.root_process.proxy_disciplines[0].mdo_discipline_wrapp.mdo_discipline self.check_jacobian(location=dirname(__file__), filename='jacobianIsolatedDiscTest_MDO_MDA_CCUS_carbon_capture_flue_gas_capture_MonoEthanolAmine.pkl', - discipline=disc_techno, step=1e-15, derr_approx='complex_step', local_data = disc_techno.local_data, + discipline=disc_techno, step=1e-15, derr_approx='complex_step', local_data=disc_techno.local_data, inputs=coupling_inputs, outputs=coupling_ouputs) - \ No newline at end of file diff --git a/energy_models/tests/to_fix/PiperazineProcessDiscipline.py b/energy_models/tests/to_fix/PiperazineProcessDiscipline.py index 5e7af477..c6427209 100644 --- a/energy_models/tests/to_fix/PiperazineProcessDiscipline.py +++ b/energy_models/tests/to_fix/PiperazineProcessDiscipline.py @@ -57,7 +57,6 @@ def test_execute(self): disc_techno = self.ee.root_process.proxy_disciplines[0].mdo_discipline_wrapp.mdo_discipline self.check_jacobian(location=dirname(__file__), filename='jacobianIsolatedDiscTest_MDO_MDA_CCUS_carbon_capture_flue_gas_capture_PiperazineProcess.pkl', - discipline=disc_techno, step=1e-15, derr_approx='complex_step', local_data = disc_techno.local_data, + discipline=disc_techno, step=1e-15, derr_approx='complex_step', local_data=disc_techno.local_data, inputs=coupling_inputs, outputs=coupling_ouputs) - \ No newline at end of file diff --git a/headers_ignore_config.json b/headers_ignore_config.json index c0debe55..954a8d36 100644 --- a/headers_ignore_config.json +++ b/headers_ignore_config.json @@ -1,31 +1,31 @@ { - "extension_to_ignore": [ - "pkl", - "png", - "jpg", - "csv", - "md", - "markdown", - "avif", - "json", - "in", - "gitignore", - "cfg", - "puml", - "pdf", - "txt", - "ipynb", - "zip", - "rst", - "ini", - "coveragerc", - "yaml", - "bat", - "toml" - ], - "files_to_ignore": [ - ".readthedocs", - "docs/Makefile" - ], - "airbus_rev_commit": "37fb4ae" -} \ No newline at end of file + "extension_to_ignore": [ + "pkl", + "png", + "jpg", + "csv", + "md", + "markdown", + "avif", + "json", + "in", + "gitignore", + "cfg", + "puml", + "pdf", + "txt", + "ipynb", + "zip", + "rst", + "ini", + "coveragerc", + "yaml", + "bat", + "toml" + ], + "files_to_ignore": [ + ".readthedocs", + "docs/Makefile" + ], + "airbus_rev_commit": "37fb4ae" +} diff --git a/parameters_glossary.csv b/parameters_glossary.csv index 67e82b84..92a2ae72 100644 --- a/parameters_glossary.csv +++ b/parameters_glossary.csv @@ -130,8 +130,8 @@ invest_objective_ref,Invest objective ref,,Reference used for the investment obj invest_sum_ref,Invest Sum ref,G$,Reference used for the investment sum constraint.,, invest_constraint_ref,Invest constraint ref,,Reference used for the investment constraint.,, invest_limit_ref,Invest Limit Reference,G$,Reference Limit used for the investment constraint.,, -invest_constraint,Investment Constraint,,Constraint applied to difference of energy investment given by macroeconomy and design space.,, -invest_objective_sum,Invest Objective,,Investment in the considered mix.,, +invest_constraint,Investment Constraint,,Constraint applied to difference of energy investment given by macroeconomy and design space.,, +invest_objective_sum,Invest Objective,,Investment in the considered mix.,, invest_sum_cons,Invest constraint ref,,Reference used for the investment constraint.,, invest_techno_mix,Techno Mix Investment Coefficients,,Coefficients of technology mix investments (not normalized) for a specific energy to distribute the energy investments.,, is_apply_ratio,Is apply ratio,,On/Off for the ratio.,, @@ -190,7 +190,7 @@ syngas_CoalGasification_array_mix,CoalGasification array mix,%,Coal gasification syngas_SMR_array_mix,SMR array mix,%,SMR data.,, syngas_array_mix,Syngas array mix,%,Syngas data.,, syngas_prod_constraint_limit,Syngas prod constraint limit,TWh,Syngas production constraint limit.,, -syngas_prod_constraint,Syngas prod constraint,,Syngas production constraint.,, +syngas_prod_constraint,Syngas prod constraint,,Syngas production constraint.,, syngas_prod_objective,Syngas prod objective,TWh,Syngas production objective.,, syngas_prod_ref,Syngas production constraint reference,TWh,Syngas production constraint reference for objective computation.,, syngas_ratio,Molar ratio of CO/H2,%,Molar ratio between monoxyde of carbon and dihydrogene.,, diff --git a/platform_version_required.txt b/platform_version_required.txt index 8a58a0dc..ac8cb4ee 100644 --- a/platform_version_required.txt +++ b/platform_version_required.txt @@ -1 +1 @@ -v4.1.3 \ No newline at end of file +v4.1.3 diff --git a/pytest.ini b/pytest.ini index 149dba41..85772ec7 100644 --- a/pytest.ini +++ b/pytest.ini @@ -1,5 +1,5 @@ [pytest] python_files = l1_test*.py -testpaths = +testpaths = energy_models/tests addopts = --numprocesses=auto From dc54ca05283b4bd1201df59cb18d4a87c8636c63 Mon Sep 17 00:00:00 2001 From: jlivatthana <145337844+jlivatthana@users.noreply.github.com> Date: Fri, 22 Nov 2024 10:42:42 +0100 Subject: [PATCH 08/13] update platform version to the latest --- platform_version_required.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/platform_version_required.txt b/platform_version_required.txt index 8a58a0dc..15a2b33b 100644 --- a/platform_version_required.txt +++ b/platform_version_required.txt @@ -1 +1 @@ -v4.1.3 \ No newline at end of file +v4.2.0 \ No newline at end of file From cab5e1af2c3dece485674a1131c596b76e33e665 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sim=C3=A3o=20Rodrigues?= Date: Fri, 22 Nov 2024 11:47:25 +0100 Subject: [PATCH 09/13] Revert "[pre-commit.ci] auto fixes from pre-commit.com hooks" This reverts commit 841a733bec553b8ce9bf469e68bfc658062747eb. --- .coveragerc | 2 +- .../issue_templates/user_story_template.md | 2 +- CREDITS.rst | 6 +- LICENSE | 2 +- LICENSES/BSD-3-Clause.txt | 2 +- LICENSES/MIT.txt | 2 +- NOTICE | 2 +- README.md | 2 +- .../fitting/clean_energy_simple_techno.py | 8 +- .../fitting/fossil_energy_simple_techno.py | 7 +- data_energy/fitting/gaseous_bioenergy.py | 34 +- data_energy/fitting/hydropower.py | 33 +- data_energy/fitting/windpower.py | 48 +- .../biomassburyingfossilization.csv | 1 + .../techno_invests/deepoceaninjection.csv | 1 + .../techno_invests/deepsalineformation.csv | 1 + data_energy/techno_invests/depletedoilgas.csv | 1 + .../techno_invests/enhancedoilrecovery.csv | 1 + .../techno_invests/geologicmineralization.csv | 1 + data_energy/techno_invests/geothermal.csv | 2 +- ...lean-energy-and-fossil-fuels-2015-2024.csv | 2 +- .../techno_invests/purecarbonsolidstorage.csv | 1 + data_energy/techno_invests/sources.txt | 2 +- data_energy/techno_invests/windoffshore.csv | 2 +- .../combinedcyclegasturbine.csv | 2 +- .../techno_production_historic/fossilgas.csv | 2 +- .../techno_production_historic/hydropower.csv | 2 +- .../techno_production_historic/oilgen.csv | 2 +- .../techno_production_historic/sources.txt | 2 +- default_process_rights.yaml | 2 +- energy_models/core/ccus/ccus.py | 10 +- energy_models/core/ccus/ccus_disc.py | 7 +- .../core/ccus/documentation/ccus_disc.md | 10 +- .../consumption_CO2_emissions.py | 32 +- .../documentation/energy_demand_disc.markdown | 10 +- energy_models/core/demand/energy_demand.py | 6 +- .../core/demand/energy_demand_disc.py | 4 +- .../energy_ghg_emissions_disc.md | 20 +- .../energy_ghg_emissions.py | 28 +- .../energy_ghg_emissions_disc.py | 2 +- .../documentation/energy_mix_disc.md | 5 +- .../core/energy_mix_study_manager.py | 2 +- energy_models/core/energy_process_builder.py | 4 +- .../investments/convex_combination_model.py | 2 +- .../documentation/energy_invest_disc.md | 3 +- .../energy_or_ccs_invest_disc.md | 3 +- .../documentation/independent_invest_disc.md | 1 + .../investments_profile_builder_disc.md | 10 +- .../investments_redistribution_disc.md | 1 + .../documentation/techno_invest_disc.md | 3 +- .../disciplines/independent_invest_disc.py | 4 +- .../investments_profile_builder_disc.py | 21 +- .../core/investments/energy_invest.py | 4 +- .../core/investments/energy_or_ccsinvest.py | 4 +- energy_models/core/investments/one_invest.py | 4 +- .../core/process_builder_database.py | 6 +- energy_models/core/stream_type/base_stream.py | 20 +- .../carbon_disciplines/carbon_capture_disc.py | 4 +- .../documentation/carbon_capture_disc.md | 2 +- .../documentation/carbon_storage_disc.md | 4 +- .../documentation/flue_gas_disc.markdown | 14 +- .../carbon_disciplines/flue_gas_disc.py | 2 +- .../carbon_models/carbon_capture.py | 8 +- .../stream_type/carbon_models/flue_gas.py | 2 +- energy_models/core/stream_type/energy_disc.py | 2 +- .../documentation/bio_diesel_disc.markdown | 8 +- .../documentation/biomass_dry_disc.markdown | 2 +- .../documentation/electricity_disc.markdown | 4 +- .../documentation/ethanol_disc.markdown | 12 +- .../documentation/fuel_disc.markdown | 2 +- .../hydrotreated_oil_fuel_disc.markdown | 4 +- .../documentation/liquid_fuel_disc.markdown | 2 +- .../liquid_hydrogen_disc.markdown | 4 +- .../documentation/methane_disc.markdown | 8 +- .../documentation/methanol_disc.markdown | 12 +- .../documentation/solid_fuel_disc.markdown | 6 +- .../energy_disciplines/electricity_disc.py | 6 +- .../energy_disciplines/syngas_disc.py | 2 +- .../energy_disciplines/wet_biomass_disc.py | 2 +- .../stream_type/energy_models/electricity.py | 4 +- .../core/stream_type/energy_models/fossil.py | 8 +- .../stream_type/energy_models/liquid_fuel.py | 4 +- .../stream_type/energy_models/solid_fuel.py | 6 +- .../core/stream_type/energy_models/syngas.py | 18 +- energy_models/core/stream_type/energy_type.py | 2 +- .../core/stream_type/resources_data_disc.py | 4 +- .../resources_models/resource_glossary.py | 2 +- energy_models/core/stream_type/stream_disc.py | 5 +- .../base_techno_models/biodiesel_techno.py | 2 + .../base_techno_models/biogas_techno.py | 2 + .../base_techno_models/biomass_dry_techno.py | 2 + .../carbon_capture_techno.py | 4 +- .../base_techno_models/electricity_techno.py | 2 + .../base_techno_models/ethanol_techno.py | 2 + .../base_techno_models/fossil_techno.py | 2 + .../gaseous_hydrogen_techno.py | 2 + .../base_techno_models/high_heat_techno.py | 3 + .../hydrotreated_oil_fuel_techno.py | 2 + .../base_techno_models/kerosene_techno.py | 2 + .../base_techno_models/liquid_fuel_techno.py | 2 + .../liquid_hydrogen_techno.py | 2 + .../base_techno_models/low_heat_techno.py | 3 + .../base_techno_models/medium_heat_techno.py | 2 + .../base_techno_models/methane_techno.py | 2 + .../base_techno_models/methanol_techno.py | 2 + .../base_techno_models/renewable_techno.py | 3 + .../base_techno_models/solid_fuel_techno.py | 2 + .../base_techno_models/syngas_techno.py | 2 + .../base_techno_models/wet_biomass_techno.py | 2 + .../disciplines/carbon_capture_techno_disc.py | 4 +- .../disciplines/syngas_techno_disc.py | 2 +- energy_models/core/techno_type/techno_disc.py | 10 +- energy_models/core/techno_type/techno_type.py | 29 +- energy_models/database_witness_energy.py | 4 +- .../datasets_database/datasets/readme.txt | 4 +- .../datasets_database/mappings/readme.txt | 3 +- energy_models/glossaryenergy.py | 182 +- .../transesterification_disc.markdown | 2 +- .../transesterification.py | 2 +- .../anaerobic_digestion_disc.markdown | 12 +- .../biomass_dry/crop_energy/crop_energy.py | 2 +- .../crop_energy/crop_energy_disc.py | 7 +- .../documentation/crop_energy_disc.markdown | 6 +- .../documentation/managed_wood_disc.markdown | 6 +- .../biomass_dry/managed_wood/managed_wood.py | 1 - .../managed_wood/managed_wood_disc.py | 6 +- .../unmanaged_wood_disc.markdown | 6 +- .../unmanaged_wood/unmanaged_wood.py | 1 + .../unmanaged_wood/unmanaged_wood_disc.py | 7 +- .../amine_scrubbing_disc.markdown | 2 +- .../calcium_potassium_scrubbing.py | 1 + .../calcium_potassium_scrubbing_disc.markdown | 2 +- .../direct_air_capture_techno.py | 2 +- .../calcium_looping/calcium_looping_disc.py | 3 +- .../chilled_ammonia_process_disc.py | 3 +- .../chilled_ammonia_process_disc.markdown | 2 +- .../co2_membranes/co2_membranes_disc.py | 3 +- .../documentation/flue_gas_disc.md | 2 +- .../flue_gas_techno/flue_gas_techno_disc.py | 1 + .../generic_flue_gas_techno_model.py | 1 + .../mono_ethanol_amine_disc.py | 3 +- .../piperazine_process_disc.py | 3 +- .../pressure_swing_adsorption_disc.py | 3 +- .../biomass_burying_fossilization_disc.py | 2 +- ...iomass_burying_fossilization_disc.markdown | 2 +- .../carbon_storage_techno_disc.py | 2 +- .../carbon_storage_techno_disc.markdown | 2 +- .../deep_ocean_injection_disc.py | 2 +- .../deep_ocean_injection_disc.markdown | 2 +- .../deep_saline_formation_disc.py | 2 +- .../deep_saline_formation_disc.markdown | 2 +- .../depleted_oil_gas/depleted_oil_gas_disc.py | 2 +- .../depleted_oil_gas_disc.markdown | 2 +- .../enhanced_oil_recovery_disc.markdown | 2 +- .../enhanced_oil_recovery_disc.py | 2 +- .../geologic_mineralization_disc.markdown | 2 +- .../geologic_mineralization_disc.py | 2 +- .../pure_carbon_solid_storage_disc.markdown | 2 +- .../pure_carbon_solid_storage_disc.py | 6 +- .../documentation/reforestation_disc.markdown | 4 +- .../reforestation/reforestation_disc.py | 2 +- .../renewable_simple_techno_disc.markdown | 3 + .../biomass_fired/biomass_fired.py | 1 + .../biomass_fired/biomass_fired_disc.py | 3 +- .../documentation/biomass_fired_disc.markdown | 10 +- .../electricity/coal_gen/coal_gen_disc.py | 4 +- .../documentation/coal_gen_disc.markdown | 18 +- .../gas/biogas_fired/biogas_fired.py | 4 +- .../gas/biogas_fired/biogas_fired_disc.py | 3 +- .../documentation/biogas_fired_disc.markdown | 24 +- .../combined_cycle_gas_turbine.py | 1 - .../combined_cycle_gas_turbine_disc.py | 4 +- .../combined_cycle_gas_turbine_disc.markdown | 26 +- .../documentation/gas_turbine_disc.markdown | 24 +- .../gas/gas_turbine/gas_turbine_disc.py | 4 +- .../documentation/geothermal_disc.markdown | 14 +- .../electricity/geothermal/geothermal_disc.py | 3 +- .../documentation/hydropower_disc.markdown | 6 +- .../electricity/hydropower/hydropower.py | 2 +- .../electricity/hydropower/hydropower_disc.py | 2 +- .../documentation/nuclear_disc.markdown | 21 +- .../models/electricity/nuclear/nuclear.py | 1 + .../electricity/nuclear/nuclear_disc.py | 3 +- .../documentation/oil_gen_disc.markdown | 17 +- .../models/electricity/oil_gen/oil_gen.py | 3 +- .../electricity/oil_gen/oil_gen_disc.py | 6 +- .../renewable_simple_techno_disc.markdown | 3 + ...enewable_electricity_simple_techno_disc.py | 3 +- .../documentation/solar_pv_disc.markdown | 18 +- .../electricity/solar_pv/solar_pv_disc.py | 4 +- .../documentation/solar_thermal_disc.markdown | 12 +- .../solar_thermal/solar_thermal_disc.py | 4 +- .../documentation/wind_offshore_disc.markdown | 20 +- .../wind_offshore/wind_offshore_disc.py | 2 +- .../documentation/wind_onshore_disc.markdown | 16 +- .../wind_onshore/wind_onshore_disc.py | 4 +- .../biomass_fermentation.py | 1 + .../biomass_fermentation_disc.markdown | 16 +- .../fossil_simple_techno_disc.markdown | 4 + .../fossil_simple_techno.py | 1 + .../fossil_simple_techno_disc.py | 2 + .../electrolysis_awe_disc.markdown | 6 +- .../electrolysis/awe/electrolysis_awe.py | 7 +- .../electrolysis_pem_disc.markdown | 8 +- .../electrolysis/pem/electrolysis_pem.py | 6 +- .../electrolysis_soec_disc.markdown | 6 +- .../electrolysis/soec/electrolysis_soec.py | 7 +- .../soec/electrolysis_soec_disc.py | 1 + .../documentation/FORMULA clean.markdown | 12 +- .../documentation/FORMULA_resume.markdown | 14 +- .../plasma_cracking_disc.markdown | 38 +- .../plasma_cracking_disc_v2.markdown | 34 +- .../plasma_cracking/plasma_cracking.py | 7 +- .../plasma_cracking/plasma_cracking_disc.py | 2 +- .../water_gas_shift_disc.markdown | 24 +- .../water_gas_shift/water_gas_shift.py | 10 +- .../water_gas_shift/water_gas_shift_disc.py | 2 +- .../heat/high/chphighheat/chphighheat_disc.py | 3 +- .../documentation/chphighheat_disc.markdown | 11 +- .../electric_boiler_high_heat_disc.markdown | 8 +- .../electric_boiler_high_heat.py | 1 + .../electric_boiler_high_heat_disc.py | 7 +- .../geothermal_high_heat_disc.markdown | 9 +- .../geothermal_high_heat.py | 1 + .../geothermal_high_heat_disc.py | 6 +- .../heat_pump_high_heat_disc.markdown | 11 +- .../heat_pump_high_heat_disc.py | 7 +- ...natural_gas_boiler_high_heat_disc.markdown | 7 +- .../natural_gas_boiler_high_heat.py | 3 +- .../natural_gas_boiler_high_heat_disc.py | 4 +- .../heat/low/chplowheat/chplowheat_disc.py | 2 +- .../documentation/chplowheat_disc.markdown | 11 +- .../electric_boiler_low_heat_disc.markdown | 4 +- .../electric_boiler_low_heat.py | 1 + .../electric_boiler_low_heat_disc.py | 8 +- .../geothermal_low_heat_disc.markdown | 9 +- .../geothermal_low_heat_disc.py | 6 +- .../heat_pump_low_heat_disc.markdown | 9 +- .../heat_pump_low_heat/heat_pump_low_heat.py | 1 + .../heat_pump_low_heat_disc.py | 7 +- .../natural_gas_boiler_low_heat_disc.markdown | 12 +- .../natural_gas_boiler_low_heat.py | 1 + .../natural_gas_boiler_low_heat_disc.py | 6 +- .../medium/chpmediumheat/chpmediumheat.py | 1 + .../chpmediumheat/chpmediumheat_disc.py | 2 +- .../documentation/chpmediumheat_disc.markdown | 11 +- .../electric_boiler_medium_heat_disc.markdown | 4 +- .../electric_boiler_medium_heat.py | 1 + .../electric_boiler_medium_heat_disc.py | 6 +- .../geothermal_medium_heat_disc.markdown | 9 +- .../geothermal_medium_heat.py | 2 +- .../geothermal_medium_heat_disc.py | 6 +- .../heat_pump_medium_heat_disc.markdown | 9 +- .../heat_pump_medium_heat_disc.py | 7 +- ...tural_gas_boiler_medium_heat_disc.markdown | 16 +- .../natural_gas_boiler_medium_heat.py | 1 + .../natural_gas_boiler_medium_heat_disc.py | 6 +- .../hefa_decarboxylation_disc.markdown | 11 +- .../hefa_decarboxylation.py | 3 +- .../hefa_decarboxylation_disc.py | 1 + .../hefa_deoxygenation_disc.markdown | 9 +- .../hefa_deoxygenation/hefa_deoxygenation.py | 4 +- .../hefa_deoxygenation_disc.py | 1 + .../fischer_tropsch_disc.markdown | 32 +- .../fischer_tropsch/fischer_tropsch.py | 13 +- .../fischer_tropsch/fischer_tropsch_disc.py | 3 +- .../documentation/refinery_disc.markdown | 18 +- .../models/liquid_fuel/refinery/refinery.py | 1 + .../liquid_fuel/refinery/refinery_disc.py | 2 +- .../hydrogen_liquefaction_disc.markdown | 5 +- .../hydrogen_liquefaction.py | 1 + .../hydrogen_liquefaction_disc.py | 2 +- .../documentation/fossil_gas_disc.markdown | 9 +- .../models/methane/fossil_gas/fossil_gas.py | 1 + .../documentation/methanation_disc.markdown | 10 +- .../models/methane/methanation/methanation.py | 5 +- .../methane/methanation/methanation_disc.py | 1 + .../upgrading_biogas_disc.markdown | 10 +- .../upgrading_biogas/upgrading_biogas.py | 5 +- .../co2_hydrogenation_disc.py | 2 + .../co2_hydrogenation_disc.markdown | 8 +- .../coal_extraction/coal_extraction_disc.py | 3 +- .../coal_extraction_disc.markdown | 6 +- .../documentation/pelletizing_disc.markdown | 4 +- .../autothermal_reforming.py | 5 +- .../autothermal_reforming_disc.markdown | 10 +- .../biomass_gasification.py | 1 + .../biomass_gasification_disc.py | 2 +- .../biomass_gasification_disc.markdown | 16 +- .../syngas/co_electrolysis/co_electrolysis.py | 5 +- .../co_electrolysis_disc.markdown | 9 +- .../coal_gasification/coal_gasification.py | 1 + .../coal_gasification_disc.py | 4 +- .../coal_gasification_disc.markdown | 20 +- .../documentation/pyrolysis_disc.markdown | 8 +- .../models/syngas/pyrolysis/pyrolysis_disc.py | 2 +- .../reversed_water_gas_shift.py | 17 +- .../reversed_water_gas_shift_disc.py | 2 +- .../smr/documentation/smr_disc.markdown | 12 +- energy_models/models/syngas/smr/smr.py | 7 +- .../animal_manure/animal_manure.py | 2 +- .../wet_crop_residue/wet_crop_residues.py | 1 + .../wet_crop_residues_disc.py | 2 +- .../energy_mix_optim_sub_process/process.py | 2 + .../energy_mix_optim_sub_process/usecase.py | 6 +- .../process.py | 2 +- .../usecase.py | 1 - .../energy/MDA/energy_process_v0/process.py | 2 +- .../sos_processes/energy/MDO/__init__.py | 2 +- .../MDO/energy_mix_optim_process/__init__.py | 2 +- .../MDO/energy_mix_optim_process/process.py | 1 - .../usecase_with_utilization_ratio.py | 1 - .../__init__.py | 2 +- .../process.py | 1 - .../techno_mix/biodiesel_mix/usecase.py | 4 +- .../energy/techno_mix/biogas_mix/usecase.py | 4 +- .../techno_mix/biomass_dry_mix/usecase.py | 2 +- .../usecase_coarse.py | 2 +- .../techno_mix/carbon_capture_mix/usecase.py | 2 +- .../usecase_coarse.py | 2 +- .../techno_mix/carbon_storage_mix/usecase.py | 4 +- .../techno_mix/clean_energy_mix/__init__.py | 2 +- .../techno_mix/electricity_mix/usecase.py | 5 +- .../energy/techno_mix/ethanol_mix/usecase.py | 2 +- .../gaseous_hydrogen_mix/usecase.py | 2 +- .../hightemperatureheat_mix/usecase.py | 2 +- .../hydrotreated_oil_fuel_mix/usecase.py | 2 +- .../techno_mix/liquid_fuel_mix/usecase.py | 4 +- .../lowtemperatureheat_mix/usecase.py | 2 +- .../mediumtemperatureheat_mix/usecase.py | 2 +- .../energy/techno_mix/methane_mix/usecase.py | 2 +- .../energy/techno_mix/methanol_mix/usecase.py | 4 +- .../energy/techno_mix/syngas_mix/usecase.py | 2 +- .../post_proc_technology_mix.py | 2 +- ...t_2024-07-14 Jul00_24technos_8streams.json | 130 +- ..._2024-07-14 Jul01_24technos_12streams.json | 170 +- ..._2024-07-14 Jul02_29technos_11streams.json | 170 +- ...ct_2024-07-14 Jul52_5technos_5streams.json | 62 +- ..._2024-07-14 Jul59_24technos_11streams.json | 160 +- ...ct_2024-07-15 Jul36_5technos_5streams.json | 62 +- ...ct_2024-07-15 Jul39_5technos_5streams.json | 62 +- .../techno_dict/data/techno_dict_test.json | 182 +- .../techno_dict/data/techno_dicts.py | 3 - .../techno_dict/techno_dict_builder.py | 28 +- .../sos_processes/techno_dict/visualistion.py | 11 +- .../witness_sub_process_builder.py | 1 - .../_l0_test_base_stream_invest_limit.py | 4 +- .../_l0_test_compute_el_nuclear_databases.py | 8 +- .../tests/_l2_test_run_optim_usecases.py | 2 +- .../tests/data_tests/data_nuclear_test.json | 4759 +---------------- .../tests/l0_test_compute_CaKOH_DAC_price.py | 1 + .../l0_test_compute_amine_scrubbing_price.py | 1 + .../l0_test_compute_anaerobic_digestion.py | 1 + ...l0_test_compute_biomass_dry_crop_energy.py | 1 + ...st_compute_biomass_dry_managed_wood_mix.py | 1 + ..._compute_biomass_dry_unmanaged_wood_mix.py | 1 + .../l0_test_compute_biomass_fermentation.py | 1 + .../tests/l0_test_compute_ccus_disc.py | 2 +- .../l0_test_compute_coal_extraction_price.py | 1 + ...ompute_cs_biomass_burying_fertilization.py | 1 + ...0_test_compute_cs_carbon_storage_techno.py | 1 + ...l0_test_compute_cs_deep_ocean_injection.py | 1 + ...0_test_compute_cs_deep_saline_formation.py | 1 + ..._test_compute_cs_depleted_oil_gaz_price.py | 1 + ...0_test_compute_cs_enhanced_oil_recovery.py | 1 + ...test_compute_cs_geologic_mineralization.py | 1 + ...st_compute_cs_pure_carbon_solid_storage.py | 1 + .../tests/l0_test_compute_cs_reforestation.py | 2 +- .../l0_test_compute_el_biogas_fired_price.py | 1 + .../l0_test_compute_el_biomass_fired_price.py | 1 + .../tests/l0_test_compute_el_ccgast_price.py | 1 + .../l0_test_compute_el_coal_gen_price.py | 2 + .../l0_test_compute_el_gas_turbine_price.py | 1 + .../tests/l0_test_compute_el_geothermal.py | 1 + .../tests/l0_test_compute_el_nuclear.py | 1 + .../tests/l0_test_compute_el_oil_gen_price.py | 1 + .../l0_test_compute_el_solar_pv_price.py | 4 +- .../l0_test_compute_el_solar_thermal_price.py | 2 + .../tests/l0_test_compute_el_wind_offshore.py | 1 + .../tests/l0_test_compute_el_wind_onshore.py | 1 + .../tests/l0_test_compute_energy_mix.py | 9 +- .../l0_test_compute_fg_calcium_looping.py | 1 + ...test_compute_fg_chilled_ammonia_process.py | 1 + .../tests/l0_test_compute_fg_co2_membranes.py | 1 + .../l0_test_compute_fg_flue_gas_techno.py | 1 + .../l0_test_compute_fg_monoethanolamine.py | 1 + .../l0_test_compute_fg_piperazine_process.py | 1 + ...st_compute_fg_pressure_swing_adsorption.py | 1 + ...0_test_compute_fsl_fossil_simple_techno.py | 1 + energy_models/tests/l0_test_compute_hefa.py | 1 + .../tests/l0_test_compute_high_heat_chp.py | 1 + ..._test_compute_high_heat_electric_boiler.py | 4 +- .../l0_test_compute_high_heat_geothermal.py | 1 + .../l0_test_compute_high_heat_natural_gas.py | 1 + .../tests/l0_test_compute_high_heatpump.py | 1 + .../tests/l0_test_compute_hydrogen_price.py | 6 +- .../tests/l0_test_compute_hydrogen_wgs.py | 3 +- .../l0_test_compute_kero_fossil_price.py | 3 +- ...0_test_compute_lh_hydrogen_liquefaction.py | 2 + .../tests/l0_test_compute_low_heat_chp.py | 1 + ...0_test_compute_low_heat_electric_boiler.py | 1 + .../l0_test_compute_low_heat_geothermal.py | 1 + .../l0_test_compute_low_heat_natural_gas.py | 1 + .../tests/l0_test_compute_low_heatpump.py | 1 + .../tests/l0_test_compute_medium_heat_chp.py | 1 + ...est_compute_medium_heat_electric_boiler.py | 1 + .../l0_test_compute_medium_heat_geothermal.py | 1 + ...l0_test_compute_medium_heat_natural_gas.py | 1 + .../tests/l0_test_compute_medium_heatpump.py | 1 + .../l0_test_compute_methane_fossil_gas.py | 1 + ..._test_compute_methane_methanation_price.py | 1 + ...0_test_compute_methane_upgrading_biogas.py | 1 + ...test_compute_methanol_co2_hydrogenation.py | 1 + .../tests/l0_test_compute_pellets_price.py | 1 + ...est_compute_syngas_biomass_gasification.py | 1 + ...0_test_compute_syngas_coal_gasification.py | 3 +- .../tests/l0_test_compute_syngas_rwgs.py | 1 + .../tests/l0_test_compute_syngas_smr.py | 1 + .../l0_test_compute_transesterification.py | 4 +- energy_models/tests/l0_test_energy_invest.py | 3 + energy_models/tests/l0_test_header.py | 10 +- .../tests/l0_test_independent_invest.py | 2 +- .../l0_test_investments_profile_builder.py | 6 +- energy_models/tests/l0_test_one_invest.py | 1 - .../tests/l1_test_base_stream_electricity.py | 6 +- .../tests/l1_test_energy_global_values.py | 14 +- .../tests/l1_test_gradient_biogas.py | 1 + .../tests/l1_test_gradient_carbon_capture.py | 12 +- .../tests/l1_test_gradient_carbon_storage.py | 3 +- ...est_gradient_clean_energy_simple_techno.py | 1 + .../tests/l1_test_gradient_electricity.py | 1 + .../l1_test_gradient_energy_mix_for_coarse.py | 4 +- .../tests/l1_test_gradient_ethanol.py | 4 +- .../tests/l1_test_gradient_flue_gas.py | 1 + .../l1_test_gradient_fossil_simple_techno.py | 2 + .../tests/l1_test_gradient_hydrogen.py | 3 +- .../l1_test_gradient_hydrotreated_oil_fuel.py | 2 +- .../tests/l1_test_gradient_liquid_fuel.py | 4 +- .../tests/l1_test_gradient_liquid_hydrogen.py | 4 + .../tests/l1_test_gradient_methane.py | 4 +- .../tests/l1_test_gradient_methanol.py | 2 +- .../tests/l1_test_gradient_one_invest.py | 2 + energy_models/tests/l1_test_gradient_ratio.py | 1 + .../tests/l1_test_gradient_solid_fuel.py | 2 + energy_models/tests/l2_test_simple.py | 4 +- .../performances/energy_models_perfos.csv | 2 + .../tests/to_fix/CalciumLoopingDiscipline.py | 3 +- .../to_fix/MonoEthanolAmineDiscipline.py | 3 +- .../to_fix/PiperazineProcessDiscipline.py | 3 +- headers_ignore_config.json | 60 +- parameters_glossary.csv | 6 +- platform_version_required.txt | 2 +- pytest.ini | 2 +- 453 files changed, 1867 insertions(+), 6321 deletions(-) diff --git a/.coveragerc b/.coveragerc index df8d576b..cc0bafcf 100644 --- a/.coveragerc +++ b/.coveragerc @@ -1,7 +1,7 @@ [run] relative_files = True branch = True -omit = +omit = */tests/* */__init__.py docs/ diff --git a/.gitlab/issue_templates/user_story_template.md b/.gitlab/issue_templates/user_story_template.md index 3693f927..4fbf43fb 100644 --- a/.gitlab/issue_templates/user_story_template.md +++ b/.gitlab/issue_templates/user_story_template.md @@ -1,4 +1,4 @@ -__As a__ type_of_user +__As a__ type_of_user __I want to__ some_goal __So that__ some_reason diff --git a/CREDITS.rst b/CREDITS.rst index a929d5c5..b719402e 100644 --- a/CREDITS.rst +++ b/CREDITS.rst @@ -8,12 +8,12 @@ witness-energy depends on software with compatible licenses that are listed belo `Pandas `_ BSD 3-Clause - + `scipy `_ BSD 3-Clause `nose2 `_ BSD 3-Clause - + `plotly `_ - MIT + MIT \ No newline at end of file diff --git a/LICENSE b/LICENSE index d6456956..7a4a3ea2 100644 --- a/LICENSE +++ b/LICENSE @@ -199,4 +199,4 @@ distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and - limitations under the License. + limitations under the License. \ No newline at end of file diff --git a/LICENSES/BSD-3-Clause.txt b/LICENSES/BSD-3-Clause.txt index dc10ebd7..78eb69c2 100644 --- a/LICENSES/BSD-3-Clause.txt +++ b/LICENSES/BSD-3-Clause.txt @@ -8,4 +8,4 @@ Redistribution and use in source and binary forms, with or without modification, 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/LICENSES/MIT.txt b/LICENSES/MIT.txt index a2874160..c6514b2e 100644 --- a/LICENSES/MIT.txt +++ b/LICENSES/MIT.txt @@ -16,4 +16,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. +SOFTWARE. \ No newline at end of file diff --git a/NOTICE b/NOTICE index fb3ad55e..573281d5 100644 --- a/NOTICE +++ b/NOTICE @@ -4,6 +4,6 @@ Copyright 2022 The Apache Software Foundation. This product includes software developed at The Apache Software Foundation (http://www.apache.org/). -This software contains code originally developed +This software contains code originally developed at Airbus SAS. (https://www.airbus.com/) Copyright 2020 - 2021 Airbus SAS. All Rights Reserved. diff --git a/README.md b/README.md index 9d2fc90b..6e85cbea 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ This package contains the following main disciplines categories: For more information, please look at the documentation associated. -The technologies are in the models folder. Most of the technology models logic is generic and is thus implemented in the +The technologies are in the models folder. Most of the technology models logic is generic and is thus implemented in the mother classes *techno type* and *techno disc*. The streams are in the core/stream_type folder. They similarly rely on mother class. diff --git a/data_energy/fitting/clean_energy_simple_techno.py b/data_energy/fitting/clean_energy_simple_techno.py index 6509b459..651e6975 100644 --- a/data_energy/fitting/clean_energy_simple_techno.py +++ b/data_energy/fitting/clean_energy_simple_techno.py @@ -33,7 +33,7 @@ df_invest_historic = DatabaseWitnessEnergy.get_techno_invest_df(techno_name=GlossaryEnergy.CleanEnergySimpleTechno) df_prod_historic = DatabaseWitnessEnergy.get_techno_prod(techno_name=GlossaryEnergy.CleanEnergySimpleTechno, year=2020)[1].value -ref_price_2023 = 70.76 # $/MWh +ref_price_2023 = 70.76 # $/MWh # data to run techno construction_delay = GlossaryEnergy.TechnoConstructionDelayDict[GlossaryEnergy.CleanEnergySimpleTechno] year_start_fitting = int(max(df_invest_historic['years'].min() + construction_delay, df_prod_historic['years'].min(), year_calibration)) @@ -111,7 +111,7 @@ def fitting_renewable(x: list): # Initial guess for the variables x0 = np.array([250., 1., 0.0, 0.2, 0.1]) -# x0 = np.array([743.8, 1.3, 0.06, 0.0, 0.06]) +#x0 = np.array([743.8, 1.3, 0.06, 0.0, 0.06]) bounds = [(0, 10000), (0, 1.1), (0.00, 0.), (0.001, 0.99), (0.0001, 0.3)] @@ -121,7 +121,7 @@ def fitting_renewable(x: list): prod_values_model, price_model_values = run_model(result.x) # Print the result -# print("Optimal solution:", result.x) +#print("Optimal solution:", result.x) print("Function value at the optimum:", result.fun) @@ -150,4 +150,4 @@ def fitting_renewable(x: list): graph_list = disc.get_post_processing_list(filters) for graph in graph_list: graph.to_plotly().show() - pass + pass \ No newline at end of file diff --git a/data_energy/fitting/fossil_energy_simple_techno.py b/data_energy/fitting/fossil_energy_simple_techno.py index 66f6cfa6..9588ae37 100644 --- a/data_energy/fitting/fossil_energy_simple_techno.py +++ b/data_energy/fitting/fossil_energy_simple_techno.py @@ -32,7 +32,7 @@ df_invest_historic = DatabaseWitnessEnergy.get_techno_invest_df(techno_name=GlossaryEnergy.FossilSimpleTechno) df_prod_historic = DatabaseWitnessEnergy.get_techno_prod(techno_name=GlossaryEnergy.FossilSimpleTechno, year=2020)[1].value -ref_price_2023 = 121.5 # $/MWh Source: chatgpt LCOE without tax +ref_price_2023 = 121.5 # $/MWh Source: chatgpt LCOE without tax # data to run techno construction_delay = GlossaryEnergy.TechnoConstructionDelayDict[GlossaryEnergy.FossilSimpleTechno] year_start_fitting = int(max(df_invest_historic['years'].min() + construction_delay, df_prod_historic['years'].min(), year_calibration)) @@ -68,6 +68,7 @@ ee.display_treeview_nodes() + def run_model(x: list, year_end: int = year_end_fitting): techno_dict_default["Capex_init"] = x[0] init_age_distrib_factor = x[1] @@ -157,7 +158,7 @@ def fitting_renewable(x: list): """ Results obtained: -Function value at the optimum: 16826745.79920797 +Function value at the optimum: 16826745.79920797 => less than 6% error at max between model and historic production between 2015 and 2023 => no error on the price Optimal capex_init : 222.638 @@ -166,4 +167,4 @@ def fitting_renewable(x: list): Optimal opex_percentage : 0.262 Optimal wacc : 0.058 Optimal utilization_ratio [100. 100. 100. 100. 100. 100.] -""" +""" \ No newline at end of file diff --git a/data_energy/fitting/gaseous_bioenergy.py b/data_energy/fitting/gaseous_bioenergy.py index a3ffca1d..f8b1bd12 100644 --- a/data_energy/fitting/gaseous_bioenergy.py +++ b/data_energy/fitting/gaseous_bioenergy.py @@ -14,22 +14,21 @@ limitations under the License. ''' import os -import pickle -from copy import deepcopy import numpy as np import pandas as pd -from climateeconomics.glossarycore import GlossaryCore +import pickle from scipy.interpolate import interp1d from scipy.optimize import minimize from sostrades_core.execution_engine.execution_engine import ExecutionEngine -from sostrades_core.tools.bspline.bspline import BSpline from sostrades_core.tools.post_processing.charts.two_axes_instanciated_chart import ( - InstanciatedSeries, - TwoAxesInstanciatedChart, + InstanciatedSeries, + TwoAxesInstanciatedChart, ) - +from sostrades_core.tools.bspline.bspline import BSpline from energy_models.glossaryenergy import GlossaryEnergy +from climateeconomics.glossarycore import GlossaryCore +from copy import deepcopy """ This script is used to calibrate the gaseous bioenergy invest so that the energy production matches the IEA NZE scenario @@ -52,14 +51,14 @@ initial_production = df_prod_iea.loc[df_prod_iea[GlossaryEnergy.Years] == year_start]["biogas AnaerobicDigestion (TWh)"].values[0] # interpolate data between 2050 and 2100 -years_IEA_interpolated = years # np.arange(years_IEA[0], years_IEA[-1] + 1, 5) +years_IEA_interpolated = years #np.arange(years_IEA[0], years_IEA[-1] + 1, 5) f = interp1d(years_IEA, df_prod_iea["biogas AnaerobicDigestion (TWh)"].values, kind='linear') prod_IEA_interpolated = f(years) # increase discretization in order to smooth production between 2020 and 2030 -years_optim = np.linspace(year_start, year_end, 8) # np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) +years_optim = np.linspace(year_start, year_end, 8) #np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) -invest_year_start = 3.432 # G$ +invest_year_start = 3.432 #G$ # chose the name so that it mathes the datamanager of the IEA vs NZE study name = 'usecase_witness_optim_nze_eval' @@ -102,13 +101,13 @@ def run_model(x: list, inputs_dict: dict = inputs_dict, year_end: int = year_end ee.factory.set_builders_to_coupling_builder(builder) ee.configure() - # ee.display_treeview_nodes() + #ee.display_treeview_nodes() inputs_dict.update({ f'{name}.{GlossaryEnergy.YearStart}': year_start, f'{name}.{GlossaryEnergy.YearEnd}': year_end, f'{name}.{model_name}.{GlossaryEnergy.InvestLevelValue}': invest_df, - # f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, + #f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, f'{name}.{model_name}.initial_production': initial_production, f'{name}.{model_name}.{GlossaryEnergy.InvestmentBeforeYearStartValue}': pd.DataFrame({GlossaryEnergy.Years: np.arange(year_start - construction_delay, year_start), GlossaryEnergy.InvestValue: invest_before_year_start}), }) @@ -117,7 +116,7 @@ def run_model(x: list, inputs_dict: dict = inputs_dict, year_end: int = year_end ee.execute() - prod_df = ee.dm.get_value(ee.dm.get_all_namespaces_from_var_name(GlossaryEnergy.TechnoProductionValue)[0]) # PWh + prod_df = ee.dm.get_value(ee.dm.get_all_namespaces_from_var_name(GlossaryEnergy.TechnoProductionValue)[0]) #PWh return prod_df[[GlossaryEnergy.Years, "biogas (TWh)"]], invest_df, ee @@ -130,11 +129,11 @@ def fitting_renewable(x: list): # Initial guess for the variables invest from year 2025 to 2100. -x0 = np.concatenate((np.array([0.]), 1 / 2.4 * np.ones(construction_delay - 1), np.ones(len(years_optim)))) -bounds = [(0., 0.)] + [(1. / 2.4 / 3., 1. / 2.4 * 3.)] * (construction_delay - 1) + (len(years_optim)) * [(1. / 3., 3. * 1.)] +x0 = np.concatenate((np.array([0.]), 1/2.4 * np.ones(construction_delay - 1), np.ones(len(years_optim)))) +bounds = [(0., 0.)] + [(1./2.4/3., 1./2.4 * 3.)] * (construction_delay - 1) + (len(years_optim)) * [(1./3., 3. * 1.)] # Use minimize to find the minimum of the function -result = minimize(fitting_renewable, x0, bounds=bounds, # method='trust-constr', - options={'disp': True, 'maxiter': 500}) # , 'maxfun': 500, 'ftol': 1.e-6, 'maxls': 50}) +result = minimize(fitting_renewable, x0, bounds=bounds, #method='trust-constr', + options={'disp': True, 'maxiter': 500}) #, 'maxfun': 500, 'ftol': 1.e-6, 'maxls': 50}) prod_df, invest_df, ee = run_model(result.x) # Print the result @@ -181,3 +180,4 @@ def fitting_renewable(x: list): df_invest_mix.to_csv(invest_mix_csv, index=False, sep=',') # values to set in the invest_design_space_NZE.csv print(f"invest at poles={result.x[construction_delay:] * invest_year_start}") + diff --git a/data_energy/fitting/hydropower.py b/data_energy/fitting/hydropower.py index 36431ec7..9d9d38aa 100644 --- a/data_energy/fitting/hydropower.py +++ b/data_energy/fitting/hydropower.py @@ -14,19 +14,19 @@ limitations under the License. ''' import os -import pickle -from copy import deepcopy import numpy as np import pandas as pd +import pickle +from copy import deepcopy from climateeconomics.glossarycore import GlossaryCore from scipy.interpolate import interp1d from scipy.optimize import minimize from sostrades_core.execution_engine.execution_engine import ExecutionEngine from sostrades_core.tools.bspline.bspline import BSpline from sostrades_core.tools.post_processing.charts.two_axes_instanciated_chart import ( - InstanciatedSeries, - TwoAxesInstanciatedChart, + InstanciatedSeries, + TwoAxesInstanciatedChart, ) from energy_models.glossaryenergy import GlossaryEnergy @@ -53,8 +53,8 @@ prod_IEA_interpolated = f(years_IEA_interpolated) # increase discretization in order to smooth production between 2020 and 2030 -years_optim = np.linspace(year_start, year_end, 8) # np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #years_IEA_interpolated #sorted(list(set(years_IEA_interpolated + list(np.arange(year_start, max(year_start, 2030) + 1))))) -invest_year_start = 18.957 # G$ +years_optim = np.linspace(year_start, year_end, 8) #np.arange(years_IEA[0], years_IEA[-1] + 1, 5) #years_IEA_interpolated #sorted(list(set(years_IEA_interpolated + list(np.arange(year_start, max(year_start, 2030) + 1))))) +invest_year_start = 18.957 #G$ name = 'usecase_witness_optim_nze_eval' model_name = f"WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.electricity.{GlossaryEnergy.Hydropower}" @@ -70,7 +70,6 @@ inputs_dict.update({f'{name}.{GlossaryEnergy.ResourcesPriceValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.{GlossaryEnergy.ResourcesPriceValue}')}) inputs_dict.update({f'{name}.{GlossaryEnergy.TransportCostValue}': inputs_dict.pop(f'usecase_witness_optim_nze_eval.WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.biogas.{GlossaryEnergy.TransportCostValue}')}) - def run_model(x: list, year_end: int = year_end): init_prod = x[0] * initial_production invest_before_year_start = x[1:1 + construction_delay] * invest_year_start @@ -98,13 +97,13 @@ def run_model(x: list, year_end: int = year_end): ee.factory.set_builders_to_coupling_builder(builder) ee.configure() - # ee.display_treeview_nodes() + #ee.display_treeview_nodes() inputs_dict.update({ f'{name}.{GlossaryEnergy.YearStart}': year_start, f'{name}.{GlossaryEnergy.YearEnd}': year_end, f'{name}.{model_name}.{GlossaryEnergy.InvestLevelValue}': invest_df, - # f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, + #f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, f'{name}.{model_name}.initial_production': init_prod, f'{name}.{model_name}.{GlossaryEnergy.InvestmentBeforeYearStartValue}': pd.DataFrame( {GlossaryEnergy.Years: np.arange(year_start - construction_delay, year_start), @@ -114,7 +113,7 @@ def run_model(x: list, year_end: int = year_end): ee.execute() - prod_df = ee.dm.get_value(ee.dm.get_all_namespaces_from_var_name(GlossaryEnergy.TechnoProductionValue)[0]) # PWh + prod_df = ee.dm.get_value(ee.dm.get_all_namespaces_from_var_name(GlossaryEnergy.TechnoProductionValue)[0]) #PWh return prod_df[[GlossaryEnergy.Years, "electricity (TWh)"]], invest_df, ee @@ -129,11 +128,11 @@ def fitting_renewable(x: list): # Initial guess for the variables invest from year 2025 to 2100. # there is a bug with the invest before year start => first value must be set to 0 # otherwise initial production at year start is not as expected -x0 = np.concatenate((np.array([1.]), np.array([0.]), 80. / invest_year_start * np.ones(construction_delay - 1), np.ones(len(years_optim)))) -bounds = [(1., 1.)] + [(0., 0.)] + [(80. / invest_year_start / 2., 80. / invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1. / 10., 10.)] +x0 = np.concatenate((np.array([1.]), np.array([0.]), 80./invest_year_start * np.ones(construction_delay - 1), np.ones(len(years_optim)))) +bounds = [(1., 1.)] + [(0., 0.)] + [(80./invest_year_start/2., 80./invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1./10., 10.)] # Use minimize to find the minimum of the function -result = minimize(fitting_renewable, x0, bounds=bounds, # method='trust-constr', +result = minimize(fitting_renewable, x0, bounds=bounds, #method='trust-constr', options={'disp': True, 'maxiter': 2000, 'xtol': 1e-20}) prod_df, invest_df, ee = run_model(result.x) @@ -141,8 +140,8 @@ def fitting_renewable(x: list): # Print the result print("Function value at the optimum:", result.fun) print("initial production", result.x[0] * initial_production) -print("invest before year start", result.x[1:1 + construction_delay] * invest_year_start) -print("invest at the poles at the optimum", result.x[1 + construction_delay:] * invest_year_start) +print("invest before year start", result.x[1:1+construction_delay] * invest_year_start) +print("invest at the poles at the optimum", result.x[1+construction_delay:] * invest_year_start) new_chart = TwoAxesInstanciatedChart('years', 'hydropower production (TWh)', @@ -161,7 +160,7 @@ def fitting_renewable(x: list): new_chart = TwoAxesInstanciatedChart('years', 'hydropower invest (G$)', chart_name='investments') -serie = InstanciatedSeries(list(years_optim), list(result.x[1 + construction_delay:] * invest_year_start), 'invests_at_poles', 'scatter') +serie = InstanciatedSeries(list(years_optim), list(result.x[1+construction_delay:] * invest_year_start), 'invests_at_poles', 'scatter') new_chart.series.append(serie) serie = InstanciatedSeries(list(years), list(invest_df[GlossaryEnergy.InvestValue]), 'invests_bspline', 'lines') new_chart.series.append(serie) @@ -185,4 +184,4 @@ def fitting_renewable(x: list): df_invest_mix.to_csv(invest_mix_csv, index=False, sep=',') # values to set in the invest_design_space_NZE.csv -print(f"invest at poles={result.x[1 + construction_delay:] * invest_year_start}") +print(f"invest at poles={result.x[1+construction_delay:] * invest_year_start}") \ No newline at end of file diff --git a/data_energy/fitting/windpower.py b/data_energy/fitting/windpower.py index 953bbab5..196d9e0c 100644 --- a/data_energy/fitting/windpower.py +++ b/data_energy/fitting/windpower.py @@ -14,26 +14,28 @@ limitations under the License. ''' import os -import pickle -from copy import deepcopy from functools import reduce - +import pickle import numpy as np import pandas as pd +from copy import deepcopy from climateeconomics.glossarycore import GlossaryCore from scipy.interpolate import interp1d from scipy.optimize import minimize from sostrades_core.execution_engine.execution_engine import ExecutionEngine -from sostrades_core.tools.bspline.bspline import BSpline from sostrades_core.tools.post_processing.charts.two_axes_instanciated_chart import ( InstanciatedSeries, TwoAxesInstanciatedChart, ) - -from energy_models.glossaryenergy import GlossaryEnergy +from sostrades_core.tools.bspline.bspline import BSpline from energy_models.models.electricity.wind_onshore.wind_onshore_disc import ( WindOnshoreDiscipline, ) +from energy_models.models.electricity.wind_offshore.wind_offshore_disc import ( + WindOffshoreDiscipline, +) +from energy_models.glossaryenergy import GlossaryEnergy + """ This script is used to calibrate the windpower invest so that the electricity production matches the IEA NZE scenario @@ -62,12 +64,12 @@ prod_IEA_interpolated = f(years_IEA_interpolated) # optimization at the poles just like in witness-full study -years_optim = np.linspace(year_start, year_end, 8) # years_IEA_interpolated #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) +years_optim = np.linspace(year_start, year_end, 8) #years_IEA_interpolated #sorted(list(set(years_IEA + list(np.arange(year_start, max(year_start, 2030) + 1))))) -invest_year_start = 80. # G$ -construction_delay = GlossaryEnergy.TechnoConstructionDelayDict['WindOffshore'] # same construction delay for windonshore and windoffshore +invest_year_start = 80. #G$ +construction_delay = GlossaryEnergy.TechnoConstructionDelayDict['WindOffshore'] # same construction delay for windonshore and windoffshore if construction_delay != GlossaryEnergy.TechnoConstructionDelayDict['WindOnshore']: - raise ValueError("must adapt script as construction delay for windOnshore and windOffshore differ") + raise ValueError(f"must adapt script as construction delay for windOnshore and windOffshore differ") name = 'usecase_witness_optim_nze_eval' model_name_onshore = f"WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.electricity.{GlossaryEnergy.WindOnshore}" @@ -82,7 +84,7 @@ mod_path_offshore = 'energy_models.models.electricity.wind_offshore.wind_offshore_disc.WindOffshoreDiscipline' # if want to modify the capex of both onshore and offshore -# dict_techno_dict_default = {model_name_onshore: WindOnshoreDiscipline.techno_infos_dict_default, +#dict_techno_dict_default = {model_name_onshore: WindOnshoreDiscipline.techno_infos_dict_default, # model_name_offshore: WindOffshoreDiscipline.techno_infos_dict_default} techno_info_dict_default = WindOnshoreDiscipline.techno_infos_dict_default Capex_init0 = WindOnshoreDiscipline.techno_infos_dict_default['Capex_init'] @@ -105,8 +107,6 @@ model_name_onshore: initial_prod * init_prod_onshore_over_offshore / (1. + init_prod_onshore_over_offshore), model_name_offshore: initial_prod / (1. + init_prod_onshore_over_offshore)} ratio_invest_onshore_offshore = 3.6689 # taken from initial witness results - - def run_model(x: list, year_end: int = year_end): techno_info_dict_default['Capex_init'] = Capex_init0 * x[0] invest_before_year_start = x[1:construction_delay + 1] * invest_year_start @@ -125,6 +125,7 @@ def run_model(x: list, year_end: int = year_end): df[model_name_onshore] = df[GlossaryCore.InvestValue] * ratio_invest_onshore_offshore / (1. + ratio_invest_onshore_offshore) df[model_name_offshore] = df[GlossaryCore.InvestValue] / (1. + ratio_invest_onshore_offshore) + ee = ExecutionEngine(name) ee.ns_manager.add_ns_def(ns_dict) builder = [] @@ -135,7 +136,7 @@ def run_model(x: list, year_end: int = year_end): ee.factory.set_builders_to_coupling_builder(builder) ee.configure() - # ee.display_treeview_nodes() + #ee.display_treeview_nodes() inputs_dict.update({ f'{name}.{GlossaryEnergy.YearStart}': year_start, @@ -144,11 +145,11 @@ def run_model(x: list, year_end: int = year_end): }) for model_name in [model_name_offshore, model_name_onshore]: inputs_dict.update({ - # f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, + #f'{name}.{model_name}.{GlossaryEnergy.InitialPlantsAgeDistribFactor}': init_age_distrib_factor, f'{name}.{model_name}.initial_production': init_prod_dict[model_name], f'{name}.{model_name}.{GlossaryEnergy.InvestLevelValue}': pd.DataFrame({GlossaryEnergy.Years: years, GlossaryCore.InvestValue: invest_df[model_name].values}), f'{name}.{model_name}.{GlossaryEnergy.InvestmentBeforeYearStartValue}': pd.DataFrame({GlossaryEnergy.Years: np.arange(year_start - construction_delay, year_start), - GlossaryEnergy.InvestValue: invest_before_year_start_df[model_name].values}), + GlossaryEnergy.InvestValue:invest_before_year_start_df[model_name].values}), }) ee.load_study_from_input_dict(inputs_dict) @@ -157,10 +158,10 @@ def run_model(x: list, year_end: int = year_end): # put electricity production for both wind techno energies in a single dataframe df_prod_names = ee.dm.get_all_namespaces_from_var_name(GlossaryEnergy.TechnoProductionValue) - df_prod_list = [ee.dm.get_value(df_prod_names[i]).rename(columns={"electricity (TWh)": df_prod_names[i]}) for i in range(len(df_prod_names))] # PWh + df_prod_list = [ee.dm.get_value(df_prod_names[i]).rename(columns={"electricity (TWh)": df_prod_names[i]}) for i in range(len(df_prod_names))] #PWh df_prod = reduce(lambda left, right: pd.merge(left, right, on=GlossaryEnergy.Years), df_prod_list) # compute the sum of onshore and offshore technos: - df_prod['electricity (TWh)'] = df_prod.drop(GlossaryEnergy.Years, axis=1).sum(axis=1) * 1000. # PWh + df_prod['electricity (TWh)'] = df_prod.drop(GlossaryEnergy.Years, axis=1).sum(axis=1) * 1000. #PWh df_prod_model = df_prod.loc[df_prod[GlossaryEnergy.Years].isin(years_IEA_interpolated)] price_df = ee.dm.get_value(f"{name}.{model_name}.{GlossaryEnergy.TechnoPricesValue}") @@ -174,12 +175,12 @@ def fitting_renewable(x: list): years_price_iea = df_price_iea['years'].values price_model_values = (price_df.loc[price_df[GlossaryEnergy.Years].isin(years_price_iea), f"{GlossaryEnergy.WindOnshore}_wotaxes"]).values - return ((((df_prod_model['electricity (TWh)'].values - prod_IEA_interpolated) / prod_IEA_interpolated.mean()) ** 2).mean() + (((price_model_values - price_iea_values) / price_iea_values.mean()) ** 2).mean()) + return ((((df_prod_model['electricity (TWh)'].values - prod_IEA_interpolated)/prod_IEA_interpolated.mean()) ** 2).mean() + (((price_model_values - price_iea_values)/price_iea_values.mean()) ** 2).mean()) # Initial guess for the variables invest from year 2025 to 2100. -x0 = np.concatenate((np.array([1.]), np.array([0.]), 80. / invest_year_start * np.ones(construction_delay - 1), np.ones(len(years_optim)))) -bounds = [(0.5, 1.5)] + [(0., 0.)] + [(80. / invest_year_start / 2., 80. / invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1. / 10., 10.)] +x0 = np.concatenate((np.array([1.]), np.array([0.]), 80./invest_year_start * np.ones(construction_delay - 1), np.ones(len(years_optim)))) +bounds = [(0.5, 1.5)] + [(0., 0.)] + [(80./invest_year_start/2., 80./invest_year_start * 2.)] * (construction_delay - 1) + (len(years_optim)) * [(1./10., 10.)] # Use minimize to find the minimum of the function result = minimize(fitting_renewable, x0, bounds=bounds) @@ -193,6 +194,7 @@ def fitting_renewable(x: list): print("invest at the poles at the optimum", result.x[construction_delay + 1:] * invest_year_start) + new_chart = TwoAxesInstanciatedChart('years', 'production (TWh)', chart_name='Windpower Production : witness vs IEA') @@ -249,5 +251,5 @@ def fitting_renewable(x: list): # values to set in the invest_design_space_NZE.csv print(f"invest at poles for WindOnshore={result.x[construction_delay + 1:] * invest_year_start * ratio_invest_onshore_offshore / (1. + ratio_invest_onshore_offshore)}") print(f"invest at poles for WindOffshore={result.x[construction_delay + 1:] * invest_year_start / (1. + ratio_invest_onshore_offshore)}") -print(f"invest before year start for WindOnshore={result.x[1:construction_delay + 1] * invest_year_start * ratio_invest_onshore_offshore / (1. + ratio_invest_onshore_offshore)}") -print(f"invest before year start for WindOffshore={result.x[1:construction_delay + 1] * invest_year_start / (1. + ratio_invest_onshore_offshore)}") +print(f"invest before year start for WindOnshore={result.x[1:construction_delay +1] * invest_year_start * ratio_invest_onshore_offshore / (1. + ratio_invest_onshore_offshore)}") +print(f"invest before year start for WindOffshore={result.x[1:construction_delay + 1] * invest_year_start / (1. + ratio_invest_onshore_offshore)}") \ No newline at end of file diff --git a/data_energy/techno_invests/biomassburyingfossilization.csv b/data_energy/techno_invests/biomassburyingfossilization.csv index c81de8fa..ad039d4a 100644 --- a/data_energy/techno_invests/biomassburyingfossilization.csv +++ b/data_energy/techno_invests/biomassburyingfossilization.csv @@ -1 +1,2 @@ years,past years,invest + diff --git a/data_energy/techno_invests/deepoceaninjection.csv b/data_energy/techno_invests/deepoceaninjection.csv index c81de8fa..ad039d4a 100644 --- a/data_energy/techno_invests/deepoceaninjection.csv +++ b/data_energy/techno_invests/deepoceaninjection.csv @@ -1 +1,2 @@ years,past years,invest + diff --git a/data_energy/techno_invests/deepsalineformation.csv b/data_energy/techno_invests/deepsalineformation.csv index c81de8fa..ad039d4a 100644 --- a/data_energy/techno_invests/deepsalineformation.csv +++ b/data_energy/techno_invests/deepsalineformation.csv @@ -1 +1,2 @@ years,past years,invest + diff --git a/data_energy/techno_invests/depletedoilgas.csv b/data_energy/techno_invests/depletedoilgas.csv index c81de8fa..ad039d4a 100644 --- a/data_energy/techno_invests/depletedoilgas.csv +++ b/data_energy/techno_invests/depletedoilgas.csv @@ -1 +1,2 @@ years,past years,invest + diff --git a/data_energy/techno_invests/enhancedoilrecovery.csv b/data_energy/techno_invests/enhancedoilrecovery.csv index c81de8fa..ad039d4a 100644 --- a/data_energy/techno_invests/enhancedoilrecovery.csv +++ b/data_energy/techno_invests/enhancedoilrecovery.csv @@ -1 +1,2 @@ years,past years,invest + diff --git a/data_energy/techno_invests/geologicmineralization.csv b/data_energy/techno_invests/geologicmineralization.csv index c81de8fa..ad039d4a 100644 --- a/data_energy/techno_invests/geologicmineralization.csv +++ b/data_energy/techno_invests/geologicmineralization.csv @@ -1 +1,2 @@ years,past years,invest + diff --git a/data_energy/techno_invests/geothermal.csv b/data_energy/techno_invests/geothermal.csv index f452ebce..ff7396f5 100644 --- a/data_energy/techno_invests/geothermal.csv +++ b/data_energy/techno_invests/geothermal.csv @@ -5,4 +5,4 @@ years,invest 2016,2.7 2017,2.4 2018,2.5 -2019,1.2 +2019,1.2 \ No newline at end of file diff --git a/data_energy/techno_invests/global-investment-in-clean-energy-and-fossil-fuels-2015-2024.csv b/data_energy/techno_invests/global-investment-in-clean-energy-and-fossil-fuels-2015-2024.csv index 91882dff..e9fb810d 100644 --- a/data_energy/techno_invests/global-investment-in-clean-energy-and-fossil-fuels-2015-2024.csv +++ b/data_energy/techno_invests/global-investment-in-clean-energy-and-fossil-fuels-2015-2024.csv @@ -8,4 +8,4 @@ Renewable power;Grids and storage;Energy efficiency and end-use;Nuclear and othe 0;470;330;562;58;11 0;605;365;655;65;17 0;735;416;646;67;20 -0;771;452;669;80;31 +0;771;452;669;80;31 \ No newline at end of file diff --git a/data_energy/techno_invests/purecarbonsolidstorage.csv b/data_energy/techno_invests/purecarbonsolidstorage.csv index c81de8fa..ad039d4a 100644 --- a/data_energy/techno_invests/purecarbonsolidstorage.csv +++ b/data_energy/techno_invests/purecarbonsolidstorage.csv @@ -1 +1,2 @@ years,past years,invest + diff --git a/data_energy/techno_invests/sources.txt b/data_energy/techno_invests/sources.txt index 3fe173bc..e1c22539 100644 --- a/data_energy/techno_invests/sources.txt +++ b/data_energy/techno_invests/sources.txt @@ -8,4 +8,4 @@ CarbonStorageTechno : https://www.iea.org/energy-system/carbon-capture-utilisati DirectAirCaptureTechno : https://www.iea.org/energy-system/carbon-capture-utilisation-and-storage/direct-air-capture SolarPV: [https://www.iea.org/energy-system/renewables/solar-pv,] WindOnShore: https://about.bnef.com/blog/offshore-wind-investment-hit-all-time-high-in-2023/#:~:text=Offshore%20wind%20investment%20surged%20to,%2476.7%20billion%2C%20jumping%2079%25. -WindOffShore: https://about.bnef.com/blog/offshore-wind-investment-hit-all-time-high-in-2023/#:~:text=Offshore%20wind%20investment%20surged%20to,%2476.7%20billion%2C%20jumping%2079%25. +WindOffShore: https://about.bnef.com/blog/offshore-wind-investment-hit-all-time-high-in-2023/#:~:text=Offshore%20wind%20investment%20surged%20to,%2476.7%20billion%2C%20jumping%2079%25. \ No newline at end of file diff --git a/data_energy/techno_invests/windoffshore.csv b/data_energy/techno_invests/windoffshore.csv index 8d2075a2..13862d2d 100644 --- a/data_energy/techno_invests/windoffshore.csv +++ b/data_energy/techno_invests/windoffshore.csv @@ -5,4 +5,4 @@ years,past years,invest 2020,,57 2021,,34 2022,,43 -2023,,78 +2023,,78 \ No newline at end of file diff --git a/data_energy/techno_production_historic/combinedcyclegasturbine.csv b/data_energy/techno_production_historic/combinedcyclegasturbine.csv index def1aadb..2f26048c 100644 --- a/data_energy/techno_production_historic/combinedcyclegasturbine.csv +++ b/data_energy/techno_production_historic/combinedcyclegasturbine.csv @@ -10,4 +10,4 @@ years,production,unit 2020,4749.16,TWh 2021,4869.71,TWh 2022,4936.04,TWh -2023,4976.33,TWh +2023,4976.33,TWh \ No newline at end of file diff --git a/data_energy/techno_production_historic/fossilgas.csv b/data_energy/techno_production_historic/fossilgas.csv index 0524830c..2b69043c 100644 --- a/data_energy/techno_production_historic/fossilgas.csv +++ b/data_energy/techno_production_historic/fossilgas.csv @@ -57,4 +57,4 @@ years,production,unit 2020,38714.09,TWh 2021,40239.016,TWh 2022,40086.875,TWh -2023,40101.74,TWh +2023,40101.74,TWh \ No newline at end of file diff --git a/data_energy/techno_production_historic/hydropower.csv b/data_energy/techno_production_historic/hydropower.csv index 5a95a9f8..00983e36 100644 --- a/data_energy/techno_production_historic/hydropower.csv +++ b/data_energy/techno_production_historic/hydropower.csv @@ -10,4 +10,4 @@ years,production,unit 2020,4343.51,TWh 2021,4275.47,TWh 2022,4298.82,TWh -2023,4197.92,TWh +2023,4197.92,TWh \ No newline at end of file diff --git a/data_energy/techno_production_historic/oilgen.csv b/data_energy/techno_production_historic/oilgen.csv index d3694491..d08261f5 100644 --- a/data_energy/techno_production_historic/oilgen.csv +++ b/data_energy/techno_production_historic/oilgen.csv @@ -37,4 +37,4 @@ years,production,unit 2020,773.0,TWh 2021,830.76,TWh 2022,849.26,TWh -2023,788.55,TWh +2023,788.55,TWh \ No newline at end of file diff --git a/data_energy/techno_production_historic/sources.txt b/data_energy/techno_production_historic/sources.txt index 4d5859aa..ac146b10 100644 --- a/data_energy/techno_production_historic/sources.txt +++ b/data_energy/techno_production_historic/sources.txt @@ -11,4 +11,4 @@ CombinedGasCycleTurbine : 75% of Natural gas electricity generation https://www. Geothermal : https://geothermal-energy-journal.springeropen.com/articles/10.1186/s40517-024-00290-w CropEnergy : 1972, by reading this graph https://www.iea.org/reports/bioenergy-2#overview we get 3966Twh in 2022 (2361 from convetional crop and 1605 from short rotation) -Clean energy simple techno : +Clean energy simple techno : \ No newline at end of file diff --git a/default_process_rights.yaml b/default_process_rights.yaml index ad00a6e0..f92f4b5a 100644 --- a/default_process_rights.yaml +++ b/default_process_rights.yaml @@ -13,4 +13,4 @@ # limitations under the License. group-name: - - All users + - All users \ No newline at end of file diff --git a/energy_models/core/ccus/ccus.py b/energy_models/core/ccus/ccus.py index 5c2a87d4..9e47254c 100644 --- a/energy_models/core/ccus/ccus.py +++ b/energy_models/core/ccus/ccus.py @@ -45,7 +45,7 @@ class CCUS: def __init__(self, name): ''' - Constructor + Constructor ''' self.year_start = None self.year_end = None @@ -67,7 +67,7 @@ def configure_parameters(self, inputs_dict): self.ccs_list = [GlossaryEnergy.carbon_capture, GlossaryEnergy.carbon_storage] def compute_carbon_storage_capacity(self): - total_carbon_storage_by_invest_mt = self.inputs_dict[f"{GlossaryEnergy.carbon_storage}.{GlossaryEnergy.EnergyProductionValue}"][GlossaryEnergy.carbon_storage].values * self.inputs_dict['scaling_factor_energy_production'] + total_carbon_storage_by_invest_mt = self.inputs_dict[f"{GlossaryEnergy.carbon_storage}.{GlossaryEnergy.EnergyProductionValue}"][ GlossaryEnergy.carbon_storage].values * self.inputs_dict['scaling_factor_energy_production'] self.outputs_dict['carbon_storage_capacity (Gt)'] = pd.DataFrame({ GlossaryEnergy.Years: self.years, @@ -100,7 +100,7 @@ def compute_co2_emissions(self): GlossaryEnergy.Years: self.years, f'{GlossaryEnergy.carbon_storage} ({GlossaryEnergy.mass_unit})': carbon_storage_gt * 1e3, f'{GlossaryEnergy.carbon_capture} to be stored (Mt)': carbon_capture_to_be_stored_gt * 1e3, - f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit}) from CC technos': carbon_capture_from_cc_technos_gt * 1e3, + f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit}) from CC technos': carbon_capture_from_cc_technos_gt * 1e3, f'{GlossaryEnergy.carbon_storage} Limited by capture (Mt)': carbon_storage_limited_by_capture_gt * 1e3, }) @@ -115,7 +115,7 @@ def compute(self): def compute_CCS_price(self): ''' - Compute CCS_price + Compute CCS_price ''' ccs_price = self.inputs_dict[f'{GlossaryEnergy.carbon_capture}.{GlossaryEnergy.StreamPricesValue}'][GlossaryEnergy.carbon_capture].values +\ self.inputs_dict[f'{GlossaryEnergy.carbon_storage}.{GlossaryEnergy.StreamPricesValue}'][GlossaryEnergy.carbon_storage].values @@ -200,4 +200,4 @@ def compute_carbon_storage_limited_by_capture_gt_der( jac_carbon_capture_from_energy_mix = jacobian(lambda *args: compute_carbon_storage_limited_by_capture_gt(*args)[1], 2) jac_co2_emissions_needed_by_energy_mix = jacobian(lambda *args: compute_carbon_storage_limited_by_capture_gt(*args)[1], 3) - return jac_carbon_capture_from_cc_prod(*args), jac_carbon_capture_from_cs_prod(*args), jac_carbon_capture_from_energy_mix(*args), jac_co2_emissions_needed_by_energy_mix(*args) + return jac_carbon_capture_from_cc_prod(*args), jac_carbon_capture_from_cs_prod(*args), jac_carbon_capture_from_energy_mix(*args), jac_co2_emissions_needed_by_energy_mix(*args) \ No newline at end of file diff --git a/energy_models/core/ccus/ccus_disc.py b/energy_models/core/ccus/ccus_disc.py index 24050b35..15f305a7 100644 --- a/energy_models/core/ccus/ccus_disc.py +++ b/energy_models/core/ccus/ccus_disc.py @@ -52,7 +52,7 @@ class CCUS_Discipline(SoSWrapp): DESC_IN = { GlossaryEnergy.YearStart: ClimateEcoDiscipline.YEAR_START_DESC_IN, GlossaryEnergy.YearEnd: {'type': 'int', - 'unit': 'year', 'visibility': 'Shared', 'namespace': 'ns_public', 'range': [2000, 2300]}, + 'unit': 'year', 'visibility': 'Shared', 'namespace': 'ns_public', 'range': [2000,2300]}, 'co2_emissions_needed_by_energy_mix': {'type': 'dataframe', 'unit': 'Gt', 'visibility': SoSWrapp.SHARED_VISIBILITY, 'namespace': 'ns_energy', 'dataframe_descriptor': {GlossaryEnergy.Years: ('float', None, True), @@ -62,7 +62,7 @@ class CCUS_Discipline(SoSWrapp): 'carbon_capture_from_energy_mix': {'type': 'dataframe', 'unit': 'Gt', 'visibility': SoSWrapp.SHARED_VISIBILITY, 'namespace': 'ns_energy', 'dataframe_descriptor': {GlossaryEnergy.Years: ('float', None, True), - 'carbon_capture from energy mix (Gt)': ('float', None, True), }, }, + 'carbon_capture from energy mix (Gt)': ('float', None, True), },}, 'co2_for_food': { 'type': 'dataframe', 'unit': 'Mt', 'visibility': SoSWrapp.SHARED_VISIBILITY, 'namespace': 'ns_energy', @@ -304,7 +304,7 @@ def get_chart_co2_limited_storage(self): def get_chart_co2_emissions_sources(self): ''' - Plot all CO2 emissions sources + Plot all CO2 emissions sources ''' chart_name = 'CO2 emissions sources' co2_emissions = self.get_sosdisc_outputs('co2_emissions') @@ -341,3 +341,4 @@ def get_chart_co2_emissions_sources(self): new_chart.add_series(serie) return new_chart + diff --git a/energy_models/core/ccus/documentation/ccus_disc.md b/energy_models/core/ccus/documentation/ccus_disc.md index b26b18c2..a280cd27 100644 --- a/energy_models/core/ccus/documentation/ccus_disc.md +++ b/energy_models/core/ccus/documentation/ccus_disc.md @@ -1,9 +1,9 @@ -# Carbon Capture and Storage model +# Carbon Capture and Storage model The Carbon Capture and Storage model in the energy mix model computes the carbon capture stored limited by the CO2 to capture. -Carbon emissions stored by carbon storage technologies are limited by the amount of CO2 captured. Gaseous CO2 storage and solid carbon storage are separated and they are both limited each by the amount of CO2 and solid carbon ready to store. +Carbon emissions stored by carbon storage technologies are limited by the amount of CO2 captured. Gaseous CO2 storage and solid carbon storage are separated and they are both limited each by the amount of CO2 and solid carbon ready to store. ![](carbon_stored.PNG) @@ -11,10 +11,12 @@ The Solid carbon ready to store is for now the one created by plasma cracking te ![](Carbon_captured_to_be_stored.PNG) -If the CO2 captured to be stored is lower than zero that means that we need more carbon capture for technos than provided. A ratio of carbon captured available is then computed as : +If the CO2 captured to be stored is lower than zero that means that we need more carbon capture for technos than provided. A ratio of carbon captured available is then computed as : $$ratio_{cc\_available} = min(1.0,\frac{cc_{provided}}{cc_{needed}})$$ -This ratio is sent to technology models that needs carbon capture and their production is consequently impacted : +This ratio is sent to technology models that needs carbon capture and their production is consequently impacted : $$production = production*ratio_{cc\_available}$$ + + diff --git a/energy_models/core/consumption_CO2_emissions/consumption_CO2_emissions.py b/energy_models/core/consumption_CO2_emissions/consumption_CO2_emissions.py index afef539c..b363e770 100644 --- a/energy_models/core/consumption_CO2_emissions/consumption_CO2_emissions.py +++ b/energy_models/core/consumption_CO2_emissions/consumption_CO2_emissions.py @@ -40,7 +40,7 @@ class ConsumptionCO2Emissions(BaseStream): def __init__(self, name): ''' - Constructor + Constructor ''' super(ConsumptionCO2Emissions, self).__init__(name) self.energy_list = None @@ -61,7 +61,7 @@ def __init__(self, name): def configure(self, inputs_dict): ''' - Configure method + Configure method ''' self.configure_parameters(inputs_dict) self.configure_parameters_update(inputs_dict) @@ -134,9 +134,9 @@ def compute_CO2_emissions(self): for col, production in self.sub_production_dict[energy].items(): if col in self.CO2_list: self.CO2_production[f'{energy} {col}'] = production.values - ''' CO2 from energy mix - CO2 expelled by energy mix technologies during the process - i.e. for machinery or tractors + ''' CO2 from energy mix + CO2 expelled by energy mix technologies during the process + i.e. for machinery or tractors ''' energy_producing_co2 = self.CO2_production[[ col for col in self.CO2_production if col.endswith(f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit})')]] @@ -167,7 +167,7 @@ def compute_CO2_emissions(self): f'{GlossaryEnergy.carbon_capture} from energy mix (Mt)'] = 0.0 ''' CO2 removed by energy mix - CO2 removed by energy mix technologies during the process + CO2 removed by energy mix technologies during the process i.e. biomass processes as managed wood or crop energy ''' energy_removing_co2 = self.CO2_consumption[[ @@ -181,13 +181,13 @@ def compute_CO2_emissions(self): self.CO2_sinks[ f'{GlossaryEnergy.carbon_capture} removed by energy mix (Mt)'] = 0.0 - '''Total CO2 by use + '''Total CO2 by use which is the sum of all CO2 emissions emitted by use of net energy production ''' self.CO2_sources['Total CO2 by use (Mt)'] = self.CO2_production[[ col for col in self.CO2_production if col.endswith('CO2 by use (Mt)')]].sum(axis=1) - ''' Total C02 from Flue gas + ''' Total C02 from Flue gas sum of all production of flue gas it could be equal to carbon capture from CC technos if enough investment but not sure ''' @@ -279,9 +279,9 @@ def compute_grad_CO2_emissions_sources(self, net_production): # self.total_co2_emissions[ # f'{GlossaryEnergy.carbon_capture} from energy mix (Mt)'] = 0.0 - ''' CO2 from energy mix - CO2 expelled by energy mix technologies during the process - i.e. for machinery or tractors + ''' CO2 from energy mix + CO2 expelled by energy mix technologies during the process + i.e. for machinery or tractors ''' energy_producing_co2 = co2_production[[ col for col in co2_production if col.endswith(f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit})')]] @@ -298,8 +298,8 @@ def compute_grad_CO2_emissions_sources(self, net_production): # self.total_co2_emissions[ # f'{GlossaryEnergy.carbon_capture} from energy mix (Mt)'] = 0.0 - ''' CO2 removed by energy mix - CO2 removed by energy mix technologies during the process + ''' CO2 removed by energy mix + CO2 removed by energy mix technologies during the process i.e. biomass processes as managed wood or crop energy ''' energy_removing_co2 = co2_consumption[[ @@ -317,7 +317,7 @@ def compute_grad_CO2_emissions_sources(self, net_production): # f'{GlossaryEnergy.carbon_capture} removed energy mix (Mt)'] = 0.0 ''' Total C02 from Flue gas - sum of all production of flue gas + sum of all production of flue gas it could be equal to carbon capture from CC technos if enough investment but not sure ''' # self.total_co2_emissions[f'Total {CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = self.co2_production[[ @@ -361,8 +361,8 @@ def compute_grad_CO2_emissions_sinks(self): # Compute the CO2 emitted during the use of the net energy # If net energy is negative, CO2 by use is equals to zero - ''' CO2 removed by energy mix - CO2 removed by energy mix technologies during the process + ''' CO2 removed by energy mix + CO2 removed by energy mix technologies during the process i.e. biomass processes as managed wood or crop energy ''' energy_removing_co2 = co2_consumption[[ diff --git a/energy_models/core/demand/documentation/energy_demand_disc.markdown b/energy_models/core/demand/documentation/energy_demand_disc.markdown index 5b9565ed..63a9f690 100644 --- a/energy_models/core/demand/documentation/energy_demand_disc.markdown +++ b/energy_models/core/demand/documentation/energy_demand_disc.markdown @@ -1,15 +1,15 @@ -# Documentation +# Documentation -The energy demand model gathers demand models for energies and computes a constraint for the optimization process. The constraint is simply to check that the energy production is higher than its demand. +The energy demand model gathers demand models for energies and computes a constraint for the optimization process. The constraint is simply to check that the energy production is higher than its demand. -## Electricity demand model +## Electricity demand model The electricity demand model computes the demand in electricity varying in time with the population number and an electrical machine efficiency $$electricity\_demand = init\_elec\_demand*\frac{population}{population[2020]}*\frac{EM\_efficiency[2020]}{EM\_efficiency}$$ -with the init_elec_demand = 22847.66 TWh by default. +with the init_elec_demand = 22847.66 TWh by default. The EM_efficiency is computed with a sigmoid function calibrated to be equals to 0.95 in 2020, 0.98 in 2025 and 0.985 in the distant future (see post-processing). @@ -17,4 +17,4 @@ The electricity demand_constraint is finally computed : $$electricity\_demand\_constraint = -\frac{electricity\_prod - electricity\_demand}{ref \Delta t}$$ -with a reference defined in input parameters. +with a reference defined in input parameters. \ No newline at end of file diff --git a/energy_models/core/demand/energy_demand.py b/energy_models/core/demand/energy_demand.py index cab5cdae..5e161b59 100644 --- a/energy_models/core/demand/energy_demand.py +++ b/energy_models/core/demand/energy_demand.py @@ -109,7 +109,7 @@ def compute(self): def compute_elec_demand_constraint(self): ''' - The constraint is the difference between the prod of electricity computed by the energy mix and the actual demand computed in this model + The constraint is the difference between the prod of electricity computed by the energy mix and the actual demand computed in this model ''' self.elec_demand['elec_demand (TWh)'] = self.compute_elec_demand_with_efficiency( ) @@ -135,10 +135,10 @@ def compute_elec_demand_with_efficiency(self): def compute_improved_efficiency_factor(self): ''' - Compute the effect of efficiency improvement based on a S-curve + Compute the effect of efficiency improvement based on a S-curve Electrical machine efficiency started at y_min =0.7 and long term efficiency is planned to be 0.985 - coeff and x0 have been tuned to fit y[2020]=0.95 and y[2025]=0.98 + coeff and x0 have been tuned to fit y[2020]=0.95 and y[2025]=0.98 ''' elec_machine_efficiency = self.electrical_machine_efficiency( diff --git a/energy_models/core/demand/energy_demand_disc.py b/energy_models/core/demand/energy_demand_disc.py index 987cd40d..985b6126 100644 --- a/energy_models/core/demand/energy_demand_disc.py +++ b/energy_models/core/demand/energy_demand_disc.py @@ -58,13 +58,13 @@ class EnergyDemandDiscipline(SoSWrapp): # old value is 20900TWh 'initial_electricity_demand': {'type': 'float', 'default': 18000., 'unit': 'TWh'}, 'long_term_elec_machine_efficiency': {'type': 'float', 'default': 0.985, 'unit': '-'}, - 'electricity_demand_constraint_ref': {'type': 'float', 'default': 2500.0, 'unit': 'TWh', }, + 'electricity_demand_constraint_ref': {'type': 'float', 'default': 2500.0, 'unit': 'TWh',}, GlossaryEnergy.PopulationDf['var_name']: GlossaryEnergy.PopulationDf, GlossaryEnergy.TransportDemandValue: {'type': 'dataframe', 'dataframe_descriptor': { GlossaryEnergy.Years: ('int', [1900, GlossaryEnergy.YearEndDefaultCore], False), GlossaryEnergy.TransportDemandValue: ('float', None, True)}, 'dataframe_edition_locked': False, 'unit': 'TWh'}, - 'transport_demand_constraint_ref': {'type': 'float', 'default': 6000.0, 'unit': 'TWh', }, + 'transport_demand_constraint_ref': {'type': 'float', 'default': 6000.0, 'unit': 'TWh',}, 'additional_demand_transport': {'type': 'float', 'default': 10., 'unit': '%'}} DESC_OUT = { diff --git a/energy_models/core/energy_ghg_emissions/documentation/energy_ghg_emissions_disc.md b/energy_models/core/energy_ghg_emissions/documentation/energy_ghg_emissions_disc.md index bd918461..1b2520c0 100644 --- a/energy_models/core/energy_ghg_emissions/documentation/energy_ghg_emissions_disc.md +++ b/energy_models/core/energy_ghg_emissions/documentation/energy_ghg_emissions_disc.md @@ -1,27 +1,31 @@ -# Energy Green House Gases Emissions +# Energy Green House Gases Emissions The objective of the model is to aggregate the information of GHG emissions coming from each energy. -## CO2 emissions model +## CO2 emissions model The CO2 emissions model is particular because it takes into account CO2 used or captured by CCUS technologies. -Four main sources are taken into account : +Four main sources are taken into account : - The CO2 in the flue gas expelled from plants (like coal generation plants) - The CO2 emitted by the use of each net energy production (energy burned) - The CO2 emitted by technos that cannot be stored, from machinery which uses fuels (tractors for biomass, coal extractors ...) - The CO2 which is captured by technologies as Upgrading biogas for example - -Other technologies acts in the favor of the removal of carbon emissions and CO2 fluxes are divided in three categories : + +Other technologies acts in the favor of the removal of carbon emissions and CO2 fluxes are divided in three categories : - The CO2 fluxes stored by carbon storage technologies (i.e. injected in oceans) - The CO2 removed by technologies (i.e. managed wood technology removes CO2 thanks to tree carbon cycle) - The CO2 needed by the chemical reaction of a technology (i.e. Fischer Tropsch plants may needs CO2 to enrich syngas in CO for Fischer Tropsch synthesis) - + ![](co2_emissions_model.PNG) -## Other GHG emissions model +## Other GHG emissions model -The other models are taking into account two sources of GHG emissions: +The other models are taking into account two sources of GHG emissions: - The GHG emissions coming from technologies (CH4 leakage in coal mines, N2O leakage when oil is burned in oil-fired electricity plants) - The GHG emissions coming from the use (burning) of fossil fuel are biomass dry net production. + + + + diff --git a/energy_models/core/energy_ghg_emissions/energy_ghg_emissions.py b/energy_models/core/energy_ghg_emissions/energy_ghg_emissions.py index 7a7cae91..8a8a12b5 100644 --- a/energy_models/core/energy_ghg_emissions/energy_ghg_emissions.py +++ b/energy_models/core/energy_ghg_emissions/energy_ghg_emissions.py @@ -162,7 +162,7 @@ def compute_ghg_emissions(self): self.compute_gwp() def sum_ghg_emissions_by_use(self): - '''Total CO2 by use + '''Total CO2 by use which is the sum of all CO2 emissions emitted by use of net energy production ''' for ghg in self.GHG_TYPE_LIST: @@ -171,9 +171,9 @@ def sum_ghg_emissions_by_use(self): col.endswith(f'{ghg} by use {self.ghg_input_unit}')]].sum(axis=1) def compute_other_co2_emissions(self): - ''' CO2 from energy mix - CO2 expelled by energy mix technologies during the process - i.e. for machinery or tractors + ''' CO2 from energy mix + CO2 expelled by energy mix technologies during the process + i.e. for machinery or tractors ''' energy_producing_co2 = self.ghg_production_dict[GlossaryEnergy.CO2][[ col for col in self.ghg_production_dict[GlossaryEnergy.CO2] if col.endswith(f'{GlossaryEnergy.carbon_capture} {self.ghg_input_unit}')]] @@ -206,7 +206,7 @@ def compute_other_co2_emissions(self): f'{GlossaryEnergy.carbon_capture} from energy mix {self.ghg_input_unit}'] = 0.0 ''' CO2 removed by energy mix - CO2 removed by energy mix technologies during the process + CO2 removed by energy mix technologies during the process i.e. biomass processes as managed wood or crop energy ''' energy_removing_co2 = self.CO2_consumption[[ @@ -220,7 +220,7 @@ def compute_other_co2_emissions(self): self.CO2_sinks[ f'{GlossaryEnergy.carbon_capture} removed by energy mix {self.ghg_input_unit}'] = 0.0 - ''' Total C02 from Flue gas + ''' Total C02 from Flue gas sum of all production of flue gas it could be equal to carbon capture from CC technos if enough investment but not sure ''' @@ -405,9 +405,9 @@ def compute_grad_CO2_emissions_sources(self, net_production): # self.total_co2_emissions[ # f'{GlossaryEnergy.carbon_capture} from energy mix {self.ghg_input_unit}'] = 0.0 - ''' CO2 from energy mix - CO2 expelled by energy mix technologies during the process - i.e. for machinery or tractors + ''' CO2 from energy mix + CO2 expelled by energy mix technologies during the process + i.e. for machinery or tractors ''' energy_producing_co2 = co2_production[[ col for col in co2_production if col.endswith(f'{GlossaryEnergy.carbon_capture} {self.ghg_input_unit}')]] @@ -425,8 +425,8 @@ def compute_grad_CO2_emissions_sources(self, net_production): # self.total_co2_emissions[ # f'{GlossaryEnergy.carbon_capture} from energy mix {self.ghg_input_unit}'] = 0.0 - ''' CO2 removed by energy mix - CO2 removed by energy mix technologies during the process + ''' CO2 removed by energy mix + CO2 removed by energy mix technologies during the process i.e. biomass processes as managed wood or crop energy ''' energy_removing_co2 = co2_consumption[[ @@ -445,7 +445,7 @@ def compute_grad_CO2_emissions_sources(self, net_production): # f'{GlossaryEnergy.carbon_capture} removed energy mix {self.ghg_input_unit}'] = 0.0 ''' Total C02 from Flue gas - sum of all production of flue gas + sum of all production of flue gas it could be equal to carbon capture from CC technos if enough investment but not sure ''' # self.total_co2_emissions[f'Total {CarbonCapture.flue_gas_name} {self.ghg_input_unit}'] = self.co2_production[[ @@ -490,8 +490,8 @@ def compute_grad_CO2_emissions_sinks(self): # Compute the CO2 emitted during the use of the net energy # If net energy is negative, CO2 by use is equals to zero - ''' CO2 removed by energy mix - CO2 removed by energy mix technologies during the process + ''' CO2 removed by energy mix + CO2 removed by energy mix technologies during the process i.e. biomass processes as managed wood or crop energy ''' energy_removing_co2 = co2_consumption[[ diff --git a/energy_models/core/energy_ghg_emissions/energy_ghg_emissions_disc.py b/energy_models/core/energy_ghg_emissions/energy_ghg_emissions_disc.py index 27934f83..7a2767ef 100644 --- a/energy_models/core/energy_ghg_emissions/energy_ghg_emissions_disc.py +++ b/energy_models/core/energy_ghg_emissions/energy_ghg_emissions_disc.py @@ -159,7 +159,7 @@ def setup_sos_disciplines(self): 'visibility': SoSWrapp.SHARED_VISIBILITY, 'namespace': 'ns_energy', 'dataframe_descriptor': {GlossaryEnergy.Years: ('float', None, True), - GlossaryEnergy.GhGPerUse.format(ghg): ('float', None, True), } + GlossaryEnergy.GhGPerUse.format(ghg): ('float', None, True),} } dynamic_inputs[f'{energy}.{GlossaryEnergy.StreamConsumptionValue}'] = { 'type': 'dataframe', 'unit': 'PWh', diff --git a/energy_models/core/energy_mix/documentation/energy_mix_disc.md b/energy_models/core/energy_mix/documentation/energy_mix_disc.md index 8fffafd6..ab996256 100644 --- a/energy_models/core/energy_mix/documentation/energy_mix_disc.md +++ b/energy_models/core/energy_mix/documentation/energy_mix_disc.md @@ -1,3 +1,4 @@ -# Energy Mix model +# Energy Mix model + +The energy mix discipline acts at the top of energies. The goal is to aggregate the information arriving from each energy, calculate the net production and the CO2 emissions per kwh of energy. These informations are then send back to technologies to compute technolo production and prices. -The energy mix discipline acts at the top of energies. The goal is to aggregate the information arriving from each energy, calculate the net production and the CO2 emissions per kwh of energy. These informations are then send back to technologies to compute technolo production and prices. diff --git a/energy_models/core/energy_mix_study_manager.py b/energy_models/core/energy_mix_study_manager.py index 93edc37c..28b39f1b 100644 --- a/energy_models/core/energy_mix_study_manager.py +++ b/energy_models/core/energy_mix_study_manager.py @@ -49,7 +49,7 @@ def setup_process(self): def configure_ds_boundaries(self, lower_bound_techno=1.0, upper_bound_techno=100.): """ - Configure design space boundaries + Configure design space boundaries """ self.lower_bound_techno = lower_bound_techno self.upper_bound_techno = upper_bound_techno diff --git a/energy_models/core/energy_process_builder.py b/energy_models/core/energy_process_builder.py index de894c89..fb9c22e7 100644 --- a/energy_models/core/energy_process_builder.py +++ b/energy_models/core/energy_process_builder.py @@ -34,8 +34,8 @@ def __init__(self, ee, invest_discipline: str = INVEST_DISCIPLINE_DEFAULT): self.techno_list = None self.invest_discipline = invest_discipline self.associate_namespace = False - # self.energy_name = filename.split('\\')[-2].replace("_mix", '') - # self.techno_list = current_techno_dict[self.energy_name] + #self.energy_name = filename.split('\\')[-2].replace("_mix", '') + #self.techno_list = current_techno_dict[self.energy_name] def setup_process(self, techno_list, invest_discipline=INVEST_DISCIPLINE_DEFAULT, associate_namespace=False): self.techno_list = techno_list diff --git a/energy_models/core/investments/convex_combination_model.py b/energy_models/core/investments/convex_combination_model.py index 6d22ebc3..d6071419 100644 --- a/energy_models/core/investments/convex_combination_model.py +++ b/energy_models/core/investments/convex_combination_model.py @@ -24,7 +24,7 @@ def __init__(self): self.convex_coefficients: dict[str: float] = {} self.dataframes: list[pd.DataFrame] = [] self.convex_combination_df: pd.DataFrame = None - self.coeffs_sum: float = 0. + self.coeffs_sum : float = 0. def store_inputs(self, positive_coefficients: dict[str: float], diff --git a/energy_models/core/investments/disciplines/documentation/energy_invest_disc.md b/energy_models/core/investments/disciplines/documentation/energy_invest_disc.md index ab5dae99..2c2ae181 100644 --- a/energy_models/core/investments/disciplines/documentation/energy_invest_disc.md +++ b/energy_models/core/investments/disciplines/documentation/energy_invest_disc.md @@ -1,5 +1,6 @@ # Investments Distribution -The distribution of investments is made according to the investments level mix dataframe in input. Each coefficient for each energy/technology over the years is normalized by the sum of coefficients for one year and multiplied by the total investments level : +The distribution of investments is made according to the investments level mix dataframe in input. Each coefficient for each energy/technology over the years is normalized by the sum of coefficients for one year and multiplied by the total investments level : $$energy\_investment = total\_investment * \frac{energy\_mix\_coefficient}{\sum energy\_mix\_coefficient}$$ + diff --git a/energy_models/core/investments/disciplines/documentation/energy_or_ccs_invest_disc.md b/energy_models/core/investments/disciplines/documentation/energy_or_ccs_invest_disc.md index 92d85aca..5e54ce6d 100644 --- a/energy_models/core/investments/disciplines/documentation/energy_or_ccs_invest_disc.md +++ b/energy_models/core/investments/disciplines/documentation/energy_or_ccs_invest_disc.md @@ -1,7 +1,8 @@ # Investments Distribution between CCS and Energy conversion -The distribution of global investments into CCS and energy conversion is made following a percentage of CCS investment compared to global investment. +The distribution of global investments into CCS and energy conversion is made following a percentage of CCS investment compared to global investment. $$ccs\_investment = global\_investment * \frac{ccs\_mix\_percentage}{100}$$ $$energy\_conversion\_investment = global\_investment *(1.0- \frac{ccs\_mix\_percentage}{100})$$ + diff --git a/energy_models/core/investments/disciplines/documentation/independent_invest_disc.md b/energy_models/core/investments/disciplines/documentation/independent_invest_disc.md index a209b029..5587e5d1 100644 --- a/energy_models/core/investments/disciplines/documentation/independent_invest_disc.md +++ b/energy_models/core/investments/disciplines/documentation/independent_invest_disc.md @@ -3,3 +3,4 @@ The distribution of investments is made according to the investments coming from the design space. A constraint is computed in order to obtain a sum of investments lower than the investment dedicated for energy production coming from the macroeconomics model. $$investment\_constraint = energy\_investment > \sum technos\_investments$$ + diff --git a/energy_models/core/investments/disciplines/documentation/investments_profile_builder_disc.md b/energy_models/core/investments/disciplines/documentation/investments_profile_builder_disc.md index 7bf3fe8f..34f20ae3 100644 --- a/energy_models/core/investments/disciplines/documentation/investments_profile_builder_disc.md +++ b/energy_models/core/investments/disciplines/documentation/investments_profile_builder_disc.md @@ -18,8 +18,10 @@ The Investments Profile Builder uses the following formulas : $$ These calculations ensure that the investments are proportionally distributed based on the given coefficients. -The output investment profile can be exported either as a dataframe 'invest_mix' where the values of the variables -are provided for each year or as a 1D array per variable (named 'variable_array_mix') where the values are provided -only for a selected number of years referred to as the poles. -Therefore, the number of poles have to be provided by the user in the second case. To activate the second case, +The output investment profile can be exported either as a dataframe 'invest_mix' where the values of the variables +are provided for each year or as a 1D array per variable (named 'variable_array_mix') where the values are provided +only for a selected number of years referred to as the poles. +Therefore, the number of poles have to be provided by the user in the second case. To activate the second case, the user must set to True the input variable 'export_invest_profiles_at_poles' + + diff --git a/energy_models/core/investments/disciplines/documentation/investments_redistribution_disc.md b/energy_models/core/investments/disciplines/documentation/investments_redistribution_disc.md index 02a17b21..37d6d560 100644 --- a/energy_models/core/investments/disciplines/documentation/investments_redistribution_disc.md +++ b/energy_models/core/investments/disciplines/documentation/investments_redistribution_disc.md @@ -6,3 +6,4 @@ We then use another input that contains percentage of investments in each techno Used formula is : $$invest\_in\_techno = GDP\_net\_of\_damage * percentage\_of\_GDP\_invest\_in\_energy * percentage\_invest\_in\_techno$$ + diff --git a/energy_models/core/investments/disciplines/documentation/techno_invest_disc.md b/energy_models/core/investments/disciplines/documentation/techno_invest_disc.md index ab5dae99..2c2ae181 100644 --- a/energy_models/core/investments/disciplines/documentation/techno_invest_disc.md +++ b/energy_models/core/investments/disciplines/documentation/techno_invest_disc.md @@ -1,5 +1,6 @@ # Investments Distribution -The distribution of investments is made according to the investments level mix dataframe in input. Each coefficient for each energy/technology over the years is normalized by the sum of coefficients for one year and multiplied by the total investments level : +The distribution of investments is made according to the investments level mix dataframe in input. Each coefficient for each energy/technology over the years is normalized by the sum of coefficients for one year and multiplied by the total investments level : $$energy\_investment = total\_investment * \frac{energy\_mix\_coefficient}{\sum energy\_mix\_coefficient}$$ + diff --git a/energy_models/core/investments/disciplines/independent_invest_disc.py b/energy_models/core/investments/disciplines/independent_invest_disc.py index 1e0ec2d9..e8ed5c93 100644 --- a/energy_models/core/investments/disciplines/independent_invest_disc.py +++ b/energy_models/core/investments/disciplines/independent_invest_disc.py @@ -72,7 +72,7 @@ class IndependentInvestDiscipline(SoSWrapp): 'float', None, False)}, 'namespace': 'ns_invest', 'dataframe_edition_locked': False}, - GlossaryEnergy.MaxBudgetValue: GlossaryEnergy.MaxBudgetDf, + GlossaryEnergy.MaxBudgetValue : GlossaryEnergy.MaxBudgetDf, GlossaryEnergy.MaxBudgetConstraintRefValue: GlossaryEnergy.MaxBudgetConstraintRef } @@ -383,4 +383,6 @@ def pimp_string(val: str): instanciated_charts.insert(1, new_chart_energy_ratio) + + return instanciated_charts diff --git a/energy_models/core/investments/disciplines/investments_profile_builder_disc.py b/energy_models/core/investments/disciplines/investments_profile_builder_disc.py index 5b7652d1..20f87412 100644 --- a/energy_models/core/investments/disciplines/investments_profile_builder_disc.py +++ b/energy_models/core/investments/disciplines/investments_profile_builder_disc.py @@ -44,10 +44,10 @@ class InvestmentsProfileBuilderDisc(SoSWrapp): 'version': '', } ''' - Discipline that generates an output invest profile based on generic input invest profiles and input weights for + Discipline that generates an output invest profile based on generic input invest profiles and input weights for each of those profiles. Based on the input boolean EXPORT_PROFILES_AT_POLES, it can either export the output profile at the poles or for all years - then, the output variable is not named the same, as in the first case it becomes an input of the design_var discipline and + then, the output variable is not named the same, as in the first case it becomes an input of the design_var discipline and in the second case it is an input of the investment distribution ''' @@ -76,6 +76,7 @@ def setup_sos_disciplines(self): for i in range(n_profiles): dynamic_inputs[f'coeff_{i}'] = {'type': 'float', 'unit': '-'} + if 'column_names' in self.get_data_in(): column_names = self.get_sosdisc_inputs('column_names') if column_names is not None and n_profiles is not None: @@ -93,6 +94,7 @@ def setup_sos_disciplines(self): if export_profiles_at_poles is not None and export_profiles_at_poles: dynamic_inputs['nb_poles'] = {'type': 'int', 'unit': '-', 'user_level': 3} + if df_descriptor is not None and export_profiles_at_poles is not None: # the output invest profile can be provided either for all the years or for some limited number of poles. if not export_profiles_at_poles: @@ -107,7 +109,7 @@ def setup_sos_disciplines(self): dynamic_outputs[f'{var}_array_mix'] = { "type": "array", "unit": "G$", - "namespace": "ns_invest", # same namespace as for design_var discipline inputs as described in design_var_descriptor + "namespace": "ns_invest", # same namespace as for design_var discipline inputs as described in design_var_descriptor "visibility": "Shared", } @@ -155,7 +157,7 @@ def run(self): # type: (...) -> None df = inputs['df_0'] nb_poles = inputs['nb_poles'] years_poles, poles_index = self.compute_poles(df, nb_poles) - for col in column_names: # extract data at the poles + for col in column_names: # extract data at the poles df = self.model.convex_combination_df[[GlossaryEnergy.Years] + [col]] outputs = {col + '_array_mix': df[df.index.isin(poles_index)][col].values} self.store_sos_outputs_values(outputs) @@ -166,7 +168,7 @@ def compute_sos_jacobian(self): n_profiles = dict_in['n_profiles'] df = dict_in['df_0'] export_profiles_at_poles = dict_in[GlossaryEnergy.EXPORT_PROFILES_AT_POLES] - poles_index = None # initialize to avoid pylint error + poles_index = None # initialize to avoid pylint error if export_profiles_at_poles: nb_poles = dict_in['nb_poles'] years_poles, poles_index = self.compute_poles(df, nb_poles) @@ -180,7 +182,7 @@ def compute_sos_jacobian(self): (f'coeff_{i}',), derivative.reshape((len(derivative), 1)) ) else: - derivative_at_poles = derivative[poles_index].reshape((len(poles_index), 1)) # extract gradient at the poles only + derivative_at_poles = derivative[poles_index].reshape((len(poles_index), 1)) #extract gradient at the poles only self.set_partial_derivative(col_name + '_array_mix', f'coeff_{i}', derivative_at_poles) def get_chart_filter_list(self): @@ -205,7 +207,7 @@ def get_post_processing_list(self, filters=None): df = self.get_sosdisc_inputs('df_0') years = list(df[GlossaryEnergy.Years].values) # all profiles should have the same years export_profiles_at_poles = self.get_sosdisc_inputs(GlossaryEnergy.EXPORT_PROFILES_AT_POLES) - years_poles = None # initialize to avoid pylint error + years_poles = None # initialize to avoid pylint error if export_profiles_at_poles: nb_poles = self.get_sosdisc_inputs('nb_poles') years_poles, poles_index = self.compute_poles(df, nb_poles) @@ -215,6 +217,7 @@ def get_post_processing_list(self, filters=None): graph_poles = TwoAxesInstanciatedChart(GlossaryEnergy.Years, 'Invest array_mix [G$]', chart_name="Output profile invest at the poles") + for idx, column in enumerate(column_names): chart_name = f"Investments in {column}" @@ -239,7 +242,7 @@ def get_post_processing_list(self, filters=None): series_values = list(invest_profile_poles) serie_obj = InstanciatedSeries(list(years_poles), series_values, column + '_array_mix', display_type="scatter", marker_symbol='circle', - # marker=dict(color='LightSkyBlue', size=20, line=dict(color='MediumPurple', width=2)) + #marker=dict(color='LightSkyBlue', size=20, line=dict(color='MediumPurple', width=2)) ) graph_poles.add_series(serie_obj) @@ -248,4 +251,4 @@ def get_post_processing_list(self, filters=None): else: instanciated_charts.append(graph_poles) - return instanciated_charts + return instanciated_charts \ No newline at end of file diff --git a/energy_models/core/investments/energy_invest.py b/energy_models/core/investments/energy_invest.py index ea018dba..49e53ad4 100644 --- a/energy_models/core/investments/energy_invest.py +++ b/energy_models/core/investments/energy_invest.py @@ -29,13 +29,13 @@ def __init__(self, name='Energy'): def set_energy_list(self, energy_list): ''' - Set the energy_list of the energy mix + Set the energy_list of the energy mix ''' self.energy_list = energy_list def set_invest_mix(self, mix_df): ''' - Set the invest mix of the energy mix + Set the invest mix of the energy mix ''' if not isinstance(self.energy_list, list): raise TypeError('energy_list must be defined as a list') diff --git a/energy_models/core/investments/energy_or_ccsinvest.py b/energy_models/core/investments/energy_or_ccsinvest.py index d2577b39..acc76eed 100644 --- a/energy_models/core/investments/energy_or_ccsinvest.py +++ b/energy_models/core/investments/energy_or_ccsinvest.py @@ -22,7 +22,7 @@ class EnergyOrCCSInvest: ''' - Model to split global investment into investment for Carbon Capture and Storage technologies and into investment for energy conversion + Model to split global investment into investment for Carbon Capture and Storage technologies and into investment for energy conversion ''' def __init__(self): @@ -44,7 +44,7 @@ def configure(self, input_dict): def compute(self): ''' - Compute the investment in to CCS and into energy_conversion + Compute the investment in to CCS and into energy_conversion ''' ccs_invest = self.global_invest[GlossaryEnergy.EnergyInvestmentsValue].values * \ self.invest_ccs_percentage['ccs_percentage'].values / 100.0 diff --git a/energy_models/core/investments/one_invest.py b/energy_models/core/investments/one_invest.py index f973eba7..9f72efaa 100644 --- a/energy_models/core/investments/one_invest.py +++ b/energy_models/core/investments/one_invest.py @@ -23,7 +23,7 @@ class OneInvest(BaseInvest): ''' - Model to split global investment into investment for each technology + Model to split global investment into investment for each technology ''' def __init__(self, name='Invest'): @@ -60,7 +60,7 @@ def compute(self, inputs_dict): def set_invest_mix(self, mix_df): ''' - Set the invest mix of the energy mix + Set the invest mix of the energy mix ''' if not isinstance(self.distribution_list, list): raise TypeError('energy_list must be defined as a list') diff --git a/energy_models/core/process_builder_database.py b/energy_models/core/process_builder_database.py index 5c8102c3..7d0e373c 100644 --- a/energy_models/core/process_builder_database.py +++ b/energy_models/core/process_builder_database.py @@ -37,7 +37,7 @@ def process_namespace(self, ns_dict=None, get_from_database=False): Parameters: ns_dict (dict): The namespace definition to add. - associate_namespace (bool): If True, associates the namespace with builders. + associate_namespace (bool): If True, associates the namespace with builders. database_name (str): The name of the database. Returns: @@ -50,7 +50,7 @@ def process_namespace(self, ns_dict=None, get_from_database=False): return ns_ids def create_builder_list(self, mods_dict, ns_dict=None, associate_namespace=False, get_from_database=False): - ''' + ''' define a base namespace instantiate builders iterating over a list of module paths return the list of disciplines built @@ -72,7 +72,7 @@ def set_builder_specific_ns_database(self, builders_list, ns_dict=None, associat Parameters: builders_list (list): A list of builder objects. ns_dict (dict): A dictionary of namespaces. - associate_namespace (bool): If True, associates the namespace dictionary with the builder(s). + associate_namespace (bool): If True, associates the namespace dictionary with the builder(s). database_location (str): The path to the directory where the database will be saved. database_name (str): The name of the database. diff --git a/energy_models/core/stream_type/base_stream.py b/energy_models/core/stream_type/base_stream.py index 1ee6dc7b..0b1b2afb 100644 --- a/energy_models/core/stream_type/base_stream.py +++ b/energy_models/core/stream_type/base_stream.py @@ -70,7 +70,7 @@ def __init__(self, name): def reload_df(self): ''' - Reload all dataframes with new year start and year end + Reload all dataframes with new year start and year end ''' self.years = np.arange(self.year_start, self.year_end + 1) base_df = pd.DataFrame({GlossaryEnergy.Years: self.years}) @@ -131,11 +131,11 @@ def configure_parameters_update(self, inputs_dict): inputs_dict['scaling_factor_techno_consumption'] self.sub_land_use_required_dict[element] = inputs_dict[f'{element}.{GlossaryEnergy.LandUseRequiredValue}'] - # print(self.name, [list(inputs_dict[f'{element}.{GlossaryEnergy.LandUseRequiredValue}'].columns) for element in self.subelements_list]) + #print(self.name, [list(inputs_dict[f'{element}.{GlossaryEnergy.LandUseRequiredValue}'].columns) for element in self.subelements_list]) def compute(self, inputs, exp_min=True): ''' - Compute all energy variables with its own technologies + Compute all energy variables with its own technologies ''' _, self.consumption_woratio, _ = self.compute_production( @@ -150,7 +150,7 @@ def compute(self, inputs, exp_min=True): self.compute_energy_type_capital(inputs) - # print(self.name, list(self.production.columns)) + #print(self.name, list(self.production.columns)) return self.total_prices, self.production, self.consumption, self.consumption_woratio, self.mix_weights def compute_production(self, sub_production_dict, sub_consumption_dict): @@ -176,8 +176,8 @@ def compute_production(self, sub_production_dict, sub_consumption_dict): production, consumption = self.compute_byproducts_consumption_and_production( element, sub_production_dict, sub_consumption_dict, production, consumption) - # print(self.name, "&&#", self.unit) - # print(self.name, list(production_by_techno.columns)) + #print(self.name, "&&#", self.unit) + #print(self.name, list(production_by_techno.columns)) return production, consumption, production_by_techno def compute_byproducts_consumption_and_production(self, element, sub_production_dict, sub_consumption_dict, production, @@ -222,7 +222,7 @@ def compute_energy_type_capital(self, inputs): def compute_price(self, exp_min=True): ''' - Compute the price with all sub_prices and sub weights computed with total production + Compute the price with all sub_prices and sub weights computed with total production ''' self.total_prices[self.name] = 0. @@ -308,12 +308,12 @@ def compute_dprod_wcutoff(self, production_by_techno, elements_dict, min_prod): def compute_prod_with_exp_min(self, production_by_techno, elements_dict, min_prod): ''' Compute the production of each element by minimizing them with and exponential function to reach min prod - Objective is to decrease gradients when prod are very low + Objective is to decrease gradients when prod are very low Be careful the objective is to increase the total production to decrease the gradient then we have to modify the sum also - BIG WARNING : there is an issue in the handling of complex number in this function that may cause small errors - in gradient tests. So far, no solution has been found. This error can be reproduced by running the test on the + BIG WARNING : there is an issue in the handling of complex number in this function that may cause small errors + in gradient tests. So far, no solution has been found. This error can be reproduced by running the test on the gradients of liquid_hydrogen stream in the case of a production of techno HydrogenLiquefaction below min_prod. elements_dict contains {Name of the prod techno or energy: full name of the column} diff --git a/energy_models/core/stream_type/carbon_disciplines/carbon_capture_disc.py b/energy_models/core/stream_type/carbon_disciplines/carbon_capture_disc.py index 24737665..a229b234 100644 --- a/energy_models/core/stream_type/carbon_disciplines/carbon_capture_disc.py +++ b/energy_models/core/stream_type/carbon_disciplines/carbon_capture_disc.py @@ -78,7 +78,7 @@ def init_execution(self): def run(self): ''' - Overwrite run to limit flue gas carbon capture + Overwrite run to limit flue gas carbon capture ''' super().run() @@ -87,7 +87,7 @@ def run(self): 'carbon_captured_type': self.energy_model.carbon_captured_type, 'carbon_captured_type_woratio': self.energy_model.carbon_captured_type_woratio, } - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_capture_disc.md b/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_capture_disc.md index 0dd9e46f..1c21f659 100644 --- a/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_capture_disc.md +++ b/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_capture_disc.md @@ -29,4 +29,4 @@ The technologies taken into account are: Flue gas capture is limited by the amount of flue gas available at the exit of CO2 emissing factories. The model limits (smoothly with an exponential to avoid zero gradients) the carbon capture via flue gas with the real amount of flue gas available. The mean CO2 concentration in the flue gas is retained to compute the economic data of each flue gas technology. -[^1]: https://en.wikipedia.org/wiki/Carbon_capture_and_storage +[^1]: https://en.wikipedia.org/wiki/Carbon_capture_and_storage \ No newline at end of file diff --git a/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_storage_disc.md b/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_storage_disc.md index c75dd2da..96b97d32 100644 --- a/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_storage_disc.md +++ b/energy_models/core/stream_type/carbon_disciplines/documentation/carbon_storage_disc.md @@ -5,7 +5,7 @@ Carbon Capture and Storage (CCS) is the process of capturing waste carbon dioxid There exist several paths for storage: -* Depleted Oil & Gas +* Depleted Oil & Gas * Enhanced Oil recovery * Deep saline formation * Deep ocean $CO_2$ injection @@ -15,4 +15,4 @@ There exist several paths for storage: [^1]: https://en.wikipedia.org/wiki/Carbon_capture_and_storage -[^2]: [Global CCS institute - CCS Image Library](https://www.globalccsinstitute.com/resources/ccs-image-library/) +[^2]: [Global CCS institute - CCS Image Library](https://www.globalccsinstitute.com/resources/ccs-image-library/) \ No newline at end of file diff --git a/energy_models/core/stream_type/carbon_disciplines/documentation/flue_gas_disc.markdown b/energy_models/core/stream_type/carbon_disciplines/documentation/flue_gas_disc.markdown index efcb75dc..b4da18d0 100644 --- a/energy_models/core/stream_type/carbon_disciplines/documentation/flue_gas_disc.markdown +++ b/energy_models/core/stream_type/carbon_disciplines/documentation/flue_gas_disc.markdown @@ -2,26 +2,26 @@ Flue gas is the gas exiting to the atmosphere via a flue, which is a pipe or channel for conveying exhaust gases from a fireplace, oven, furnace, boiler or steam generator. Quite often, the flue gas refers to the combustion exhaust gas produced at power plants. Its composition depends on what is being burned, but it will usually consist of mostly nitrogen (typically more than two-thirds) derived from the combustion of air, carbon dioxide (CO2), and water vapor as well as excess oxygen (also derived from the combustion air). It further contains a small percentage of a number of pollutants, such as particulate matter (like soot), carbon monoxide, nitrogen oxides, and sulfur oxides. Flue gas from London's Bankside Power Station, 1975[^1] -![](flue_gas.PNG) +![](flue_gas.PNG) Flue gas composition[^2] -![](flue_gas_composition.PNG) +![](flue_gas_composition.PNG) In the model, we focus on C02 concentration in the flue gas to calculate costs variation of CAPEX and electricity needs. The table below describes common flue gases from industries and their related concentration of CO2. CO2 concentration in different flue gases[^3] -![](co2_concentration_flue_gas.PNG) +![](co2_concentration_flue_gas.PNG) -Once each energy production flue gases specified, the model calculates average CO2 concentration in flue gas stream and applies variations. +Once each energy production flue gases specified, the model calculates average CO2 concentration in flue gas stream and applies variations. -The total flue gas production is finally computed and send to the carbon capture model to get the potential fluegas to be captured. +The total flue gas production is finally computed and send to the carbon capture model to get the potential fluegas to be captured. CO2 concentration and evolution of costs related to capture[^4] -![](co2_cost_evolution_by_concentration.PNG) +![](co2_cost_evolution_by_concentration.PNG) [^1]: Robin Webster, Bankside power station and St George the Martyr church, licensed under [CC BY-SA 2.0](https://creativecommons.org/licenses/by-sa/2.0/), https://en.wikipedia.org/wiki/Flue_gas [^2]: Constituents of flue gas, https://www.sciencedirect.com/topics/earth-and-planetary-sciences/flue-gas [^3]: Wang, X. and Song, C., 2020. Carbon Capture From Flue Gas and the Atmosphere: A Perspective. Frontiers in Energy Research, 8, p.265. Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), https://www.frontiersin.org/articles/10.3389/fenrg.2020.560849/full -[^4]: IEM capture cost estimates for representative oil sands flue gas streams according to CO2 concentration, https://www.researchgate.net/figure/IEM-capture-cost-estimates-for-representative-oil-sands-flue-gas-streams-according-to_tbl4_251711920 +[^4]: IEM capture cost estimates for representative oil sands flue gas streams according to CO2 concentration, https://www.researchgate.net/figure/IEM-capture-cost-estimates-for-representative-oil-sands-flue-gas-streams-according-to_tbl4_251711920 \ No newline at end of file diff --git a/energy_models/core/stream_type/carbon_disciplines/flue_gas_disc.py b/energy_models/core/stream_type/carbon_disciplines/flue_gas_disc.py index 32b04229..0166a053 100644 --- a/energy_models/core/stream_type/carbon_disciplines/flue_gas_disc.py +++ b/energy_models/core/stream_type/carbon_disciplines/flue_gas_disc.py @@ -188,7 +188,7 @@ def run(self): GlossaryEnergy.FlueGasMean: flue_gas_mean, 'flue_gas_production': self.energy_model.get_total_flue_gas_production(), 'flue_gas_prod_ratio': self.energy_model.get_total_flue_gas_prod_ratio()} - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/stream_type/carbon_models/carbon_capture.py b/energy_models/core/stream_type/carbon_models/carbon_capture.py index a83f1d58..fc0f2af0 100644 --- a/energy_models/core/stream_type/carbon_models/carbon_capture.py +++ b/energy_models/core/stream_type/carbon_models/carbon_capture.py @@ -88,7 +88,7 @@ def compute(self, inputs, exp_min=True): def compute_production(self, sub_production_dict, sub_consumption_dict): ''' - Specific compute energy production where we compute carbon captured from flue gas + Specific compute energy production where we compute carbon captured from flue gas ''' # Initialize dataframe out @@ -196,11 +196,11 @@ def compute_grad_element_mix_vs_prod(self, production_by_techno, elements_dict, ptot = p1 + p2 + p3 for dpi/dpi Old : dtechno_mix(p1)/dprod1 = dp1(ptot-p1)/ptot**2 - with p1 = fexp(prod1) + with p1 = fexp(prod1) dp1/dprod1 = 1.0*f'exp(prod1) dp2/dprod1 = 0.0 New : dtechno_mix(p1)/dprod1 = dp1/ptot-(dp1+dp2)p1/ptot**2 = dp1(ptot-p1)/ptot**2 -dp2p1/ptot**2 - with p1 = fexp(pbis1) + with p1 = fexp(pbis1) pbis1 = prod1*fg_perc = prod1*fexpp(fg_prod/(prod1+prod2)) dp1/dprod1 = f'exp(pbis1)*dpbis1 = f'exp(pbis1)*(fexpp(fg_ratio) + prod1*f'expp(fg_ratio)*dfg_ratio = dp1old * (fexpp(fg_ratio) + prod1*f'expp(fg_ratio)*dfg_ratio @@ -216,7 +216,7 @@ def compute_grad_element_mix_vs_prod(self, production_by_techno, elements_dict, with dp1/dprod1 = 1.0*f'exp(prod1) New : dtechno_mix(p2)/dprod1 = -p2dp1/ptot**2 -p2dp2/ptot**2 - with + with dp1/dprod1 = f'exp(pbis1)*dpbis1 = f'exp(pbis1)*(fexpp(fg_ratio) + prod1*f'expp(fg_ratio)*dfg_ratio = dp2old * (fexpp(fg_ratio) + prod1*f'expp(fg_ratio)*dfg_ratio dp2/dprod1 = f'exp(pbis2)*dpbis2 =f'exp(pbis2)*prod2*f'expp(fg_ratio)*dfg_ratio diff --git a/energy_models/core/stream_type/carbon_models/flue_gas.py b/energy_models/core/stream_type/carbon_models/flue_gas.py index 28ff464e..36254799 100644 --- a/energy_models/core/stream_type/carbon_models/flue_gas.py +++ b/energy_models/core/stream_type/carbon_models/flue_gas.py @@ -71,7 +71,7 @@ def get_total_flue_gas_production(self): def get_total_flue_gas_prod_ratio(self): ''' - Return mix weights which is flue gas production ratio + Return mix weights which is flue gas production ratio ''' return self.mix_weights diff --git a/energy_models/core/stream_type/energy_disc.py b/energy_models/core/stream_type/energy_disc.py index 7dd14b68..bdd5420d 100644 --- a/energy_models/core/stream_type/energy_disc.py +++ b/energy_models/core/stream_type/energy_disc.py @@ -101,7 +101,7 @@ def run(self): outputs_dict = {GlossaryEnergy.CO2EmissionsValue: CO2_emissions} outputs_dict.update(ghg_per_use) - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/bio_diesel_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/bio_diesel_disc.markdown index d606e123..b47a8549 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/bio_diesel_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/bio_diesel_disc.markdown @@ -14,16 +14,16 @@ The production process we consider uses the chemical reactions of transesterific World bio fuel production was estimated around 1 100 TWh in 2019, including bioethanol and hydrotreated vegetable oil. Production in liter for 2019 and forecast to 2025: -![](biodiesel_production.PNG) +![](biodiesel_production.PNG) (Image credit: IEA, taken from [^2]) **Price:** -Price for FAME (Fatty Acid Methyl Ester) and SME (Soy Methyl Ester) is highly versatile, since end of 2020 it became more expensive (> 1000 USD/ton). With a calorific value of 37.27 MJ/kg, it appears to be superior to 100 USD/MWh. +Price for FAME (Fatty Acid Methyl Ester) and SME (Soy Methyl Ester) is highly versatile, since end of 2020 it became more expensive (> 1000 USD/ton). With a calorific value of 37.27 MJ/kg, it appears to be superior to 100 USD/MWh. - ![](biodiesel_price.PNG) + ![](biodiesel_price.PNG) (Image credit: NESTE, taken from [^3]) [^1]: Biodiesel, Wikipedia, https://en.wikipedia.org/wiki/Biodiesel [^2]: IEA 2022; Global Biofuel production, https://www.iea.org/data-and-statistics/charts/global-biofuel-production-in-2019-and-forecast-to-2025, License: CC BY 4.0. -[^3]: Biodiesel price, neste.com, https://www.neste.com/investors/market-data/biodiesel-prices-sme-fame +[^3]: Biodiesel price, neste.com, https://www.neste.com/investors/market-data/biodiesel-prices-sme-fame \ No newline at end of file diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/biomass_dry_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/biomass_dry_disc.markdown index 0ca84952..7169264e 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/biomass_dry_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/biomass_dry_disc.markdown @@ -8,7 +8,7 @@ Biomass sources for energy include: * Wood and wood processing wastes—firewood, wood pellets, and wood chips, lumber and furniture mill sawdust and waste, and black liquor from pulp and paper mills * Agricultural crops and waste materials—corn, soybeans, sugar cane, switchgrass, woody plants, and algae, and crop and food processing residues -* Biogenic materials in municipal solid waste—paper, cotton, and wool products, and food, yard, and wood wastes +* Biogenic materials in municipal solid waste—paper, cotton, and wool products, and food, yard, and wood wastes * Animal manure and human sewage ![](biomass_prod.PNG) [^1] diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/electricity_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/electricity_disc.markdown index 478eb1fb..c80bffc2 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/electricity_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/electricity_disc.markdown @@ -22,7 +22,7 @@ World electricity production was estimated around 26 000 TWh in 2019, and the fo **Price:** -The selection of electricity production modes and their economic viability varies in accordance with demand and region. The economics vary considerably around the world, resulting in widespread residential selling prices, e.g. the price in Serbia is around 6 cents per kWh while Germany has one of the most expensive electricity price around 30 cents per kWh (€). +The selection of electricity production modes and their economic viability varies in accordance with demand and region. The economics vary considerably around the world, resulting in widespread residential selling prices, e.g. the price in Serbia is around 6 cents per kWh while Germany has one of the most expensive electricity price around 30 cents per kWh (€). **CO2 impact:** @@ -32,4 +32,4 @@ Electricity generation accounts for nearly 40% of total CO2 emissions, the large [^1]: Electricity Generation, Wikipedia, https://en.wikipedia.org/wiki/Electricity_generation [^2]: Electricity Mix, Our World in Data, https://ourworldindata.org/electricity-mix -[^3]: Electricity Map, electricitymap.org, https://www.electricitymap.org/map +[^3]: Electricity Map, electricitymap.org, https://www.electricitymap.org/map \ No newline at end of file diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/ethanol_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/ethanol_disc.markdown index a500671c..c421d10a 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/ethanol_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/ethanol_disc.markdown @@ -1,13 +1,13 @@ ## Ethanol ## Description -Bioethanol is a form of renewable energy that can be produced from agricultural feedstocks. -It can be made from very common crops such as hemp, sugarcane, potato, cassava and corn. -There has been considerable debate about how useful bioethanol is in replacing gasoline. +Bioethanol is a form of renewable energy that can be produced from agricultural feedstocks. +It can be made from very common crops such as hemp, sugarcane, potato, cassava and corn. +There has been considerable debate about how useful bioethanol is in replacing gasoline. Concerns about its production and use relate to increased food prices due to the large amount of arable land required for crops, as well as the energy and pollution balance of the whole cycle of ethanol production, especially from corn.[^1] -## Engines Fuel -Ethanol contains approximately 34% less energy per unit volume than gasoline, and therefore in theory, burning pure ethanol in a vehicle reduces range per unit measure by 34%, given the same fuel economy, compared to burning pure gasoline. +## Engines Fuel +Ethanol contains approximately 34% less energy per unit volume than gasoline, and therefore in theory, burning pure ethanol in a vehicle reduces range per unit measure by 34%, given the same fuel economy, compared to burning pure gasoline. However, since ethanol has a higher octane rating, the engine can be made more efficient by raising its compression ratio. More than 98% of U.S. gasoline contains ethanol, typically E10 (10% ethanol, 90% gasoline), to oxygenate the fuel, which reduces air pollution. @@ -22,7 +22,7 @@ Compared with conventional unleaded gasoline, ethanol is a particulate-free burn The most common production process is "Dry Milling" representing 90% of total production.[^3] ![](ethanol_world_prod_rfa.PNG) -(Image Credit: +(Image Credit: [Renewable Fuels Association](https://ethanolrfa.org/markets-and-statistics/annual-ethanol-production)) ## Sources diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/fuel_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/fuel_disc.markdown index 498c221e..702e3893 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/fuel_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/fuel_disc.markdown @@ -18,4 +18,4 @@ The general formula of fuels is CnH2n+2. Here, we consider several types of fuel [^1]: Hydrocarbons, [Wikipedia](https://en.wikipedia.org/wiki/Hydrocarbon) -[^2]: Ed Vitz, John W. Moore, Justin Shorb, Xavier Prat-Resina, Tim Wendorff, and Adam Hahn. Organic Compounds- Hydrocarbons,[Chemistry, Libretext](https://chem.libretexts.org/Bookshelves/General_Chemistry/Book%3A_ChemPRIME_(Moore_et_al.)/08%3A_Properties_of_Organic_Compounds/8.05%3A_Organic_Compounds-_Hydrocarbons) +[^2]: Ed Vitz, John W. Moore, Justin Shorb, Xavier Prat-Resina, Tim Wendorff, and Adam Hahn. Organic Compounds- Hydrocarbons,[Chemistry, Libretext](https://chem.libretexts.org/Bookshelves/General_Chemistry/Book%3A_ChemPRIME_(Moore_et_al.)/08%3A_Properties_of_Organic_Compounds/8.05%3A_Organic_Compounds-_Hydrocarbons) \ No newline at end of file diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/hydrotreated_oil_fuel_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/hydrotreated_oil_fuel_disc.markdown index b4ca9c10..49e8da0e 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/hydrotreated_oil_fuel_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/hydrotreated_oil_fuel_disc.markdown @@ -1,3 +1,3 @@ ## What is Hydrotreated Vegetable Oil Fuel ? -Vegetable oil can be used as an alternative fuel in diesel engines and in heating oil burners. When vegetable oil is used directly as a fuel, in either modified or unmodified equipment, it is referred to as straight vegetable oil (SVO) or pure plant oil (PPO). Conventional diesel engines can be modified to help ensure that the viscosity of the vegetable oil is low enough to allow proper atomization of the fuel. This prevents incomplete combustion, which would damage the engine by causing a build-up of carbon. Straight vegetable oil can also be blended with conventional diesel or processed into biodiesel, HVO or bioliquids for use under a wider range of conditions. -(see [HVO](https://en.wikipedia.org/wiki/Vegetable_oil_fuel)) +Vegetable oil can be used as an alternative fuel in diesel engines and in heating oil burners. When vegetable oil is used directly as a fuel, in either modified or unmodified equipment, it is referred to as straight vegetable oil (SVO) or pure plant oil (PPO). Conventional diesel engines can be modified to help ensure that the viscosity of the vegetable oil is low enough to allow proper atomization of the fuel. This prevents incomplete combustion, which would damage the engine by causing a build-up of carbon. Straight vegetable oil can also be blended with conventional diesel or processed into biodiesel, HVO or bioliquids for use under a wider range of conditions. +(see [HVO](https://en.wikipedia.org/wiki/Vegetable_oil_fuel)) \ No newline at end of file diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/liquid_fuel_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/liquid_fuel_disc.markdown index b75f8a87..b5f3cd3d 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/liquid_fuel_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/liquid_fuel_disc.markdown @@ -19,4 +19,4 @@ We consider several methods for production: [^1]: Liquid Fuel, Wikipedia, https://en.wikipedia.org/wiki/Liquid_fuel [^2]: Engineering Toolbox, https://www.engineeringtoolbox.com [^3]: https://www.icao.int/environmental-protection/CarbonOffset/Documents/Methodology%20ICAO%20Carbon%20Calculator_v10-2017.pdf -[^4]: https://www.ipcc-nggip.iges.or.jp/public/gp/bgp/2_4_Water-borne_Navigation.pdf +[^4]: https://www.ipcc-nggip.iges.or.jp/public/gp/bgp/2_4_Water-borne_Navigation.pdf \ No newline at end of file diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/liquid_hydrogen_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/liquid_hydrogen_disc.markdown index 2c223b0b..7e019372 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/liquid_hydrogen_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/liquid_hydrogen_disc.markdown @@ -16,8 +16,8 @@ Significant datas for liquid hydrogen [^2]: Water is often considered harmless for the environment and it can be considered "zero emission", however, water vapor emitted in the athmosphere contribute to global warming (to a lesser extent than CO2). The energy cost to produce 1 kg of LH2 is between 10 to 12 kWh/kg for current industrial liquefaction plants, and goes down to around 6.0 kWh/kg in R&D prototypes. -The losses of H2 to produce LH2 are estimated around 1.6 % from input stream to output stream. +The losses of H2 to produce LH2 are estimated around 1.6 % from input stream to output stream. [^1]: Hydrogen Fuel Wikipedia page, https://en.wikipedia.org/wiki/Hydrogen_fuel -[^2]: Engineering Toolbox, https://www.engineeringtoolbox.com +[^2]: Engineering Toolbox, https://www.engineeringtoolbox.com \ No newline at end of file diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/methane_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/methane_disc.markdown index 0f7749b0..7c01f536 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/methane_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/methane_disc.markdown @@ -1,6 +1,6 @@ **Definition ([^1]):** -Methane is the simplest gaseous hydrocarbon, consisting of one carbon and four hydrogen atoms. It is also a powerful greenhouse gas that is found in small quantities in Earth's atmosphere. +Methane is the simplest gaseous hydrocarbon, consisting of one carbon and four hydrogen atoms. It is also a powerful greenhouse gas that is found in small quantities in Earth's atmosphere. Significant datas for methane [^2]: @@ -13,7 +13,7 @@ Significant datas for methane [^2]: * Density, Liquid at -161 °C, 422.62 kg/m^3 * Density, Gas at 15 °C, 1 atm, 0.6709 kg/m^3 * CH4 leakage at consumption : 0.08 kt/PJ [^6] - * CH4 leakage at transport,distribution : 0.195 kt/PJ [^6] + * CH4 leakage at transport,distribution : 0.195 kt/PJ [^6] * N2O after use : 0.0001 kt/PJ [^5] Methane is mostly used as a fuel, it produce carbon dioxide and water vapor when burned with oxygen: @@ -34,7 +34,7 @@ Although the concentration of methane in Earth's atmosphere is small (around 1.8 The following figure shows the balance between CH4 emissions and sinks, and the main sectors: -![](1200px-The_Global_Methane_Budget_2008–2017.PNG) +![](1200px-The_Global_Methane_Budget_2008–2017.PNG) (Image credit: The Global Carbon Project, taken from [^3]) @@ -47,4 +47,4 @@ The average price of methane around the world is 0.80 U.S. Dollar per liter. How [^3]: By The Global Carbon Project - http://www.globalcarbonatlas.org/en/CH4-emissions / https://essd.copernicus.org/articles/12/1561/2020/, CC BY 4.0, https://commons.wikimedia.org/w/index.php?curid=93292720 [^4]: Methane prices, 28 Jun 2021, GlobalPetrolPrices.com, https://www.globalpetrolprices.com/methane_prices/ [^5]: Winiwarter, W., 2005. The GAINS model for greenhouse gases-version 1.0: nitrous oxide (N2O).https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR55-GAINS-N2O.pdf -[^6]: Hoglund-Isaksson, L. and Mechler, R., 2005. The GAINS Model for Greenhouse gases-Version 1.0: Methane (CH4), IIASA Interim Report IR-05-054. International Institute for Applied Systems Analysis, Laxenburg. https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR54-GAINS-CH4.pdf +[^6]: Hoglund-Isaksson, L. and Mechler, R., 2005. The GAINS Model for Greenhouse gases-Version 1.0: Methane (CH4), IIASA Interim Report IR-05-054. International Institute for Applied Systems Analysis, Laxenburg. https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR54-GAINS-CH4.pdf \ No newline at end of file diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/methanol_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/methanol_disc.markdown index f14c7b6f..43ddd8d1 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/methanol_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/methanol_disc.markdown @@ -1,6 +1,6 @@ **Definition:** -Methanol (CH3OH) is the simplest alcohol molecule. It has many applications and is mostly used in chemicals and +Methanol (CH3OH) is the simplest alcohol molecule. It has many applications and is mostly used in chemicals and industrial areas. But it also has potential as a fuel, through combustion reaction: $$2CH_{3}OH + 3O_{2} --> 2CO_{2} + 4H_{2}O$$ @@ -8,7 +8,7 @@ $$2CH_{3}OH + 3O_{2} --> 2CO_{2} + 4H_{2}O$$ ![](Methanol_production.PNG) (Image Credit: [^3]) -Methanol production can be separated into 2 kinds of processes [^2] [^4] : +Methanol production can be separated into 2 kinds of processes [^2] [^4] : * through syngas reforming: in(syngas), out(methanol, water) [NOT IMPLEMENTED YET] * through direct CO2 hydrogenation: in(carbon dioxide, hydrogen), out(methanol, water) @@ -25,10 +25,10 @@ Significant datas for liquid fuel [^1]: * CH4 after use : 0.0 kg/kg * N2O after use : 0.0 kg/kg * High Calorific value : 6.39 kWh/kg - + The advantages of Methanol as a fuel[^3]: - * It could be used in the auto industry without having to replace the current thermic engine automobile fleet + * It could be used in the auto industry without having to replace the current thermic engine automobile fleet * It is liquid at room temperature and thus easier to store The disadvantages of Methanol as a fuel: @@ -38,11 +38,11 @@ The disadvantages of Methanol as a fuel: [^1] [Engineering Toolbox](https://www.engineeringtoolbox.com) -[^2] [Eco-Techno-Economic Analysis of Methanol Production from Biogas and Power-to-X, +[^2] [Eco-Techno-Economic Analysis of Methanol Production from Biogas and Power-to-X, Emanuele Moioli and Tilman Schildhauer, Industrial & Engineering Chemistry Research 2022 61 (21), 7335-7348](https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.1c04682) [^3] [Schröder, J., Müller-Langer, F., Aakko-Saksa, P., Winther, K., Baumgarten, W. and Lindgren, M., 2020. Methanol as motor fuel: Summary Report.](https://www.iea-amf.org/content/fuel_information/methanol#general) [^4] [Nyári, J., 2018. Techno-economic feasibility study of a methanol plant using carbon dioxide and hydrogen.](http://kth.diva-portal.org/smash/get/diva2:1290829/FULLTEXT01.pdf) -[^5] [IRENA, 2021, Innovation Outlook : Renewable Methanol](https://www.irena.org/publications/2021/Jan/Innovation-Outlook-Renewable-Methanol) +[^5] [IRENA, 2021, Innovation Outlook : Renewable Methanol](https://www.irena.org/publications/2021/Jan/Innovation-Outlook-Renewable-Methanol) \ No newline at end of file diff --git a/energy_models/core/stream_type/energy_disciplines/documentation/solid_fuel_disc.markdown b/energy_models/core/stream_type/energy_disciplines/documentation/solid_fuel_disc.markdown index 3912a182..5e04fbc5 100644 --- a/energy_models/core/stream_type/energy_disciplines/documentation/solid_fuel_disc.markdown +++ b/energy_models/core/stream_type/energy_disciplines/documentation/solid_fuel_disc.markdown @@ -10,8 +10,8 @@ Significant datas for liquid fuel [^2]: * Molecular Weight : 170.0 g/mol * Density: 1300 kg/m^3 - * CO2 after use : 2.42 kgCO2/kg - * N2O after use : 0.0014 kt/PJ [^4] + * CO2 after use : 2.42 kgCO2/kg + * N2O after use : 0.0014 kt/PJ [^4] * Calorific value : 4.86 kWh/kg[^3] @@ -25,4 +25,4 @@ The solid fuel section deals with the coal extraction part, for the burning part [^1]: Solid Fuel Wikipedia page, https://en.wikipedia.org/wiki/Solid_fuel [^2]: Engineering Toolbox, https://www.engineeringtoolbox.com [^3]: Lee, J.S., 2015. Calorific value of wood pellets (Doctoral dissertation, University of British Columbia). -[^4]: Winiwarter, W., 2005. The GAINS model for greenhouse gases-version 1.0: nitrous oxide (N2O).https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR55-GAINS-N2O.pdf +[^4]: Winiwarter, W., 2005. The GAINS model for greenhouse gases-version 1.0: nitrous oxide (N2O).https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR55-GAINS-N2O.pdf \ No newline at end of file diff --git a/energy_models/core/stream_type/energy_disciplines/electricity_disc.py b/energy_models/core/stream_type/energy_disciplines/electricity_disc.py index ba6e319c..284525b8 100644 --- a/energy_models/core/stream_type/energy_disciplines/electricity_disc.py +++ b/energy_models/core/stream_type/energy_disciplines/electricity_disc.py @@ -53,11 +53,11 @@ class ElectricityDiscipline(EnergyDiscipline): # 4400TWh is total production, # we use a 50% higher value 'unit': 'Twh', - 'user_level': 2, }, + 'user_level': 2,}, 'hydropower_constraint_ref': {'type': 'float', 'default': 1000., 'unit': 'Twh', - 'user_level': 2, }, + 'user_level': 2,}, 'data_fuel_dict': {'type': 'dict', 'visibility': EnergyDiscipline.SHARED_VISIBILITY, 'namespace': 'ns_electricity', @@ -103,7 +103,7 @@ def run(self): } else: outputs_dict = {} - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/stream_type/energy_disciplines/syngas_disc.py b/energy_models/core/stream_type/energy_disciplines/syngas_disc.py index 0d062d5a..2e0324e9 100644 --- a/energy_models/core/stream_type/energy_disciplines/syngas_disc.py +++ b/energy_models/core/stream_type/energy_disciplines/syngas_disc.py @@ -113,7 +113,7 @@ def run(self): 'syngas_ratio': syngas_ratio, 'syngas_ratio_technos': self.energy_model.syngas_ratio} outputs_dict.update(ghg_per_use_dict) - + self.store_sos_outputs_values(outputs_dict) def compute_data_energy_dict(self): diff --git a/energy_models/core/stream_type/energy_disciplines/wet_biomass_disc.py b/energy_models/core/stream_type/energy_disciplines/wet_biomass_disc.py index 0b892bec..311d17aa 100644 --- a/energy_models/core/stream_type/energy_disciplines/wet_biomass_disc.py +++ b/energy_models/core/stream_type/energy_disciplines/wet_biomass_disc.py @@ -73,5 +73,5 @@ def run(self): GlossaryEnergy.EnergyProductionValue: production / inputs_dict[ 'scaling_factor_energy_production'], 'techno_mix': techno_mix} - + self.store_sos_outputs_values(outputs_dict) diff --git a/energy_models/core/stream_type/energy_models/electricity.py b/energy_models/core/stream_type/energy_models/electricity.py index 0fd84658..bf71823f 100644 --- a/energy_models/core/stream_type/energy_models/electricity.py +++ b/energy_models/core/stream_type/energy_models/electricity.py @@ -26,7 +26,7 @@ class Electricity(EnergyType): default_techno_list = ['WindOffshore', GlossaryEnergy.WindOnshore, GlossaryEnergy.SolarPv, 'SolarThermal', GlossaryEnergy.Hydropower, GlossaryEnergy.CoalGen, 'OilGen', 'Nuclear', 'CombinedCycleGasTurbine', GlossaryEnergy.GasTurbine, 'BiogasFired', 'BiomassFired', - # è'Geothermal' + #è'Geothermal' ] def __init__(self, name): @@ -45,7 +45,7 @@ def configure_parameters(self, inputs_dict): def compute_hydropower_constraint(self): ''' - Compute hydropower production constraint so that + Compute hydropower production constraint so that ''' self.hydropower_constraint = pd.DataFrame( {GlossaryEnergy.Years: self.production[GlossaryEnergy.Years]}) diff --git a/energy_models/core/stream_type/energy_models/fossil.py b/energy_models/core/stream_type/energy_models/fossil.py index 3ac2cadf..a7629e29 100644 --- a/energy_models/core/stream_type/energy_models/fossil.py +++ b/energy_models/core/stream_type/energy_models/fossil.py @@ -92,12 +92,12 @@ def compute_ghg_per_use(self, ghg_type): """ def compute_ghg_per_use(self, data_energy_dict): ''' - Specific computation for the CO2 per use taking into account the use of fossil in - petrochemical plants (plastic and textile), construction, cement and steel + Specific computation for the CO2 per use taking into account the use of fossil in + petrochemical plants (plastic and textile), construction, cement and steel - We only take into account energy emissions and not industrial emissions in energy mix - --> CO2 per use petrochemical and construction will be used in an industrial co2 emissions model + We only take into account energy emissions and not industrial emissions in energy mix + --> CO2 per use petrochemical and construction will be used in an industrial co2 emissions model This CO2_per_use is used in CO2 emitted by net energy ''' diff --git a/energy_models/core/stream_type/energy_models/liquid_fuel.py b/energy_models/core/stream_type/energy_models/liquid_fuel.py index e536a6c5..3385c5c0 100644 --- a/energy_models/core/stream_type/energy_models/liquid_fuel.py +++ b/energy_models/core/stream_type/energy_models/liquid_fuel.py @@ -81,8 +81,8 @@ def compute_ghg_per_use(self, ghg_type): Specific computation for the CO2 per use taking into account the use of oil in petrochemical plants (plastic and textile) and construction - We only take into account energy emissions and not industrial emissions in energy mix - --> CO2 per use petrochemical and construction will be used in an industrial co2 emissions model + We only take into account energy emissions and not industrial emissions in energy mix + --> CO2 per use petrochemical and construction will be used in an industrial co2 emissions model ''' # kgcoal_per_kgsteel = 1 / 1.7 diff --git a/energy_models/core/stream_type/energy_models/solid_fuel.py b/energy_models/core/stream_type/energy_models/solid_fuel.py index 06d2020d..01ed2a61 100644 --- a/energy_models/core/stream_type/energy_models/solid_fuel.py +++ b/energy_models/core/stream_type/energy_models/solid_fuel.py @@ -71,7 +71,7 @@ class SolidFuel(EnergyType): def compute_ghg_per_use(self, ghg_type): ''' - Specific computation for the CO2 per use taking into account the use of coal for cement and steel + Specific computation for the CO2 per use taking into account the use of coal for cement and steel 1t of steel --> 1.852 t of CO2 @@ -79,8 +79,8 @@ def compute_ghg_per_use(self, ghg_type): 1T of cement --> 0.9 t of CO2 1T of cement --> 0.25 t of coal - We only take into account energy emissions and not industrial emissions in energy mix - --> CO2 per use for iron and steel and cement will be used in an industrial co2 emissions model + We only take into account energy emissions and not industrial emissions in energy mix + --> CO2 per use for iron and steel and cement will be used in an industrial co2 emissions model ''' # Source: IEA 2022, Iron and Steel technology roadmap, diff --git a/energy_models/core/stream_type/energy_models/syngas.py b/energy_models/core/stream_type/energy_models/syngas.py index 00448004..45d2fadf 100644 --- a/energy_models/core/stream_type/energy_models/syngas.py +++ b/energy_models/core/stream_type/energy_models/syngas.py @@ -70,7 +70,7 @@ def configure_parameters_update(self, inputs_dict): def compute_syngas_ratio(self): """ - Method to compute syngas ratio using production by + Method to compute syngas ratio using production by """ self.syngas_ratio_mean = np.zeros(len(self.years)) for techno in self.subelements_list: @@ -83,8 +83,8 @@ def compute_syngas_ratio(self): def compute_molar_mass(syngas_ratio): ''' - syngas ratio is the molar ratio of CO over H2 - We compute the molar mass following this ratio + syngas ratio is the molar ratio of CO over H2 + We compute the molar mass following this ratio if ratio is equal to zero syngas is h2 syngas_ratio must be between 0 and 1 (not in %) ''' @@ -95,8 +95,8 @@ def compute_molar_mass(syngas_ratio): def compute_calorific_value(syngas_ratio): ''' - syngas ratio is the molar ratio of CO over H2 - We compute the calorific_value following this ratio + syngas ratio is the molar ratio of CO over H2 + We compute the calorific_value following this ratio Ratio is on mol not kg !! So we need molar_mass ratio in the computation if ratio is equal to zero syngas is h2 syngas_ratio must be between 0 and 1 (not in %) @@ -112,8 +112,8 @@ def compute_calorific_value(syngas_ratio): def compute_high_calorific_value(syngas_ratio): ''' - syngas ratio is the molar ratio of CO over H2 - We compute the calorific_value following this ratio + syngas ratio is the molar ratio of CO over H2 + We compute the calorific_value following this ratio Ratio is on mol not kg !! So we need molar_mass ratio in the computation if ratio is equal to zero syngas is h2 syngas_ratio must be between 0 and 1 (not in %) @@ -147,8 +147,8 @@ def compute_dcal_val_dsyngas_ratio(syngas_ratio, type_cal='calorific_value'): def compute_density(syngas_ratio): ''' - syngas ratio is the molar ratio of CO over H2 - We compute the density following this ratio + syngas ratio is the molar ratio of CO over H2 + We compute the density following this ratio Ratio is on mol not kg !! So we need molar_mass ratio in the computation if ratio is equal to zero syngas is h2 ''' diff --git a/energy_models/core/stream_type/energy_type.py b/energy_models/core/stream_type/energy_type.py index 6e21d9de..3edd6dcf 100644 --- a/energy_models/core/stream_type/energy_type.py +++ b/energy_models/core/stream_type/energy_type.py @@ -89,6 +89,6 @@ def compute_ghg_per_use(self, ghg_type): elif self.data_energy_dict_input[f'{ghg_type}_per_use_unit'] == 'kg/kWh' or self.data_energy_dict_input[ f'{ghg_type}_per_use_unit'] == 'Mt/TWh': ghg_type_per_use = self.data_energy_dict_input[f'{ghg_type}_per_use'] - else: + else : raise Exception("ghg per use unit is not handled") return ghg_type_per_use diff --git a/energy_models/core/stream_type/resources_data_disc.py b/energy_models/core/stream_type/resources_data_disc.py index 1711ce09..83b5a374 100644 --- a/energy_models/core/stream_type/resources_data_disc.py +++ b/energy_models/core/stream_type/resources_data_disc.py @@ -85,7 +85,7 @@ class ResourcesDisc(SoSWrapp): df_desc_resource = { GlossaryEnergy.Years: ('int', [1900, GlossaryEnergy.YearEndDefaultCore], False), - **{resource: ('float', None, True) for resource in GlossaryEnergy.ResourcesList} + **{resource : ('float', None, True) for resource in GlossaryEnergy.ResourcesList} } DESC_IN = {GlossaryEnergy.YearStart: ClimateEcoDiscipline.YEAR_START_DESC_IN, GlossaryEnergy.YearEnd: GlossaryEnergy.YearEndVar, @@ -136,7 +136,7 @@ def run(self): outputs_dict = {GlossaryEnergy.ResourcesPriceValue: resources_price, GlossaryEnergy.RessourcesCO2EmissionsValue: co2_emissions} - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/stream_type/resources_models/resource_glossary.py b/energy_models/core/stream_type/resources_models/resource_glossary.py index 7b871010..09d09513 100644 --- a/energy_models/core/stream_type/resources_models/resource_glossary.py +++ b/energy_models/core/stream_type/resources_models/resource_glossary.py @@ -126,7 +126,7 @@ class ResourceGlossary: SolidCarbon = {'name': GlossaryEnergy.SolidCarbon, GlossaryEnergy.CO2EmissionsValue: 0.0, - 'price': 1180.} # https://www.made-in-china.com/price/solid-carbon-price.html + 'price': 1180.} # https://www.made-in-china.com/price/solid-carbon-price.html GlossaryDict = { 'Uranium': Uranium, 'Water': Water, 'SeaWater': SeaWater, GlossaryEnergy.CO2: CO2, 'BiomassDry': BiomassDry, diff --git a/energy_models/core/stream_type/stream_disc.py b/energy_models/core/stream_type/stream_disc.py index 810b4a42..f11ae537 100644 --- a/energy_models/core/stream_type/stream_disc.py +++ b/energy_models/core/stream_type/stream_disc.py @@ -129,7 +129,8 @@ def run(self): self.energy_model.configure(inputs_dict) # -- compute informations cost_details, production, consumption, consumption_woratio, techno_mix = self.energy_model.compute(inputs_dict, - exp_min=inputs_dict[ + exp_min= + inputs_dict[ 'exp_min']) cost_details_technos = self.energy_model.sub_prices @@ -153,7 +154,7 @@ def run(self): GlossaryEnergy.LandUseRequiredValue: self.energy_model.land_use_required, GlossaryEnergy.EnergyTypeCapitalDfValue: self.energy_model.energy_type_capital } - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/techno_type/base_techno_models/biodiesel_techno.py b/energy_models/core/techno_type/base_techno_models/biodiesel_techno.py index 2f9bb711..62b72401 100644 --- a/energy_models/core/techno_type/base_techno_models/biodiesel_techno.py +++ b/energy_models/core/techno_type/base_techno_models/biodiesel_techno.py @@ -23,3 +23,5 @@ class BioDieselTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) self.energy_name = BioDiesel.name + + diff --git a/energy_models/core/techno_type/base_techno_models/biogas_techno.py b/energy_models/core/techno_type/base_techno_models/biogas_techno.py index 34c083ff..7faf5190 100644 --- a/energy_models/core/techno_type/base_techno_models/biogas_techno.py +++ b/energy_models/core/techno_type/base_techno_models/biogas_techno.py @@ -24,3 +24,5 @@ class BioGasTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) + + diff --git a/energy_models/core/techno_type/base_techno_models/biomass_dry_techno.py b/energy_models/core/techno_type/base_techno_models/biomass_dry_techno.py index 57ba5d08..873d64d7 100644 --- a/energy_models/core/techno_type/base_techno_models/biomass_dry_techno.py +++ b/energy_models/core/techno_type/base_techno_models/biomass_dry_techno.py @@ -42,3 +42,5 @@ def compute_land_use(self): if 'recyle_part' in self.techno_infos_dict: self.land_use[f'{self.name} (Gha)'] *= ( 1 - self.techno_infos_dict['recyle_part']) + + diff --git a/energy_models/core/techno_type/base_techno_models/carbon_capture_techno.py b/energy_models/core/techno_type/base_techno_models/carbon_capture_techno.py index b6dac86e..f908aeb7 100644 --- a/energy_models/core/techno_type/base_techno_models/carbon_capture_techno.py +++ b/energy_models/core/techno_type/base_techno_models/carbon_capture_techno.py @@ -64,6 +64,8 @@ def check_energy_demand_unit(self, energy_demand_unit, energy_demand): return energy_demand + + @staticmethod def compute_capex_variation_from_fg_ratio(fg_mean_ratio, fg_ratio_effect): @@ -199,7 +201,7 @@ def compute_dnon_usecapital_dfluegas(self, dcapex_dfluegas, dprod_dfluegas): (len(self.years), 1))) dnon_usecapital_dfluegas = dtechnocapital_dfluegas * ( - 1.0 - self.applied_ratio['applied_ratio'].values * self.utilisation_ratio / 100.).reshape((len(self.years), 1)) + 1.0 - self.applied_ratio['applied_ratio'].values * self.utilisation_ratio/ 100.).reshape((len(self.years), 1)) # we do not divide by / self.scaling_factor_invest_level because invest # and non_use_capital are in G$ diff --git a/energy_models/core/techno_type/base_techno_models/electricity_techno.py b/energy_models/core/techno_type/base_techno_models/electricity_techno.py index 1a52d94b..70736154 100644 --- a/energy_models/core/techno_type/base_techno_models/electricity_techno.py +++ b/energy_models/core/techno_type/base_techno_models/electricity_techno.py @@ -29,3 +29,5 @@ def compute_transport(self): self.transport_margin[GlossaryEnergy.MarginValue].values / 100.0 return transport_cost + + diff --git a/energy_models/core/techno_type/base_techno_models/ethanol_techno.py b/energy_models/core/techno_type/base_techno_models/ethanol_techno.py index 2e2fb77b..20b2a86d 100644 --- a/energy_models/core/techno_type/base_techno_models/ethanol_techno.py +++ b/energy_models/core/techno_type/base_techno_models/ethanol_techno.py @@ -23,3 +23,5 @@ class EthanolTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) self.energy_name = Ethanol.name + + diff --git a/energy_models/core/techno_type/base_techno_models/fossil_techno.py b/energy_models/core/techno_type/base_techno_models/fossil_techno.py index 4356b544..8e3dc174 100644 --- a/energy_models/core/techno_type/base_techno_models/fossil_techno.py +++ b/energy_models/core/techno_type/base_techno_models/fossil_techno.py @@ -30,3 +30,5 @@ def compute_transport(self): self.transport_margin[GlossaryEnergy.MarginValue].values / 100.0 return transport_cost + + diff --git a/energy_models/core/techno_type/base_techno_models/gaseous_hydrogen_techno.py b/energy_models/core/techno_type/base_techno_models/gaseous_hydrogen_techno.py index 62a59c0a..8e68d835 100644 --- a/energy_models/core/techno_type/base_techno_models/gaseous_hydrogen_techno.py +++ b/energy_models/core/techno_type/base_techno_models/gaseous_hydrogen_techno.py @@ -26,3 +26,5 @@ class GaseousHydrogenTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) + + diff --git a/energy_models/core/techno_type/base_techno_models/high_heat_techno.py b/energy_models/core/techno_type/base_techno_models/high_heat_techno.py index 56ed04ea..a76f013d 100644 --- a/energy_models/core/techno_type/base_techno_models/high_heat_techno.py +++ b/energy_models/core/techno_type/base_techno_models/high_heat_techno.py @@ -29,3 +29,6 @@ def compute_transport(self): self.transport_margin[GlossaryEnergy.MarginValue].values / 100.0 return transport_cost + + + diff --git a/energy_models/core/techno_type/base_techno_models/hydrotreated_oil_fuel_techno.py b/energy_models/core/techno_type/base_techno_models/hydrotreated_oil_fuel_techno.py index a5902e7d..e18fb450 100644 --- a/energy_models/core/techno_type/base_techno_models/hydrotreated_oil_fuel_techno.py +++ b/energy_models/core/techno_type/base_techno_models/hydrotreated_oil_fuel_techno.py @@ -25,3 +25,5 @@ class HydrotreatedOilFuelTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) self.energy_name = HydrotreatedOilFuel.name + + diff --git a/energy_models/core/techno_type/base_techno_models/kerosene_techno.py b/energy_models/core/techno_type/base_techno_models/kerosene_techno.py index 3abb986a..b5d19691 100644 --- a/energy_models/core/techno_type/base_techno_models/kerosene_techno.py +++ b/energy_models/core/techno_type/base_techno_models/kerosene_techno.py @@ -24,3 +24,5 @@ class KeroseneTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) + + diff --git a/energy_models/core/techno_type/base_techno_models/liquid_fuel_techno.py b/energy_models/core/techno_type/base_techno_models/liquid_fuel_techno.py index a83672ab..9ce4765b 100644 --- a/energy_models/core/techno_type/base_techno_models/liquid_fuel_techno.py +++ b/energy_models/core/techno_type/base_techno_models/liquid_fuel_techno.py @@ -24,3 +24,5 @@ class LiquidFuelTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) + + diff --git a/energy_models/core/techno_type/base_techno_models/liquid_hydrogen_techno.py b/energy_models/core/techno_type/base_techno_models/liquid_hydrogen_techno.py index f4fcd512..df79193e 100644 --- a/energy_models/core/techno_type/base_techno_models/liquid_hydrogen_techno.py +++ b/energy_models/core/techno_type/base_techno_models/liquid_hydrogen_techno.py @@ -24,3 +24,5 @@ class LiquidHydrogenTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) + + diff --git a/energy_models/core/techno_type/base_techno_models/low_heat_techno.py b/energy_models/core/techno_type/base_techno_models/low_heat_techno.py index fb215280..7070f952 100644 --- a/energy_models/core/techno_type/base_techno_models/low_heat_techno.py +++ b/energy_models/core/techno_type/base_techno_models/low_heat_techno.py @@ -29,3 +29,6 @@ def compute_transport(self): self.transport_margin[GlossaryEnergy.MarginValue].values / 100.0 return transport_cost + + + diff --git a/energy_models/core/techno_type/base_techno_models/medium_heat_techno.py b/energy_models/core/techno_type/base_techno_models/medium_heat_techno.py index 1845981e..c7a62a80 100644 --- a/energy_models/core/techno_type/base_techno_models/medium_heat_techno.py +++ b/energy_models/core/techno_type/base_techno_models/medium_heat_techno.py @@ -29,3 +29,5 @@ def compute_transport(self): self.transport_margin[GlossaryEnergy.MarginValue].values / 100.0 return transport_cost + + diff --git a/energy_models/core/techno_type/base_techno_models/methane_techno.py b/energy_models/core/techno_type/base_techno_models/methane_techno.py index 1a7d69ae..b8a249b5 100644 --- a/energy_models/core/techno_type/base_techno_models/methane_techno.py +++ b/energy_models/core/techno_type/base_techno_models/methane_techno.py @@ -24,3 +24,5 @@ class MethaneTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) + + diff --git a/energy_models/core/techno_type/base_techno_models/methanol_techno.py b/energy_models/core/techno_type/base_techno_models/methanol_techno.py index 177acaca..3c480b49 100644 --- a/energy_models/core/techno_type/base_techno_models/methanol_techno.py +++ b/energy_models/core/techno_type/base_techno_models/methanol_techno.py @@ -23,3 +23,5 @@ class MethanolTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) self.energy_name = Methanol.name + + diff --git a/energy_models/core/techno_type/base_techno_models/renewable_techno.py b/energy_models/core/techno_type/base_techno_models/renewable_techno.py index 1ac56a81..6021d840 100644 --- a/energy_models/core/techno_type/base_techno_models/renewable_techno.py +++ b/energy_models/core/techno_type/base_techno_models/renewable_techno.py @@ -30,3 +30,6 @@ def compute_transport(self): self.transport_margin[GlossaryEnergy.MarginValue].values / 100.0 return transport_cost + + + diff --git a/energy_models/core/techno_type/base_techno_models/solid_fuel_techno.py b/energy_models/core/techno_type/base_techno_models/solid_fuel_techno.py index d57fea77..d58b8db4 100644 --- a/energy_models/core/techno_type/base_techno_models/solid_fuel_techno.py +++ b/energy_models/core/techno_type/base_techno_models/solid_fuel_techno.py @@ -24,3 +24,5 @@ class SolidFuelTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) + + diff --git a/energy_models/core/techno_type/base_techno_models/syngas_techno.py b/energy_models/core/techno_type/base_techno_models/syngas_techno.py index 6b6d4a1a..48107d7a 100644 --- a/energy_models/core/techno_type/base_techno_models/syngas_techno.py +++ b/energy_models/core/techno_type/base_techno_models/syngas_techno.py @@ -42,6 +42,8 @@ def configure_energy_data(self, inputs_dict): self.data_energy_dict['high_calorific_value'] = calorific_value self.data_energy_dict['density'] = density + + def compute_transport(self): # Electricity has no Calorific value overload # Warning transport cost unit must $/kWh diff --git a/energy_models/core/techno_type/base_techno_models/wet_biomass_techno.py b/energy_models/core/techno_type/base_techno_models/wet_biomass_techno.py index 2a2abffe..42c96716 100644 --- a/energy_models/core/techno_type/base_techno_models/wet_biomass_techno.py +++ b/energy_models/core/techno_type/base_techno_models/wet_biomass_techno.py @@ -24,3 +24,5 @@ class WetBiomassTechno(TechnoType): def __init__(self, name): TechnoType.__init__(self, name) + + diff --git a/energy_models/core/techno_type/disciplines/carbon_capture_techno_disc.py b/energy_models/core/techno_type/disciplines/carbon_capture_techno_disc.py index 79bae2ec..c845ecbb 100644 --- a/energy_models/core/techno_type/disciplines/carbon_capture_techno_disc.py +++ b/energy_models/core/techno_type/disciplines/carbon_capture_techno_disc.py @@ -115,7 +115,7 @@ def set_partial_derivatives_flue_gas(self, energy_name=GlossaryEnergy.electricit self.set_partial_derivative_for_other_types( (GlossaryEnergy.TechnoProductionValue, f'{self.energy_name} ({self.techno_model.product_unit})'), ( GlossaryEnergy.FlueGasMean, GlossaryEnergy.FlueGasMean), - dprod_dfluegas * (self.techno_model.applied_ratio['applied_ratio'].values * utilisation_ratio / 100.)[:, + dprod_dfluegas * (self.techno_model.applied_ratio['applied_ratio'].values * utilisation_ratio/ 100.)[:, np.newaxis] * scaling_factor_invest_level / scaling_factor_techno_production) production, consumption = self.get_sosdisc_outputs( @@ -132,7 +132,7 @@ def set_partial_derivatives_flue_gas(self, energy_name=GlossaryEnergy.electricit self.set_partial_derivative_for_other_types( (GlossaryEnergy.TechnoConsumptionValue, column), (GlossaryEnergy.FlueGasMean, GlossaryEnergy.FlueGasMean), - dprod_column_dfluegas * (self.techno_model.applied_ratio['applied_ratio'].values * utilisation_ratio / 100.)[:, + dprod_column_dfluegas * (self.techno_model.applied_ratio['applied_ratio'].values * utilisation_ratio/ 100.)[:, np.newaxis] * scaling_factor_invest_level / scaling_factor_techno_production) self.set_partial_derivative_for_other_types( (GlossaryEnergy.TechnoConsumptionWithoutRatioValue, diff --git a/energy_models/core/techno_type/disciplines/syngas_techno_disc.py b/energy_models/core/techno_type/disciplines/syngas_techno_disc.py index 7c4e60f2..cbbdb317 100644 --- a/energy_models/core/techno_type/disciplines/syngas_techno_disc.py +++ b/energy_models/core/techno_type/disciplines/syngas_techno_disc.py @@ -71,7 +71,7 @@ def run(self): super().run() outputs_dict = {'syngas_ratio': np.array([self.syngas_ratio])} - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/core/techno_type/techno_disc.py b/energy_models/core/techno_type/techno_disc.py index b12605ea..ccec5346 100644 --- a/energy_models/core/techno_type/techno_disc.py +++ b/energy_models/core/techno_type/techno_disc.py @@ -74,7 +74,7 @@ class TechnoDiscipline(SoSWrapp): 'visibility': SoSWrapp.SHARED_VISIBILITY, 'namespace': 'ns_public', 'user_level': 2}, 'smooth_type': {'type': 'string', 'default': 'smooth_max', - 'possible_values': ['smooth_max', 'soft_max', ], # 'cons_smooth_max' : deactivated cause gradients are wrong when all ratios are 1. (no limiting stream) + 'possible_values': ['smooth_max', 'soft_max', ], # 'cons_smooth_max' : deactivated cause gradients are wrong when all ratios are 1. (no limiting stream) 'user_level': 2, 'structuring': False, 'visibility': SoSWrapp.SHARED_VISIBILITY, 'namespace': 'ns_public'}, GlossaryEnergy.BoolApplyRatio: {'type': 'bool', 'default': True, 'user_level': 2, 'structuring': True, @@ -216,7 +216,7 @@ def setup_sos_disciplines(self): energy_name=self.energy_name, byproducts_list=GlossaryEnergy.techno_byproducts[self.techno_name]), GlossaryEnergy.LandUseRequiredValue: GlossaryEnergy.get_land_use_df(techno_name=self.techno_name), - 'age_distrib_production': GlossaryEnergy.get_age_distrib_prod_df(energy_name=self.energy_name), # todo: not coupled, rename cols and move to DESC_OUT + 'age_distrib_production': GlossaryEnergy.get_age_distrib_prod_df(energy_name=self.energy_name), # todo: not coupled, rename cols and move to DESC_OUT GlossaryEnergy.TechnoDetailedPricesValue: GlossaryEnergy.get_techno_detailed_price_df(techno_name=self.techno_name), }) self.add_inputs(dynamic_inputs) @@ -303,7 +303,7 @@ def update_default_values(self): def run(self): ''' - Generic run for all technologies + Generic run for all technologies ''' # -- get inputs inputs_dict = self.get_sosdisc_inputs() @@ -524,7 +524,7 @@ def compute_sos_jacobian(self): var_cons = (consumption[column] / installed_power['new_power_production']).fillna(0) self.dcons_column_dinvest = self.dpower_dinvest.copy() else: - var_cons = (consumption[column] / production[f'{self.energy_name} ({self.techno_model.product_unit})']).fillna(0) + var_cons = (consumption[column] /production[f'{self.energy_name} ({self.techno_model.product_unit})']).fillna(0) self.dcons_column_dinvest = self.dprod_dinvest.copy() for line in range(len(years)): @@ -1398,7 +1398,7 @@ def get_chart_capex(self): years = cost_details[GlossaryEnergy.Years] capex = cost_details[f'Capex_{self.techno_name}'] new_chart = TwoAxesInstanciatedChart(GlossaryEnergy.Years, '$/MWh', chart_name=chart_name) - serie = InstanciatedSeries(years, capex, '', 'lines') + serie = InstanciatedSeries( years, capex, '','lines') new_chart.series.append(serie) return new_chart diff --git a/energy_models/core/techno_type/techno_type.py b/energy_models/core/techno_type/techno_type.py index aa4d5dd1..f770ddb1 100644 --- a/energy_models/core/techno_type/techno_type.py +++ b/energy_models/core/techno_type/techno_type.py @@ -201,6 +201,7 @@ def configure_parameters_update(self, inputs_dict): <= self.year_end] self.stream_prices = inputs_dict[GlossaryEnergy.StreamPricesValue] + self.invest_level = inputs_dict[GlossaryEnergy.InvestLevelValue].loc[ inputs_dict[GlossaryEnergy.InvestLevelValue][GlossaryEnergy.Years] <= self.year_end] @@ -279,7 +280,7 @@ def select_resources_ratios(self): def apply_resources_ratios(self, apply_ressources_ratio: bool = True): """! Select the most constraining ratio and apply it to production and consumption. - To avoid clipping effects, the applied ratio is not the minimum value between all the ratios, + To avoid clipping effects, the applied ratio is not the minimum value between all the ratios, but the smoothed minimum value between all the ratio (see func_manager documentation for more). A model variables is set in this method: -self.applied_ratio: the effective ratio applied for each year @@ -350,11 +351,11 @@ def compute_capital(self): ''' Compute Capital & loss of capital because of the unusability of the technology. When the applied ratio is below 1, the technology does not produce all the energy possible. - Investments on this technology is consequently non_use. - This method computes the non_use of capital + Investments on this technology is consequently non_use. + This method computes the non_use of capital Capex is in $/MWh - Prod in TWh + Prod in TWh then capex*prod_wo_ratio is in $/MWh*(1e6MWh)= M$ We divide by scaling_factor_invest_level to put non_use_capital in G$ @@ -469,7 +470,7 @@ def compute_cost_of_resources_usage(self): """ Cost of resource R = need of resource R x price of resource R """ - cost_of_resource_usage = { + cost_of_resource_usage = { GlossaryEnergy.Years: self.years, } for resource in self.resources_used_for_production: @@ -615,7 +616,7 @@ def check_capex_unity(self, data_tocheck): """ Put all capex in $/MWh """ - capex_init = None # intialize capex init variable + capex_init = None # intialize capex init variable if data_tocheck['Capex_init_unit'] == 'euro': # it is a total capital requirement TCR , need to be divided by # full_load_hours available power and capacity factor @@ -741,6 +742,7 @@ def get_electricity_needs(self): return elec_need + def check_energy_demand_unit(self, energy_demand_unit, energy_demand): """ Compute energy demand in kWh/kWh or MWh/MWh (equivalent) @@ -785,7 +787,7 @@ def compute_efficiency(self): self.cost_details['efficiency'] = efficiency return efficiency - + def sigmoid_function(self, x, eff_max, eff_ini, x_shift, slope): x = x - x_shift # Logistic function @@ -880,7 +882,7 @@ def compute_co2_tax(self): @abstractmethod def get_theoretical_co2_prod(self, unit='kg/kWh'): - ''' + ''' Get the theoretical CO2 production for a given technology, Need to be overloaded in each technology model (example in SMR) ''' @@ -889,7 +891,7 @@ def get_theoretical_co2_prod(self, unit='kg/kWh'): def compute_primary_energy_production(self): ''' Compute the primary energy production for each technology - (primary energy is H2 for H2 techno , Kero for Kero techno ...etc) + (primary energy is H2 for H2 techno , Kero for Kero techno ...etc) ''' # First compute the initial aging distribution with the initial # production found in the techno discipline @@ -916,6 +918,7 @@ def compute_primary_energy_production(self): def compute_primary_installed_power(self): + if 'full_load_hours' in self.techno_infos_dict: full_load_hours = self.techno_infos_dict['full_load_hours'] else: @@ -945,9 +948,9 @@ def compute_primary_installed_power(self): def compute_aging_distribution_production(self): ''' Compute the aging distribution production of primary energy for years of study - Start with the initial distribution and add a year on the age each year + Start with the initial distribution and add a year on the age each year Add also the yearly production regarding the investment - All productions older than the lifetime are removed from the dataframe + All productions older than the lifetime are removed from the dataframe ''' # To break the object link with initial distrib aging_distrib_year_df = pd.DataFrame( @@ -1207,6 +1210,7 @@ def d_non_use_capital_d_invest_level(self, d_capital_d_invest_level): utilisation_ratio = self.utilisation_ratio return np.diag(applied_ratio * utilisation_ratio / 100.) * d_capital_d_invest_level + def compute_dlanduse_dinvest(self): """ compute grad d_land_use / d_invest @@ -1548,6 +1552,7 @@ def compute_dcapex_dinvest(self, invest_list, data_config): return (1.0 - maximum_learning_capex_ratio) * dcapex_calc_list_dinvest_list * dinvest_func.reshape( len(invest_list)) + "---------END OF GRADIENTS---------" def compute_initial_age_distribution(self): @@ -1571,4 +1576,4 @@ def compute_initial_plants_historical_prod(self): }) self.initial_plants_historical_prod.sort_values(GlossaryEnergy.Years, inplace=True) - self.initial_plants_historical_prod[f'cum energy ({self.product_unit})'] = self.initial_plants_historical_prod[f'energy ({self.product_unit})'].cumsum() + self.initial_plants_historical_prod[f'cum energy ({self.product_unit})'] = self.initial_plants_historical_prod[f'energy ({self.product_unit})'].cumsum() \ No newline at end of file diff --git a/energy_models/database_witness_energy.py b/energy_models/database_witness_energy.py index 8d75c4cc..851b01ec 100644 --- a/energy_models/database_witness_energy.py +++ b/energy_models/database_witness_energy.py @@ -148,8 +148,8 @@ def get_techno_invest_before_year_start(cls, techno_name: str, year_start: int, out_df = heavy_collected_data.get_between_years(year_start=year_start - construction_delay, year_end=year_start - 1) return out_df, heavy_collected_data - techno_production_historic_folder = join(Path(__file__).parents[1], "data_energy", "techno_production_historic") + techno_production_historic_folder = join(Path(__file__).parents[1], "data_energy", "techno_production_historic") @classmethod def get_techno_prod(cls, techno_name: str, year: int, is_available_at_year: bool = False): name_formatted = techno_name.replace(".", "_") @@ -172,6 +172,7 @@ def get_techno_prod(cls, techno_name: str, year: int, is_available_at_year: bool out = heavy_collected_data.get_value_at_year(year=year) return out, heavy_collected_data + techno_age_distrib_folder = join(Path(__file__).parents[1], "data_energy", "techno_factories_age") @classmethod @@ -194,3 +195,4 @@ def get_techno_age_distrib_factor(cls, techno_name: str, year: int, is_available out = heavy_collected_data.get_value_at_year(year=year) return out, heavy_collected_data + diff --git a/energy_models/datasets_database/datasets/readme.txt b/energy_models/datasets_database/datasets/readme.txt index d2adfefa..ec39174a 100644 --- a/energy_models/datasets_database/datasets/readme.txt +++ b/energy_models/datasets_database/datasets/readme.txt @@ -1,4 +1,4 @@ -The dataset database (the folder "datasets_database\datasets") have to contain the parameters you want to update in your usecase. +The dataset database (the folder "datasets_database\datasets") have to contain the parameters you want to update in your usecase. To create a new dataset: @@ -17,4 +17,4 @@ Example of descriptor.json file content: "y_array":"@array@y_array.csv", "z_list":[1.0,2.0,3.0], "d":"@dataframe@d.csv" -} +} \ No newline at end of file diff --git a/energy_models/datasets_database/mappings/readme.txt b/energy_models/datasets_database/mappings/readme.txt index bda58bc9..0c94152d 100644 --- a/energy_models/datasets_database/mappings/readme.txt +++ b/energy_models/datasets_database/mappings/readme.txt @@ -1,6 +1,6 @@ To describe wich dataset goes with wich data in a specific usecase, the usecase will need a dataset_mapping file. -This file can be created next to the usecase.py file For testing purpose, or in the "datasets_database\mappings" folder if the mapping is official and where everyone can retrieve it. +This file can be created next to the usecase.py file For testing purpose, or in the "datasets_database\mappings" folder if the mapping is official and where everyone can retrieve it. The dataset mapping file has the following structure: @@ -31,3 +31,4 @@ A dataset information has the following format: - dataset_disc1 → name of the dataset - |* → means that it is for all parameters of this dataset. For now you can't specify a single parameter but it will be possible in the future. If several dataset are specified for one namespace, if a parameter is present in several datasets, the value of the parameter will be the value of the last dataset to have the parameter value. + diff --git a/energy_models/glossaryenergy.py b/energy_models/glossaryenergy.py index 06b13b42..8b0feaa9 100644 --- a/energy_models/glossaryenergy.py +++ b/energy_models/glossaryenergy.py @@ -1072,25 +1072,25 @@ class GlossaryEnergy(GlossaryWitnessCore): Transesterification: [electricity], # heat -> low, no electricity AnaerobicDigestion: [electricity], # produce heat -> low, dont consume electricity, consume biomass_dry and wet ManagedWood: [electricity], # consume fuel and electricity .. les tronçonneuses et les camions (donc transport fuel) - UnmanagedWood: [electricity], # consume fuel and electricity .. les tronçonneuses et les camions (donc transport fuel) + UnmanagedWood: [electricity], # consume fuel and electricity .. les tronçonneuses et les camions (donc transport fuel) f"{direct_air_capture}.{AmineScrubbing}": [electricity, methane], # put heat instead of methane - f"{direct_air_capture}.{CalciumPotassiumScrubbing}": [electricity, methane], # put heat instead of methane - f"{direct_air_capture}.{DirectAirCaptureTechno}": [GlossaryWitnessCore.clean_energy, fossil], # dont touch + f"{direct_air_capture}.{CalciumPotassiumScrubbing}": [electricity, methane], # put heat instead of methane + f"{direct_air_capture}.{DirectAirCaptureTechno}": [GlossaryWitnessCore.clean_energy, fossil], # dont touch # in flue gas techno: heat comes directly from plant so just electricity - f"{flue_gas_capture}.{CalciumLooping}": [electricity], # heat and electricity - f"{flue_gas_capture}.{ChilledAmmoniaProcess}": [electricity], # heat and electricity - f"{flue_gas_capture}.{CO2Membranes}": [electricity], # heat and electricity - f"{flue_gas_capture}.{FlueGasTechno}": [GlossaryWitnessCore.clean_energy], # heat and electricity -> not heat because - f"{flue_gas_capture}.{MonoEthanolAmine}": [electricity], # heat and electricity - f"{flue_gas_capture}.{PiperazineProcess}": [electricity], # heat and electricity - f"{flue_gas_capture}.{PressureSwingAdsorption}": [electricity], # heat and electricity + f"{flue_gas_capture}.{CalciumLooping}": [electricity], # heat and electricity + f"{flue_gas_capture}.{ChilledAmmoniaProcess}": [electricity], # heat and electricity + f"{flue_gas_capture}.{CO2Membranes}": [electricity], # heat and electricity + f"{flue_gas_capture}.{FlueGasTechno}": [GlossaryWitnessCore.clean_energy], # heat and electricity -> not heat because + f"{flue_gas_capture}.{MonoEthanolAmine}": [electricity], # heat and electricity + f"{flue_gas_capture}.{PiperazineProcess}": [electricity], # heat and electricity + f"{flue_gas_capture}.{PressureSwingAdsorption}": [electricity], # heat and electricity BiomassFired: [biomass_dry], CoalGen: [solid_fuel], GasTurbine: [methane], CombinedCycleGasTurbine: [methane], BiogasFired: [biogas], OilGen: [f"{fuel}.{liquid_fuel}"], - BiomassFermentation: [biomass_dry, electricity], # heat instead of electricity + BiomassFermentation: [biomass_dry, electricity], # heat instead of electricity ElectrolysisAWE: [electricity], ElectrolysisPEM: [electricity], ElectrolysisSOEC: [electricity], @@ -1116,9 +1116,9 @@ class GlossaryEnergy(GlossaryWitnessCore): HefaDeoxygenation: [f"{hydrogen}.{gaseous_hydrogen}", electricity], # heat instead of electricity, use resource natural_oil (trygliceride) Refinery: [f"{hydrogen}.{gaseous_hydrogen}", electricity], # idea : creer une techno puit de pétrole (CrudeOil) HydrogenLiquefaction: [f"{hydrogen}.{gaseous_hydrogen}", electricity], # might need some heat ? produced or consumed, not clear - FossilGas: [electricity], # "transport fuel" + FossilGas: [electricity], # "transport fuel" Methanation: [f"{hydrogen}.{gaseous_hydrogen}", carbon_capture], - UpgradingBiogas: [electricity, biogas], # heat not electricity + UpgradingBiogas: [electricity, biogas], # heat not electricity CO2Hydrogenation: [ f"{hydrogen}.{gaseous_hydrogen}", electricity, @@ -1126,14 +1126,14 @@ class GlossaryEnergy(GlossaryWitnessCore): ], CoalExtraction: [electricity], # transport fuel instead of electricity Pelletizing: [electricity, biomass_dry], # might be heat instead of electricity - AutothermalReforming: [methane, carbon_capture], # add heat + AutothermalReforming: [methane, carbon_capture], # add heat BiomassGasification: [electricity, biomass_dry], # heat instead of electricity, produce syngas CoElectrolysis: [electricity, carbon_capture], CoalGasification: [solid_fuel], # add heat - RWGS: [electricity, syngas], # heat instead of electricity, CO2 instead of carbon_capture + RWGS: [electricity, syngas], # heat instead of electricity, CO2 instead of carbon_capture SMR: [electricity, methane], # heat instead of elec AnimalManure: [electricity], # transport fuel in stead of elec - WetCropResidues: [electricity], # transport fuel in stead of elec + WetCropResidues: [electricity], # transport fuel in stead of elec Geothermal: [f"{heat}.{mediumtemperatureheat}"], # just electricity BiomassBuryingFossilization: [biomass_dry], # add transport fuel DeepOceanInjection: [], # add transport fuel @@ -1183,7 +1183,7 @@ class GlossaryEnergy(GlossaryWitnessCore): PureCarbonSolidStorage: [SolidCarbon], # note : could be a stream but we prefered to let it as a resource for the moment } - # TechnoResourceUsedDict[FischerTropsch] = TechnoResourceUsedDict[ReversedWaterGasShift] + TechnoResourceUsedDict[WaterGasShift] + #TechnoResourceUsedDict[FischerTropsch] = TechnoResourceUsedDict[ReversedWaterGasShift] + TechnoResourceUsedDict[WaterGasShift] TechnoBuildingResourceDict = { CoalGen: [CopperResource], @@ -1273,7 +1273,7 @@ class GlossaryEnergy(GlossaryWitnessCore): GeothermalLowHeat: f"{heat}.{lowtemperatureheat}", ElectricBoilerMediumHeat: f"{heat}.{mediumtemperatureheat}", GeothermalMediumHeat: f"{heat}.{mediumtemperatureheat}", - # CO2Hydrogenation: f'{fuel}.{methanol}', + #CO2Hydrogenation: f'{fuel}.{methanol}', NaturalGasBoilerMediumHeat: f"{heat}.{mediumtemperatureheat}", HeatPumpLowHeat: f"{heat}.{lowtemperatureheat}", CHPLowHeat: f"{heat}.{lowtemperatureheat}", @@ -1286,8 +1286,8 @@ class GlossaryEnergy(GlossaryWitnessCore): RWGS: 2, UpgradingBiogas: 2, Methanation: 2, # Thema, M., Bauer, F. and Sterner, M., 2019. Power-to-Gas: Electrolysis and methanation status review. Renewable and Sustainable Energy Reviews, 112, pp.775-787. the average time needed for planning and constructing was about 1.5years from Thema2019 - WaterGasShift: 2, # Giuliano, A., Freda, C. and Catizzone, E., 2020. Techno-economic assessment of bio-syngas production for methanol synthesis: A focus on the water gas shift and carbon capture sections. Bioengineering, 7(3), p.70. - ElectrolysisSOEC: 1, # Haldor Topsoe, 2021 Haldor Topsoe to build large-scale SOEC electrolyzer manufacturing facility to meet customer needs for green hydrogen production https://blog.topsoe.com/haldor-topsoe-to-build-large-scale-soec-electrolyzer-manufacturing-facility-to-meet-customer-needs-for-green-hydrogen-production Construction will start in 2022 and will ends in 2023 + WaterGasShift: 2, # Giuliano, A., Freda, C. and Catizzone, E., 2020. Techno-economic assessment of bio-syngas production for methanol synthesis: A focus on the water gas shift and carbon capture sections. Bioengineering, 7(3), p.70. + ElectrolysisSOEC: 1, # Haldor Topsoe, 2021 Haldor Topsoe to build large-scale SOEC electrolyzer manufacturing facility to meet customer needs for green hydrogen production https://blog.topsoe.com/haldor-topsoe-to-build-large-scale-soec-electrolyzer-manufacturing-facility-to-meet-customer-needs-for-green-hydrogen-production Construction will start in 2022 and will ends in 2023 ElectrolysisPEM: 2, ElectrolysisAWE: 1, PlasmaCracking: 2, @@ -1310,11 +1310,11 @@ class GlossaryEnergy(GlossaryWitnessCore): WindOffshore: 3, # ATB NREL 2020 WindOnshore: 3, # ATB NREL 2020 SolarPv: 1, - SolarThermal: 3, # JRC, ATB NREL, database https://solarpaces.nrel.gov/ + SolarThermal: 3, # JRC, ATB NREL, database https://solarpaces.nrel.gov/ Hydropower: 3, - Nuclear: 6, # Timilsina, G.R., 2020. Demystifying the Costs of Electricity Generation # Technologies., average + Nuclear: 6, # Timilsina, G.R., 2020. Demystifying the Costs of Electricity Generation # Technologies., average CombinedCycleGasTurbine: 2, - GasTurbine: 2, # Lazard + GasTurbine: 2, #Lazard BiogasFired: 2, CoalGen: 5, # For 1000MW hypercritical in Korea OilGen: 5, # For 1000MW hypercritical in Korea @@ -1346,104 +1346,104 @@ class GlossaryEnergy(GlossaryWitnessCore): ElectricBoilerHighHeat: 2, GeothermalHighHeat: 1, ElectricBoilerLowHeat: 2, - CHPHighHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University - NaturalGasBoilerHighHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University + CHPHighHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University + NaturalGasBoilerHighHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University NaturalGasBoilerLowHeat: 2, GeothermalLowHeat: 1, HeatPumpLowHeat: 1, GeothermalMediumHeat: 1, HeatPumpMediumHeat: 1, - CHPLowHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University + CHPLowHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University FossilSimpleTechno: 3, - NaturalGasBoilerMediumHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University + NaturalGasBoilerMediumHeat: 2, # Economic and Technical Analysis of Heat Dry Milling: Model Description. Rhys T.Dale and Wallace E.Tyner Staff Paper Agricultural Economics Department Purdue University CO2Hydrogenation: 3, - GlossaryWitnessCore.CleanEnergySimpleTechno: 3, # Timilsina, G.R., 2020. Demystifying the Costs of Electricity Generation # Technologies., average + GlossaryWitnessCore.CleanEnergySimpleTechno: 3, # Timilsina, G.R., 2020. Demystifying the Costs of Electricity Generation # Technologies., average CHPMediumHeat: 2, ElectricBoilerMediumHeat: 2, } TechnoLifetimeDict = { - RWGS: 40, # for now constant in time but should increase with time - FossilGas: 23, # for now constant in time but should increase with time - UpgradingBiogas: 20, # for now constant in time but should increase with time - Methanation: 15, # for now constant in time but should increase with time - WaterGasShift: 20, # Giuliano2020 amortized on 20 years # for now constant in time but should increase with time - ElectrolysisSOEC: 8, # Around 60000hours - ElectrolysisPEM: 11, # Around 90000 operating hours with 8000 hours a year - ElectrolysisAWE: 25, # David, M., Ocampo-Martinez, C. and Sanchez-Pena, R., 2019. Advances in alkaline water electrolyzers: A review. Journal of Energy Storage, 23, pp.392-403. Around 20 and 30 years + RWGS: 40, # for now constant in time but should increase with time + FossilGas: 23, # for now constant in time but should increase with time + UpgradingBiogas: 20, # for now constant in time but should increase with time + Methanation: 15, # for now constant in time but should increase with time + WaterGasShift: 20, # Giuliano2020 amortized on 20 years # for now constant in time but should increase with time + ElectrolysisSOEC: 8, # Around 60000hours + ElectrolysisPEM: 11, # Around 90000 operating hours with 8000 hours a year + ElectrolysisAWE: 25, # David, M., Ocampo-Martinez, C. and Sanchez-Pena, R., 2019. Advances in alkaline water electrolyzers: A review. Journal of Energy Storage, 23, pp.392-403. Around 20 and 30 years PlasmaCracking: 25, HydrogenLiquefaction: 39, AnaerobicDigestion: 20, - BiomassGasification: 25, # Wang2019 Rosenfeld2020 says 20 # for now constant in time but should increase with time + BiomassGasification: 25, # Wang2019 Rosenfeld2020 says 20 # for now constant in time but should increase with time SMR: 25, CoalGasification: 20, Pyrolysis: 20, - AutothermalReforming: 15, # for now constant in time but should increase with time + AutothermalReforming: 15, # for now constant in time but should increase with time CoElectrolysis: 40, - Refinery: 35, # should be modified - FischerTropsch: 30, # for now constant in time but should increase with time - HefaDecarboxylation: 30, # https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3/tables/2 # for now constant in time but should increase with time - HefaDeoxygenation: 30, # Tao, L., Milbrandt, A., Zhang, Y. and Wang, W.C., 2017. Techno-economic and resource analysis of hydroprocessed renewable jet fuel. # Biotechnology for biofuels, 10(1), pp.1-16.# https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3/tables/2 - Transesterification: 15, # for now constant in time but should increase with time - BiomassFermentation: 45, # http://www.ethanolproducer.com/articles/2005/time-testing#:~:text=Most%20experts%20suggest%20dry%2Dmill,of%20%22useful%22%20life%20expectancy. - CoalExtraction: 35, # should be modified - Pelletizing: 25, # Wang2019 Rosenfeld2020 says 20 # for now constant in time but should increase with time - WindOffshore: 30, # ATB NREL 2020 - WindOnshore: 30, # ATB NREL 2020 - SolarPv: 25, # IRENA, EOLES model - SolarThermal: 25, # JRC, IRENA, SolarPACES - Hydropower: 50, # should be modified + Refinery: 35, # should be modified + FischerTropsch: 30, # for now constant in time but should increase with time + HefaDecarboxylation: 30, # https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3/tables/2 # for now constant in time but should increase with time + HefaDeoxygenation: 30,# Tao, L., Milbrandt, A., Zhang, Y. and Wang, W.C., 2017. Techno-economic and resource analysis of hydroprocessed renewable jet fuel. # Biotechnology for biofuels, 10(1), pp.1-16.# https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3/tables/2 + Transesterification: 15, # for now constant in time but should increase with time + BiomassFermentation: 45, # http://www.ethanolproducer.com/articles/2005/time-testing#:~:text=Most%20experts%20suggest%20dry%2Dmill,of%20%22useful%22%20life%20expectancy. + CoalExtraction: 35, # should be modified + Pelletizing: 25, # Wang2019 Rosenfeld2020 says 20 # for now constant in time but should increase with time + WindOffshore: 30, # ATB NREL 2020 + WindOnshore: 30, # ATB NREL 2020 + SolarPv: 25, # IRENA, EOLES model + SolarThermal: 25, # JRC, IRENA, SolarPACES + Hydropower: 50, # should be modified Nuclear: 60, # Cole, W.J., Gates, N., Mai, T.T., Greer, D. and Das, P., 2020. 2019 standard scenarios report: a US electric sector outlook (No. NREL/PR-6A20-75798). National Renewable Energy Lab.(NREL), Golden, CO (United States). - CombinedCycleGasTurbine: 30, # # Source: U.S. Energy Information Administration 2020, for now constant in time but should increase with time - GasTurbine: 30, # Source U.S. Energy Information Administration 2020, # for now constant in time but should increase with time - BiogasFired: 20, # Value for CHP units - CoalGen: 46, # Source: Cui, R.Y., Hultman, N., Edwards, M.R., He, L., Sen, A., Surana, K., McJeon, H., Iyer, G., Patel, P., Yu, S. and Nace, T., 2019. Quantifying operational lifetimes for coal power plants under the Paris goals. Nature communications, 10(1), pp.1-9. - OilGen: 46, # Source: Cui, R.Y., Hultman, N., Edwards, M.R., He, L., Sen, A., Surana, K., McJeon, H., Iyer, G., Patel, P., Yu, S. and Nace, T., 2019. Quantifying operational lifetimes for coal power plants under the Paris goals. Nature communications, 10(1), pp.1-9. - BiomassFired: 25, # Value for CHP units - f"{direct_air_capture}.{AmineScrubbing}": 35, # should be modified - f"{direct_air_capture}.{CalciumPotassiumScrubbing}": 35, # should be modified + CombinedCycleGasTurbine: 30, # # Source: U.S. Energy Information Administration 2020, for now constant in time but should increase with time + GasTurbine: 30, # Source U.S. Energy Information Administration 2020, # for now constant in time but should increase with time + BiogasFired: 20, # Value for CHP units + CoalGen: 46, # Source: Cui, R.Y., Hultman, N., Edwards, M.R., He, L., Sen, A., Surana, K., McJeon, H., Iyer, G., Patel, P., Yu, S. and Nace, T., 2019. Quantifying operational lifetimes for coal power plants under the Paris goals. Nature communications, 10(1), pp.1-9. + OilGen: 46, # Source: Cui, R.Y., Hultman, N., Edwards, M.R., He, L., Sen, A., Surana, K., McJeon, H., Iyer, G., Patel, P., Yu, S. and Nace, T., 2019. Quantifying operational lifetimes for coal power plants under the Paris goals. Nature communications, 10(1), pp.1-9. + BiomassFired: 25, # Value for CHP units + f"{direct_air_capture}.{AmineScrubbing}": 35, # should be modified + f"{direct_air_capture}.{CalciumPotassiumScrubbing}": 35, # should be modified f"{flue_gas_capture}.{CalciumLooping}": 25, # SAEECCT Coal USC plant lifetime - f"{flue_gas_capture}.{ChilledAmmoniaProcess}": 25, # SAEECCT Coal USC plant lifetime - f"{flue_gas_capture}.{CO2Membranes}": 25, # SAEECCT Coal USC plant lifetime - f"{flue_gas_capture}.{MonoEthanolAmine}": 25, # SAEECCT Coal USC plant lifetime - f"{flue_gas_capture}.{PiperazineProcess}": 25, # SAEECCT Coal USC plant lifetime - f"{flue_gas_capture}.{PressureSwingAdsorption}": 25, # SAEECCT Coal USC plant lifetime - BiomassBuryingFossilization: 35, # should be modified - DeepOceanInjection: 35, # should be modified - DeepSalineFormation: 35, # should be modified - DepletedOilGas: 35, # should be modified - EnhancedOilRecovery: 35, # should be modified - GeologicMineralization: 35, # should be modified - PureCarbonSolidStorage: 35, # should be modified - ManagedWood: 150, # for now constant in time but should increase with time - UnmanagedWood: 150, # for now constant in time but should increase with time - CropEnergy: 50, # for now constant in time but should increase with time + f"{flue_gas_capture}.{ChilledAmmoniaProcess}": 25, # SAEECCT Coal USC plant lifetime + f"{flue_gas_capture}.{CO2Membranes}": 25, # SAEECCT Coal USC plant lifetime + f"{flue_gas_capture}.{MonoEthanolAmine}": 25, # SAEECCT Coal USC plant lifetime + f"{flue_gas_capture}.{PiperazineProcess}": 25, # SAEECCT Coal USC plant lifetime + f"{flue_gas_capture}.{PressureSwingAdsorption}": 25, # SAEECCT Coal USC plant lifetime + BiomassBuryingFossilization: 35, # should be modified + DeepOceanInjection: 35, # should be modified + DeepSalineFormation: 35, # should be modified + DepletedOilGas: 35, # should be modified + EnhancedOilRecovery: 35, # should be modified + GeologicMineralization: 35, # should be modified + PureCarbonSolidStorage: 35, # should be modified + ManagedWood: 150, # for now constant in time but should increase with time + UnmanagedWood: 150, # for now constant in time but should increase with time + CropEnergy: 50, # for now constant in time but should increase with time FossilSimpleTechno: 25, - NaturalGasBoilerHighHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. - HeatPumpHighHeat: 25, # years # https://www.energy.gov/energysaver/heat-pump-systems - GeothermalHighHeat: 25, # in years # https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power - CHPHighHeat: 45, # Heat Producer [Online] # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. - NaturalGasBoilerLowHeat: 45, # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 + NaturalGasBoilerHighHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. + HeatPumpHighHeat: 25, # years # https://www.energy.gov/energysaver/heat-pump-systems + GeothermalHighHeat: 25, # in years # https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power + CHPHighHeat: 45, # Heat Producer [Online] # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. + NaturalGasBoilerLowHeat: 45, # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 ElectricBoilerLowHeat: 45, HeatPumpLowHeat: 25, - GeothermalLowHeat: 25, # in years # https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power - CHPLowHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. - NaturalGasBoilerMediumHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. - ElectricBoilerMediumHeat: 45, # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 - HeatPumpMediumHeat: 25, # years # https://www.energy.gov/energysaver/heat-pump-systems - GeothermalMediumHeat: 25, # in years # https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power - CHPMediumHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. + GeothermalLowHeat: 25, # in years # https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power + CHPLowHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. + NaturalGasBoilerMediumHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. + ElectricBoilerMediumHeat: 45, # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 + HeatPumpMediumHeat: 25, # years # https://www.energy.gov/energysaver/heat-pump-systems + GeothermalMediumHeat: 25, # in years # https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power + CHPMediumHeat: 45, # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. CO2Hydrogenation: 20, - GlossaryWitnessCore.CleanEnergySimpleTechno: 30, # Cole, W.J., Gates, N., Mai, T.T., Greer, D. and Das, P., 2020. 2019 standard scenarios report: a US electric sector outlook (No. NREL/PR-6A20-75798). National Renewable Energy Lab.(NREL), Golden, CO (United States). - f"{ElectricBoilerHighHeat}": 45, # Heat Producer [Online] # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 + GlossaryWitnessCore.CleanEnergySimpleTechno: 30, # Cole, W.J., Gates, N., Mai, T.T., Greer, D. and Das, P., 2020. 2019 standard scenarios report: a US electric sector outlook (No. NREL/PR-6A20-75798). National Renewable Energy Lab.(NREL), Golden, CO (United States). + f"{ElectricBoilerHighHeat}": 45, # Heat Producer [Online] # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 f"{CarbonStorageTechno}": 35, f"{direct_air_capture}.{DirectAirCaptureTechno}": 35, f"{flue_gas_capture}.{FlueGasTechno}": 25, Reforestation: 150, # for now constant in time but should increase with time, - Geothermal: 30, # Tsiropoulos, I., Tarvydas, D. and Zucker, A., 2018. Cost development of low carbon energy technologies-Scenario-based cost trajectories to 2050, 2017 Edition. Publications Office of the European Union, Luxemburgo. - AnimalManure: 25, # for now constant in time but should increase with time + Geothermal: 30, # Tsiropoulos, I., Tarvydas, D. and Zucker, A., 2018. Cost development of low carbon energy technologies-Scenario-based cost trajectories to 2050, 2017 Edition. Publications Office of the European Union, Luxemburgo. + AnimalManure: 25, # for now constant in time but should increase with time WetCropResidues: 25, # for now constant in time but should increase with time - CO2Membranes: 25, # SAEECCT Coal USC plant lifetime + CO2Membranes: 25, # SAEECCT Coal USC plant lifetime } @classmethod diff --git a/energy_models/models/biodiesel/transesterification/documentation/transesterification_disc.markdown b/energy_models/models/biodiesel/transesterification/documentation/transesterification_disc.markdown index f7f38779..8e545112 100644 --- a/energy_models/models/biodiesel/transesterification/documentation/transesterification_disc.markdown +++ b/energy_models/models/biodiesel/transesterification/documentation/transesterification_disc.markdown @@ -84,4 +84,4 @@ Technico-economic information (Capex, Opex) are coming from [Biodiesel productio [^1]: Biodiesel Handbook (2010), AOCS Press [^2]: https://www.sciencedirect.com/topics/chemical-engineering/transesterification -[^3]: https://www.sciencedirect.com/topics/engineering/transesterification#:~:text=Transesterification%20is%20the%20conversion%20of,presence%20of%20an%20acid%20catalyst. +[^3]: https://www.sciencedirect.com/topics/engineering/transesterification#:~:text=Transesterification%20is%20the%20conversion%20of,presence%20of%20an%20acid%20catalyst. \ No newline at end of file diff --git a/energy_models/models/biodiesel/transesterification/transesterification.py b/energy_models/models/biodiesel/transesterification/transesterification.py index b709e3c7..76322bd9 100644 --- a/energy_models/models/biodiesel/transesterification/transesterification.py +++ b/energy_models/models/biodiesel/transesterification/transesterification.py @@ -44,7 +44,7 @@ def compute_resources_needs(self): # need in kg/kwh biodiesel self.cost_details[f'{Water.name}_needs'] = self.get_theoretical_water_needs() / self.cost_details['efficiency'] # need in kWh/kwh biodiesel - + def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] diff --git a/energy_models/models/biogas/anaerobic_digestion/documentation/anaerobic_digestion_disc.markdown b/energy_models/models/biogas/anaerobic_digestion/documentation/anaerobic_digestion_disc.markdown index 52539217..421ba720 100644 --- a/energy_models/models/biogas/anaerobic_digestion/documentation/anaerobic_digestion_disc.markdown +++ b/energy_models/models/biogas/anaerobic_digestion/documentation/anaerobic_digestion_disc.markdown @@ -5,21 +5,21 @@ A mixture of methane and carbon dioxide (CO2), biogas can be produced from organ **How Does an Anaerobic Digester Work? [^6]** -Anaerobic digestion, or methanization, uses the process of fermentation to break down organic matter from animals. The biomass is heated to approximately 37°C to 38°C (sometimes more than 50°C) and stirred continuously. After at least 20 days and a series of bacteria-induced chemical transformations, the fermented biomass produces biogas . +Anaerobic digestion, or methanization, uses the process of fermentation to break down organic matter from animals. The biomass is heated to approximately 37°C to 38°C (sometimes more than 50°C) and stirred continuously. After at least 20 days and a series of bacteria-induced chemical transformations, the fermented biomass produces biogas . The composition of biogas depends on the type of feedstock and the production pathway. The methane content of biogas typically ranges from 45% to 75% and the carbon dioxyde content from 25% to 55% by volume. -Biogas and biomethane production pathways -![](Biogasbiomethaneinfographic.png) +Biogas and biomethane production pathways +![](Biogasbiomethaneinfographic.png) (Image Credit: IEA [^5]) -## Data -Most of the data used for this model is extracted from the paper of Rajendran & al [^1] which is a review of all papers on anaerobic digestion. +## Data +Most of the data used for this model is extracted from the paper of Rajendran & al [^1] which is a review of all papers on anaerobic digestion. World initial production is extracted from the IEA site [^5]. -Typical prices of Biogas from anaerobic digestion can be found on IRENA site[^2] : "between USD 0.22 and USD 0.39 per cubic meter of methane for manure-based biogas production, and USD 0.11 to USD 0.50 per cubic meter of methane for industrial waste-based biogas production". This corresponds to a biogas price between 0.02 and 0.078 $/kWh. +Typical prices of Biogas from anaerobic digestion can be found on IRENA site[^2] : "between USD 0.22 and USD 0.39 per cubic meter of methane for manure-based biogas production, and USD 0.11 to USD 0.50 per cubic meter of methane for industrial waste-based biogas production". This corresponds to a biogas price between 0.02 and 0.078 $/kWh. The efficiency of the anaerobic digester is mentionned in Carlini & al [^3]. The age distribution of biogas plants from anaerobic digestion has been computed with the plant list in [^4]. diff --git a/energy_models/models/biomass_dry/crop_energy/crop_energy.py b/energy_models/models/biomass_dry/crop_energy/crop_energy.py index bcbc9069..84c98a6d 100644 --- a/energy_models/models/biomass_dry/crop_energy/crop_energy.py +++ b/energy_models/models/biomass_dry/crop_energy/crop_energy.py @@ -188,4 +188,4 @@ def compute_dlanduse_dinvest(self): return dlanduse_dinvest def compute_resources_needs(self): - self.cost_details[f'{GlossaryEnergy.carbon_capture}_needs'] = -self.techno_infos_dict['CO2_from_production'] / self.data_energy_dict['high_calorific_value'] + self.cost_details[f'{GlossaryEnergy.carbon_capture}_needs'] = -self.techno_infos_dict['CO2_from_production'] / self.data_energy_dict['high_calorific_value'] \ No newline at end of file diff --git a/energy_models/models/biomass_dry/crop_energy/crop_energy_disc.py b/energy_models/models/biomass_dry/crop_energy/crop_energy_disc.py index bb5b5675..8e6e6f90 100644 --- a/energy_models/models/biomass_dry/crop_energy/crop_energy_disc.py +++ b/energy_models/models/biomass_dry/crop_energy/crop_energy_disc.py @@ -50,6 +50,7 @@ class CropEnergyDiscipline(BiomassDryTechnoDiscipline): } techno_name = GlossaryEnergy.CropEnergy + # mdpi: according to the NASU recommendations, # a fixed value of 0.25 is applied to all crops # 50% of crops are left on the field, @@ -143,7 +144,7 @@ def init_execution(self): def run(self): ''' - specific run for crops + specific run for crops ''' super().run() self.specific_run() @@ -154,7 +155,7 @@ def specific_run(self): ''' outputs_dict = {'mix_detailed_prices': self.techno_model.price_mix, 'mix_detailed_production': self.techno_model.production_mix} - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): @@ -234,7 +235,7 @@ def get_post_processing_list(self, filters=None): def get_production_chart(self): ''' - Create chart with production details for industry/energy + Create chart with production details for industry/energy ''' production_mix_df = self.get_sosdisc_outputs('mix_detailed_production') diff --git a/energy_models/models/biomass_dry/crop_energy/documentation/crop_energy_disc.markdown b/energy_models/models/biomass_dry/crop_energy/documentation/crop_energy_disc.markdown index c799ed49..9768132f 100644 --- a/energy_models/models/biomass_dry/crop_energy/documentation/crop_energy_disc.markdown +++ b/energy_models/models/biomass_dry/crop_energy/documentation/crop_energy_disc.markdown @@ -77,7 +77,7 @@ The computed land-use amount of hectares is the agricultural area for energy cro $$NumberOfHa=\frac{CropProductionForEnergy}{density\_per\_ha*calorific\_value}$$ -With: +With: - CropProductionForEnergy, the production of crop and residue for energy sector computed by this model **Costs** @@ -88,7 +88,7 @@ For CAPEX computation: For OPEX computation: - crop harvest and processing: 87.74 €/ha (264.4$/acre)[^7] - residue harvest (22$/t) + fertilizing (23$/t): 37.54 €/ha[^8] - + The computed price is the mixed price of crop and residue. Details in the composition of prices of crop and residue is shown in the graphics named "Detailed Price of energy crop technology over the years". Prices are computed with the input parameter crop_residue_price_percent_dif. ## Other Data @@ -102,4 +102,4 @@ Information regarding the age distribution of agricultural lands comes from Our [^5]: Bioenergy Europe, Biomass for energy: agricultural residues and energy crops, https://bioenergyeurope.org/component/attachments/attachments.html?id=561&task=download [^6]: The world bank, Cereal yield kg per hectare, https://data.worldbank.org/indicator/AG.YLD.CREL.KG [^7]: Manitoba, Crops production costs - 2021, gov.mb.ca/agriculture/farm-management/production-economics/pubs/cop-crop-production.pdf -[^8]: United States Department of Agriculture, 2016, Harvesting Crop Residue: What’s it worth?, https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcseprd1298023.pdf +[^8]: United States Department of Agriculture, 2016, Harvesting Crop Residue: What’s it worth?, https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcseprd1298023.pdf \ No newline at end of file diff --git a/energy_models/models/biomass_dry/managed_wood/documentation/managed_wood_disc.markdown b/energy_models/models/biomass_dry/managed_wood/documentation/managed_wood_disc.markdown index 051b46b0..0c0aefb2 100644 --- a/energy_models/models/biomass_dry/managed_wood/documentation/managed_wood_disc.markdown +++ b/energy_models/models/biomass_dry/managed_wood/documentation/managed_wood_disc.markdown @@ -84,7 +84,7 @@ The computed land-use amount of hectares is the global amount of managed forest $$NumberOfHa=\frac{WoodProductionForEnergy+WoodProductionForNonEnergy}{mean\_density\_per\_ha * mean\_calorific\_value} \\ * years\_between\_harvest * (1 - recycle\_part)$$ -With: +With: - WoodProductionForEnergy, the production of Managed wood and residue computed by this model - WoodProductionForNonEnergy, the computed amount of Managed Wood used for production using the inputs data wood_percentage_for_energy and wood_percentage_for_energy. @@ -97,7 +97,7 @@ For CAPEX computation: For OPEX computation: - planting (5%), manual cleaning: 269 $/ha[^5] - cutting, chipping, off_road transportation : 8 $/Mwh[^6] - + The computed price is the mixed price of wood and residue. Details in the composition of prices of wood and residue is shown in the graphics named "Detailed Price of Unmanaged wood technology over the years". Prices are computed with the input parameter wood_residue_price_percent_dif. ## Other Data @@ -110,4 +110,4 @@ Information regarding the age distribution of planted forests comes from Our Wor [^4]: European Biomass Industry Association, Recovery of forest residues, found online at https://www.eubia.org/cms/wiki-biomass/biomass-resources/challenges-related-to-biomass/recovery-of-forest-residues/ [^5]: Agriculture And Food Developement Authority, Reforestation, https://www.teagasc.ie/crops/forestry/advice/establishment/reforestation/ [^6]: Eubia, Recovery of forests residues, https://www.eubia.org/cms/wiki-biomass/biomass-resources/challenges-related-to-biomass/recovery-of-forest-residues/ -[^7]: OurworldInData, Primary vs. planted forest, https://ourworldindata.org/forest-area#primary-vs-planted-forest +[^7]: OurworldInData, Primary vs. planted forest, https://ourworldindata.org/forest-area#primary-vs-planted-forest \ No newline at end of file diff --git a/energy_models/models/biomass_dry/managed_wood/managed_wood.py b/energy_models/models/biomass_dry/managed_wood/managed_wood.py index 0856a6c7..633a764c 100644 --- a/energy_models/models/biomass_dry/managed_wood/managed_wood.py +++ b/energy_models/models/biomass_dry/managed_wood/managed_wood.py @@ -32,7 +32,6 @@ def __init__(self, name): self.production_mix = None self.price_mix = None self.mean_age_df = None - def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() diff --git a/energy_models/models/biomass_dry/managed_wood/managed_wood_disc.py b/energy_models/models/biomass_dry/managed_wood/managed_wood_disc.py index c6b0c9c1..f6a12515 100644 --- a/energy_models/models/biomass_dry/managed_wood/managed_wood_disc.py +++ b/energy_models/models/biomass_dry/managed_wood/managed_wood_disc.py @@ -49,6 +49,7 @@ class ManagedWoodDiscipline(BiomassDryTechnoDiscipline): techno_name = GlossaryEnergy.ManagedWood + # available planted forests in 2020: 294 Mha (worldbioenergy.org) # reference: @@ -152,6 +153,7 @@ class ManagedWoodDiscipline(BiomassDryTechnoDiscipline): years_between_harvest / (1 - recycle_part) # in Twh # distrib computed, for planted forests since 150 years + # distrib computed, for planted forests since 1980 (40years) # 'distrib': [3.25, 3.26, 3.27, 3.27, 3.27, 3.24, 3.21, 3.17, 3.14, 3.1, @@ -170,7 +172,7 @@ class ManagedWoodDiscipline(BiomassDryTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'define in dict'}, - + } # -- add specific techno inputs to this DESC_IN.update(BiomassDryTechnoDiscipline.DESC_IN) @@ -195,7 +197,7 @@ def specific_run(self): outputs_dict = {'mix_detailed_prices': self.techno_model.price_mix, 'mix_detailed_production': self.techno_model.production_mix} - + self.store_sos_outputs_values(outputs_dict) def get_post_processing_list(self, filters=None): diff --git a/energy_models/models/biomass_dry/unmanaged_wood/documentation/unmanaged_wood_disc.markdown b/energy_models/models/biomass_dry/unmanaged_wood/documentation/unmanaged_wood_disc.markdown index 4f968838..205c9f93 100644 --- a/energy_models/models/biomass_dry/unmanaged_wood/documentation/unmanaged_wood_disc.markdown +++ b/energy_models/models/biomass_dry/unmanaged_wood/documentation/unmanaged_wood_disc.markdown @@ -84,7 +84,7 @@ The computed land-use amount of hectares is the global amount of managed forest $$NumberOfHa=\frac{WoodProductionForEnergy+WoodProductionForNonEnergy}{mean\_density\_per\_ha * mean\_calorific\_value} \\ * years\_between\_harvest * (1 - recycle\_part)$$ -With: +With: - WoodProductionForEnergy, the production of Managed wood and residue computed by this model - WoodProductionForNonEnergy, the computed amount of Managed Wood used for production using the inputs data wood_percentage_for_energy and wood_percentage_for_energy. @@ -95,7 +95,7 @@ For CAPEX computation: For OPEX computation: - cutting, chipping, off_road transportation : 8 $/Mwh[^5] - + The computed price is the mixed price of wood and residue. Details in the composition of prices of wood and residue is shown in the graphics named "Detailed Price of Unmanaged wood technology over the years". Prices are computed with the input parameter wood_residue_price_percent_dif. ## Other Data @@ -107,4 +107,4 @@ Information regarding the age distribution of planted forests comes from Our Wor [^3]: SCA, We use the entire tree, https://www.sca.com/en/about-us/sustainability/sustainable-development/Efficient-use-of-resources/we-use-the-entire-tree/ [^4]: European Biomass Industry Association, Recovery of forest residues, found online at https://www.eubia.org/cms/wiki-biomass/biomass-resources/challenges-related-to-biomass/recovery-of-forest-residues/ [^5]: Eubia, Recovery of forests residues, https://www.eubia.org/cms/wiki-biomass/biomass-resources/challenges-related-to-biomass/recovery-of-forest-residues/ -[^6]: OurworldInData, Primary vs. planted forest, https://ourworldindata.org/forest-area#primary-vs-planted-forest +[^6]: OurworldInData, Primary vs. planted forest, https://ourworldindata.org/forest-area#primary-vs-planted-forest \ No newline at end of file diff --git a/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood.py b/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood.py index ee36e7e4..fe681840 100644 --- a/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood.py +++ b/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood.py @@ -36,6 +36,7 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() + def grad_production_invest(self, capex, production, production_mix): dcapex_dinvest = self.compute_dcapex_dinvest(self.invest_level.loc[self.invest_level[GlossaryEnergy.Years] diff --git a/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood_disc.py b/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood_disc.py index b28e2431..fc004812 100644 --- a/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood_disc.py +++ b/energy_models/models/biomass_dry/unmanaged_wood/unmanaged_wood_disc.py @@ -50,6 +50,7 @@ class UnmanagedWoodDiscipline(BiomassDryTechnoDiscipline): techno_name = GlossaryEnergy.UnmanagedWood + # reference: # https://qtimber.daf.qld.gov.au/guides/wood-density-and-hardness wood_density = 600.0 # kg/m3 @@ -148,7 +149,7 @@ class UnmanagedWoodDiscipline(BiomassDryTechnoDiscipline): wood_density * 3.36) / years_between_harvest / (1 - recycle_part) # in Twh # distrib computed, for planted forests since 150 years - + # distrib computed, for planted forests since 1980 (40years) # 'distrib': [3.25, 3.26, 3.27, 3.27, 3.27, 3.24, 3.21, 3.17, 3.14, 3.1, # 3.04, 2.99, 2.94, 2.89, 2.83, 2.77, 2.71, 2.66, 2.57, 2.51, @@ -166,7 +167,7 @@ class UnmanagedWoodDiscipline(BiomassDryTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'define in dict'}, - + } # -- add specific techno inputs to this DESC_IN.update(BiomassDryTechnoDiscipline.DESC_IN) @@ -191,7 +192,7 @@ def specific_run(self): outputs_dict = {'mix_detailed_prices': self.techno_model.price_mix, 'mix_detailed_production': self.techno_model.production_mix} - + self.store_sos_outputs_values(outputs_dict) def compute_sos_jacobian(self): diff --git a/energy_models/models/carbon_capture/direct_air_capture/amine_scrubbing/documentation/amine_scrubbing_disc.markdown b/energy_models/models/carbon_capture/direct_air_capture/amine_scrubbing/documentation/amine_scrubbing_disc.markdown index de5240e5..4218ceb9 100644 --- a/energy_models/models/carbon_capture/direct_air_capture/amine_scrubbing/documentation/amine_scrubbing_disc.markdown +++ b/energy_models/models/carbon_capture/direct_air_capture/amine_scrubbing/documentation/amine_scrubbing_disc.markdown @@ -32,4 +32,4 @@ Plus, detailed about the sharing of Capex and Opex are given [^2]: Buijs, W. and De Flart, S., 2017. Direct air capture of CO2 with an amine resin: A molecular modeling study of the CO2 capturing process. Industrial & engineering chemistry research, 56(43), pp.12297-12304. [^3]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. [^4]: Keith, D.W., Holmes, G., Angelo, D.S. and Heidel, K., 2018. A process for capturing CO2 from the atmosphere. Joule, 2(8), pp.1573-1594. -[^5]: Vo, T.T., Wall, D.M., Ring, D., Rajendran, K. and Murphy, J.D., 2018. Techno-economic analysis of biogas upgrading via amine scrubber, carbon capture and ex-situ methanation. Applied energy, 212, pp.1191-1202. +[^5]: Vo, T.T., Wall, D.M., Ring, D., Rajendran, K. and Murphy, J.D., 2018. Techno-economic analysis of biogas upgrading via amine scrubber, carbon capture and ex-situ methanation. Applied energy, 212, pp.1191-1202. \ No newline at end of file diff --git a/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/calcium_potassium_scrubbing.py b/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/calcium_potassium_scrubbing.py index 01d51187..d9364a90 100644 --- a/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/calcium_potassium_scrubbing.py +++ b/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/calcium_potassium_scrubbing.py @@ -55,6 +55,7 @@ def compute_other_streams_needs(self): def compute_byproducts_production(self): + self.production_detailed[f'{CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = self.cost_details[ f'{Methane.name}_needs'] * \ self.production_detailed[ diff --git a/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/documentation/calcium_potassium_scrubbing_disc.markdown b/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/documentation/calcium_potassium_scrubbing_disc.markdown index c8231ba8..ee8170b0 100644 --- a/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/documentation/calcium_potassium_scrubbing_disc.markdown +++ b/energy_models/models/carbon_capture/direct_air_capture/calcium_potassium_scrubbing/documentation/calcium_potassium_scrubbing_disc.markdown @@ -30,4 +30,4 @@ Technical data was found in [^1], [^2], [^3] and [^4]. [^1]: https://www.cell.com/joule/pdfExtended/S2542-4351(18)30225-3 [^2]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. [^3]: Keith, D.W., Holmes, G., Angelo, D.S. and Heidel, K., 2018. A process for capturing CO2 from the atmosphere. Joule, 2(8), pp.1573-1594. -[^4]: Vo, T.T., Wall, D.M., Ring, D., Rajendran, K. and Murphy, J.D., 2018. Techno-economic analysis of biogas upgrading via amine scrubber, carbon capture and ex-situ methanation. Applied energy, 212, pp.1191-1202. +[^4]: Vo, T.T., Wall, D.M., Ring, D., Rajendran, K. and Murphy, J.D., 2018. Techno-economic analysis of biogas upgrading via amine scrubber, carbon capture and ex-situ methanation. Applied energy, 212, pp.1191-1202. \ No newline at end of file diff --git a/energy_models/models/carbon_capture/direct_air_capture/direct_air_capture_techno/direct_air_capture_techno.py b/energy_models/models/carbon_capture/direct_air_capture/direct_air_capture_techno/direct_air_capture_techno.py index 423371b0..c4976590 100644 --- a/energy_models/models/carbon_capture/direct_air_capture/direct_air_capture_techno/direct_air_capture_techno.py +++ b/energy_models/models/carbon_capture/direct_air_capture/direct_air_capture_techno/direct_air_capture_techno.py @@ -49,4 +49,4 @@ def compute_byproducts_production(self): self.production_detailed[f'{CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = \ self.cost_details[f'{Fossil.name}_needs'] * self.production_detailed[f'{CCTechno.energy_name} ({self.product_unit})'] * \ Fossil.data_energy_dict[GlossaryEnergy.CO2PerUse] / Fossil.data_energy_dict[ - 'calorific_value'] + 'calorific_value'] \ No newline at end of file diff --git a/energy_models/models/carbon_capture/flue_gas_capture/calcium_looping/calcium_looping_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/calcium_looping/calcium_looping_disc.py index 5c148bfa..41a7ce79 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/calcium_looping/calcium_looping_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/calcium_looping/calcium_looping_disc.py @@ -43,6 +43,7 @@ class CalciumLoopingDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.CalciumLooping}' + # Most of the data from this model come from : # Guandalini, G., Romano, M.C., Ho, M., Wiley, D., Rubin, E.S. and Abanades, J.C., 2019. # A sequential approach for the economic evaluation of new CO2 capture technologies for power plants. @@ -98,7 +99,7 @@ class CalciumLoopingDiscipline(CCTechnoDiscipline): initial_capture = 5 # Mt # We assume 0.5 MT increase per year, with a capex ~ 40$/ton - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, GlossaryEnergy.FlueGasMean: {'type': 'dataframe', 'namespace': 'ns_flue_gas', diff --git a/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/chilled_ammonia_process_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/chilled_ammonia_process_disc.py index a5796b21..29453ca3 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/chilled_ammonia_process_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/chilled_ammonia_process_disc.py @@ -43,6 +43,7 @@ class ChilledAmmoniaProcessDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.ChilledAmmoniaProcess}' + # Most of the data from this model come from : # Guandalini, G., Romano, M.C., Ho, M., Wiley, D., Rubin, E.S. and Abanades, J.C., 2019. # A sequential approach for the economic evaluation of new CO2 capture technologies for power plants. @@ -98,7 +99,7 @@ class ChilledAmmoniaProcessDiscipline(CCTechnoDiscipline): initial_capture = 5 # Mt # We assume 0.5 MT increase per year, with a capex ~ 40$/ton - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, GlossaryEnergy.FlueGasMean: {'type': 'dataframe', 'namespace': 'ns_flue_gas', diff --git a/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/documentation/chilled_ammonia_process_disc.markdown b/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/documentation/chilled_ammonia_process_disc.markdown index 4de99b42..2d2c6bb1 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/documentation/chilled_ammonia_process_disc.markdown +++ b/energy_models/models/carbon_capture/flue_gas_capture/chilled_ammonia_process/documentation/chilled_ammonia_process_disc.markdown @@ -1,6 +1,6 @@ **Definition[^1] :** -The Chilled Ammonia Process is based on the chemistry of the NH3-CO2-H2O system and the ability of the ammoniated solution to absorb CO2 at low temperature and to release the CO2 at moderately elevated temperature. +The Chilled Ammonia Process is based on the chemistry of the NH3-CO2-H2O system and the ability of the ammoniated solution to absorb CO2 at low temperature and to release the CO2 at moderately elevated temperature. Fundamentals of Chilled ammonia process[^1] ![](cap.PNG) diff --git a/energy_models/models/carbon_capture/flue_gas_capture/co2_membranes/co2_membranes_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/co2_membranes/co2_membranes_disc.py index b5467d06..9ddcfacc 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/co2_membranes/co2_membranes_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/co2_membranes/co2_membranes_disc.py @@ -43,6 +43,7 @@ class CO2MembranesDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.CO2Membranes}' + # Most of the data from this model come from : # Guandalini, G., Romano, M.C., Ho, M., Wiley, D., Rubin, E.S. and Abanades, J.C., 2019. # A sequential approach for the economic evaluation of new CO2 capture technologies for power plants. @@ -93,7 +94,7 @@ class CO2MembranesDiscipline(CCTechnoDiscipline): initial_capture = 5 # Mt # We assume 0.5 MT increase per year, with a capex ~ 40$/ton - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, GlossaryEnergy.FlueGasMean: {'type': 'dataframe', 'namespace': 'ns_flue_gas', diff --git a/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/documentation/flue_gas_disc.md b/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/documentation/flue_gas_disc.md index b004c27d..250f8afa 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/documentation/flue_gas_disc.md +++ b/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/documentation/flue_gas_disc.md @@ -2,4 +2,4 @@ ** Simplified Flue Gas technology, used in the WITNESS Full Coarse process to demonstrate the Energy Mix** -** This technology uses the energy produced in the Renewable stream as a consumption ** +** This technology uses the energy produced in the Renewable stream as a consumption ** \ No newline at end of file diff --git a/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/flue_gas_techno_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/flue_gas_techno_disc.py index 93de742b..f56b72c8 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/flue_gas_techno_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/flue_gas_techno/flue_gas_techno_disc.py @@ -45,6 +45,7 @@ class FlueGasTechnoDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.FlueGasTechno}' + heat_to_power_lost = 0.243 heat_duty = 18 elec_demand_capture = 338 diff --git a/energy_models/models/carbon_capture/flue_gas_capture/generic_flue_gas_techno_model.py b/energy_models/models/carbon_capture/flue_gas_capture/generic_flue_gas_techno_model.py index f3731d32..58afa5fc 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/generic_flue_gas_techno_model.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/generic_flue_gas_techno_model.py @@ -53,3 +53,4 @@ def compute_streams_consumption(self): self.consumption_detailed[f'{GlossaryEnergy.electricity} ({self.energy_unit})'] = self.cost_details[f'{GlossaryEnergy.electricity}_needs'] * \ self.production_detailed[f'{CCTechno.energy_name} ({self.product_unit})'] / self.compute_electricity_variation_from_fg_ratio( self.flue_gas_ratio[GlossaryEnergy.FlueGasMean].values, self.fg_ratio_effect) + diff --git a/energy_models/models/carbon_capture/flue_gas_capture/mono_ethanol_amine/mono_ethanol_amine_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/mono_ethanol_amine/mono_ethanol_amine_disc.py index d93a094e..509c80ac 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/mono_ethanol_amine/mono_ethanol_amine_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/mono_ethanol_amine/mono_ethanol_amine_disc.py @@ -44,6 +44,7 @@ class MonoEthanolAmineDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.MonoEthanolAmine}' + # Most of the data from this model come from : # Guandalini, G., Romano, M.C., Ho, M., Wiley, D., Rubin, E.S. and Abanades, J.C., 2019. # A sequential approach for the economic evaluation of new CO2 capture technologies for power plants. @@ -99,7 +100,7 @@ class MonoEthanolAmineDiscipline(CCTechnoDiscipline): initial_capture = 15 # Mt # We assume 0.5 MT increase per year, with a capex ~ 40$/ton - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, GlossaryEnergy.FlueGasMean: {'type': 'dataframe', 'namespace': 'ns_flue_gas', diff --git a/energy_models/models/carbon_capture/flue_gas_capture/piperazine_process/piperazine_process_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/piperazine_process/piperazine_process_disc.py index abaa031b..90e0ff1c 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/piperazine_process/piperazine_process_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/piperazine_process/piperazine_process_disc.py @@ -43,6 +43,7 @@ class PiperazineProcessDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.PiperazineProcess}' + # Most of the data from this model come from : # Guandalini, G., Romano, M.C., Ho, M., Wiley, D., Rubin, E.S. and Abanades, J.C., 2019. # A sequential approach for the economic evaluation of new CO2 capture technologies for power plants. @@ -98,7 +99,7 @@ class PiperazineProcessDiscipline(CCTechnoDiscipline): initial_capture = 5 # Mt # We assume 0.5 MT increase per year, with a capex ~ 40$/ton - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, GlossaryEnergy.FlueGasMean: {'type': 'dataframe', 'namespace': 'ns_flue_gas', diff --git a/energy_models/models/carbon_capture/flue_gas_capture/pressure_swing_adsorption/pressure_swing_adsorption_disc.py b/energy_models/models/carbon_capture/flue_gas_capture/pressure_swing_adsorption/pressure_swing_adsorption_disc.py index b83143c9..d8c23ce8 100644 --- a/energy_models/models/carbon_capture/flue_gas_capture/pressure_swing_adsorption/pressure_swing_adsorption_disc.py +++ b/energy_models/models/carbon_capture/flue_gas_capture/pressure_swing_adsorption/pressure_swing_adsorption_disc.py @@ -43,6 +43,7 @@ class PressureSwingAdsorptionDiscipline(CCTechnoDiscipline): } techno_name = f'{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.PressureSwingAdsorption}' + # Most of the data from this model come from : # Guandalini, G., Romano, M.C., Ho, M., Wiley, D., Rubin, E.S. and Abanades, J.C., 2019. # A sequential approach for the economic evaluation of new CO2 capture technologies for power plants. @@ -93,7 +94,7 @@ class PressureSwingAdsorptionDiscipline(CCTechnoDiscipline): initial_capture = 5 # Mt # We assume 0.5 MT increase per year, with a capex ~ 40$/ton - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, GlossaryEnergy.FlueGasMean: {'type': 'dataframe', 'namespace': 'ns_flue_gas', diff --git a/energy_models/models/carbon_storage/biomass_burying_fossilization/biomass_burying_fossilization_disc.py b/energy_models/models/carbon_storage/biomass_burying_fossilization/biomass_burying_fossilization_disc.py index d6e8bc42..5e2e6293 100644 --- a/energy_models/models/carbon_storage/biomass_burying_fossilization/biomass_burying_fossilization_disc.py +++ b/energy_models/models/carbon_storage/biomass_burying_fossilization/biomass_burying_fossilization_disc.py @@ -65,7 +65,7 @@ class BiomassBuryingFossilizationDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 # in kg at year_start - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/biomass_burying_fossilization/documentation/biomass_burying_fossilization_disc.markdown b/energy_models/models/carbon_storage/biomass_burying_fossilization/documentation/biomass_burying_fossilization_disc.markdown index 1db239d1..fd9be6d3 100644 --- a/energy_models/models/carbon_storage/biomass_burying_fossilization/documentation/biomass_burying_fossilization_disc.markdown +++ b/energy_models/models/carbon_storage/biomass_burying_fossilization/documentation/biomass_burying_fossilization_disc.markdown @@ -11,4 +11,4 @@ According to [^3], dry biomass is composed of 42.5% of carbon and using molar ma [^1]: The East Africa Sisal Company Ltd, http://www.eastafricasisal.com/peatland-restoration/ [^2]: Carbon sequestration via wood burial, BMC,https://cbmjournal.biomedcentral.com/articles/10.1186/1750-0680-3-1 -[^3]: INTA, http://repo-desa.inta.gob.ar/xmlui/bitstream/handle/20.500.12123/1303/INTA_CRPatagoniaNorte-EEABariloche_WarrenRaffa_D_How_does_crop_residue_removal_affect_soil.pdf?sequence=3 +[^3]: INTA, http://repo-desa.inta.gob.ar/xmlui/bitstream/handle/20.500.12123/1303/INTA_CRPatagoniaNorte-EEABariloche_WarrenRaffa_D_How_does_crop_residue_removal_affect_soil.pdf?sequence=3 \ No newline at end of file diff --git a/energy_models/models/carbon_storage/carbon_storage_techno/carbon_storage_techno_disc.py b/energy_models/models/carbon_storage/carbon_storage_techno/carbon_storage_techno_disc.py index e3c5a6b8..8adea9b3 100644 --- a/energy_models/models/carbon_storage/carbon_storage_techno/carbon_storage_techno_disc.py +++ b/energy_models/models/carbon_storage/carbon_storage_techno/carbon_storage_techno_disc.py @@ -67,7 +67,7 @@ class CarbonStorageTechnoDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 # in kg at year_start - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/carbon_storage_techno/documentation/carbon_storage_techno_disc.markdown b/energy_models/models/carbon_storage/carbon_storage_techno/documentation/carbon_storage_techno_disc.markdown index 44824bc6..c555ce8a 100644 --- a/energy_models/models/carbon_storage/carbon_storage_techno/documentation/carbon_storage_techno_disc.markdown +++ b/energy_models/models/carbon_storage/carbon_storage_techno/documentation/carbon_storage_techno_disc.markdown @@ -1,3 +1,3 @@ **Definition :** -A simplified techno used to demonstrate EnergyMix +A simplified techno used to demonstrate EnergyMix \ No newline at end of file diff --git a/energy_models/models/carbon_storage/deep_ocean_injection/deep_ocean_injection_disc.py b/energy_models/models/carbon_storage/deep_ocean_injection/deep_ocean_injection_disc.py index 5b4f8ac4..89109eef 100644 --- a/energy_models/models/carbon_storage/deep_ocean_injection/deep_ocean_injection_disc.py +++ b/energy_models/models/carbon_storage/deep_ocean_injection/deep_ocean_injection_disc.py @@ -70,7 +70,7 @@ class DeepOceanInjectionDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/deep_ocean_injection/documentation/deep_ocean_injection_disc.markdown b/energy_models/models/carbon_storage/deep_ocean_injection/documentation/deep_ocean_injection_disc.markdown index 4d7f0b38..01e80c67 100644 --- a/energy_models/models/carbon_storage/deep_ocean_injection/documentation/deep_ocean_injection_disc.markdown +++ b/energy_models/models/carbon_storage/deep_ocean_injection/documentation/deep_ocean_injection_disc.markdown @@ -10,4 +10,4 @@ The overall cost varies between 2.2$ / tCO2 for direct injection and up to 15.7$ [^1]: IPCC, https://www.ipcc.ch/site/assets/uploads/2018/03/srccs_chapter6-1.pdf [^2]: Ocean Storage of CO2, The Liquid Grid, http://theliquidgrid.com/2018/07/22/ocean-storage-of-co2 -[^3]: Ocean Storage of CO2, The Liquid Grid, https://www.ipcc.ch/site/assets/uploads/2018/03/srccs_chapter6-1.pdf +[^3]: Ocean Storage of CO2, The Liquid Grid, https://www.ipcc.ch/site/assets/uploads/2018/03/srccs_chapter6-1.pdf \ No newline at end of file diff --git a/energy_models/models/carbon_storage/deep_saline_formation/deep_saline_formation_disc.py b/energy_models/models/carbon_storage/deep_saline_formation/deep_saline_formation_disc.py index 465b0542..1ef902c0 100644 --- a/energy_models/models/carbon_storage/deep_saline_formation/deep_saline_formation_disc.py +++ b/energy_models/models/carbon_storage/deep_saline_formation/deep_saline_formation_disc.py @@ -70,7 +70,7 @@ class DeepSalineFormationDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 # in kg at year_start - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/deep_saline_formation/documentation/deep_saline_formation_disc.markdown b/energy_models/models/carbon_storage/deep_saline_formation/documentation/deep_saline_formation_disc.markdown index bcd6c538..333bce90 100644 --- a/energy_models/models/carbon_storage/deep_saline_formation/documentation/deep_saline_formation_disc.markdown +++ b/energy_models/models/carbon_storage/deep_saline_formation/documentation/deep_saline_formation_disc.markdown @@ -15,4 +15,4 @@ Technical data was found in [^3]. [^1]: Carbon Storage, Equinor.com, https://www.equinor.com/en/what-we-do/carbon-capture-and-storage.html [^2]: Carbon Storage, Global CCS Institute, https://www.globalccsinstitute.com/archive/hub/publications/119816/costs-co2-storage-post-demonstration-ccs-eu.pdf -[^3]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. +[^3]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. \ No newline at end of file diff --git a/energy_models/models/carbon_storage/depleted_oil_gas/depleted_oil_gas_disc.py b/energy_models/models/carbon_storage/depleted_oil_gas/depleted_oil_gas_disc.py index fc36c7a7..dfa1cb89 100644 --- a/energy_models/models/carbon_storage/depleted_oil_gas/depleted_oil_gas_disc.py +++ b/energy_models/models/carbon_storage/depleted_oil_gas/depleted_oil_gas_disc.py @@ -72,7 +72,7 @@ class DepletedOilGasDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/depleted_oil_gas/documentation/depleted_oil_gas_disc.markdown b/energy_models/models/carbon_storage/depleted_oil_gas/documentation/depleted_oil_gas_disc.markdown index e24434d0..f8a8dc4d 100644 --- a/energy_models/models/carbon_storage/depleted_oil_gas/documentation/depleted_oil_gas_disc.markdown +++ b/energy_models/models/carbon_storage/depleted_oil_gas/documentation/depleted_oil_gas_disc.markdown @@ -13,4 +13,4 @@ Technical data was found in [^3]. [^1]: Scottish Centre for Carbon Storage, 2012, https://www.researchgate.net/figure/Injection-of-carbon-dioxide-into-depleted-oil-and-gas-reservoir-Image-source-Scottish_fig13_327750644 [^2]: Global CCS institute, https://www.globalccsinstitute.com/archive/hub/publications/119816/costs-co2-storage-post-demonstration-ccs-eu.pdf -[^3]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. +[^3]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. \ No newline at end of file diff --git a/energy_models/models/carbon_storage/enhanced_oil_recovery/documentation/enhanced_oil_recovery_disc.markdown b/energy_models/models/carbon_storage/enhanced_oil_recovery/documentation/enhanced_oil_recovery_disc.markdown index 05d54735..121785fb 100644 --- a/energy_models/models/carbon_storage/enhanced_oil_recovery/documentation/enhanced_oil_recovery_disc.markdown +++ b/energy_models/models/carbon_storage/enhanced_oil_recovery/documentation/enhanced_oil_recovery_disc.markdown @@ -8,4 +8,4 @@ Oil & Gas companies such as Oxy or Total invest in CO2 capture and underground i -[^1]: Energy.gov, https://www.energy.gov/fe/science-innovation/oil-gas-research/enhanced-oil-recoveryupdate_of_financial_data_for_coal_fired_chp_plants_may17_july17.pdf +[^1]: Energy.gov, https://www.energy.gov/fe/science-innovation/oil-gas-research/enhanced-oil-recoveryupdate_of_financial_data_for_coal_fired_chp_plants_may17_july17.pdf \ No newline at end of file diff --git a/energy_models/models/carbon_storage/enhanced_oil_recovery/enhanced_oil_recovery_disc.py b/energy_models/models/carbon_storage/enhanced_oil_recovery/enhanced_oil_recovery_disc.py index 28fc4507..100925a5 100644 --- a/energy_models/models/carbon_storage/enhanced_oil_recovery/enhanced_oil_recovery_disc.py +++ b/energy_models/models/carbon_storage/enhanced_oil_recovery/enhanced_oil_recovery_disc.py @@ -70,7 +70,7 @@ class EnhancedOilRecoveryDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/geologic_mineralization/documentation/geologic_mineralization_disc.markdown b/energy_models/models/carbon_storage/geologic_mineralization/documentation/geologic_mineralization_disc.markdown index c089f3fa..b065da8b 100644 --- a/energy_models/models/carbon_storage/geologic_mineralization/documentation/geologic_mineralization_disc.markdown +++ b/energy_models/models/carbon_storage/geologic_mineralization/documentation/geologic_mineralization_disc.markdown @@ -13,4 +13,4 @@ The researchers report that approximately 80% of the carbon becomes embedded in Technical data was found in [^2]. [^1]: Carbfix Website, https://www.carbfix.com/ -[^2]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. +[^2]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. \ No newline at end of file diff --git a/energy_models/models/carbon_storage/geologic_mineralization/geologic_mineralization_disc.py b/energy_models/models/carbon_storage/geologic_mineralization/geologic_mineralization_disc.py index 8db35bfb..706788c4 100644 --- a/energy_models/models/carbon_storage/geologic_mineralization/geologic_mineralization_disc.py +++ b/energy_models/models/carbon_storage/geologic_mineralization/geologic_mineralization_disc.py @@ -70,7 +70,7 @@ class GeologicMineralizationDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/carbon_storage/pure_carbon_solid_storage/documentation/pure_carbon_solid_storage_disc.markdown b/energy_models/models/carbon_storage/pure_carbon_solid_storage/documentation/pure_carbon_solid_storage_disc.markdown index 4e6646b1..799990ea 100644 --- a/energy_models/models/carbon_storage/pure_carbon_solid_storage/documentation/pure_carbon_solid_storage_disc.markdown +++ b/energy_models/models/carbon_storage/pure_carbon_solid_storage/documentation/pure_carbon_solid_storage_disc.markdown @@ -15,4 +15,4 @@ Using molar masses we know that in order to obtain 1 kg of carbon we need 3,67 k **Datas :** Technical data was found in [^1]. -[^1]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. +[^1]: Fasihi, M., Efimova, O. and Breyer, C., 2019. Techno-economic assessment of CO2 direct air capture plants. Journal of cleaner production, 224, pp.957-980. \ No newline at end of file diff --git a/energy_models/models/carbon_storage/pure_carbon_solid_storage/pure_carbon_solid_storage_disc.py b/energy_models/models/carbon_storage/pure_carbon_solid_storage/pure_carbon_solid_storage_disc.py index ed59465c..05ecfcf2 100644 --- a/energy_models/models/carbon_storage/pure_carbon_solid_storage/pure_carbon_solid_storage_disc.py +++ b/energy_models/models/carbon_storage/pure_carbon_solid_storage/pure_carbon_solid_storage_disc.py @@ -78,13 +78,13 @@ class PureCarbonSolidStorageDiscipline(CSTechnoDiscipline): techno_info_dict = techno_infos_dict_default initial_storage = 0 - + carbon_zero_quantity_to_be_stored = pd.DataFrame( {GlossaryEnergy.Years: range(GlossaryEnergy.YearStartDefault, GlossaryEnergy.YearEndDefault + 1), GlossaryEnergy.carbon_storage: 0.}) DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'carbon_quantity_to_be_stored': {'type': 'dataframe', 'unit': 'Mt', 'default': carbon_zero_quantity_to_be_stored, 'namespace': 'ns_carb', 'visibility': 'Shared', 'structuring': True, @@ -129,7 +129,7 @@ def init_execution(self): def run(self): ''' - specific run for carbon storage + specific run for carbon storage ''' # -- get inputs CSTechnoDiscipline.run(self) diff --git a/energy_models/models/carbon_storage/reforestation/documentation/reforestation_disc.markdown b/energy_models/models/carbon_storage/reforestation/documentation/reforestation_disc.markdown index 2b5fe400..7ab26b0d 100644 --- a/energy_models/models/carbon_storage/reforestation/documentation/reforestation_disc.markdown +++ b/energy_models/models/carbon_storage/reforestation/documentation/reforestation_disc.markdown @@ -29,7 +29,7 @@ The worldwide gain in forest cover totalled 5.2 million hectares per year, the a ## Carbon storage -The intensity of the metabolic processes in trees depends not only on their age but also on certain environmental factors, such as density, temperature, humidity, availability of nutrients, and presence of weather anomalies. There are so many variables that any estimates as to the amount of CO2 absorbed by forests are beset with uncertainty. Consequently, there is a risk that important political decisions regarding climate change mitigation will be based on uncertain predictions. +The intensity of the metabolic processes in trees depends not only on their age but also on certain environmental factors, such as density, temperature, humidity, availability of nutrients, and presence of weather anomalies. There are so many variables that any estimates as to the amount of CO2 absorbed by forests are beset with uncertainty. Consequently, there is a risk that important political decisions regarding climate change mitigation will be based on uncertain predictions. A tree absorbs 30 kgCO2 per year in average [^2]. The world mean density of trees per hectare is 225.40[3]. @@ -45,4 +45,4 @@ Investments are taken to corresponds to actual trend of 1.6Mha new planted fores [^1]: Food and Agriculture Organisation of the United Nations, http://www.fao.org/documents/card/en/c/ca8642en [^2]: ecotree, https://ecotree.green/en/how-much-co2-does-a-tree-absorb [^3]: OurworldInData, Number of trees per km2, 2014, https://ourworldindata.org/grapher/number-of-trees-per-km?tab=chart&country=World+%28mean%29~FRA~RUS~USA -[^4]: OurworldInData, Primary vs. planted forest, https://ourworldindata.org/forest-area#primary-vs-planted-forest +[^4]: OurworldInData, Primary vs. planted forest, https://ourworldindata.org/forest-area#primary-vs-planted-forest \ No newline at end of file diff --git a/energy_models/models/carbon_storage/reforestation/reforestation_disc.py b/energy_models/models/carbon_storage/reforestation/reforestation_disc.py index 95f9361a..98099ce6 100644 --- a/energy_models/models/carbon_storage/reforestation/reforestation_disc.py +++ b/energy_models/models/carbon_storage/reforestation/reforestation_disc.py @@ -96,7 +96,7 @@ class ReforestationDiscipline(CSTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default}, - + } # -- add specific techno inputs to this DESC_IN.update(CSTechnoDiscipline.DESC_IN) diff --git a/energy_models/models/clean_energy/clean_energy_simple_techno/documentation/renewable_simple_techno_disc.markdown b/energy_models/models/clean_energy/clean_energy_simple_techno/documentation/renewable_simple_techno_disc.markdown index 5ef54454..9abeadb4 100644 --- a/energy_models/models/clean_energy/clean_energy_simple_techno/documentation/renewable_simple_techno_disc.markdown +++ b/energy_models/models/clean_energy/clean_energy_simple_techno/documentation/renewable_simple_techno_disc.markdown @@ -3,3 +3,6 @@ ** Simplified techno that produces energy cleanly, used to demonstrate EnergyMix.** ** The energy produced is used in simplified carbon capture techno ** + + + diff --git a/energy_models/models/electricity/biomass_fired/biomass_fired.py b/energy_models/models/electricity/biomass_fired/biomass_fired.py index 9e90dcea..73f10fe5 100644 --- a/energy_models/models/electricity/biomass_fired/biomass_fired.py +++ b/energy_models/models/electricity/biomass_fired/biomass_fired.py @@ -38,6 +38,7 @@ def compute_byproducts_production(self): self.consumption_detailed[f'{BiomassDry.name} ({self.product_unit})'] - \ self.production_detailed[f'{ElectricityTechno.energy_name} ({self.product_unit})'] # TWh + def get_theoretical_co2_prod(self, unit='kg/kWh'): ''' Get co2 needs in kg co2 /kWh diff --git a/energy_models/models/electricity/biomass_fired/biomass_fired_disc.py b/energy_models/models/electricity/biomass_fired/biomass_fired_disc.py index c1b1697b..d58594ee 100644 --- a/energy_models/models/electricity/biomass_fired/biomass_fired_disc.py +++ b/energy_models/models/electricity/biomass_fired/biomass_fired_disc.py @@ -45,6 +45,7 @@ class BiomassFiredDiscipline(ElectricityTechnoDiscipline): techno_name = GlossaryEnergy.BiomassFired + # Source for Initial prod in TWh (2019): # IEA 2022, Data Tables # https://www.iea.org/data-and-statistics/data-tables?country=WORLD&energy=Renewables%20%26%20waste&year=2019, @@ -80,7 +81,7 @@ class BiomassFiredDiscipline(ElectricityTechnoDiscipline): 'efficiency': 1, 'techno_evo_eff': 'no', # yes or no 'full_load_hours': 8760, - f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9 # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW, + f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9 #No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW, # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } diff --git a/energy_models/models/electricity/biomass_fired/documentation/biomass_fired_disc.markdown b/energy_models/models/electricity/biomass_fired/documentation/biomass_fired_disc.markdown index f91c8bc3..2b8fcf1d 100644 --- a/energy_models/models/electricity/biomass_fired/documentation/biomass_fired_disc.markdown +++ b/energy_models/models/electricity/biomass_fired/documentation/biomass_fired_disc.markdown @@ -1,15 +1,15 @@ # Biomass Fired Electricity -Most biopower plants use direct-fired combustion systems. They burn biomass directly to produce high-pressure steam +Most biopower plants use direct-fired combustion systems. They burn biomass directly to produce high-pressure steam that drives a turbine generator to make electricity. Direct combustion systems feed a biomass feedstock into a combustor or furnace, where the biomass is burned with excess air to heat water in a boiler to create steam. Instead of direct combustion, some developing technologies gasify -the biomass to produce a combustible gas, and others produce pyrolysis oils that can be used to replace liquid fuels. -Boiler fuel can include wood chips, pellets, sawdust, or bio-oil. Steam from the boiler is then expanded through a +the biomass to produce a combustible gas, and others produce pyrolysis oils that can be used to replace liquid fuels. +Boiler fuel can include wood chips, pellets, sawdust, or bio-oil. Steam from the boiler is then expanded through a steam turbine, which spins to run a generator and produce electricity. -Drying the biomass before combusting or gasifying it improves the overall process efficiency, +Drying the biomass before combusting or gasifying it improves the overall process efficiency, but may not be economically viable in many cases (see: Biogas Fired technology).[^1] Production of high heat is calculated in TWh. Where, consumption of biomass dry(TWh) is more than production of electricity(TWh). @@ -23,4 +23,4 @@ The data used for this model is extracted from the IEA Data & statistics[^2] and [^3][IRENA Power Generation Costs, 2019, (p110-119)](https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2020/Jun/IRENA_Power_Generation_Costs_2019.pdf) -[^4]https://en.wikipedia.org/wiki/Biomass_heating_system +[^4]https://en.wikipedia.org/wiki/Biomass_heating_system \ No newline at end of file diff --git a/energy_models/models/electricity/coal_gen/coal_gen_disc.py b/energy_models/models/electricity/coal_gen/coal_gen_disc.py index 827504ed..7dccde66 100644 --- a/energy_models/models/electricity/coal_gen/coal_gen_disc.py +++ b/energy_models/models/electricity/coal_gen/coal_gen_disc.py @@ -114,7 +114,7 @@ class CoalGenDiscipline(ElectricityTechnoDiscipline): 'efficiency': 0.41, 'efficiency_max': 0.48, 'efficiency evolution slope': 0.5, - f"{GlossaryEnergy.CopperResource}_needs": 1150 / 1e9 # According to the IEA, Coal powered stations need 1150 kg of copper for each MW implemented. Computing the need in Mt/MW., + f"{GlossaryEnergy.CopperResource}_needs": 1150 /1e9 #According to the IEA, Coal powered stations need 1150 kg of copper for each MW implemented. Computing the need in Mt/MW., # IEA Executive summary - Role of critical minerals in clean energy transitions 2022 } @@ -124,7 +124,7 @@ class CoalGenDiscipline(ElectricityTechnoDiscipline): # License: CC BY 4.0. initial_production = 9914.45 # in TWh at year_start # Invest before year start in $ - + FLUE_GAS_RATIO = np.array([0.13]) DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, diff --git a/energy_models/models/electricity/coal_gen/documentation/coal_gen_disc.markdown b/energy_models/models/electricity/coal_gen/documentation/coal_gen_disc.markdown index 1a7e66f2..01ea319e 100644 --- a/energy_models/models/electricity/coal_gen/documentation/coal_gen_disc.markdown +++ b/energy_models/models/electricity/coal_gen/documentation/coal_gen_disc.markdown @@ -1,5 +1,5 @@ **Definition (extracted from [^1]):** -A coal-fired power station is a thermal power station that burns coal to generate electricity. Thermal energy produced by coal combustion converts boiler water into steam. This steam is then used to spin turbines and in turn activate generators. Coal-generated electricity represents about 38% of global electricity and produces over 10 billion tonnes of carbon dioxide every year. Coal-fired power plant technology development has been focused on two targets: +A coal-fired power station is a thermal power station that burns coal to generate electricity. Thermal energy produced by coal combustion converts boiler water into steam. This steam is then used to spin turbines and in turn activate generators. Coal-generated electricity represents about 38% of global electricity and produces over 10 billion tonnes of carbon dioxide every year. Coal-fired power plant technology development has been focused on two targets: -Improving the average efficiency from 38% to 48% by increasing the pressure and temperature of the process (up to 650 C and 330 bar for advanced ultra-supercritical or retrofitted subcritical power plants) which allows a drop from 900 to 670 kg/MWh in CO2 emissions. @@ -7,29 +7,29 @@ A coal-fired power station is a thermal power station that burns coal to generat Production of high heat is calculated in TWh. Where, consumption of solid fuel(TWh) is more than production of electricity(TWh). -![](Coal_fired_power_plant_diagram.png) +![](Coal_fired_power_plant_diagram.png) (Image Credit: Tennessee Valley Authority, taken from [^1]) -## Data +## Data According to [^2] and based on 227 power stations, median water consumption is 2220 L/MWh. -![](water_consumption_coalgen.jpg) +![](water_consumption_coalgen.jpg) (Image Credit: [^2]) According to [^5], CAPEX median value is 1900 USD/kW and OPEX median value is 3.39%. -![](CAPEX_OPEX.PNG) -(Table taken from [^5], source IEA) +![](CAPEX_OPEX.PNG) +(Table taken from [^5], source IEA) Fuel and electricity average consumption on worldwide coal-fired power plant for several technologies.[^4] -![](Ressources_costs_coalgen.png) +![](Ressources_costs_coalgen.png) (Table taken from [^4], source IEA) Of the 2 080 GW existing coal fleet, 62% is 20 years old (YO) or less, 16% over 40 YO, 22% from 20 to 40 YO. [^6] -### GHG emissions +### GHG emissions The GAINS model also predicts N2O fugitive emissions from coal energy. The emission factor is equal to 0.0014 kt/PJ. [^7] @@ -50,4 +50,4 @@ The GAINS model also predicts N2O fugitive emissions from coal energy. The emiss [^8]: https://en.wikipedia.org/wiki/Coal-fired_power_station -[^9]: https://www.tva.com/energy/our-power-system/coal/how-a-coal-plant-works#:~:text=Coal%2Dfired%20plants%20produce%20electricity,to%20start%20the%20process%20over. +[^9]: https://www.tva.com/energy/our-power-system/coal/how-a-coal-plant-works#:~:text=Coal%2Dfired%20plants%20produce%20electricity,to%20start%20the%20process%20over. \ No newline at end of file diff --git a/energy_models/models/electricity/gas/biogas_fired/biogas_fired.py b/energy_models/models/electricity/gas/biogas_fired/biogas_fired.py index 9b9e754e..39382388 100644 --- a/energy_models/models/electricity/gas/biogas_fired/biogas_fired.py +++ b/energy_models/models/electricity/gas/biogas_fired/biogas_fired.py @@ -40,8 +40,8 @@ def compute_byproducts_production(self): self.production_detailed[f'{ElectricityTechno.energy_name} ({self.product_unit})'] def get_theoretical_co2_prod(self, unit='kg/kWh'): - ''' - Get co2 needs in kg co2 /kWh + ''' + Get co2 needs in kg co2 /kWh ''' biogas_data = BioGas.data_energy_dict # kg of C02 per kWh of biogas burnt diff --git a/energy_models/models/electricity/gas/biogas_fired/biogas_fired_disc.py b/energy_models/models/electricity/gas/biogas_fired/biogas_fired_disc.py index 7cd6bc53..177fcd64 100644 --- a/energy_models/models/electricity/gas/biogas_fired/biogas_fired_disc.py +++ b/energy_models/models/electricity/gas/biogas_fired/biogas_fired_disc.py @@ -45,6 +45,7 @@ class BiogasFiredDiscipline(ElectricityTechnoDiscipline): techno_name = GlossaryEnergy.BiogasFired + # IEA 2022, Data Tables, # https://www.iea.org/data-and-statistics/data-tables?country=WORLD&energy=Renewables%20%26%20waste&year=2019 # License: CC BY 4.0. @@ -70,7 +71,7 @@ class BiogasFiredDiscipline(ElectricityTechnoDiscipline): 'efficiency': 1, 'techno_evo_eff': 'no', # yes or no 'full_load_hours': 8760, - f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW + f"{GlossaryEnergy.CopperResource}_needs": 1100 /1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } diff --git a/energy_models/models/electricity/gas/biogas_fired/documentation/biogas_fired_disc.markdown b/energy_models/models/electricity/gas/biogas_fired/documentation/biogas_fired_disc.markdown index 38ce6eb1..9712693d 100644 --- a/energy_models/models/electricity/gas/biogas_fired/documentation/biogas_fired_disc.markdown +++ b/energy_models/models/electricity/gas/biogas_fired/documentation/biogas_fired_disc.markdown @@ -1,30 +1,30 @@ # Biogas Fired Electricity -Biogas can be used in different types of internal combustion engines. Other internal combustion -engines such as gas turbines are suitable for the conversion of biogas into both electricity and -heat. The digestate is the remaining inorganic matter that was not transformed into biogas. +Biogas can be used in different types of internal combustion engines. Other internal combustion +engines such as gas turbines are suitable for the conversion of biogas into both electricity and +heat. The digestate is the remaining inorganic matter that was not transformed into biogas. It can be used as an agricultural fertiliser. -Biogas can be used as the fuel in the system of producing biogas from agricultural wastes and -co-generating heat and electricity in a combined heat and power (CHP) plant. +Biogas can be used as the fuel in the system of producing biogas from agricultural wastes and +co-generating heat and electricity in a combined heat and power (CHP) plant. -Unlike the other green energy such as wind and solar, the biogas can be quickly accessed on demand. -The global warming potential can also be greatly reduced when using biogas as the fuel +Unlike the other green energy such as wind and solar, the biogas can be quickly accessed on demand. +The global warming potential can also be greatly reduced when using biogas as the fuel instead of fossil fuel.[^1] Production of high heat is calculated in TWh. Where, consumption of biogas(TWh) is more than production of electricity(TWh). ## Combined Heat and Power (CHP) -The biogas is fed into a combustion engine that turns an electrical generator producing electricity that is injected +The biogas is fed into a combustion engine that turns an electrical generator producing electricity that is injected into the electrical grid via a set of electrical protections and transformers, generating a lot of heat in the process. Typically, a biogas CHP will convert 40% of the biogas energy into electricity, and 50% into hot water. CHP have high capacity factor (about 95%) which means that they produce electricity steadily throughout the year.[^5] -## Data -The data used for this model is extracted partly from the IEA Data & statistics[^2] and survey on Biogas installed power -generation capacity, 2010-2018[^3] and partly from an IRENA Report[^4]. +## Data +The data used for this model is extracted partly from the IEA Data & statistics[^2] and survey on Biogas installed power +generation capacity, 2010-2018[^3] and partly from an IRENA Report[^4]. [^1][Wikipedia](https://en.wikipedia.org/wiki/Biogas#Biogas_generated_heat/electricity) @@ -38,4 +38,4 @@ generation capacity, 2010-2018[^3] and partly from an IRENA Report[^4]. [^6]https://www.sciencedirect.com/science/article/abs/pii/S0306261911008348 -[^7]https://en.wikipedia.org/wiki/Biogas#Biogas_generated_heat/electricity +[^7]https://en.wikipedia.org/wiki/Biogas#Biogas_generated_heat/electricity \ No newline at end of file diff --git a/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine.py b/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine.py index 602ffabe..023a2cc2 100644 --- a/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine.py +++ b/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine.py @@ -26,7 +26,6 @@ class CCGasT(ElectricityTechno): COPPER_RESOURCE_NAME = GlossaryEnergy.CopperResource - def compute_other_streams_needs(self): self.cost_details[f'{Methane.name}_needs'] = self.techno_infos_dict[f'{Methane.name}_needs'] diff --git a/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine_disc.py b/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine_disc.py index 1774b7ae..02ee1dc3 100644 --- a/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine_disc.py +++ b/energy_models/models/electricity/gas/combined_cycle_gas_turbine/combined_cycle_gas_turbine_disc.py @@ -84,7 +84,7 @@ class CombinedCycleGasTurbineDiscipline(ElectricityTechnoDiscipline): # 'efficiency': 0.55, #https://www.ipieca.org/resources/energy-efficiency-solutions/combined-cycle-gas-turbines-2022#:~:text=The%20overall%20efficiency%20of%20an,drops%20significantly%20at%20partial%20load. 'techno_evo_eff': 'no', # yes or no 'full_load_hours': 8760, - f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW. + f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW. # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } @@ -100,7 +100,7 @@ class CombinedCycleGasTurbineDiscipline(ElectricityTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + } # -- add specific techno inputs to this DESC_IN.update(ElectricityTechnoDiscipline.DESC_IN) diff --git a/energy_models/models/electricity/gas/combined_cycle_gas_turbine/documentation/combined_cycle_gas_turbine_disc.markdown b/energy_models/models/electricity/gas/combined_cycle_gas_turbine/documentation/combined_cycle_gas_turbine_disc.markdown index c42dfdb7..c789edaa 100644 --- a/energy_models/models/electricity/gas/combined_cycle_gas_turbine/documentation/combined_cycle_gas_turbine_disc.markdown +++ b/energy_models/models/electricity/gas/combined_cycle_gas_turbine/documentation/combined_cycle_gas_turbine_disc.markdown @@ -2,42 +2,42 @@ **Definition:** -Gas power plants generate electricity by burning gas. There exists different types of gas power plant to generate electricity. All of them use a gas turbine: "natural gas is added, along with a stream of air, which combusts and expands through this turbine causing a generator to spin a magnet, making electricity."[^1]. Within this process, waste heat is generated. Some types of plant use this waste heat (see below). -Natural gas power plants are cheap and quick to build. They also have very high thermodynamic efficiencies compared to other power plants. +Gas power plants generate electricity by burning gas. There exists different types of gas power plant to generate electricity. All of them use a gas turbine: "natural gas is added, along with a stream of air, which combusts and expands through this turbine causing a generator to spin a magnet, making electricity."[^1]. Within this process, waste heat is generated. Some types of plant use this waste heat (see below). +Natural gas power plants are cheap and quick to build. They also have very high thermodynamic efficiencies compared to other power plants. There are two types of natural gas power plants: **Simple cycle gas plants** and **combined cycle gas plants**. The former consists of a gas turbine connected to a generator and the latter consists of a simple cycle plant, combined with another external combustion engine. -## Simple Cycle +## Simple Cycle "The simple cycle is simpler but less efficient than the combined cycle. However, simple cycle plants are able to dispatch faster than coal-fired power plants or nuclear plants. This means they can be turned on or off faster in order to meet societies electricity needs. Often needed on the grid with wind power and solar power, its purpose is to meet the fluctuating electricity needs of society, known as peaking power."[^1] ## Combined Cycle Gas Plant "Combined cycle plants are more efficient because it makes use of the hot exhaust gases that would otherwise be dispelled from the system. These exhaust gases are used to boil water into steam which can then spin another turbine and generate more electricity. The thermal efficiency of the combined cycle can get up to 60%. Moreover, these plants produce one third of the waste heat of a plant with a 33% efficiency (like a typical nuclear power plant or an older coal power plant). The cost of a combined cycle plants is generally higher since they cost more to build and run."[^1] - -## Data + +## Data Production of high heat is calculated in TWh. Where, consumption of methane(TWh) is more than production of electricity(TWh). The efficiency drops significantly at partial load. The typical simple-cycle efficiency is 33–43% at maximum load and drops significantly at partial load. The efficiency for a CCGT will vary with size and type of turbine selected. But tends to increase with the size of the turbine. -The data used for this model is extracted from World Bank[^2], the International Energy Agency[^3], the Energy Information Administration[^4], Lazard[^5] and Fraunhofer[^6]. -In its document[^2], the World Bank gather data from several sources to compute the Levelized Cost of Energy and compare the different results. +The data used for this model is extracted from World Bank[^2], the International Energy Agency[^3], the Energy Information Administration[^4], Lazard[^5] and Fraunhofer[^6]. +In its document[^2], the World Bank gather data from several sources to compute the Levelized Cost of Energy and compare the different results. -### GHG emissions +### GHG emissions The GAINS model predicts methane fugitive emissions from gas energy. Emission factors from gas production are adapted from IPCC guidelines and a mean value has been taken for the leakage at industrial and power plants of 0.1025 kt/PJ [^8]. The GAINS model also predict N2O fugitive emissions from gas energy. The emission factor is equal to 0.0001 kt/PJ. [^7] ### Hypotheses -For global investment and production we only have data for gas electricity without the detail for each technology. For the production, the Energy Information Agency[^3] explains that in 2017, 53% of the gas electricity was produced by Combined Cycle Gas Plant and the left 47% by gas turbine. This information was used for our assumption that 55% of global production comes from CCGT and 45% from GT. -Regarding investment, the only information we found is also from the Energy Information Agency[^3]. It states that the majority of the investment goes into CCGT plant. Our hypothesis is that 75% of investment of the 2 past years in gas plant was for CCGT plant and 25% for GT plant. +For global investment and production we only have data for gas electricity without the detail for each technology. For the production, the Energy Information Agency[^3] explains that in 2017, 53% of the gas electricity was produced by Combined Cycle Gas Plant and the left 47% by gas turbine. This information was used for our assumption that 55% of global production comes from CCGT and 45% from GT. +Regarding investment, the only information we found is also from the Energy Information Agency[^3]. It states that the majority of the investment goes into CCGT plant. Our hypothesis is that 75% of investment of the 2 past years in gas plant was for CCGT plant and 25% for GT plant. ## Some insight on gas Electricity evolution Global electricity generation by source and scenario (TWh)[^3] -![Global electricity generation by source and scenario (TWh)[^3]](electricitybysourceIEA.PNG) +![Global electricity generation by source and scenario (TWh)[^3]](electricitybysourceIEA.PNG) Global power generation capacity by source and scenario[^3] -![](byscenarioprodelecIEA.PNG) +![](byscenarioprodelecIEA.PNG) Global annual average power sector investment, historical and by scenario, 2019-2040[^3] ![](investIEA.PNG) @@ -57,4 +57,4 @@ by scenario, 2019-2040[^3] [^7]: Winiwarter, W., 2005. The GAINS model for greenhouse gases-version 1.0: nitrous oxide (N2O).https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR55-GAINS-N2O.pdf [^8]: Höglund-Isaksson, L. and Mechler, R., 2005. The GAINS Model for Greenhouse gases–Version 1.0: Methane (CH4), IIASA Interim Report IR-05-054. International Institute for Applied Systems Analysis, Laxenburg. https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR54-GAINS-CH4.pdf -[^9]: https://www.ipieca.org/resources/energy-efficiency-solutions/combined-cycle-gas-turbines-2022#:~:text=A%20combined%2Dcycle%20gas%20turbine,or%20as%20a%20mechanical%20drive +[^9]: https://www.ipieca.org/resources/energy-efficiency-solutions/combined-cycle-gas-turbines-2022#:~:text=A%20combined%2Dcycle%20gas%20turbine,or%20as%20a%20mechanical%20drive \ No newline at end of file diff --git a/energy_models/models/electricity/gas/gas_turbine/documentation/gas_turbine_disc.markdown b/energy_models/models/electricity/gas/gas_turbine/documentation/gas_turbine_disc.markdown index d94b4e0e..f2611a8c 100644 --- a/energy_models/models/electricity/gas/gas_turbine/documentation/gas_turbine_disc.markdown +++ b/energy_models/models/electricity/gas/gas_turbine/documentation/gas_turbine_disc.markdown @@ -2,11 +2,11 @@ **Definition:** -Gas power plants generate electricity by burning gas. There exists different types of gas power plant to generate electricity. All of them use a gas turbine: "natural gas is added, along with a stream of air, which combusts and expands through this turbine causing a generator to spin a magnet, making electricity."[^1]. Within this process, waste heat is generated. Some types of plant use this waste heat (see below). -Natural gas power plants are cheap and quick to build. They also have very high thermodynamic efficiencies compared to other power plants. +Gas power plants generate electricity by burning gas. There exists different types of gas power plant to generate electricity. All of them use a gas turbine: "natural gas is added, along with a stream of air, which combusts and expands through this turbine causing a generator to spin a magnet, making electricity."[^1]. Within this process, waste heat is generated. Some types of plant use this waste heat (see below). +Natural gas power plants are cheap and quick to build. They also have very high thermodynamic efficiencies compared to other power plants. There are two types of natural gas power plants: **Simple cycle gas plants** and **combined cycle gas plants**. The former consists of a gas turbine connected to a generator and the latter consists of a simple cycle plant, combined with another external combustion engine. -## Simple Cycle +## Simple Cycle "The simple cycle is simpler but less efficient than the combined cycle. However, simple cycle plants are able to dispatch faster than coal-fired power plants or nuclear plants. This means they can be turned on or off faster in order to meet societies electricity needs. Often needed on the grid with wind power and solar power, its purpose is to meet the fluctuating electricity needs of society, known as peaking power."[^1] ## Combined Cycle Gas Plant @@ -15,28 +15,28 @@ There are two types of natural gas power plants: **Simple cycle gas plants** and Production of high heat is calculated in TWh. Where, consumption of methane(TWh) is more than production of electricity(TWh). -## Data -The data used for this model is extracted from World Bank[^2], the International Energy Agency[^3], the Energy Information Administration[^4], Lazard[^5] and Fraunhofer[^6]. -In its document[^2], the World Bank gather data from several sources to compute the Levelized Cost of Energy and compare the different results. +## Data +The data used for this model is extracted from World Bank[^2], the International Energy Agency[^3], the Energy Information Administration[^4], Lazard[^5] and Fraunhofer[^6]. +In its document[^2], the World Bank gather data from several sources to compute the Levelized Cost of Energy and compare the different results. -### GHG emissions +### GHG emissions The GAINS model predicts methane fugitive emissions from gas energy. Emission factors from gas production are adapted from IPCC guidelines and a mean value has been taken for the leakage at industrial and power plants of 0.1025 kt/PJ [^8]. The GAINS model also predicts N2O fugitive emissions from gas energy. The emission factor is equal to 0.0001 kt/PJ. [^7] ### Hypotheses -For global investment and production we only have data for gas electricity without the detail for each technology. For the production, the Energy Information Agency[^3] explains that in 2017, 53% of the gas electricity was produced by Combined Cycle Gas Plant and the left 47% by gas turbine. This information was used for our assumption that 55% of global production comes from CCGT and 45% from GT. -Regarding investment, the only information we found is also from the Energy Information Agency[^3]. It states that the majority of the investment goes into CCGT plant. Our hypothesis is that 75% of investment of the 2 past years in gas plant was for CCGT plant and 25% for GT plant. +For global investment and production we only have data for gas electricity without the detail for each technology. For the production, the Energy Information Agency[^3] explains that in 2017, 53% of the gas electricity was produced by Combined Cycle Gas Plant and the left 47% by gas turbine. This information was used for our assumption that 55% of global production comes from CCGT and 45% from GT. +Regarding investment, the only information we found is also from the Energy Information Agency[^3]. It states that the majority of the investment goes into CCGT plant. Our hypothesis is that 75% of investment of the 2 past years in gas plant was for CCGT plant and 25% for GT plant. ## Some insight on gas Electricity evolution Global electricity generation by source and scenario (TWh)[^3] -![Global electricity generation by source and scenario (TWh)[^3]](electricitybysourceIEA.PNG) +![Global electricity generation by source and scenario (TWh)[^3]](electricitybysourceIEA.PNG) Global power generation capacity by source and scenario[^3] -![](byscenarioprodelecIEA.PNG) +![](byscenarioprodelecIEA.PNG) Global annual average power sector investment, historical and by scenario, 2019-2040[^3] @@ -58,4 +58,4 @@ by scenario, 2019-2040[^3] [^7]: Winiwarter, W., 2005. The GAINS model for greenhouse gases-version 1.0: nitrous oxide (N2O).https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR55-GAINS-N2O.pdf [^8]: Hoglund-Isaksson, L. and Mechler, R., 2005. The GAINS Model for Greenhouse gases–Version 1.0: Methane (CH4), IIASA Interim Report IR-05-054. International Institute for Applied Systems Analysis, Laxenburg. https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR54-GAINS-CH4.pdf -[^9]: https://www.energy.gov/fecm/how-gas-turbine-power-plants-work +[^9]: https://www.energy.gov/fecm/how-gas-turbine-power-plants-work \ No newline at end of file diff --git a/energy_models/models/electricity/gas/gas_turbine/gas_turbine_disc.py b/energy_models/models/electricity/gas/gas_turbine/gas_turbine_disc.py index 1cffd578..c60a3f5c 100644 --- a/energy_models/models/electricity/gas/gas_turbine/gas_turbine_disc.py +++ b/energy_models/models/electricity/gas/gas_turbine/gas_turbine_disc.py @@ -78,7 +78,7 @@ class GasTurbineDiscipline(ElectricityTechnoDiscipline): 'efficiency': 1, 'techno_evo_eff': 'no', # yes or no 'full_load_hours': 8760, - f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9 # According to the IEA, Gaz powered stations need 1100 kg of copper for each MW implemented. Computing the need in Mt/MW., + f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9# According to the IEA, Gaz powered stations need 1100 kg of copper for each MW implemented. Computing the need in Mt/MW., # IEA Executive summary - Role of critical minerals in clean energy transitions 2022 } @@ -95,7 +95,7 @@ class GasTurbineDiscipline(ElectricityTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + } # -- add specific techno inputs to this DESC_IN.update(ElectricityTechnoDiscipline.DESC_IN) diff --git a/energy_models/models/electricity/geothermal/documentation/geothermal_disc.markdown b/energy_models/models/electricity/geothermal/documentation/geothermal_disc.markdown index d18838a3..7a216117 100644 --- a/energy_models/models/electricity/geothermal/documentation/geothermal_disc.markdown +++ b/energy_models/models/electricity/geothermal/documentation/geothermal_disc.markdown @@ -3,7 +3,7 @@ **Definition[^1] :** Geothermal energy is a type of renewable energy which is generated within the earth and can be used directly for heating or transformed into electricity. An advantage of geothermal energy over some other renewable energy sources is that it is available year-long (whereas solar and wind energy present higher variability and intermittence) and can be found around the globe. However, for electricity generation, medium- to high-temperature resources, which are usually close to volcanically active regions, are needed. -![](geothermal_schema.PNG) +![](geothermal_schema.PNG) (Image Credit Geothermal Tomorrow 2008, [^1b]) Geothermal energy technologies are distinguished into three main subtechnologies[^2], namely flash geothermal, Organic Rankine Cycle (ORC binary) geothermal and Enhanced Geothermal System (EGS). The capital investment costs of geothermal power plants depend highly on local sites. @@ -12,17 +12,17 @@ A geothermal power plant is not producing electricity full time, in order to mod Consumption of heat is calculated in TWh for medium range temperature. It depends on production of electricity(TWh) and efficiency. -**Capacity factor[^3] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. - -## Data -Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. +**Capacity factor[^3] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. +## Data +Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. + Typical uncertainty and expenditure profiles for a geothermal project[^1] -![](geothermal_uncertainty.PNG) +![](geothermal_uncertainty.PNG) ## Some insight on Geothermal electricity evolution IEA Geothermal electricity power generation prediction[^7] -![](geothermal_forecast.PNG) +![](geothermal_forecast.PNG) [^1]: [IRENA Geothermal power (2017)](https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2017/Aug/IRENA_Geothermal_Power_2017.pdf) [^1b]: [Vision, G.T.P. and Mission, E.G.S., Geothermal Tomorrow 08](https://www.nrel.gov/docs/fy08osti/43504.pdf) diff --git a/energy_models/models/electricity/geothermal/geothermal_disc.py b/energy_models/models/electricity/geothermal/geothermal_disc.py index 792ae61d..e2c6f6b1 100644 --- a/energy_models/models/electricity/geothermal/geothermal_disc.py +++ b/energy_models/models/electricity/geothermal/geothermal_disc.py @@ -49,6 +49,7 @@ class GeothermalDiscipline(ElectricityTechnoDiscipline): # Cost development of low carbon energy technologies-Scenario-based cost trajectories to 2050, 2017 Edition. # Publications Office of the European Union, Luxemburgo. + techno_infos_dict_default = {'maturity': 0, 'Opex_percentage': 0.045, # Fixed 4.0% and recurrent 0.5 % @@ -70,7 +71,7 @@ class GeothermalDiscipline(ElectricityTechnoDiscipline): # https://www.sciencedirect.com/science/article/abs/pii/S0375650513001120 'CO2_from_production': 0.0, 'CO2_from_production_unit': 'kg/kg', - f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW + f"{GlossaryEnergy.CopperResource}_needs": 1100 /1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } diff --git a/energy_models/models/electricity/hydropower/documentation/hydropower_disc.markdown b/energy_models/models/electricity/hydropower/documentation/hydropower_disc.markdown index ac8b5543..e42f6aac 100644 --- a/energy_models/models/electricity/hydropower/documentation/hydropower_disc.markdown +++ b/energy_models/models/electricity/hydropower/documentation/hydropower_disc.markdown @@ -11,7 +11,7 @@ Hydropower is mainly associated with the Hoover Dam a huge facility harnessing t Hydropower itself does not produce heat as a primary output. Instead, it generates electricity through the kinetic energy of flowing water. any heat generated in these incidental ways is relatively small compared to the electrical energy produced by the hydropower system. Overall, hydropower is considered an efficient and low-heat-producing method of electricity generation. -## Data +## Data Most of the data used for this model is extracted from International Energy Agency (IEA)[^4] and International Renewable Energy Agency (IRENA)[^5]. @@ -19,11 +19,11 @@ Data's about Hydropower plant commissioning year has been extracted from a power ![](hydropower_plant_world_age_distribution.png) -## Some insight on hydropower evolution +## Some insight on hydropower evolution IEA hydropower generation in the Sustainable Development Scenario, 2000-2030 [^7] -![](hydropower-generation-in-the-sustainable-development-scenario-2000-2030.png) +![](hydropower-generation-in-the-sustainable-development-scenario-2000-2030.png) Global weighted average total installed costs, capacity factors and LCOE for hydropower, 2010-2019 [^8] diff --git a/energy_models/models/electricity/hydropower/hydropower.py b/energy_models/models/electricity/hydropower/hydropower.py index 44f5d517..af207a5a 100644 --- a/energy_models/models/electricity/hydropower/hydropower.py +++ b/energy_models/models/electricity/hydropower/hydropower.py @@ -21,4 +21,4 @@ class Hydropower(ElectricityTechno): - pass + pass \ No newline at end of file diff --git a/energy_models/models/electricity/hydropower/hydropower_disc.py b/energy_models/models/electricity/hydropower/hydropower_disc.py index 8d73a75f..ace54eea 100644 --- a/energy_models/models/electricity/hydropower/hydropower_disc.py +++ b/energy_models/models/electricity/hydropower/hydropower_disc.py @@ -64,7 +64,7 @@ class HydropowerDiscipline(ElectricityTechnoDiscipline): 'efficiency': 1.0, # No need of efficiency here 'learning_rate': 0.0, 'techno_evo_eff': 'no', - f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW + f"{GlossaryEnergy.CopperResource}_needs": 1100 /1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } diff --git a/energy_models/models/electricity/nuclear/documentation/nuclear_disc.markdown b/energy_models/models/electricity/nuclear/documentation/nuclear_disc.markdown index 185b13f8..89c94343 100644 --- a/energy_models/models/electricity/nuclear/documentation/nuclear_disc.markdown +++ b/energy_models/models/electricity/nuclear/documentation/nuclear_disc.markdown @@ -3,38 +3,38 @@ **Definition[^1] :** Nuclear power is the use of nuclear reactions that release nuclear energy to generate heat, which most frequently is then used in steam turbines to produce electricity in a nuclear power plant. Nuclear power can be obtained from nuclear fission, nuclear decay and nuclear fusion reactions. Presently, the vast majority of electricity from nuclear power is produced by nuclear fission of uranium and plutonium. Nuclear decay processes are used in niche applications such as radioisotope thermoelectric generators in some space probes such as Voyager 2. Generating electricity from fusion power remains at the focus of international research. This article mostly deals with nuclear fission power for electricity generation. -![](nuclearpowerpic.jpg) +![](nuclearpowerpic.jpg) (Image Credit: TTstudio/ Shutterstock) A nuclear power plant is not producing electricity full time, in order to model yearly production a variable is set : **Capacity Factor (%)**. -**Capacity factor[^2] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. +**Capacity factor[^2] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. -## Data +## Data Most of the data used for this model is extracted from Internation Atomic Energy Agency (IAEA) [^3] -World Nuclear Association (WNA) [^5], National Renewable Energy Laboratory (NREL)[^6]. +World Nuclear Association (WNA) [^5], National Renewable Energy Laboratory (NREL)[^6]. Production of high heat in TWh is calculated as waste heat of its electricity production to the efficiency generation. Water Consumption[^7] -![](nuclear_water.png) +![](nuclear_water.png) Uranium enrichment, from uranium ore[^8] -![](uranium_enrichment.PNG) +![](uranium_enrichment.PNG) Uranium as fuel cost[^9] -![](uranium_cost.PNG) +![](uranium_cost.PNG) Nuclear power plants age distribution[^3] -![](nuclear_age_distribution.png) +![](nuclear_age_distribution.png) ## Waste disposal and Decommissioning Following an analysis from the World Nuclear Waste Report [^10], waste disposal cost is added as a levy and is part of the energy price. -Decommissioning costs are integrated to the Factory Capex and is consequently spread on its lifespan. +Decommissioning costs are integrated to the Factory Capex and is consequently spread on its lifespan. ## Some insight on Nuclear evolution IEA Nuclear power generation prediction[^4] -![](nuclear_capacity_forecast.PNG) +![](nuclear_capacity_forecast.PNG) ## Fuel comparison With a complete combustion or fission, approx. 8 kWh of heat can be generated from 1 kg of coal, approx. 12 kWh from 1 kg of mineral oil and around 24,000,000 kWh from 1 kg of uranium-235.[^13] @@ -54,3 +54,4 @@ With a complete combustion or fission, approx. 8 kWh of heat can be generated fr [^13]: Data - uranium used per kg, https://www.euronuclear.org/glossary/fuel-comparison/ [^14]: Data - efficiency and heat recovery factor, https://www.sciencedirect.com/science/article/abs/pii/S0306261921001975 [^15]: Working: https://www.eia.gov/energyexplained/nuclear/nuclear-power-plants.php#:~:text=Nuclear%20power%20comes%20from%20nuclear,magnetic%20generators%20to%20produce%20electricity. + diff --git a/energy_models/models/electricity/nuclear/nuclear.py b/energy_models/models/electricity/nuclear/nuclear.py index 1d010c96..0a276757 100644 --- a/energy_models/models/electricity/nuclear/nuclear.py +++ b/energy_models/models/electricity/nuclear/nuclear.py @@ -47,6 +47,7 @@ def compute_byproducts_production(self): # self.production[f'{ElectricityTechno.energy_name} ({self.product_unit})']) / \ # self.techno_infos_dict['efficiency'] + def get_theoretical_uranium_fuel_needs(self): """ Get Uranium fuel needs in kg Uranium fuel /kWh electricty diff --git a/energy_models/models/electricity/nuclear/nuclear_disc.py b/energy_models/models/electricity/nuclear/nuclear_disc.py index a73a24ba..c3dcc2f8 100644 --- a/energy_models/models/electricity/nuclear/nuclear_disc.py +++ b/energy_models/models/electricity/nuclear/nuclear_disc.py @@ -48,6 +48,7 @@ class NuclearDiscipline(ElectricityTechnoDiscipline): # 2019 standard scenarios report: a US electric sector outlook (No. NREL/PR-6A20-75798). # National Renewable Energy Lab.(NREL), Golden, CO (United States). + techno_infos_dict_default = {'maturity': 0, 'Opex_percentage': 0.024, # Fixed 1.9 and recurrent 0.5 % @@ -71,7 +72,7 @@ class NuclearDiscipline(ElectricityTechnoDiscipline): 'decommissioning_cost_unit': '$/kW', # World Nuclear Waste Report 2019, Chapter 6 (https://worldnuclearwastereport.org) # average of 1000 $/kW - f"{GlossaryEnergy.CopperResource}_needs": 1473 / 1e9, # According to the IEA, Nuclear power stations need 1473 kg of copper for each MW implemented. Computing the need in Mt/MW + f"{GlossaryEnergy.CopperResource}_needs": 1473/ 1e9, # According to the IEA, Nuclear power stations need 1473 kg of copper for each MW implemented. Computing the need in Mt/MW # IEA Executive summary - Role of critical minerals in clean energy transitions 2022 } diff --git a/energy_models/models/electricity/oil_gen/documentation/oil_gen_disc.markdown b/energy_models/models/electricity/oil_gen/documentation/oil_gen_disc.markdown index b04fcd9a..37876500 100644 --- a/energy_models/models/electricity/oil_gen/documentation/oil_gen_disc.markdown +++ b/energy_models/models/electricity/oil_gen/documentation/oil_gen_disc.markdown @@ -1,13 +1,13 @@ **Definition** -Oil-fired generating plants burn oil to produce electricity. They are similar in construction and operation +Oil-fired generating plants burn oil to produce electricity. They are similar in construction and operation to coal-fired and natural gas-fired facilities.[^2] -Heavy fuel oil was once a significant source of energy for electric power generation. -After oil price increases of the 1970s, oil was displaced by coal and later natural gas. -Distillate oil is still important as the fuel source for diesel engine power plants used especially in -isolated communities not interconnected to a grid. Liquid fuels may also be used by gas turbine power plants, -especially for peaking or emergency service. Of the three fossil fuel sources, oil has the advantages of easier +Heavy fuel oil was once a significant source of energy for electric power generation. +After oil price increases of the 1970s, oil was displaced by coal and later natural gas. +Distillate oil is still important as the fuel source for diesel engine power plants used especially in +isolated communities not interconnected to a grid. Liquid fuels may also be used by gas turbine power plants, +especially for peaking or emergency service. Of the three fossil fuel sources, oil has the advantages of easier transportation and handling than solid coal, and easier on-site storage than natural gas.[^1] Production of high heat is calculated in TWh. Where, consumption of liquid fuel(TWh) is more than production of electricity(TWh). @@ -16,13 +16,13 @@ Production of high heat is calculated in TWh. Where, consumption of liquid fuel( Data are extracted from IEA data tables[^3] and balances[^4]. CO2 emissions information from RTE French data[^5]. -### GHG emissions +### GHG emissions The GAINS model also predicts N2O fugitive emissions from oil energy. The emission factor is equal to 0.008 kt/PJ for oil combustion via electricity plants. [^7] -## Sources +## Sources [^1]: [Fossil Fuel Power on Wikipedia](https://en.wikipedia.org/wiki/Fossil_fuel_power_station#Oil) @@ -38,3 +38,4 @@ The GAINS model also predicts N2O fugitive emissions from oil energy. The emissi [^7]: Winiwarter, W., 2005. The GAINS model for greenhouse gases-version 1.0: nitrous oxide (N2O).https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR55-GAINS-N2O.pdf [^8]: https://www.eia.gov/energyexplained/oil-and-petroleum-products/refining-crude-oil-the-refining-process.php + diff --git a/energy_models/models/electricity/oil_gen/oil_gen.py b/energy_models/models/electricity/oil_gen/oil_gen.py index 01a7c7d4..9b65fedd 100644 --- a/energy_models/models/electricity/oil_gen/oil_gen.py +++ b/energy_models/models/electricity/oil_gen/oil_gen.py @@ -54,13 +54,14 @@ def compute_byproducts_production(self): def grad_price_vs_stream_price(self): ''' - Compute the gradient of global price vs energy prices + Compute the gradient of global price vs energy prices Work also for total CO2_emissions vs energy CO2 emissions ''' liquid_fuel_needs = self.techno_infos_dict['fuel_demand'] efficiency = self.compute_efficiency() return {LiquidFuel.name: np.diag(liquid_fuel_needs / efficiency)} + def compute_dprod_dinvest(self, capex_list, invest_list, invest_before_year_start, techno_dict, dcapex_list_dinvest_list): dprod_dinvest = ElectricityTechno.compute_dprod_dinvest( diff --git a/energy_models/models/electricity/oil_gen/oil_gen_disc.py b/energy_models/models/electricity/oil_gen/oil_gen_disc.py index bc07abdb..eb090c35 100644 --- a/energy_models/models/electricity/oil_gen/oil_gen_disc.py +++ b/energy_models/models/electricity/oil_gen/oil_gen_disc.py @@ -115,7 +115,7 @@ class OilGenDiscipline(ElectricityTechnoDiscipline): 'transport_cost_unit': '$/kg', # check if pertinent 'techno_evo_eff': 'no', 'efficiency': 1, - f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW + f"{GlossaryEnergy.CopperResource}_needs": 1100 /1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } @@ -131,12 +131,12 @@ class OilGenDiscipline(ElectricityTechnoDiscipline): # https://www.iea.org/reports/world-energy-investment-2019/power-sector # License: CC BY 4.0. # (linear from 2016, 2017, 2018 data) - + oil_flue_gas_ratio = np.array([0.12]) DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flue_gas_co2_ratio': {'type': 'array', 'default': oil_flue_gas_ratio, 'unit': ''}, } diff --git a/energy_models/models/electricity/renewable_electricity_simple_techno/documentation/renewable_simple_techno_disc.markdown b/energy_models/models/electricity/renewable_electricity_simple_techno/documentation/renewable_simple_techno_disc.markdown index 5ef54454..9abeadb4 100644 --- a/energy_models/models/electricity/renewable_electricity_simple_techno/documentation/renewable_simple_techno_disc.markdown +++ b/energy_models/models/electricity/renewable_electricity_simple_techno/documentation/renewable_simple_techno_disc.markdown @@ -3,3 +3,6 @@ ** Simplified techno that produces energy cleanly, used to demonstrate EnergyMix.** ** The energy produced is used in simplified carbon capture techno ** + + + diff --git a/energy_models/models/electricity/renewable_electricity_simple_techno/renewable_electricity_simple_techno_disc.py b/energy_models/models/electricity/renewable_electricity_simple_techno/renewable_electricity_simple_techno_disc.py index 25a852c9..713f304d 100644 --- a/energy_models/models/electricity/renewable_electricity_simple_techno/renewable_electricity_simple_techno_disc.py +++ b/energy_models/models/electricity/renewable_electricity_simple_techno/renewable_electricity_simple_techno_disc.py @@ -53,6 +53,7 @@ class RenewableElectricitySimpleTechnoDiscipline(ElectricityTechnoDiscipline): # 2019 standard scenarios report: a US electric sector outlook (No. NREL/PR-6A20-75798). # National Renewable Energy Lab.(NREL), Golden, CO (United States). + techno_infos_dict_default = {'maturity': 0, 'Opex_percentage': 0.12, # Fixed 1.9 and recurrent 0.5 % @@ -77,7 +78,7 @@ class RenewableElectricitySimpleTechnoDiscipline(ElectricityTechnoDiscipline): # and Nuclear Power Estimates up to 2050 initial_production = 6590.0 # Invest in 2019 => 29.6 bn - + # Age distribution => IAEA OPEX Nuclear 2020 - Number of Reactors by Age # (as of 1 January 2020) DESC_IN = {'techno_infos_dict': {'type': 'dict', diff --git a/energy_models/models/electricity/solar_pv/documentation/solar_pv_disc.markdown b/energy_models/models/electricity/solar_pv/documentation/solar_pv_disc.markdown index d938da61..90f0d087 100644 --- a/energy_models/models/electricity/solar_pv/documentation/solar_pv_disc.markdown +++ b/energy_models/models/electricity/solar_pv/documentation/solar_pv_disc.markdown @@ -1,4 +1,4 @@ -# Solar Photovoltaic +# Solar Photovoltaic **Definition[^1]:** "Solar cells, also called photovoltaic cells, convert sunlight directly into electricity. @@ -6,26 +6,26 @@ Photovoltaics (often shortened as PV) gets its name from the process of converti The maximum possible energy output of a given installation assumes its continuous operation at full nameplate capacity over the relevant period. The actual energy output during that period and the capacity factor vary greatly depending on a range of factors, for renewable energy the main factor being the weather conditions. For solar PV it is then important to take into account the $capacity factor$ defined as "the ratio of the net electricity generated, for the time considered, to the energy that could have been generated at continuous full-power operation during the same period"[^2]. -Solar photovoltaic (PV) systems primarily generate electricity, not heat. These systems convert sunlight directly into electrical energy through the photovoltaic effect, which occurs in solar cells. +Solar photovoltaic (PV) systems primarily generate electricity, not heat. These systems convert sunlight directly into electrical energy through the photovoltaic effect, which occurs in solar cells. In summary, while solar PV systems themselves do not produce heat, they can indirectly impact heat production by supplying electricity for heating purposes, reducing the demand for traditional heating sources, and contributing to overall energy savings. -## Data +## Data To generate the data for this model we used International Energy Agency (IEA)[^3] and International Renewable Energy Agency (IRENA)[^4] reports. ## Some insight on Solar PV evolution IEA solar PV power generation in the Sustainable Development Scenario, 2000-2030[^7] -![](IEAsolarprodsds.png) +![](IEAsolarprodsds.png) Global weighted average total installed costs, capacity factors and LCOE for PV, 2010–2019 ![](IRENAcostevol.png) - + ## Land use -Solar PV are disposed in lands and most of it on crops category of lands.[^5] +Solar PV are disposed in lands and most of it on crops category of lands.[^5] Because in developed countries, where solar PV are the most deployed, barren lands and desert are scarce (around 10% of the global barren lands surface), it will not be considered in this model. -Moreover, only 3% of the urban surface can be used for solar PV, very few rooftops are eligible to solar PV. So for this first version of land use model it will not be considered either. +Moreover, only 3% of the urban surface can be used for solar PV, very few rooftops are eligible to solar PV. So for this first version of land use model it will not be considered either. The power by hectare value has been computed on the base of 357 MWh/acre[^6], giving 315059 kWh/ha. -[^1]: Solar Photovoltaic Technology Basics. NREL.gov, www.nrel.gov/research/re-photovoltaics.html +[^1]: Solar Photovoltaic Technology Basics. NREL.gov, www.nrel.gov/research/re-photovoltaics.html [^2]: Capacity factor. NRC.gov, https://www.nrc.gov/reading-rm/basic-ref/glossary/capacity-factor-net.html [^3]: IEA 2022, World Energy Outlook 2019, IEA, Paris https://www.iea.org/reports/world-energy-outlook-2019, License: CC BY 4.0. [^4]: IRENA (2020), Renewable Power Generation Costs in 2019, @@ -33,4 +33,4 @@ International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/publicat [^5]: Scientific report (2021), https://www.nature.com/articles/s41598-021-82042-5 [^6]: greenCoast, 2019, Solar Farm Land Requirements: How Much Land Do You Need?, https://greencoast.org/solar-farm-land-requirements/ [^7]: IEA 2022, Solar PV power generation in the Sustainable Development Scenario, 2000-2030, IEA, Paris https://www.iea.org/data-and-statistics/charts/solar-pv-power-generation-in-the-sustainable-development-scenario-2000-2030, License: CC BY 4.0. -[^8]: https://www.eia.gov/energyexplained/solar/photovoltaics-and-electricity.php#:~:text=A%20photovoltaic%20(PV)%20cell%2C,convert%20artificial%20light%20into%20electricity +[^8]: https://www.eia.gov/energyexplained/solar/photovoltaics-and-electricity.php#:~:text=A%20photovoltaic%20(PV)%20cell%2C,convert%20artificial%20light%20into%20electricity \ No newline at end of file diff --git a/energy_models/models/electricity/solar_pv/solar_pv_disc.py b/energy_models/models/electricity/solar_pv/solar_pv_disc.py index 483b6f27..ad9119d4 100644 --- a/energy_models/models/electricity/solar_pv/solar_pv_disc.py +++ b/energy_models/models/electricity/solar_pv/solar_pv_disc.py @@ -77,14 +77,14 @@ class SolarPvDiscipline(ElectricityTechnoDiscipline): 'transport_cost_unit': '$/kg', # check if pertient 'techno_evo_eff': 'no', GlossaryEnergy.EnergyEfficiency: 1.0, - f"{GlossaryEnergy.CopperResource}_needs": 2822 / 1e9 # According to the IEA, Solar PV panels need 2822 kg of copper for each MW implemented. Computing the need in Mt/MW, + f"{GlossaryEnergy.CopperResource}_needs": 2822 / 1e9 # According to the IEA, Solar PV panels need 2822 kg of copper for each MW implemented. Computing the need in Mt/MW, # IEA Executive summary - Role of critical minerals in clean energy transitions 2022 } techno_info_dict = techno_infos_dict_default initial_production = 700 # in TWh at year_start source IEA 2019 # Invest before year start in $ source IEA 2019 - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/electricity/solar_thermal/documentation/solar_thermal_disc.markdown b/energy_models/models/electricity/solar_thermal/documentation/solar_thermal_disc.markdown index 6eb8cdc3..7fc2d8fe 100644 --- a/energy_models/models/electricity/solar_thermal/documentation/solar_thermal_disc.markdown +++ b/energy_models/models/electricity/solar_thermal/documentation/solar_thermal_disc.markdown @@ -18,20 +18,20 @@ Production of heat for high temperature is calculated in TWh. It depends on prod ![](solar_thermal_type.png) (Image Credit: IEA [^1b]) -## Data +## Data Most of the data used for this model is extracted from Greenpeace International, SolarPACES and ESTELA report[^2], National Renewable Energy Laboratory (NREL) [^3], Joint Research Center [^1] and International Renewable Energy Agency (IRENA)[^4]. ## Some insight on SolarThermal evolution IEA solar thermal power generation in the Sustainable Development Scenario, 2000-2030 -![](concentrating-solar-power-generation-in-the-sustainable-development-scenario-2000-2030.png) +![](concentrating-solar-power-generation-in-the-sustainable-development-scenario-2000-2030.png) Global weighted average total installed costs, capacity factors and LCOE for CSP, 2010-2019 ![](irena_csp.png) - + ## Land use -Solar thermal are disposed in lands and most of it on crops category of lands.[^5] +Solar thermal are disposed in lands and most of it on crops category of lands.[^5] Because in developed countries, where solar thermal are the most deployed, barren lands and desert are scarce (around 10% of the global barren lands surface), it will not be considered in this model. -Moreover, only 3% of the urban surface can be used for solar thermal, very few rooftops are eligible to solar panels. So for this first version of land use model it will not be considered either. +Moreover, only 3% of the urban surface can be used for solar thermal, very few rooftops are eligible to solar panels. So for this first version of land use model it will not be considered either. The power by hectare value has been computed on the base of 357 MWh/acre[^6] for photovoltaic panels, and solar thermal uses 10% less space than Solar photovoltaic, giving 346564,9 kWh/ha. @@ -45,4 +45,4 @@ International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/publicat [^5]: Scientific report (2021), https://www.nature.com/articles/s41598-021-82042-5 [^6]: greenCoast, 2019, Solar Farm Land Requirements: How Much Land Do You Need?, https://greencoast.org/solar-farm-land-requirements/ [^7]: Working principle: https://www.bmwk.de/Redaktion/EN/Artikel/Energy/research-priorities-solar-thermal-power-plants.html#:~:text=In%20solar%20thermal%20power%20plants,transforms%20this%20energy%20into%20electricity. -[^8]: Efficiency: https://www.volker-quaschning.de/articles/fundamentals2/index.php#:~:text=The%20efficiency%20of%20a%20solar,losses%20are%20usually%20below%2010%25. +[^8]: Efficiency: https://www.volker-quaschning.de/articles/fundamentals2/index.php#:~:text=The%20efficiency%20of%20a%20solar,losses%20are%20usually%20below%2010%25. \ No newline at end of file diff --git a/energy_models/models/electricity/solar_thermal/solar_thermal_disc.py b/energy_models/models/electricity/solar_thermal/solar_thermal_disc.py index a8a014c1..207b585b 100644 --- a/energy_models/models/electricity/solar_thermal/solar_thermal_disc.py +++ b/energy_models/models/electricity/solar_thermal/solar_thermal_disc.py @@ -71,7 +71,7 @@ class SolarThermalDiscipline(ElectricityTechnoDiscipline): 'density_per_ha_unit': 'kWh/ha', 'CO2_from_production': 0.0, 'CO2_from_production_unit': 'kg/kg', - f"{GlossaryEnergy.CopperResource}_needs": 1100 / 1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW + f"{GlossaryEnergy.CopperResource}_needs": 1100 /1e9, # No data found, therefore we make the assumption that it needs at least a generator which uses the same amount of copper as a gaz powered station. It needs 1100 kg / MW. Computing the need in Mt/MW # no data, assuming it needs at least enough copper for a generator (such as the gas_turbine) } @@ -80,7 +80,7 @@ class SolarThermalDiscipline(ElectricityTechnoDiscipline): # Invest before year start # from # https://www.irena.org/Statistics/View-Data-by-Topic/Finance-and-Investment/Investment-Trends - + # from database https://solarpaces.nrel.gov/ # Nb plants 'Operational' and not pilot/demo/proto # only commercial or production diff --git a/energy_models/models/electricity/wind_offshore/documentation/wind_offshore_disc.markdown b/energy_models/models/electricity/wind_offshore/documentation/wind_offshore_disc.markdown index ee4a6619..72daa95d 100644 --- a/energy_models/models/electricity/wind_offshore/documentation/wind_offshore_disc.markdown +++ b/energy_models/models/electricity/wind_offshore/documentation/wind_offshore_disc.markdown @@ -8,24 +8,24 @@ Offshore electricity production, through wind farms can be split in two contruct - offshore floating Fixed bottom foundations and floating offshore concepts[^2] -![](Fixed-bottom-foundations-and-floating-offshore-concepts-9.png) - +![](Fixed-bottom-foundations-and-floating-offshore-concepts-9.png) + Wind is split into 14 class (speed class m/s). A wind farm is not producing electricity full time, in order to model yearly production a variable is set : **Capacity Factor (%)**. -**Capacity factor[^3] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. +**Capacity factor[^3] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. -Wind offshore energy production, like wind onshore energy production, does not directly produce heat in the form of thermal energy. Instead, it generates electricity from the kinetic energy of moving air (wind). -## Data -Most of the data used for this model is extracted from International Energy Agency (IEA)[^4], -International Renewable Energy Agency (IRENA)[^5], Global Wind Energy Council (GWEC)[^6] and National Renewable Energy Laboratory (NREL)[^7]. +Wind offshore energy production, like wind onshore energy production, does not directly produce heat in the form of thermal energy. Instead, it generates electricity from the kinetic energy of moving air (wind). +## Data +Most of the data used for this model is extracted from International Energy Agency (IEA)[^4], +International Renewable Energy Agency (IRENA)[^5], Global Wind Energy Council (GWEC)[^6] and National Renewable Energy Laboratory (NREL)[^7]. Some IRENA data -![](irena-offshore.png) +![](irena-offshore.png) ## Some insight on Wind offshore evolution IEA Offshore power generation prediction -![](IEA-offshore-prediction.PNG) +![](IEA-offshore-prediction.PNG) [^1]: https://en.wikipedia.org/wiki/Offshore_wind_power [^2]: IOPscience, 2016, Wind turbines: current status, obstacles, trends and technologies, https://iopscience.iop.org/article/10.1088/1757-899X/161/1/012079 @@ -35,4 +35,4 @@ IEA Offshore power generation prediction [^6]: Global Wind Energy Council (GWEC), Global wind report 2019, https://gwec.net/global-wind-report-2019/ [^7]: National Renewable Energy Laboratory USA (NREL), Annual Technology Baseline 2020, https://atb.nrel.gov/electricity/2020/data.php [^8]: https://www.sciencedirect.com/topics/engineering/offshore-wind-energy -[^9]: https://en.wikipedia.org/wiki/Offshore_wind_power +[^9]: https://en.wikipedia.org/wiki/Offshore_wind_power \ No newline at end of file diff --git a/energy_models/models/electricity/wind_offshore/wind_offshore_disc.py b/energy_models/models/electricity/wind_offshore/wind_offshore_disc.py index f08d715d..9ef3fa76 100644 --- a/energy_models/models/electricity/wind_offshore/wind_offshore_disc.py +++ b/energy_models/models/electricity/wind_offshore/wind_offshore_disc.py @@ -71,7 +71,7 @@ class WindOffshoreDiscipline(ElectricityTechnoDiscipline): # techno_infos_dict_default['capacity_factor'] initial_production = 89 # IEA in 2019 # Invest in 2019 => 29.6 bn - + # Age distribution => GWEC Annual-Wind-Report_2019_digital_final_2r DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, diff --git a/energy_models/models/electricity/wind_onshore/documentation/wind_onshore_disc.markdown b/energy_models/models/electricity/wind_onshore/documentation/wind_onshore_disc.markdown index 2c75f0bb..36dc8b53 100644 --- a/energy_models/models/electricity/wind_onshore/documentation/wind_onshore_disc.markdown +++ b/energy_models/models/electricity/wind_onshore/documentation/wind_onshore_disc.markdown @@ -4,25 +4,25 @@ Wind farms consist of many individual wind turbines, which are connected to the electric power transmission network. Onshore wind is an inexpensive source of electric power, competitive with or in many places cheaper than coal or gas plants. Onshore wind farms have a greater visual impact on the landscape than other power stations, as they need to be spread over more land and need to be built away from dense population Growth of wind energy, AIP[^2] -![](growthofwind.jpg) +![](growthofwind.jpg) Wind is split into 10 class (speed class m/s). A wind farm is not producing electricity full time, in order to model yearly production a variable is set : **Capacity Factor (%)**. -**Capacity factor[^3] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. +**Capacity factor[^3] :** Generally defined as the ratio of actual annual output to output at rated capacity for an entire year. The mechanical energy generated by the rotating blades is then transferred to a generator inside the turbine. The generator converts this mechanical energy into electrical energy. This electricity is then typically fed into the electrical grid. While wind onshore energy production doesn't directly produce heat, it can indirectly impact heat production and consumption in a region. -## Data -Most of the data used for this model is extracted from International Energy Agency (IEA)[^4], -International Renewable Energy Agency (IRENA)[^5], Global Wind Energy Council (GWEC)[^6] and National Renewable Energy Laboratory (NREL)[^7]. +## Data +Most of the data used for this model is extracted from International Energy Agency (IEA)[^4], +International Renewable Energy Agency (IRENA)[^5], Global Wind Energy Council (GWEC)[^6] and National Renewable Energy Laboratory (NREL)[^7]. Some IRENA data -![](irena-onshore.png) +![](irena-onshore.png) ## Some insight on Wind onshore evolution IEA Onshore power generation prediction -![](IEA-onshore-prediction.PNG) +![](IEA-onshore-prediction.PNG) [^1]: https://en.wikipedia.org/wiki/Wind_power [^2]: American Institute of Physics, Growth of wind energy points to future challenges, promise, https://techxplore.com/news/2019-08-growth-energy-future.html @@ -31,4 +31,4 @@ IEA Onshore power generation prediction [^5]: IRENA (2020), Future of wind 2019, International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Oct/IRENA_Future_of_wind_2019.pdf [^6]: Global Wind Energy Council (GWEC), Global wind report 2019, https://gwec.net/global-wind-report-2019/ [^7]: National Renewable Energy Laboratory USA (NREL), Annual Technology Baseline 2020, https://atb.nrel.gov/electricity/2020/data.php -[^8]: https://www.iberdrola.com/sustainability/renewables-energy-wind-power#:~:text=Onshore%20wind%20energy%20is%20responsible,it%20to%20the%20distribution%20network. +[^8]: https://www.iberdrola.com/sustainability/renewables-energy-wind-power#:~:text=Onshore%20wind%20energy%20is%20responsible,it%20to%20the%20distribution%20network. \ No newline at end of file diff --git a/energy_models/models/electricity/wind_onshore/wind_onshore_disc.py b/energy_models/models/electricity/wind_onshore/wind_onshore_disc.py index ff1f8b4c..afb1ae35 100644 --- a/energy_models/models/electricity/wind_onshore/wind_onshore_disc.py +++ b/energy_models/models/electricity/wind_onshore/wind_onshore_disc.py @@ -59,7 +59,7 @@ class WindOnshoreDiscipline(ElectricityTechnoDiscipline): 'efficiency': 1.0, 'CO2_from_production': 0.0, 'CO2_from_production_unit': 'kg/kg', - f"{GlossaryEnergy.CopperResource}_needs": 2900 / 1e9 # According to the IEA, Onshore Wind turbines need 2900 kg of copper for each MW implemented. Computing the need in Mt/MW, + f"{GlossaryEnergy.CopperResource}_needs": 2900 / 1e9 # According to the IEA, Onshore Wind turbines need 2900 kg of copper for each MW implemented. Computing the need in Mt/MW, # IEA Executive summary - Role of critical minerals in clean energy transitions 2022 } @@ -70,7 +70,7 @@ class WindOnshoreDiscipline(ElectricityTechnoDiscipline): # techno_infos_dict_default['capacity_factor'] initial_production = 1323 # IEA in 2019 # Invest in 2019 => 138.2 bn less 29.6 bn offshore => 108.6 bn - + # Age distribution => GWEC Annual-Wind-Report_2019_digital_final_2r DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, diff --git a/energy_models/models/ethanol/biomass_fermentation/biomass_fermentation.py b/energy_models/models/ethanol/biomass_fermentation/biomass_fermentation.py index df1f2626..b1f4179b 100644 --- a/energy_models/models/ethanol/biomass_fermentation/biomass_fermentation.py +++ b/energy_models/models/ethanol/biomass_fermentation/biomass_fermentation.py @@ -42,6 +42,7 @@ def compute_other_streams_needs(self): self.cost_details[f'{BiomassDry.name}_needs'] = self.get_theoretical_biomass_needs() / self.cost_details['efficiency'] self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] + def compute_byproducts_production(self): carbon_production_factor = self.get_theoretical_co2_prod() self.production_detailed[f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit})'] = carbon_production_factor * \ diff --git a/energy_models/models/ethanol/biomass_fermentation/documentation/biomass_fermentation_disc.markdown b/energy_models/models/ethanol/biomass_fermentation/documentation/biomass_fermentation_disc.markdown index 30d49f13..110120d1 100644 --- a/energy_models/models/ethanol/biomass_fermentation/documentation/biomass_fermentation_disc.markdown +++ b/energy_models/models/ethanol/biomass_fermentation/documentation/biomass_fermentation_disc.markdown @@ -1,11 +1,11 @@ ## Definition -Bioethanol is a form of renewable energy that can be produced from agricultural feedstocks. -It can be made from very common crops such as hemp, sugarcane, potato, cassava and corn. -There has been considerable debate about how useful bioethanol is in replacing gasoline. +Bioethanol is a form of renewable energy that can be produced from agricultural feedstocks. +It can be made from very common crops such as hemp, sugarcane, potato, cassava and corn. +There has been considerable debate about how useful bioethanol is in replacing gasoline. Concerns about its production and use relate to increased food prices due to the large amount of arable land required for crops, as well as the energy and pollution balance of the whole cycle of ethanol production, especially from corn.[^1] -What does fermentation of biomass produce? +What does fermentation of biomass produce? Fermentation is an anaerobic process that breaks down the glucose within organic materials. It is a series of chemical reactions that convert sugars to alcohol or acid. Yeast or bacteria are added to the biomass material, which feed on the sugars to produce ethanol and carbon dioxide. ## Dry Milling Process @@ -13,14 +13,14 @@ Fermentation is an anaerobic process that breaks down the glucose within organic Over 90 percent of the grain ethanol produced today comes from the dry milling process, with the remaining coming from wet mills. The main difference between the two is in the initial treatment of the grain. ![](drymill.jpg) -(Image Credit: +(Image Credit: [Renewable Fuels Association, How is Ethanol Made ?](https://ethanolrfa.org/ethanol-101/how-is-ethanol-made)) In dry milling, the entire grain kernel is first ground into “meal,” then slurried with water to form a “mash.” -Enzymes are added to the mash to convert starch to sugar. The mash is cooked, then cooled and transferred to fermenters. Yeast is added and the conversion of sugar to alcohol begins. After fermentation, +Enzymes are added to the mash to convert starch to sugar. The mash is cooked, then cooled and transferred to fermenters. Yeast is added and the conversion of sugar to alcohol begins. After fermentation, the resulting “beer” is separated from the remaining “stillage.” The ethanol is then distilled and dehydrated, then blended with about 2% denaturant (such as gasoline) to render it undrinkable. It is then ready for shipment. -The stillage is sent through a centrifuge that separates the solids from the solubles. +The stillage is sent through a centrifuge that separates the solids from the solubles. These co-products eventually become distillers grains, as well as corn distillers oil.[^2] ## Sources @@ -29,4 +29,4 @@ These co-products eventually become distillers grains, as well as corn distiller [^2] [Renewable Fuels Association, How is Ethanol Made ?](https://ethanolrfa.org/ethanol-101/how-is-ethanol-made) -[^3] https://www.eia.gov/energyexplained/biomass/ +[^3] https://www.eia.gov/energyexplained/biomass/ \ No newline at end of file diff --git a/energy_models/models/fossil/fossil_simple_techno/documentation/fossil_simple_techno_disc.markdown b/energy_models/models/fossil/fossil_simple_techno/documentation/fossil_simple_techno_disc.markdown index 9169ef07..120ec36b 100644 --- a/energy_models/models/fossil/fossil_simple_techno/documentation/fossil_simple_techno_disc.markdown +++ b/energy_models/models/fossil/fossil_simple_techno/documentation/fossil_simple_techno_disc.markdown @@ -1,3 +1,7 @@ # Fossil Techno ** Simplified techno that produces fossil energy , used to demonstrate EnergyMix.** + + + + diff --git a/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno.py b/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno.py index 6225a766..9c8dd8a2 100644 --- a/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno.py +++ b/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno.py @@ -25,6 +25,7 @@ class FossilSimpleTechno(FossilTechno): + def compute_specifif_costs_of_technos(self): self.specific_costs = pd.DataFrame({ GlossaryEnergy.Years: self.years, diff --git a/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno_disc.py b/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno_disc.py index cb6ea67f..44cbfca0 100644 --- a/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno_disc.py +++ b/energy_models/models/fossil/fossil_simple_techno/fossil_simple_techno_disc.py @@ -53,6 +53,7 @@ class FossilSimpleTechnoDiscipline(FossilTechnoDiscipline): } techno_name = GlossaryEnergy.FossilSimpleTechno + prod_solid_fuel = 45000. # TWh prod_liquid_fuel = 53000. # TWh prod_methane = 39106.77 # TWh @@ -89,6 +90,7 @@ class FossilSimpleTechnoDiscipline(FossilTechnoDiscipline): # net production = 90717.76 TWh initial_production = 136917.16 # TWh + FLUE_GAS_RATIO = np.array([0.12]) DESC_IN = {'techno_infos_dict': {'type': 'dict', diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/awe/documentation/electrolysis_awe_disc.markdown b/energy_models/models/gaseous_hydrogen/electrolysis/awe/documentation/electrolysis_awe_disc.markdown index a908ebbc..81dfd89f 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/awe/documentation/electrolysis_awe_disc.markdown +++ b/energy_models/models/gaseous_hydrogen/electrolysis/awe/documentation/electrolysis_awe_disc.markdown @@ -4,9 +4,9 @@ AWE is the most established technology for electrolysis with relatively low capi -At the cathode side, two molecules of water reacts with electrons to form the hydrogen and hydroxyl ions ($OH^-$) .Hydroxyl ions transfer through the porous diaphragm to the anode,resulting in half molecule of oxygen (O2) and one molecule of water (H2O). +At the cathode side, two molecules of water reacts with electrons to form the hydrogen and hydroxyl ions ($OH^-$) .Hydroxyl ions transfer through the porous diaphragm to the anode,resulting in half molecule of oxygen (O2) and one molecule of water (H2O). -![](alkaline_electrolysis.PNG) +![](alkaline_electrolysis.PNG) (Image Credit: S.Shiva Kumar & V.Himabindu [^2]) @@ -30,7 +30,7 @@ The lifetime of an alkaline electrolyser is around 25 years and the construction -As alkaline electrolysers are the most mature electrolysis technology, they dominate the market, especially for large-scale projects. In 2020, the global electrolysis capacity is 200 MW [^6]. Assuming 8000 yearly load hours, the global production of electrolysis is around 1.6 TWh. The PEM hydrogen production is of 0.24TWh per year (see PEM documentation) and others electrolysis technologies are not yet commercialized. Then we assume that AWE world production in 2020 is 0.76 TWh. +As alkaline electrolysers are the most mature electrolysis technology, they dominate the market, especially for large-scale projects. In 2020, the global electrolysis capacity is 200 MW [^6]. Assuming 8000 yearly load hours, the global production of electrolysis is around 1.6 TWh. The PEM hydrogen production is of 0.24TWh per year (see PEM documentation) and others electrolysis technologies are not yet commercialized. Then we assume that AWE world production in 2020 is 0.76 TWh. Alkakine electrolysers is the lowest efficient electrolysis technology we assume that no investments have been made during the past years. diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/awe/electrolysis_awe.py b/energy_models/models/gaseous_hydrogen/electrolysis/awe/electrolysis_awe.py index c768c10a..2661d746 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/awe/electrolysis_awe.py +++ b/energy_models/models/gaseous_hydrogen/electrolysis/awe/electrolysis_awe.py @@ -31,14 +31,14 @@ class ElectrolysisAWE(GaseousHydrogenTechno): def compute_resources_needs(self): # Cost of water for 1 kWH of H2 self.cost_details[f"{GlossaryEnergy.WaterResource}_needs"] = self.get_water_needs() - def compute_other_streams_needs(self): # Efficiency ifor electrolysis means electric efficiency and is here to # compute the elec needs in kWh/kWh 1/efficiency self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = 1.0 / self.cost_details['efficiency'] + def get_water_needs(self): - ''' + ''' Get water needs in kg Water /kWh H2 1 mol of H20 for 1 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -54,7 +54,7 @@ def get_water_needs(self): return water_needs def get_oxygen_produced(self): - ''' + ''' Get oxygen needs in kg O2 /kWh H2 1 mol of O2 for 2 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -79,3 +79,4 @@ def compute_byproducts_production(self): # self.production[f'{lowheattechno.energy_name} ({self.product_unit})'] = \ # self.consumption[f'{GlossaryEnergy.electricity} ({self.product_unit})'] \ # - self.production[f'{GaseousHydrogenTechno.energy_name} ({self.product_unit})'] # in TWH + diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/pem/documentation/electrolysis_pem_disc.markdown b/energy_models/models/gaseous_hydrogen/electrolysis/pem/documentation/electrolysis_pem_disc.markdown index 9fa0f61a..6354f697 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/pem/documentation/electrolysis_pem_disc.markdown +++ b/energy_models/models/gaseous_hydrogen/electrolysis/pem/documentation/electrolysis_pem_disc.markdown @@ -2,7 +2,7 @@ PEM electrolysers use a proton exchange membrane to separate the protons (H+) from water and oxygen. Operating conditions range between 20-100 C and up to 100 bar. -![](PEM_electrolysis.PNG) +![](PEM_electrolysis.PNG) (Image Credit: S.Shiva Kumar & V.Himabindu [^2]) @@ -26,10 +26,10 @@ PEM electrolyser OPEX costs are 2 - 3 % of CAPEX excluding electricity [^4]. It is worth to note, that electrolysers are still built in small volumes for niche markets, which puts the large expected costs reductions into context. Significant cost reductions are expected to come from technology innovations, improved supply chains and bigger production volumes, which enable more efficient manufacturing techniques [^4]. -PEM electrolysers need expensive noble metals (platinum, iridium) which makes them more expensive and less efficient than alkaline electrolysers [^2]. +PEM electrolysers need expensive noble metals (platinum, iridium) which makes them more expensive and less efficient than alkaline electrolysers [^2]. -PEM electrolysers are not yet fully developped but Buttler [^6] reported around 6MW of PEM electrolysers nominal power around the world in 2017. With new project emerging, the nominal power of PEM is around 50MW in 2021 (a new 10MW hydrogen electrolysis plant, the largest of its kind in Europe operates in 2020[^7] and another 20 MW, the largest of its kind in the world, will start operating in 2021 in Quebec (Canada) [^10] ) leading to a global hydrogen production of 0.4TWh per year. +PEM electrolysers are not yet fully developped but Buttler [^6] reported around 6MW of PEM electrolysers nominal power around the world in 2017. With new project emerging, the nominal power of PEM is around 50MW in 2021 (a new 10MW hydrogen electrolysis plant, the largest of its kind in Europe operates in 2020[^7] and another 20 MW, the largest of its kind in the world, will start operating in 2021 in Quebec (Canada) [^10] ) leading to a global hydrogen production of 0.4TWh per year. Public investment in Europe for electrolysers is handled by the Fuel Cell and Hydrogen Joint Undertaking (FCH-JU) organism [^8].In 2019, european investments was around 156 MEUR or around 190 MDollars. We assume half of it is dedicated tor PEM. Around 36% of PEM electrolysers is financed by European union worldwide [^9]. Consequently the hypothesis investment in 2019 for PEM is around : $$\frac{190*100}{2*36}= 263.88 \ MDollars$$ @@ -55,4 +55,4 @@ In Electrolysis, heat production assumed the net difference between total electr [^9]:https://www.euractiv.com/section/energy/news/europe-china-battle-for-global-supremacy-on-electrolyser-manufacturing/ -[^10]: https://www.cummins.com/news/releases/2021/01/26/cummins-hydrogen-technology-powers-largest-proton-exchange-membrane-pemCUMMINS +[^10]: https://www.cummins.com/news/releases/2021/01/26/cummins-hydrogen-technology-powers-largest-proton-exchange-membrane-pemCUMMINS \ No newline at end of file diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/pem/electrolysis_pem.py b/energy_models/models/gaseous_hydrogen/electrolysis/pem/electrolysis_pem.py index 115ae829..7d50187e 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/pem/electrolysis_pem.py +++ b/energy_models/models/gaseous_hydrogen/electrolysis/pem/electrolysis_pem.py @@ -38,7 +38,7 @@ def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = 1.0 / self.cost_details['efficiency'] def get_water_needs(self): - ''' + ''' Get water needs in kg Water /kWh H2 1 mol of H20 for 1 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -54,7 +54,7 @@ def get_water_needs(self): return water_needs def get_oxygen_produced(self): - ''' + ''' Get oxygen needs in kg O2 /kWh H2 1 mol of O2 for 2 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -72,7 +72,7 @@ def get_oxygen_produced(self): def get_theoretical_platinum_needs(self): """ Get platinum needs in kg platinum /kWh H2 - + https://www.energy.gov/sites/prod/files/2016/03/f30/At_A_GLANCE%20%28FCTO%29.pdf According to the Fuel Cell Technologies Office, 1g of platinum enables the production of 8K of H2 diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/soec/documentation/electrolysis_soec_disc.markdown b/energy_models/models/gaseous_hydrogen/electrolysis/soec/documentation/electrolysis_soec_disc.markdown index 6b9a81f7..f6641467 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/soec/documentation/electrolysis_soec_disc.markdown +++ b/energy_models/models/gaseous_hydrogen/electrolysis/soec/documentation/electrolysis_soec_disc.markdown @@ -1,8 +1,8 @@ # SOEC (Solid Oxyde Electrolyser Cell) Electrolysis -The solid oxide electrolysis (SOE) first introduced by Donitz and Erdle in the 1980s. Solid oxide electrolysis has attracted an abundant deal of attention due to the electrical energy converts into the chemical energy along with producing the ultra-pure hydrogen with greater efficiency. Solid oxide electrolysis operates at high pressure and high temperatures 500-850 C and utilizes the water in the form of steam. [^1] +The solid oxide electrolysis (SOE) first introduced by Donitz and Erdle in the 1980s. Solid oxide electrolysis has attracted an abundant deal of attention due to the electrical energy converts into the chemical energy along with producing the ultra-pure hydrogen with greater efficiency. Solid oxide electrolysis operates at high pressure and high temperatures 500-850 C and utilizes the water in the form of steam. [^1] -![](SOEC.PNG) +![](SOEC.PNG) (Image Credit: S.Shiva Kumar & V.Himabindu [^1]) @@ -22,7 +22,7 @@ CAPEX estimates for SOEC electrolysers range across 2 800 to 600 USD/kW dependin SOEC is in a pre-commercial and fundamental research stage although Sunfire is already offering systems of 150 kW in a 20 ft or 40 ft container. Other companies investigating the SOEC technologies are Haldor Topsoe (incooperation with Riso DTU), Cermatec (in cooperation with the -Idaho National Laboratory), FuelCell Energy and Toshiba. The initial production of the technology is assumed negligible. +Idaho National Laboratory), FuelCell Energy and Toshiba. The initial production of the technology is assumed negligible. ## Heat In Electrolysis, heat production assumed the net difference between total electricity consumption and total hydrogen production. diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec.py b/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec.py index 060b422b..7658fe1e 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec.py +++ b/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec.py @@ -37,8 +37,9 @@ def compute_other_streams_needs(self): # compute the elec needs in kWh/kWh 1/efficiency self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = 1.0 / self.cost_details['efficiency'] + def get_water_needs(self): - ''' + ''' Get water needs in kg Water /kWh H2 1 mol of H20 for 1 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -54,7 +55,7 @@ def get_water_needs(self): return water_needs def get_oxygen_produced(self): - ''' + ''' Get oxygen needs in kg O2 /kWh H2 1 mol of O2 for 2 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -80,3 +81,5 @@ def compute_byproducts_production(self): # self.production[f'{lowheattechno.energy_name} ({self.product_unit})'] = \ # self.consumption[f'{GlossaryEnergy.electricity} ({self.product_unit})'] \ # - self.production[f'{GaseousHydrogenTechno.energy_name} ({self.product_unit})'] # in TWH + + diff --git a/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec_disc.py b/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec_disc.py index 4854a8a4..bf74f7c7 100644 --- a/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec_disc.py +++ b/energy_models/models/gaseous_hydrogen/electrolysis/soec/electrolysis_soec_disc.py @@ -45,6 +45,7 @@ class ElectrolysisSOECDiscipline(GaseousHydrogenTechnoDiscipline): } techno_name = GlossaryEnergy.ElectrolysisSOEC + techno_infos_dict_default = {'maturity': 5, 'Opex_percentage': 0.03, 'CO2_from_production': 0.0, diff --git a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA clean.markdown b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA clean.markdown index 9b9270cb..f17382ed 100644 --- a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA clean.markdown +++ b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA clean.markdown @@ -16,14 +16,14 @@ $$X = \dfrac {H2\_revenue}{SUM\_revenues}$$ $$= \dfrac {H2\_revenue}{H2\_revenue+ Carbon\_sold\_revenue+Carbon\_storage\_revenue}$$ -if Carbon\_prod - Carbon\_demand < 0: +if Carbon\_prod - Carbon\_demand < 0: $$X = \dfrac {H2\_revenue}{H2\_revenue+ Carbon\_sold\_revenue+ 0}$$ $$= \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+ [Carbon\_prod * Carbon\_price]}$$ -if Carbon\_prod - Carbon\_demand > 0 : - +if Carbon\_prod - Carbon\_demand > 0 : +        Carbon\_storage = Carbon\_prod - Carbon\_demand        if Carbon\_storage < Carbon\_storage\_max : @@ -43,7 +43,7 @@ $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+[Carbon\_demand * Ca \ ### Gradient computation: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+ [Carbon\_prod * Carbon\_price]}$$ @@ -72,7 +72,7 @@ $$=\dfrac {[ \dfrac {\partial H2\_prod}{\partial invest} * H2\_price * Carbon\_s \ \ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand : $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ] + [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}]+ [Carbon\_demand * (Carbon\_price- \dfrac {Carbon\_mol * CO2\_credit}{CO2\_mol})]}$$ @@ -131,4 +131,4 @@ $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\ finally, the only gradient to compute is: -$$\dfrac {\partial H2\_price}{\partial energy\_prices} = (Id_{H2\_column}, Zero_{other\_column})$$ +$$\dfrac {\partial H2\_price}{\partial energy\_prices} = (Id_{H2\_column}, Zero_{other\_column})$$ \ No newline at end of file diff --git a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA_resume.markdown b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA_resume.markdown index 9a66efd2..3be45e5f 100644 --- a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA_resume.markdown +++ b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/FORMULA_resume.markdown @@ -11,7 +11,7 @@ $$H2_{price}= PC_{cost} * Margin * X$$ $$\dfrac {\partial H2_{price}}{\partial energy\_prices}= Margin * X * \dfrac {\partial PC_{cost}}{\partial energy\_prices} + PC_{cost} * Margin * \dfrac {\partial X}{\partial energy\_prices}$$ - $$\dfrac {\partial H2_{price}}{\partial energy\_CO2\_emission}= Margin * X * \dfrac {\partial PC_{cost}}{\partial energy\_CO2\_emission} + $$\dfrac {\partial H2_{price}}{\partial energy\_CO2\_emission}= Margin * X * \dfrac {\partial PC_{cost}}{\partial energy\_CO2\_emission} + 0 $$ ### X computation: @@ -24,14 +24,14 @@ with: if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand        if Carbon\_storage < Carbon\_storage\_max : $$A = [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}] + [Carbon\_demand * (Carbon\_price - \dfrac {Carbon\_mol * CO2\_credit}{CO2\_mol})]$$ -       if Carbon\_storage > Carbon\_storage\_max : +       if Carbon\_storage > Carbon\_storage\_max : $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\_mol * CO2\_credit}{CO2\_mol}]$$ \ @@ -42,7 +42,7 @@ $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\ $$\dfrac {\partial X}{\partial energy\_prices} = \dfrac { - \dfrac {\partial H2\_price}{\partial energy\_prices} * H2\_prod * + \dfrac {\partial H2\_price}{\partial energy\_prices} * H2\_prod * A }{[H2\_revenue + A ]^2 @@ -53,7 +53,7 @@ with: if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -85,7 +85,7 @@ if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ $$B = Carbon\_price$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -117,4 +117,4 @@ finally, the only gradient to compute is: $$ \dfrac {\partial H2\_price}{\partial energy\_prices} = (Id_{H2\_column}, Zero_{other\_column}) -$$ +$$ \ No newline at end of file diff --git a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc.markdown b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc.markdown index 936f1ffa..7f44f77f 100644 --- a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc.markdown +++ b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc.markdown @@ -1,6 +1,6 @@ # PlasmaCracking -## Introduction +## Introduction The plasma cracking process consists in breaking the connection between the carbon and the hydrogen of the methane using high frequency microwaves. The reaction is the following: $$CH_4 --> C + 2H_2$$ @@ -13,13 +13,13 @@ This process allows to extract solid carbon out of methane and if used with biom -![](plasmacracking.png) +![](plasmacracking.png) ## Gradient computation - Summary H2_price : techno price \ X : percentage resource \ -PC_cost : Plasma Cracking cost +PC_cost : Plasma Cracking cost ### Formula: @@ -53,11 +53,11 @@ $$X = \dfrac {H2\_revenue}{H2\_revenue with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -71,7 +71,7 @@ $${\footnotesize A = [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol} $$\dfrac {\partial X}{\partial energy\_prices} = \dfrac { - \dfrac {\partial H2\_price}{\partial energy\_prices} * H2\_prod * + \dfrac {\partial H2\_price}{\partial energy\_prices} * H2\_prod * A }{[H2\_revenue + A ]^2 @@ -80,11 +80,11 @@ $$ with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -107,13 +107,13 @@ $$ with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ $$B = Carbon\_price$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -139,13 +139,13 @@ $$ with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ $$B = Carbon\_price$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -168,13 +168,13 @@ $$ with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ $$B = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -203,14 +203,14 @@ $$X = \dfrac {H2\_revenue}{SUM\_revenues}$$ $$= \dfrac {H2\_revenue}{H2\_revenue+ Carbon\_sold\_revenue+Carbon\_storage\_revenue}$$ -if Carbon\_prod - Carbon\_demand < 0: +if Carbon\_prod - Carbon\_demand < 0: $$X = \dfrac {H2\_revenue}{H2\_revenue+ Carbon\_sold\_revenue+ 0}$$ $$= \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+ [Carbon\_prod * Carbon\_price]}$$ -if Carbon\_prod > Carbon\_demand : - +if Carbon\_prod > Carbon\_demand : +        Carbon\_storage = Carbon\_prod - Carbon\_demand $${\scriptsize X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+[Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_prod -Carbon\_demand)* Carbon\_mol * CO2\_credit}{CO2\_mol}]}}$$ @@ -224,7 +224,7 @@ $$= \dfrac {H2\_revenue}{H2\_revenue + A}$$ ### Gradient computation: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+ [Carbon\_prod * Carbon\_price]}$$ @@ -257,7 +257,7 @@ $$=\dfrac {[ \dfrac {\partial H2\_prod}{\partial invest} * H2\_price * Carbon\_s -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand : $${\scriptsize X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ] + [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}]+ [Carbon\_demand * (Carbon\_price- \dfrac {Carbon\_mol * CO2\_credit}{CO2\_mol})]} }$$ diff --git a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc_v2.markdown b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc_v2.markdown index 31e1f903..9c1326f6 100644 --- a/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc_v2.markdown +++ b/energy_models/models/gaseous_hydrogen/plasma_cracking/documentation/plasma_cracking_disc_v2.markdown @@ -1,6 +1,6 @@ # PlasmaCracking -## Introduction +## Introduction The plasma cracking process consists in breaking the connection between the carbon and the hydrogen of the methane using high frequency microwaves. The reaction is the following: $$CH_4 --> C + 2H_2$$ @@ -38,11 +38,11 @@ $$X = \dfrac {H2\_revenue}{H2\_revenue with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -52,7 +52,7 @@ $$A = [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}] + [Carbon\_demand * (Carbon\_price - \dfrac {Carbon\_mol * CO2\_credit}{CO2\_mol})]$$ -       if Carbon\_storage > Carbon\_storage\_max : +       if Carbon\_storage > Carbon\_storage\_max : $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\_mol * CO2\_credit}{CO2\_mol}]$$ \ @@ -63,7 +63,7 @@ $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\ $$\dfrac {\partial X}{\partial energy\_prices} = \dfrac { - \dfrac {\partial H2\_price}{\partial energy\_prices} * H2\_prod * + \dfrac {\partial H2\_price}{\partial energy\_prices} * H2\_prod * A }{[H2\_revenue + A ]^2 @@ -72,15 +72,15 @@ $$ with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand -       if Carbon\_storage < Carbon\_storage\_max : +       if Carbon\_storage < Carbon\_storage\_max : $$A = [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}] + [Carbon\_demand * (Carbon\_price @@ -105,13 +105,13 @@ $$ with: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$A = Carbon\_sold\_revenue$$ $$B = Carbon\_price$$ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand :        Carbon\_storage = Carbon\_prod - Carbon\_demand @@ -123,7 +123,7 @@ $$A = [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}] $$B = \dfrac {Carbon\_mol * CO2\_credit}{CO2\_mol}$$ -       if Carbon\_storage > Carbon\_storage\_max : +       if Carbon\_storage > Carbon\_storage\_max : $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\_mol * CO2\_credit}{CO2\_mol}]$$ @@ -149,14 +149,14 @@ $$X = \dfrac {H2\_revenue}{SUM\_revenues}$$ $$= \dfrac {H2\_revenue}{H2\_revenue+ Carbon\_sold\_revenue+Carbon\_storage\_revenue}$$ -if Carbon\_prod - Carbon\_demand < 0: +if Carbon\_prod - Carbon\_demand < 0: $$X = \dfrac {H2\_revenue}{H2\_revenue+ Carbon\_sold\_revenue+ 0}$$ $$= \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+ [Carbon\_prod * Carbon\_price]}$$ -if Carbon\_prod - Carbon\_demand > 0 : - +if Carbon\_prod - Carbon\_demand > 0 : +        Carbon\_storage = Carbon\_prod - Carbon\_demand        if Carbon\_storage < Carbon\_storage\_max : @@ -176,7 +176,7 @@ $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+[Carbon\_demand * Ca \ ### Gradient computation: -if Carbon\_prod < Carbon\_demand : +if Carbon\_prod < Carbon\_demand : $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ]+ [Carbon\_prod * Carbon\_price]}$$ @@ -205,7 +205,7 @@ $$=\dfrac {[ \dfrac {\partial H2\_prod}{\partial invest} * H2\_price * Carbon\_s \ \ -if Carbon\_prod > Carbon\_demand : +if Carbon\_prod > Carbon\_demand : $$X = \dfrac {H2\_prod * H2\_price}{[H2\_prod * H2\_price ] + [\dfrac {Carbon\_prod* Carbon\_mol * CO2\_credit}{CO2\_mol}]+ [Carbon\_demand * (Carbon\_price- \dfrac {Carbon\_mol * CO2\_credit}{CO2\_mol})]}$$ @@ -254,4 +254,4 @@ with: $$A = [Carbon\_demand * Carbon\_price]+ [\dfrac {(Carbon\_storage\_max)* Carbon\_mol * CO2\_credit}{CO2\_mol}]$$ \ \ -\ +\ \ No newline at end of file diff --git a/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking.py b/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking.py index 3bbef3b7..539fbb0a 100644 --- a/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking.py +++ b/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking.py @@ -50,6 +50,7 @@ def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() self.cost_details[f'{GlossaryEnergy.methane}_needs'] = self.get_theoretical_methane_needs() / self.cost_details['efficiency'] + def compute_byproducts_production(self): C_per_h2 = self.get_theoretical_solid_carbon_production() @@ -57,7 +58,7 @@ def compute_byproducts_production(self): C_per_h2 * self.production_detailed[f'{GaseousHydrogenTechno.energy_name} ({self.product_unit})'] def get_theoretical_solid_carbon_production(self): - ''' + ''' Get methane needs in kg C /kWh H2 1 mol of C for 2 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -74,7 +75,7 @@ def get_theoretical_solid_carbon_production(self): return methane_needs def get_theoretical_methane_needs(self): - ''' + ''' Get methane needs in kWh CH4 /kWh H2 1 mol of CH4 for 2 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -91,7 +92,7 @@ def get_theoretical_methane_needs(self): return methane_needs def get_theoretical_cO2_prod(self): - ''' + ''' Need to model the fact that carbon is created but not CO2 ''' diff --git a/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking_disc.py b/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking_disc.py index 0340c724..054aaed6 100644 --- a/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking_disc.py +++ b/energy_models/models/gaseous_hydrogen/plasma_cracking/plasma_cracking_disc.py @@ -89,7 +89,7 @@ class PlasmaCrackingDiscipline(GaseousHydrogenTechnoDiscipline): }, 'market_demand': {'type': 'dataframe', 'default': market_demand, 'unit': 'Mt/year', 'structuring': True, 'dataframe_descriptor': {GlossaryEnergy.Years: ('float', None, True), - 'carbon_demand': ('float', None, True), } + 'carbon_demand': ('float', None, True),} } } diff --git a/energy_models/models/gaseous_hydrogen/water_gas_shift/documentation/water_gas_shift_disc.markdown b/energy_models/models/gaseous_hydrogen/water_gas_shift/documentation/water_gas_shift_disc.markdown index c543f390..595b5c10 100644 --- a/energy_models/models/gaseous_hydrogen/water_gas_shift/documentation/water_gas_shift_disc.markdown +++ b/energy_models/models/gaseous_hydrogen/water_gas_shift/documentation/water_gas_shift_disc.markdown @@ -4,20 +4,20 @@ Water Gas Shift reaction involves reaction between carbon monoxyde ($CO$) and wa The reaction can be used to enrich a syngas to obtain a given $CO$ to $H_2$ ratio for a specific technology (Fischer Tropsch reaction or methanol synthesis for example) or to produce $CO$-free hydrogen by cleaning $CO$ residues from syngas which are poisonous and deadly. -In order to achieve large-scale hydrogen production from syngas, an appropriate catalyst must be chosen to facilitate the reaction. +In order to achieve large-scale hydrogen production from syngas, an appropriate catalyst must be chosen to facilitate the reaction. -![](WGS_catalysts.PNG) +![](WGS_catalysts.PNG) (Image Credit: Pal, D. [^1]) -The figure above shows a broad classification of catalysts that have been commonly used for the WGS reaction. WGS catalysts may be divided into five categories: High-Temperature Catalysts, Low-Temperature Catalysts, Ceria and Noble Metal based Catalysts; Carbon based Catalysts and Nanostructured Catalysts. All processes to obtain the catalysts and a comparison of them can be found in [^1]. +The figure above shows a broad classification of catalysts that have been commonly used for the WGS reaction. WGS catalysts may be divided into five categories: High-Temperature Catalysts, Low-Temperature Catalysts, Ceria and Noble Metal based Catalysts; Carbon based Catalysts and Nanostructured Catalysts. All processes to obtain the catalysts and a comparison of them can be found in [^1]. -The syngas in the model is defined with a syngas ratio ($r_1$) which is the molar ratio of CO over $H_2$. The objective of the reaction is to eliminate the CO inside the syngas to obtain another syngas at a different molar ratio ($r_2$). With a zero $r_2$ ratio, the syngas is fully converted into hydrogen. +The syngas in the model is defined with a syngas ratio ($r_1$) which is the molar ratio of CO over $H_2$. The objective of the reaction is to eliminate the CO inside the syngas to obtain another syngas at a different molar ratio ($r_2$). With a zero $r_2$ ratio, the syngas is fully converted into hydrogen. -However, the reaction products carbon dioxyde ($CO_2$) which can be captured and stored with suitable technologies (see Carbon Capture and Storage technologies on flue gas). - -The main reaction of this technology is : +However, the reaction products carbon dioxyde ($CO_2$) which can be captured and stored with suitable technologies (see Carbon Capture and Storage technologies on flue gas). +The main reaction of this technology is : + $$(H_2 +r_1 CO) + cH_20 --> dCO_2 + e(H_2 +r_2CO)$$ @@ -25,7 +25,7 @@ with $r_1$ and $r_2$ syngas ratios before and after the reaction : $$r_i = \frac{mol CO}{mol H2}$$ -and with $c$,$d$ and $e$ coefficients of the reaction that can be computed with $r_1$ and $r_2$ to satisfy chemical equilibrium : +and with $c$,$d$ and $e$ coefficients of the reaction that can be computed with $r_1$ and $r_2$ to satisfy chemical equilibrium : $$c = \frac{r1-r2}{1+r2}$$ @@ -34,15 +34,15 @@ $$d = r1 - \frac{r2(1+r1)}{1+r2}$$ $$e = \frac{1+r1}{1+r2}$$ -## Data +## Data -Economic datas are computed following the work in [^2] where a techno-economic analysis is performed on a two-stage WGS combining Low-Temperature and High-Temperature catalysts. -Theoretical datas about production and consumption have been computed with coefficients above depending on $CO$ to $H_2$ ratios ($r_1$ and $r_2$). Other technical datas (i.e. construction delay, lifetime or learning rate, efficiency) can be found in [^2] or [^3]. +Economic datas are computed following the work in [^2] where a techno-economic analysis is performed on a two-stage WGS combining Low-Temperature and High-Temperature catalysts. +Theoretical datas about production and consumption have been computed with coefficients above depending on $CO$ to $H_2$ ratios ($r_1$ and $r_2$). Other technical datas (i.e. construction delay, lifetime or learning rate, efficiency) can be found in [^2] or [^3]. The initial world production has ## Heat -[^4] WGSR is the reaction of an equimolar mixture of steam and carbon monoxide and the process is moderately exothermic. +[^4] WGSR is the reaction of an equimolar mixture of steam and carbon monoxide and the process is moderately exothermic. It is an important step in the reforming process. CO + O(a) → CO2 + ∗ .... ΔH°= −283kJ/mol diff --git a/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift.py b/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift.py index 66fd15f2..c264e3c4 100644 --- a/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift.py +++ b/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift.py @@ -57,7 +57,7 @@ def configure_parameters_update(self, inputs_dict): def check_capex_unity(self, data_config): ''' - Overload the check_capex_unity for this particular model + Overload the check_capex_unity for this particular model ''' capex_list = np.array(data_config['Capex_init_vs_CO_conversion']) @@ -506,7 +506,7 @@ def compute_dprice_WGS_wo_taxes_dsyngas_ratio(self): def compute_resources_needs(self): # need in kg - self.cost_details[f"{GlossaryEnergy.WaterResource}_needs"] = self.get_theoretical_water_needs() / self.cost_details['efficiency'] + self.cost_details[f"{GlossaryEnergy.WaterResource}_needs"] = self.get_theoretical_water_needs()/ self.cost_details['efficiency'] def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() @@ -552,7 +552,7 @@ def compute_byproducts_production(self): # self.production[f'{GaseousHydrogenTechno.energy_name} ({self.product_unit})'] # in TWH def get_theoretical_syngas_needs(self, syngas_ratio): - ''' + ''' (H2 +r1CO) + cH20 --> dCO2 + e(H2 +r2CO) e = (1+r1)/(1+r2) @@ -580,7 +580,7 @@ def get_theoretical_syngas_needs(self, syngas_ratio): return syngas_needs def get_theoretical_water_needs(self): - ''' + ''' (H2 +r1CO) + cH20 --> dCO2 + e(H2 +r2CO) e = (1+r1)/(1+r2) @@ -606,7 +606,7 @@ def get_theoretical_water_needs(self): return water_needs def get_theoretical_co2_prod(self, unit='kg/kWh'): - ''' + ''' Get co2 needs in kg co2 /kWh H2 1 mol of CO2 for 4 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one diff --git a/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift_disc.py b/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift_disc.py index be17c180..de53890f 100644 --- a/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift_disc.py +++ b/energy_models/models/gaseous_hydrogen/water_gas_shift/water_gas_shift_disc.py @@ -100,7 +100,7 @@ class WaterGasShiftDiscipline(GaseousHydrogenTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'syngas_ratio': {'type': 'array', 'unit': '%', 'visibility': GaseousHydrogenTechnoDiscipline.SHARED_VISIBILITY, 'namespace': 'ns_syngas'}, diff --git a/energy_models/models/heat/high/chphighheat/chphighheat_disc.py b/energy_models/models/heat/high/chphighheat/chphighheat_disc.py index 7ed1ce75..b91258a5 100644 --- a/energy_models/models/heat/high/chphighheat/chphighheat_disc.py +++ b/energy_models/models/heat/high/chphighheat/chphighheat_disc.py @@ -49,6 +49,7 @@ class CHPHighHeatDiscipline(HighHeatTechnoDiscipline): # Heat Producer [Online] # https://www.serviceone.com/blog/article/how-long-does-a-home-boiler-last#:~:text=Estimated%20lifespan,most%20parts%20of%20the%20nation. + techno_infos_dict_default = { 'Capex_init': 1300, # https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW @@ -90,7 +91,7 @@ class CHPHighHeatDiscipline(HighHeatTechnoDiscipline): # Renewable Methane Association [online] DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + } DESC_IN.update(HighHeatTechnoDiscipline.DESC_IN) # -- add specific techno outputs to this diff --git a/energy_models/models/heat/high/chphighheat/documentation/chphighheat_disc.markdown b/energy_models/models/heat/high/chphighheat/documentation/chphighheat_disc.markdown index 07f6b0c9..c944c674 100644 --- a/energy_models/models/heat/high/chphighheat/documentation/chphighheat_disc.markdown +++ b/energy_models/models/heat/high/chphighheat/documentation/chphighheat_disc.markdown @@ -22,7 +22,7 @@ initial production for high heat temp = 39 TWh ## Working: CHP can use different fuels in the energy generation process, including fossil fuels and renewable fuels such as biofuels. -CHP is a technology that is used on a local scale. The energy produced is distributed to adjacent communities or buildings. Well insulated pipes underground, distribute the energy to local houses, and commercial buildings. +CHP is a technology that is used on a local scale. The energy produced is distributed to adjacent communities or buildings. Well insulated pipes underground, distribute the energy to local houses, and commercial buildings. CHP is only suitable where there is a year round demand. Buildings such as schools, hospitals, leisure centres, university halls, care homes and similar settings benefit from CHP. @@ -36,7 +36,7 @@ Combustion turbine, or reciprocating engine, with heat recovery unit Steam boiler with steam turbine. ![img.png](img.png) - + Combustion turbine or reciprocating engine CHP systems burn fuel (natural gas, oil, or biogas) to turn generators to produce electricity and use heat recovery devices to capture the heat from the turbine or engine. This heat is converted into useful thermal energy, usually in the form of steam or hot water. ## Data @@ -51,7 +51,7 @@ The data used for this model is extracted from the EPA, IEA Data, ScienceDirect [^4]: [CHP(Data study and comparison) - IEA(International Energy Agency)](https://iea.blob.core.windows.net/assets/d459f7d5-1ba7-49d9-ad56-915fba22f267/chp_report.pdf) -[^5]: Capex, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW +[^5]: Capex, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW [^6]: Efficiency, https://www.epa.gov/chp/chp-benefits#:~:text=By%20recovering%20and%20using%20heat,of%2065%20to%2080%20percent. @@ -61,8 +61,9 @@ The data used for this model is extracted from the EPA, IEA Data, ScienceDirect [^9]: co2 captured production, https://odr.chalmers.se/server/api/core/bitstreams/65470fdd-f00a-4607-8d0f-59152df05ea8/content -[^10]: Opex percentage, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1, 40$/kW for 1000$/kW capex +[^10]: Opex percentage, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1, 40$/kW for 1000$/kW capex [^11]: Electric demand, https://www.carboncommentary.com/blog/2007/10/01/domestic-combined-heat-and-power - + [^12]: Initial production, https://www.statista.com/statistics/678192/chp-electricity-generation-germany/ + diff --git a/energy_models/models/heat/high/electric_boiler_high_heat/documentation/electric_boiler_high_heat_disc.markdown b/energy_models/models/heat/high/electric_boiler_high_heat/documentation/electric_boiler_high_heat_disc.markdown index ca9420fe..36cc6961 100644 --- a/energy_models/models/heat/high/electric_boiler_high_heat/documentation/electric_boiler_high_heat_disc.markdown +++ b/energy_models/models/heat/high/electric_boiler_high_heat/documentation/electric_boiler_high_heat_disc.markdown @@ -3,7 +3,7 @@ What is the Electric Boiler? An Electric Boiler is a Boiler which operates using electricity rather than a combustible fossil fuel such as gas or oil boilers. -Electrically heated boilers make use of electric current running through a heating element to create heat, to boil water into high-temperature and high-pressure steam. +Electrically heated boilers make use of electric current running through a heating element to create heat, to boil water into high-temperature and high-pressure steam. They can be classified into the automatically controlled and the manually controlled types. High temperature heat in electric boiler technology, the temperature range is above 400 degree C. @@ -28,7 +28,7 @@ The cold water is passed over the metal heating element and heat is transferred, The exact way in which the boiler works in terms of heating and storing the water depends on the type of electric boiler/heater used. ![img.png](img.png) -## Data +## Data The data used for this model is extracted from EHC, WBA, EHB Leaflet & Rio heating [^1]: [Electric boiler Working - rio heating](https://www.rioheating.com/how-do-electric-heaters-work/#:~:text=Electric%20heating%20is%20the%20process,where%20the%20heat%20is%20created.) @@ -39,7 +39,7 @@ The data used for this model is extracted from EHC, WBA, EHB Leaflet & Rio heati [^4]: [About Electric Boiler – EHB Leaflet](https://www.labour.gov.hk/eng/public/bpvd/EHB_Leaflet_2016_Eng_2%20web.pdf) -[^5]: [Electric Boiler: World heat production – WBA(World Bioenergy Association)](https://www.worldbioenergy.org/uploads/211214%20WBA%20GBS%202021.pdf) +[^5]: [Electric Boiler: World heat production – WBA(World Bioenergy Association)](https://www.worldbioenergy.org/uploads/211214%20WBA%20GBS%202021.pdf) [^6]: Capex, https://capgemini-my.sharepoint.com/personal/valentin_joncquieres_capgemini_com/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fvalentin%5Fjoncquieres%5Fcapgemini%5Fcom%2FDocuments%2FFichiers%20de%20conversation%20Microsoft%20Teams%2FPriyankaChintada%5Ffinal%5Fthesis%2Epdf&parent=%2Fpersonal%2Fvalentin%5Fjoncquieres%5Fcapgemini%5Fcom%2FDocuments%2FFichiers%20de%20conversation%20Microsoft%20Teams&ga=1 # table 5.2. @@ -49,4 +49,4 @@ The data used for this model is extracted from EHC, WBA, EHB Leaflet & Rio heati [^9]: Electricity demand, https://billswiz.com/electric-boiler-electricity-use -[^10]: Initial production, https://www.worldbioenergy.org/uploads/211214%20WBA%20GBS%202021.pdf +[^10]: Initial production, https://www.worldbioenergy.org/uploads/211214%20WBA%20GBS%202021.pdf \ No newline at end of file diff --git a/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat.py b/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat.py index bfd7eba6..0232e784 100644 --- a/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat.py +++ b/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat.py @@ -32,6 +32,7 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] + def configure_input(self, inputs_dict): ''' Configure with inputs_dict from the discipline diff --git a/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat_disc.py b/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat_disc.py index 3fd29732..73fe0784 100644 --- a/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat_disc.py +++ b/energy_models/models/heat/high/electric_boiler_high_heat/electric_boiler_high_heat_disc.py @@ -46,6 +46,7 @@ class ElectricBoilerHighHeatDiscipline(HighHeatTechnoDiscipline): # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 + techno_infos_dict_default = { 'Capex_init': 42.86, @@ -71,11 +72,11 @@ class ElectricBoilerHighHeatDiscipline(HighHeatTechnoDiscipline): initial_production = 139.67 # Renewable Association [online] - + flux_input_dict = {'land_rate': 22000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - - + + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(HighHeatTechnoDiscipline.DESC_IN) diff --git a/energy_models/models/heat/high/geothermal_high_heat/documentation/geothermal_high_heat_disc.markdown b/energy_models/models/heat/high/geothermal_high_heat/documentation/geothermal_high_heat_disc.markdown index 933d6286..10fc4b83 100644 --- a/energy_models/models/heat/high/geothermal_high_heat/documentation/geothermal_high_heat_disc.markdown +++ b/energy_models/models/heat/high/geothermal_high_heat/documentation/geothermal_high_heat_disc.markdown @@ -3,13 +3,13 @@ **Definition[2] :** Geothermal heating is the direct use of geothermal energy for some heating applications. Humans have taken advantage of geothermal heat this way since the Paleolithic era. Approximately seventy countries made direct use of a total of 270 PJ of geothermal heating in 2004. As of 2007, 28 GW of geothermal heating capacity is installed around the world, satisfying 0.07% of global primary energy consumption.[1] Thermal efficiency is high since no energy conversion is needed, but capacity factors tend to be low (around 20%) since the heat is mostly needed in the winter -![](geothermal-heat-pump.jpg) +![](geothermal-heat-pump.jpg) (Image Credit Geothermal Heating System, [^1]) Geothermal energy originates from the heat retained within the Earth since the original formation of the planet, from radioactive decay of minerals, and from solar energy absorbed at the surface.Most high temperature geothermal heat is harvested in regions close to tectonic plate boundaries where volcanic activity rises close to the surface of the Earth. In these areas, ground and groundwater can be found with temperatures higher than the target temperature of the application. However, even cold ground contains heat. Below 6 metres (20 ft), the undisturbed ground temperature is consistently at the mean annual air temperature, and this heat can be extracted with a ground source heat pump. -## Data -Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. -![](Data.PNG) +## Data +Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. +![](Data.PNG) (Image Credit Geothermal Tomorrow 2008, [^2]) @@ -30,3 +30,4 @@ Most of the data used for this model is extracted from and International Renewa [^8]: Steel needs, https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power Page:21 [^9]: Initial production, https://www.iea.org/data-and-statistics/charts/direct-use-of-geothermal-energy-world-2012-2024 + diff --git a/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat.py b/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat.py index 743e65e2..44a64238 100644 --- a/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat.py +++ b/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat.py @@ -34,6 +34,7 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] + def compute_byproducts_production(self): carbon_production_factor = self.get_theoretical_co2_prod() self.production_detailed[f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit})'] = carbon_production_factor * \ diff --git a/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat_disc.py b/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat_disc.py index ce148037..af51df8b 100644 --- a/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat_disc.py +++ b/energy_models/models/heat/high/geothermal_high_heat/geothermal_high_heat_disc.py @@ -47,6 +47,8 @@ class GeothermalHighHeatDiscipline(HighHeatTechnoDiscipline): techno_name = GlossaryEnergy.GeothermalHighHeat energy_name = hightemperatureheat.name + + techno_infos_dict_default = { 'Capex_init': 3830, # https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2017/Aug/IRENA_Geothermal_Power_2017.pdf @@ -79,7 +81,7 @@ class GeothermalHighHeatDiscipline(HighHeatTechnoDiscipline): flux_input_dict = {'land_rate': 23000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } @@ -116,7 +118,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/high/heat_pump_high_heat/documentation/heat_pump_high_heat_disc.markdown b/energy_models/models/heat/high/heat_pump_high_heat/documentation/heat_pump_high_heat_disc.markdown index dc7befa5..e4bc51ab 100644 --- a/energy_models/models/heat/high/heat_pump_high_heat/documentation/heat_pump_high_heat_disc.markdown +++ b/energy_models/models/heat/high/heat_pump_high_heat/documentation/heat_pump_high_heat_disc.markdown @@ -3,7 +3,7 @@ [^2] A heat pump can be an energy-efficient alternative to fossil-fuel furnaces and water heaters that use natural gas and heating oil, which emit CO2 directly. Heat pumps also are more efficient than electric resistance heat. Switching from fossil fuel heat-generating devices, such as furnaces and boilers, to an efficient electric heat pump can be step toward a zero-carbon system. -![](HeatPumps.avif) +![](HeatPumps.avif) (Image Credit: IEA – International Energy Agency, taken from [^1]) COP (Coefficient of Performance) [^3] @@ -12,10 +12,10 @@ The Co-efficient of performance (COP) is an expression of the efficiency of a he COP is defined as the relationship between the power (kWh) that is drawn out of the heat pump as cooling or heat, and the power (kWh) that is supplied to the compressor. -## Data -![](Heat_Model_Assumptions.png) +## Data +![](Heat_Model_Assumptions.png) (Heat Model Assumptions, taken from [^4]) -## References +## References [^1]: [How a heat pump works – The Future of Heat Pumps - IEA](https://www.iea.org/reports/the-future-of-heat-pumps/how-a-heat-pump-works) [^2]: [The Important Role of Heat Pumps in a Sustainable Future](https://www.reuters.com/article/sponsored/the-important-role-of-heat-pumps-in-a-sustainable-future) @@ -33,3 +33,6 @@ COP is defined as the relationship between the power (kWh) that is drawn out of [^8]: Opex, https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx [^9]: Initial production, https://www.iea.org/reports/heat-pumps + + + diff --git a/energy_models/models/heat/high/heat_pump_high_heat/heat_pump_high_heat_disc.py b/energy_models/models/heat/high/heat_pump_high_heat/heat_pump_high_heat_disc.py index 2dfd0420..cb179340 100644 --- a/energy_models/models/heat/high/heat_pump_high_heat/heat_pump_high_heat_disc.py +++ b/energy_models/models/heat/high/heat_pump_high_heat/heat_pump_high_heat_disc.py @@ -47,6 +47,7 @@ class HeatPumpHighHeatDiscipline(HighHeatTechnoDiscipline): techno_name = GlossaryEnergy.HeatPumpHighHeat energy_name = hightemperatureheat.name + # Heat pumps offer an energy-efficient alternative to furnaces and air conditioners for all climates. # Heat pump can reduce your electricity use for heating by approximately 50% compared to # electric resistance heating such as furnaces and baseboard heaters. @@ -62,7 +63,7 @@ class HeatPumpHighHeatDiscipline(HighHeatTechnoDiscipline): # 660euro/kW/(lifetime * Number of hours in year) # Source:- https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx 'Capex_init_unit': '$/kWh', 'Opex_percentage': 0.04, - # https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx + ## https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx 'efficiency': 1.0, # consumptions and productions already have efficiency included 'CO2_from_production': 0.0, 'CO2_from_production_unit': 'kg/kg', @@ -100,7 +101,7 @@ class HeatPumpHighHeatDiscipline(HighHeatTechnoDiscipline): flux_input_dict = {'land_rate': 24000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(HighHeatTechnoDiscipline.DESC_IN) @@ -136,7 +137,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/high/natural_gas_boiler_high_heat/documentation/natural_gas_boiler_high_heat_disc.markdown b/energy_models/models/heat/high/natural_gas_boiler_high_heat/documentation/natural_gas_boiler_high_heat_disc.markdown index 5c282e7e..4df7eaa8 100644 --- a/energy_models/models/heat/high/natural_gas_boiler_high_heat/documentation/natural_gas_boiler_high_heat_disc.markdown +++ b/energy_models/models/heat/high/natural_gas_boiler_high_heat/documentation/natural_gas_boiler_high_heat_disc.markdown @@ -42,11 +42,14 @@ The data used for this model is extracted from the IEA Data, ScienceDirect & MET [^4]: [Natural Gas Model - ScienceDirect](https://www.sciencedirect.com/topics/engineering/natural-gas) [^5]: Methane demand, https://www.google.com/search?q=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat&rlz=1C1UEAD_enIN1000IN1000&oq=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat+&aqs=chrome..69i57.90503j0j7&sourceid=chrome&ie=UTF-8 - + [^6]: https://www.google.com/search?q=co2+captured+production+to+produce+heat+in+natural+gas+boiler&rlz=1C1UEAD_enIN1000IN1000&oq=co2+captured+production+to+produce+heat+in+natural+gas+boiler&aqs=chrome..69i57.37619j0j7&sourceid=chrome&ie=UTF-8 - + [^7]: co2 captured production, https://www.google.com/search?q=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat&rlz=1C1UEAD_enIN1000IN1000&oq=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat+&aqs=chrome..69i57.90503j0j7&sourceid=chrome&ie=UTF-8 [^8]: Initial production, https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=Electricity%20and%20heat&indicator=HeatGenByFuel [^9]: https://www.google.com/search?q=TJ+to+TWh&rlz=1C1UEAD_enIN1000IN1000&oq=TJ+to+TWh&aqs=chrome..69i57.35591j0j7&sourceid=chrome&ie=UTF-8 + + + diff --git a/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat.py b/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat.py index 8963a51a..c25db254 100644 --- a/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat.py +++ b/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat.py @@ -40,6 +40,7 @@ def compute_other_streams_needs(self): # and then we divide by efficiency self.cost_details[f'{Methane.name}_needs'] = self.get_theoretical_methane_needs() / self.cost_details['efficiency'] + def compute_byproducts_production(self): # CO2 production self.production_detailed[f'{CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = Methane.data_energy_dict[ @@ -50,7 +51,7 @@ def compute_byproducts_production(self): f'{Methane.name} ({self.product_unit})'] def get_theoretical_methane_needs(self): - # we need as output kwh/kwh + # we need as output kwh/kwh methane_demand = self.techno_infos_dict['methane_demand'] methane_needs = methane_demand diff --git a/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat_disc.py b/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat_disc.py index c12beb4a..a64f4d15 100644 --- a/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat_disc.py +++ b/energy_models/models/heat/high/natural_gas_boiler_high_heat/natural_gas_boiler_high_heat_disc.py @@ -106,7 +106,7 @@ class NaturalGasBoilerHighHeatDiscipline(HighHeatTechnoDiscipline): # Renewable Methane Association [online] flux_input_dict = {'land_rate': 20000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(HighHeatTechnoDiscipline.DESC_IN) @@ -141,7 +141,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/low/chplowheat/chplowheat_disc.py b/energy_models/models/heat/low/chplowheat/chplowheat_disc.py index 77e6d98d..88cc2425 100644 --- a/energy_models/models/heat/low/chplowheat/chplowheat_disc.py +++ b/energy_models/models/heat/low/chplowheat/chplowheat_disc.py @@ -94,7 +94,7 @@ class CHPLowHeatDiscipline(LowHeatTechnoDiscipline): # Renewable Methane Association [online] DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + } DESC_IN.update(LowHeatTechnoDiscipline.DESC_IN) # -- add specific techno outputs to this diff --git a/energy_models/models/heat/low/chplowheat/documentation/chplowheat_disc.markdown b/energy_models/models/heat/low/chplowheat/documentation/chplowheat_disc.markdown index 2aa70f74..c1af5ef5 100644 --- a/energy_models/models/heat/low/chplowheat/documentation/chplowheat_disc.markdown +++ b/energy_models/models/heat/low/chplowheat/documentation/chplowheat_disc.markdown @@ -22,7 +22,7 @@ initial production for low heat temp = 39 TWh ## Working: CHP can use different fuels in the energy generation process, including fossil fuels and renewable fuels such as biofuels. -CHP is a technology that is used on a local scale. The energy produced is distributed to adjacent communities or buildings. Well insulated pipes underground, distribute the energy to local houses, and commercial buildings. +CHP is a technology that is used on a local scale. The energy produced is distributed to adjacent communities or buildings. Well insulated pipes underground, distribute the energy to local houses, and commercial buildings. CHP is only suitable where there is a year round demand. Buildings such as schools, hospitals, leisure centres, university halls, care homes and similar settings benefit from CHP. @@ -36,7 +36,7 @@ Combustion turbine, or reciprocating engine, with heat recovery unit Steam boiler with steam turbine. ![img.png](img.png) - + Combustion turbine or reciprocating engine CHP systems burn fuel (natural gas, oil, or biogas) to turn generators to produce electricity and use heat recovery devices to capture the heat from the turbine or engine. This heat is converted into useful thermal energy, usually in the form of steam or hot water. ## Data @@ -51,7 +51,7 @@ The data used for this model is extracted from the EPA, IEA Data, ScienceDirect [^4]: [CHP(Data study and comparison) - IEA(International Energy Agency)](https://iea.blob.core.windows.net/assets/d459f7d5-1ba7-49d9-ad56-915fba22f267/chp_report.pdf) -[^5]: Capex, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW +[^5]: Capex, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW [^6]: Efficiency, https://www.epa.gov/chp/chp-benefits#:~:text=By%20recovering%20and%20using%20heat,of%2065%20to%2080%20percent. @@ -61,8 +61,9 @@ The data used for this model is extracted from the EPA, IEA Data, ScienceDirect [^9]: co2 captured production, https://odr.chalmers.se/server/api/core/bitstreams/65470fdd-f00a-4607-8d0f-59152df05ea8/content -[^10]: Opex percentage, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1, 40$/kW for 1000$/kW capex +[^10]: Opex percentage, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1, 40$/kW for 1000$/kW capex [^11]: Electric demand, https://www.carboncommentary.com/blog/2007/10/01/domestic-combined-heat-and-power - + [^12]: Initial production, https://www.statista.com/statistics/678192/chp-electricity-generation-germany/ + diff --git a/energy_models/models/heat/low/electric_boiler_low_heat/documentation/electric_boiler_low_heat_disc.markdown b/energy_models/models/heat/low/electric_boiler_low_heat/documentation/electric_boiler_low_heat_disc.markdown index d9455e09..a630f693 100644 --- a/energy_models/models/heat/low/electric_boiler_low_heat/documentation/electric_boiler_low_heat_disc.markdown +++ b/energy_models/models/heat/low/electric_boiler_low_heat/documentation/electric_boiler_low_heat_disc.markdown @@ -3,7 +3,7 @@ What is the Electric Boiler? An Electric Boiler is a Boiler which operates using electricity rather than a combustible fossil fuel such as gas or oil boilers. -Electrically heated boilers make use of electric current running through a heating element to create heat, to boil water into high-temperature and high-pressure steam. +Electrically heated boilers make use of electric current running through a heating element to create heat, to boil water into high-temperature and high-pressure steam. They can be classified into the automatically controlled and the manually controlled types. Low temperature heat in electric boiler technology, the temperature range is below 100 degree C. @@ -28,7 +28,7 @@ The cold water is passed over the metal heating element and heat is transferred, The exact way in which the boiler works in terms of heating and storing the water depends on the type of electric boiler/heater used. ![img.png](img.png) -## Data +## Data The data used for this model is extracted from EHC, WBA, EHB Leaflet & Rio heating. [^1]: [Electric boiler Working - rio heating](https://www.rioheating.com/how-do-electric-heaters-work/#:~:text=Electric%20heating%20is%20the%20process,where%20the%20heat%20is%20created.) diff --git a/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat.py b/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat.py index a7f321d8..e79ae4f4 100644 --- a/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat.py +++ b/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat.py @@ -32,6 +32,7 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] + def get_theoretical_electricity_needs(self): # we need as output kwh/kwh elec_demand = self.techno_infos_dict['elec_demand'] diff --git a/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat_disc.py b/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat_disc.py index d7e983e2..499ee5ae 100644 --- a/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat_disc.py +++ b/energy_models/models/heat/low/electric_boiler_low_heat/electric_boiler_low_heat_disc.py @@ -50,6 +50,8 @@ class ElectricBoilerLowHeatDiscipline(LowHeatTechnoDiscipline): # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 + + techno_infos_dict_default = { 'Capex_init': 42.86, @@ -77,8 +79,8 @@ class ElectricBoilerLowHeatDiscipline(LowHeatTechnoDiscipline): # Renewable Association [online] flux_input_dict = {'land_rate': 21000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - - + + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(LowHeatTechnoDiscipline.DESC_IN) @@ -113,7 +115,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/low/geothermal_low_heat/documentation/geothermal_low_heat_disc.markdown b/energy_models/models/heat/low/geothermal_low_heat/documentation/geothermal_low_heat_disc.markdown index 933d6286..10fc4b83 100644 --- a/energy_models/models/heat/low/geothermal_low_heat/documentation/geothermal_low_heat_disc.markdown +++ b/energy_models/models/heat/low/geothermal_low_heat/documentation/geothermal_low_heat_disc.markdown @@ -3,13 +3,13 @@ **Definition[2] :** Geothermal heating is the direct use of geothermal energy for some heating applications. Humans have taken advantage of geothermal heat this way since the Paleolithic era. Approximately seventy countries made direct use of a total of 270 PJ of geothermal heating in 2004. As of 2007, 28 GW of geothermal heating capacity is installed around the world, satisfying 0.07% of global primary energy consumption.[1] Thermal efficiency is high since no energy conversion is needed, but capacity factors tend to be low (around 20%) since the heat is mostly needed in the winter -![](geothermal-heat-pump.jpg) +![](geothermal-heat-pump.jpg) (Image Credit Geothermal Heating System, [^1]) Geothermal energy originates from the heat retained within the Earth since the original formation of the planet, from radioactive decay of minerals, and from solar energy absorbed at the surface.Most high temperature geothermal heat is harvested in regions close to tectonic plate boundaries where volcanic activity rises close to the surface of the Earth. In these areas, ground and groundwater can be found with temperatures higher than the target temperature of the application. However, even cold ground contains heat. Below 6 metres (20 ft), the undisturbed ground temperature is consistently at the mean annual air temperature, and this heat can be extracted with a ground source heat pump. -## Data -Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. -![](Data.PNG) +## Data +Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. +![](Data.PNG) (Image Credit Geothermal Tomorrow 2008, [^2]) @@ -30,3 +30,4 @@ Most of the data used for this model is extracted from and International Renewa [^8]: Steel needs, https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power Page:21 [^9]: Initial production, https://www.iea.org/data-and-statistics/charts/direct-use-of-geothermal-energy-world-2012-2024 + diff --git a/energy_models/models/heat/low/geothermal_low_heat/geothermal_low_heat_disc.py b/energy_models/models/heat/low/geothermal_low_heat/geothermal_low_heat_disc.py index 21dceb30..b36978e3 100644 --- a/energy_models/models/heat/low/geothermal_low_heat/geothermal_low_heat_disc.py +++ b/energy_models/models/heat/low/geothermal_low_heat/geothermal_low_heat_disc.py @@ -47,6 +47,8 @@ class GeothermalLowHeatDiscipline(LowHeatTechnoDiscipline): techno_name = GlossaryEnergy.GeothermalLowHeat energy_name = lowtemperatureheat.name + + techno_infos_dict_default = { 'Capex_init': 3830, # https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2017/Aug/IRENA_Geothermal_Power_2017.pdf @@ -79,7 +81,7 @@ class GeothermalLowHeatDiscipline(LowHeatTechnoDiscipline): flux_input_dict = {'land_rate': 18000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(LowHeatTechnoDiscipline.DESC_IN) @@ -115,7 +117,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/low/heat_pump_low_heat/documentation/heat_pump_low_heat_disc.markdown b/energy_models/models/heat/low/heat_pump_low_heat/documentation/heat_pump_low_heat_disc.markdown index dc7befa5..43eb372e 100644 --- a/energy_models/models/heat/low/heat_pump_low_heat/documentation/heat_pump_low_heat_disc.markdown +++ b/energy_models/models/heat/low/heat_pump_low_heat/documentation/heat_pump_low_heat_disc.markdown @@ -3,7 +3,7 @@ [^2] A heat pump can be an energy-efficient alternative to fossil-fuel furnaces and water heaters that use natural gas and heating oil, which emit CO2 directly. Heat pumps also are more efficient than electric resistance heat. Switching from fossil fuel heat-generating devices, such as furnaces and boilers, to an efficient electric heat pump can be step toward a zero-carbon system. -![](HeatPumps.avif) +![](HeatPumps.avif) (Image Credit: IEA – International Energy Agency, taken from [^1]) COP (Coefficient of Performance) [^3] @@ -12,10 +12,10 @@ The Co-efficient of performance (COP) is an expression of the efficiency of a he COP is defined as the relationship between the power (kWh) that is drawn out of the heat pump as cooling or heat, and the power (kWh) that is supplied to the compressor. -## Data -![](Heat_Model_Assumptions.png) +## Data +![](Heat_Model_Assumptions.png) (Heat Model Assumptions, taken from [^4]) -## References +## References [^1]: [How a heat pump works – The Future of Heat Pumps - IEA](https://www.iea.org/reports/the-future-of-heat-pumps/how-a-heat-pump-works) [^2]: [The Important Role of Heat Pumps in a Sustainable Future](https://www.reuters.com/article/sponsored/the-important-role-of-heat-pumps-in-a-sustainable-future) @@ -33,3 +33,4 @@ COP is defined as the relationship between the power (kWh) that is drawn out of [^8]: Opex, https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx [^9]: Initial production, https://www.iea.org/reports/heat-pumps + diff --git a/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat.py b/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat.py index e098f13b..976239e5 100644 --- a/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat.py +++ b/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat.py @@ -33,6 +33,7 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] + def compute_byproducts_production(self): # Production self.production_detailed[f'{lowtemperatureheat.name} ({self.product_unit})'] = \ diff --git a/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat_disc.py b/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat_disc.py index c2b8f745..dbd8d1a0 100644 --- a/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat_disc.py +++ b/energy_models/models/heat/low/heat_pump_low_heat/heat_pump_low_heat_disc.py @@ -45,6 +45,7 @@ class HeatPumpLowHeatDiscipline(LowHeatTechnoDiscipline): techno_name = GlossaryEnergy.HeatPumpLowHeat energy_name = lowtemperatureheat.name + # https://www.energy.gov/energysaver/heat-pump-systems # Heat pumps offer an energy-efficient alternative to furnaces and air conditioners for all climates. # Heat pump can reduce your electricity use for heating by approximately 50% compared to @@ -61,7 +62,7 @@ class HeatPumpLowHeatDiscipline(LowHeatTechnoDiscipline): # 660euro/kW/(lifetime * Number of hours in year) # Source:- https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx 'Capex_init_unit': '$/kWh', 'Opex_percentage': 0.04, - # https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx + ## https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx 'efficiency': 1, # consumptions and productions already have efficiency included 'CO2_from_production': 0.0, 'CO2_from_production_unit': 'kg/kg', @@ -85,7 +86,7 @@ class HeatPumpLowHeatDiscipline(LowHeatTechnoDiscipline): flux_input_dict = {'land_rate': 19000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(LowHeatTechnoDiscipline.DESC_IN) @@ -121,7 +122,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/low/natural_gas_boiler_low_heat/documentation/natural_gas_boiler_low_heat_disc.markdown b/energy_models/models/heat/low/natural_gas_boiler_low_heat/documentation/natural_gas_boiler_low_heat_disc.markdown index a7bc7d23..e0693def 100644 --- a/energy_models/models/heat/low/natural_gas_boiler_low_heat/documentation/natural_gas_boiler_low_heat_disc.markdown +++ b/energy_models/models/heat/low/natural_gas_boiler_low_heat/documentation/natural_gas_boiler_low_heat_disc.markdown @@ -11,8 +11,8 @@ price of methane is in $/kWh of heat. CO2 production is in Kg/KWh. That values from methane: CO2 per use(kg/kg) / calorific value(KWh/kg) production in 2020 that is total heat produced by Natural gas is 6236761 TJ = 1683 TWh - -initial production for low heat temp = 561 TWh + +initial production for low heat temp = 561 TWh Gas boilers work by releasing the fuel through a gas valve into a sealed combustion chamber in the boiler through small jets. With an electrical ignition, the gas is combusted to create heat. This heat is absorbed by a connected pipe carrying cold water. @@ -22,7 +22,7 @@ Hot gases are produced by burning fuel in the furnace. These hot gases are made Natural gas consists mostly methane (typical >85%) with the balance being varying amounts of ethane, propane, butane and some inert components (nitrogen, carbon dioxide and helium). ![img_1.png](img_1.png) -## Data +## Data The data used for this model is extracted from the IEA Data, ScienceDirect & MET [^1]: [How Does a Gas Boiler Work – always70wade & IEA](https://always70wade.com/b/what-is-a-boiler-how-does-it-work#:~:text=Gas%20boilers%20work%20by%20releasing,connected%20pipe%20carrying%20cold%20water) @@ -34,11 +34,13 @@ The data used for this model is extracted from the IEA Data, ScienceDirect & MET [^4]: [Natural Gas Model - ScienceDirect](https://www.sciencedirect.com/topics/engineering/natural-gas) [^5]: Methane demand, https://www.google.com/search?q=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat&rlz=1C1UEAD_enIN1000IN1000&oq=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat+&aqs=chrome..69i57.90503j0j7&sourceid=chrome&ie=UTF-8 - + [^6]: https://www.google.com/search?q=co2+captured+production+to+produce+heat+in+natural+gas+boiler&rlz=1C1UEAD_enIN1000IN1000&oq=co2+captured+production+to+produce+heat+in+natural+gas+boiler&aqs=chrome..69i57.37619j0j7&sourceid=chrome&ie=UTF-8 - + [^7]: co2 captured production, https://www.google.com/search?q=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat&rlz=1C1UEAD_enIN1000IN1000&oq=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat+&aqs=chrome..69i57.90503j0j7&sourceid=chrome&ie=UTF-8 [^8]: Initial production, https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=Electricity%20and%20heat&indicator=HeatGenByFuel [^9]: https://www.google.com/search?q=TJ+to+TWh&rlz=1C1UEAD_enIN1000IN1000&oq=TJ+to+TWh&aqs=chrome..69i57.35591j0j7&sourceid=chrome&ie=UTF-8 + + diff --git a/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat.py b/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat.py index 9236d818..ffcb9831 100644 --- a/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat.py +++ b/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat.py @@ -34,6 +34,7 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{Methane.name}_needs'] = self.get_theoretical_methane_needs() / self.cost_details['efficiency'] + # methane_needs # output needed in this method is in $/kwh of heat diff --git a/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat_disc.py b/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat_disc.py index 9596cb17..fadf01a8 100644 --- a/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat_disc.py +++ b/energy_models/models/heat/low/natural_gas_boiler_low_heat/natural_gas_boiler_low_heat_disc.py @@ -106,8 +106,8 @@ class NaturalGasBoilerLowHeatDiscipline(LowHeatTechnoDiscipline): # Renewable Methane Association [online] flux_input_dict = {'land_rate': 17000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - - + + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(LowHeatTechnoDiscipline.DESC_IN) @@ -142,7 +142,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/medium/chpmediumheat/chpmediumheat.py b/energy_models/models/heat/medium/chpmediumheat/chpmediumheat.py index f292f00a..c64d85e3 100644 --- a/energy_models/models/heat/medium/chpmediumheat/chpmediumheat.py +++ b/energy_models/models/heat/medium/chpmediumheat/chpmediumheat.py @@ -32,6 +32,7 @@ class CHPMediumHeat(mediumheattechno): def compute_other_streams_needs(self): self.cost_details[f'{Methane.name}_needs'] = self.get_theoretical_methane_needs() + # methane_needs # output needed in this method is in $/kwh of heat diff --git a/energy_models/models/heat/medium/chpmediumheat/chpmediumheat_disc.py b/energy_models/models/heat/medium/chpmediumheat/chpmediumheat_disc.py index e3c225c5..90f3246b 100644 --- a/energy_models/models/heat/medium/chpmediumheat/chpmediumheat_disc.py +++ b/energy_models/models/heat/medium/chpmediumheat/chpmediumheat_disc.py @@ -92,7 +92,7 @@ class CHPMediumHeatDiscipline(MediumHeatTechnoDiscipline): # Renewable Methane Association [online] DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + } DESC_IN.update(MediumHeatTechnoDiscipline.DESC_IN) # -- add specific techno outputs to this diff --git a/energy_models/models/heat/medium/chpmediumheat/documentation/chpmediumheat_disc.markdown b/energy_models/models/heat/medium/chpmediumheat/documentation/chpmediumheat_disc.markdown index 62e064e2..985eff97 100644 --- a/energy_models/models/heat/medium/chpmediumheat/documentation/chpmediumheat_disc.markdown +++ b/energy_models/models/heat/medium/chpmediumheat/documentation/chpmediumheat_disc.markdown @@ -22,7 +22,7 @@ initial production for medium heat temp = 39 TWh ## Working: CHP can use different fuels in the energy generation process, including fossil fuels and renewable fuels such as biofuels. -CHP is a technology that is used on a local scale. The energy produced is distributed to adjacent communities or buildings. Well insulated pipes underground, distribute the energy to local houses, and commercial buildings. +CHP is a technology that is used on a local scale. The energy produced is distributed to adjacent communities or buildings. Well insulated pipes underground, distribute the energy to local houses, and commercial buildings. CHP is only suitable where there is a year round demand. Buildings such as schools, hospitals, leisure centres, university halls, care homes and similar settings benefit from CHP. @@ -36,7 +36,7 @@ Combustion turbine, or reciprocating engine, with heat recovery unit Steam boiler with steam turbine. ![img.png](img.png) - + Combustion turbine or reciprocating engine CHP systems burn fuel (natural gas, oil, or biogas) to turn generators to produce electricity and use heat recovery devices to capture the heat from the turbine or engine. This heat is converted into useful thermal energy, usually in the form of steam or hot water. ## Data @@ -51,7 +51,7 @@ The data used for this model is extracted from the EPA, IEA Data, ScienceDirect [^4]: [CHP(Data study and comparison) - IEA(International Energy Agency)](https://iea.blob.core.windows.net/assets/d459f7d5-1ba7-49d9-ad56-915fba22f267/chp_report.pdf) -[^5]: Capex, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW +[^5]: Capex, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1 # average between 900$/kW to 1500$/kW [^6]: Efficiency, https://www.epa.gov/chp/chp-benefits#:~:text=By%20recovering%20and%20using%20heat,of%2065%20to%2080%20percent. @@ -61,8 +61,9 @@ The data used for this model is extracted from the EPA, IEA Data, ScienceDirect [^9]: co2 captured production, https://odr.chalmers.se/server/api/core/bitstreams/65470fdd-f00a-4607-8d0f-59152df05ea8/content -[^10]: Opex percentage, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1, 40$/kW for 1000$/kW capex +[^10]: Opex percentage, https://iea-etsap.org/E-TechDS/PDF/E04-CHP-GS-gct_ADfinal.pdf # page no-1, 40$/kW for 1000$/kW capex [^11]: Electric demand, https://www.carboncommentary.com/blog/2007/10/01/domestic-combined-heat-and-power - + [^12]: Initial production, https://www.statista.com/statistics/678192/chp-electricity-generation-germany/ + diff --git a/energy_models/models/heat/medium/electric_boiler_medium_heat/documentation/electric_boiler_medium_heat_disc.markdown b/energy_models/models/heat/medium/electric_boiler_medium_heat/documentation/electric_boiler_medium_heat_disc.markdown index 7869857b..8d90b466 100644 --- a/energy_models/models/heat/medium/electric_boiler_medium_heat/documentation/electric_boiler_medium_heat_disc.markdown +++ b/energy_models/models/heat/medium/electric_boiler_medium_heat/documentation/electric_boiler_medium_heat_disc.markdown @@ -28,7 +28,7 @@ The cold water is passed over the metal heating element and heat is transferred, The exact way in which the boiler works in terms of heating and storing the water depends on the type of electric boiler/heater used. ![img.png](img.png) -## Data +## Data The data used for this model is extracted from EHC, WBA, EHB Leaflet & Rio heating. [^1]: [Electric boiler Working - rio heating](https://www.rioheating.com/how-do-electric-heaters-work/#:~:text=Electric%20heating%20is%20the%20process,where%20the%20heat%20is%20created.) @@ -36,7 +36,7 @@ The data used for this model is extracted from EHC, WBA, EHB Leaflet & Rio heati [^2]: [Electric Boiler Overview - ehc](https://www.electric-heatingcompany.co.uk/article/electric-boiler-guide/#:~:text=An%20Electric%20Boiler%20is%20a%20device%20used%20to%20heat%20your,designed%20to%20maximise%20surface%20area.) [^3]: [Electric Boiler Process - ehc](https://www.electric-heatingcompany.co.uk/article/how-do-electric-boilers-heaters-work/) - + [^4]: [About Electric Boiler – EHB Leaflet](https://www.labour.gov.hk/eng/public/bpvd/EHB_Leaflet_2016_Eng_2%20web.pdf) [^5]: [Electric Boiler: World heat production – WBA(World Bioenergy Association)](https://www.worldbioenergy.org/uploads/211214%20WBA%20GBS%202021.pdf) diff --git a/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat.py b/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat.py index 62529685..bfd17259 100644 --- a/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat.py +++ b/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat.py @@ -32,6 +32,7 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] + def get_theoretical_electricity_needs(self): # we need as output kwh/kwh elec_demand = self.techno_infos_dict['elec_demand'] diff --git a/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat_disc.py b/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat_disc.py index 15d88ea5..23ee1454 100644 --- a/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat_disc.py +++ b/energy_models/models/heat/medium/electric_boiler_medium_heat/electric_boiler_medium_heat_disc.py @@ -50,6 +50,8 @@ class ElectricBoilerMediumHeatDiscipline(MediumHeatTechnoDiscipline): # https://www.google.com/search?q=electric+boiler+maximum+heat+temperature+in+degree+celcius&rlz=1C1UEAD_enIN1000IN1000&sxsrf=APwXEdf5IN3xbJw5uB3tC7-M-5nvtg8TKg%3A1683626939090&ei=uxtaZNOCBYWeseMP6ZuEwAM&ved=0ahUKEwiTzI2N_-f-AhUFT2wGHekNATgQ4dUDCA8&uact=5&oq=electric+boiler+maximum+heat+temperature+in+degree+celcius&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCCEQoAEyBQghEKABMgUIIRCgATIFCCEQoAE6CwgAEIoFEIYDELADOggIIRAWEB4QHToHCCEQoAEQCjoECCEQFUoECEEYAVDPB1izUGDqoQVoAXAAeACAAZ0BiAGUBJIBAzAuNJgBAKABAcgBA8ABAQ&sclient=gws-wiz-serp # https://www.google.com/search?q=electric+boiler+lifetime&rlz=1C1UEAD_enIN1000IN1000&oq=electric+boiler+lifetime&aqs=chrome..69i57j0i22i30l4j0i390i650l4.14155j0j7&sourceid=chrome&ie=UTF-8 + + techno_infos_dict_default = { 'Capex_init': 42.86, @@ -77,7 +79,7 @@ class ElectricBoilerMediumHeatDiscipline(MediumHeatTechnoDiscipline): # Renewable Association [online] flux_input_dict = {'land_rate': 26000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(MediumHeatTechnoDiscipline.DESC_IN) @@ -113,7 +115,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/medium/geothermal_medium_heat/documentation/geothermal_medium_heat_disc.markdown b/energy_models/models/heat/medium/geothermal_medium_heat/documentation/geothermal_medium_heat_disc.markdown index 933d6286..10fc4b83 100644 --- a/energy_models/models/heat/medium/geothermal_medium_heat/documentation/geothermal_medium_heat_disc.markdown +++ b/energy_models/models/heat/medium/geothermal_medium_heat/documentation/geothermal_medium_heat_disc.markdown @@ -3,13 +3,13 @@ **Definition[2] :** Geothermal heating is the direct use of geothermal energy for some heating applications. Humans have taken advantage of geothermal heat this way since the Paleolithic era. Approximately seventy countries made direct use of a total of 270 PJ of geothermal heating in 2004. As of 2007, 28 GW of geothermal heating capacity is installed around the world, satisfying 0.07% of global primary energy consumption.[1] Thermal efficiency is high since no energy conversion is needed, but capacity factors tend to be low (around 20%) since the heat is mostly needed in the winter -![](geothermal-heat-pump.jpg) +![](geothermal-heat-pump.jpg) (Image Credit Geothermal Heating System, [^1]) Geothermal energy originates from the heat retained within the Earth since the original formation of the planet, from radioactive decay of minerals, and from solar energy absorbed at the surface.Most high temperature geothermal heat is harvested in regions close to tectonic plate boundaries where volcanic activity rises close to the surface of the Earth. In these areas, ground and groundwater can be found with temperatures higher than the target temperature of the application. However, even cold ground contains heat. Below 6 metres (20 ft), the undisturbed ground temperature is consistently at the mean annual air temperature, and this heat can be extracted with a ground source heat pump. -## Data -Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. -![](Data.PNG) +## Data +Most of the data used for this model is extracted from and International Renewable Energy Agency (IRENA)[^2], National Renewable Energy Laboratory (NREL)[^5]. +![](Data.PNG) (Image Credit Geothermal Tomorrow 2008, [^2]) @@ -30,3 +30,4 @@ Most of the data used for this model is extracted from and International Renewa [^8]: Steel needs, https://www.energy.gov/eere/geothermal/articles/life-cycle-analysis-results-geothermal-systems-comparison-other-power Page:21 [^9]: Initial production, https://www.iea.org/data-and-statistics/charts/direct-use-of-geothermal-energy-world-2012-2024 + diff --git a/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat.py b/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat.py index 8448d9fa..bc05b8f6 100644 --- a/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat.py +++ b/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat.py @@ -33,7 +33,7 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_theoretical_electricity_needs() / self.cost_details['efficiency'] - + def compute_byproducts_production(self): # Production carbon_production_factor = self.get_theoretical_co2_prod() diff --git a/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat_disc.py b/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat_disc.py index 00368c8a..db3f1b92 100644 --- a/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat_disc.py +++ b/energy_models/models/heat/medium/geothermal_medium_heat/geothermal_medium_heat_disc.py @@ -47,6 +47,8 @@ class GeothermalMediumHeatDiscipline(MediumHeatTechnoDiscipline): techno_name = GlossaryEnergy.GeothermalMediumHeat energy_name = mediumtemperatureheat.name + + techno_infos_dict_default = { 'Capex_init': 3830, # https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2017/Aug/IRENA_Geothermal_Power_2017.pdf @@ -79,7 +81,7 @@ class GeothermalMediumHeatDiscipline(MediumHeatTechnoDiscipline): flux_input_dict = {'land_rate': 15000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(MediumHeatTechnoDiscipline.DESC_IN) @@ -115,7 +117,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/medium/heat_pump_medium_heat/documentation/heat_pump_medium_heat_disc.markdown b/energy_models/models/heat/medium/heat_pump_medium_heat/documentation/heat_pump_medium_heat_disc.markdown index dc7befa5..43eb372e 100644 --- a/energy_models/models/heat/medium/heat_pump_medium_heat/documentation/heat_pump_medium_heat_disc.markdown +++ b/energy_models/models/heat/medium/heat_pump_medium_heat/documentation/heat_pump_medium_heat_disc.markdown @@ -3,7 +3,7 @@ [^2] A heat pump can be an energy-efficient alternative to fossil-fuel furnaces and water heaters that use natural gas and heating oil, which emit CO2 directly. Heat pumps also are more efficient than electric resistance heat. Switching from fossil fuel heat-generating devices, such as furnaces and boilers, to an efficient electric heat pump can be step toward a zero-carbon system. -![](HeatPumps.avif) +![](HeatPumps.avif) (Image Credit: IEA – International Energy Agency, taken from [^1]) COP (Coefficient of Performance) [^3] @@ -12,10 +12,10 @@ The Co-efficient of performance (COP) is an expression of the efficiency of a he COP is defined as the relationship between the power (kWh) that is drawn out of the heat pump as cooling or heat, and the power (kWh) that is supplied to the compressor. -## Data -![](Heat_Model_Assumptions.png) +## Data +![](Heat_Model_Assumptions.png) (Heat Model Assumptions, taken from [^4]) -## References +## References [^1]: [How a heat pump works – The Future of Heat Pumps - IEA](https://www.iea.org/reports/the-future-of-heat-pumps/how-a-heat-pump-works) [^2]: [The Important Role of Heat Pumps in a Sustainable Future](https://www.reuters.com/article/sponsored/the-important-role-of-heat-pumps-in-a-sustainable-future) @@ -33,3 +33,4 @@ COP is defined as the relationship between the power (kWh) that is drawn out of [^8]: Opex, https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx [^9]: Initial production, https://www.iea.org/reports/heat-pumps + diff --git a/energy_models/models/heat/medium/heat_pump_medium_heat/heat_pump_medium_heat_disc.py b/energy_models/models/heat/medium/heat_pump_medium_heat/heat_pump_medium_heat_disc.py index cdb21d0f..79df2c6f 100644 --- a/energy_models/models/heat/medium/heat_pump_medium_heat/heat_pump_medium_heat_disc.py +++ b/energy_models/models/heat/medium/heat_pump_medium_heat/heat_pump_medium_heat_disc.py @@ -47,6 +47,7 @@ class HeatPumpMediumHeatDiscipline(MediumHeatTechnoDiscipline): techno_name = GlossaryEnergy.HeatPumpMediumHeat energy_name = mediumtemperatureheat.name + # Heat pumps offer an energy-efficient alternative to furnaces and air conditioners for all climates. # Heat pump can reduce your electricity use for heating by approximately 50% compared to # electric resistance heating such as furnaces and baseboard heaters. @@ -61,7 +62,7 @@ class HeatPumpMediumHeatDiscipline(MediumHeatTechnoDiscipline): # 660euro/kW/(lifetime * Number of hours in year) # Source:- https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx 'Capex_init_unit': '$/kWh', 'Opex_percentage': 0.04, - # https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx + ## https://europeanclimate.org/wp-content/uploads/2019/11/14-03-2019-ffe-2050-cost-assumptions.xlsx 'efficiency': 1, # consumptions and productions already have efficiency included 'CO2_from_production': 0.0, 'CO2_from_production_unit': 'kg/kg', @@ -93,7 +94,7 @@ class HeatPumpMediumHeatDiscipline(MediumHeatTechnoDiscipline): flux_input_dict = {'land_rate': 14000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(MediumHeatTechnoDiscipline.DESC_IN) @@ -129,7 +130,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/documentation/natural_gas_boiler_medium_heat_disc.markdown b/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/documentation/natural_gas_boiler_medium_heat_disc.markdown index 677f3f0c..41ec6e75 100644 --- a/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/documentation/natural_gas_boiler_medium_heat_disc.markdown +++ b/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/documentation/natural_gas_boiler_medium_heat_disc.markdown @@ -4,15 +4,15 @@ A gas-fired boiler provides hot water to taps throughout the home and also to ra Medium temperature heat in natural gas technology, the temperature range is 100 to 400 degree C. -In Methane demand calculation, Methane is used to produce 1kwh of heat (this information in kwh) : methane_needs is in kwh of methane / kwh of heat +In Methane demand calculation, Methane is used to produce 1kwh of heat (this information in kwh) : methane_needs is in kwh of methane / kwh of heat price of methane is in $/kWh of heat. - + CO2 production is in Kg/KWh. That values from methane: CO2 per use(kg/kg) / calorific value(KWh/kg) production in 2020 that is total heat produced by Natural gas is 6236761 TJ = 1683 TWh - -initial production for medium heat temp = 561 TWh + +initial production for medium heat temp = 561 TWh Gas boilers work by releasing the fuel through a gas valve into a sealed combustion chamber in the boiler through small jets. With an electrical ignition, the gas is combusted to create heat. This heat is absorbed by a connected pipe carrying cold water. @@ -22,7 +22,7 @@ Hot gases are produced by burning fuel in the furnace. These hot gases are made Natural gas consists mostly methane (typical >85%) with the balance being varying amounts of ethane, propane, butane and some inert components (nitrogen, carbon dioxide and helium). ![img_2.png](img_2.png) -## Data +## Data The data used for this model is extracted from the IEA Data, ScienceDirect & MET [^1]: [How Does a Gas Boiler Work – always70wade & IEA](https://always70wade.com/b/what-is-a-boiler-how-does-it-work#:~:text=Gas%20boilers%20work%20by%20releasing,connected%20pipe%20carrying%20cold%20water) @@ -34,11 +34,13 @@ The data used for this model is extracted from the IEA Data, ScienceDirect & MET [^4]: [Natural Gas Model - ScienceDirect](https://www.sciencedirect.com/topics/engineering/natural-gas) [^5]: Methane demand, https://www.google.com/search?q=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat&rlz=1C1UEAD_enIN1000IN1000&oq=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat+&aqs=chrome..69i57.90503j0j7&sourceid=chrome&ie=UTF-8 - + [^6]: https://www.google.com/search?q=co2+captured+production+to+produce+heat+in+natural+gas+boiler&rlz=1C1UEAD_enIN1000IN1000&oq=co2+captured+production+to+produce+heat+in+natural+gas+boiler&aqs=chrome..69i57.37619j0j7&sourceid=chrome&ie=UTF-8 - + [^7]: co2 captured production, https://www.google.com/search?q=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat&rlz=1C1UEAD_enIN1000IN1000&oq=how+much+KWh+of+methane+required+in+natural+gas+boiler+to+produce+1KWh+of+heat+&aqs=chrome..69i57.90503j0j7&sourceid=chrome&ie=UTF-8 [^8]: Initial production, https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=Electricity%20and%20heat&indicator=HeatGenByFuel [^9]: https://www.google.com/search?q=TJ+to+TWh&rlz=1C1UEAD_enIN1000IN1000&oq=TJ+to+TWh&aqs=chrome..69i57.35591j0j7&sourceid=chrome&ie=UTF-8 + + diff --git a/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat.py b/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat.py index 5afd614c..cb8d5547 100644 --- a/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat.py +++ b/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat.py @@ -34,6 +34,7 @@ def __init__(self, name): def compute_other_streams_needs(self): self.cost_details[f'{Methane.name}_needs'] = self.get_theoretical_methane_needs() / self.cost_details['efficiency'] + # methane_needs # output needed in this method is in $/kwh of heat diff --git a/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat_disc.py b/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat_disc.py index a32c03af..3aa48ea5 100644 --- a/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat_disc.py +++ b/energy_models/models/heat/medium/natural_gas_boiler_medium_heat/natural_gas_boiler_medium_heat_disc.py @@ -106,8 +106,8 @@ class NaturalGasBoilerMediumHeatDiscipline(MediumHeatTechnoDiscipline): # Renewable Methane Association [online] flux_input_dict = {'land_rate': 13000, 'land_rate_unit': '$/Gha', } DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - - + + 'flux_input_dict': {'type': 'dict', 'default': flux_input_dict, 'unit': 'defined in dict'}, } DESC_IN.update(MediumHeatTechnoDiscipline.DESC_IN) @@ -142,7 +142,7 @@ def run(self): self.techno_model.compute_heat_flux() outputs_dict = {'heat_flux': self.techno_model.heat_flux_distribution} - + self.store_sos_outputs_values(outputs_dict) @staticmethod diff --git a/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/documentation/hefa_decarboxylation_disc.markdown b/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/documentation/hefa_decarboxylation_disc.markdown index 5d45675f..dd5a141d 100644 --- a/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/documentation/hefa_decarboxylation_disc.markdown +++ b/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/documentation/hefa_decarboxylation_disc.markdown @@ -4,8 +4,8 @@ Hydroprocessing uses hydrogen to convert unsaturated compounds such as alkenes and aromatics into saturated alkanes (paraffins) and cycloalkanes, which are more stable and less reactive. The conversion is usually a two-staged process. -The first stage, the so-called hydrotreatment, takes place at reaction temperatures between 300 °C and 390 °C. -First hydrogen is added to saturate the double bonds of the unsaturated vegetable oil triglycerides. +The first stage, the so-called hydrotreatment, takes place at reaction temperatures between 300 °C and 390 °C. +First hydrogen is added to saturate the double bonds of the unsaturated vegetable oil triglycerides. Then more hydrogen is added to remove the propane backbone, hereby cleaving the saturated vegetable oil triglycerides to fatty acids. Finally, the fatty acids either undergo hydrooxygenation (by addition of more hydrogen the oxygen leaves as H2O) or decarboxylation (oxygen leaves as CO2 without further addition of hydrogen), or a combination of these two. The result is a mixture of straight chain, branched chain, and cyclic paraffinic hydrocarbons. (see [Hydrotreatment](https://www.etipbioenergy.eu/value-chains/conversion-technologies/conventional-technologies/hydrotreatment-to-hvo)) @@ -15,7 +15,7 @@ from: [ETIP Bioteenergy](https://www.etipbioenergy.eu/value-chains/conversion-te ## Decarboxylation -This technology is focused on the second reaction, producing C02 and fuel from oil and hydrogen. This reaction consumes less hydrogen but produces CO2. It is a cheaper reaction to perform, but it is likely to be less attractive than the deoxygenation reaction following environmental concerns. +This technology is focused on the second reaction, producing C02 and fuel from oil and hydrogen. This reaction consumes less hydrogen but produces CO2. It is a cheaper reaction to perform, but it is likely to be less attractive than the deoxygenation reaction following environmental concerns. ## Use in Jet Fuel @@ -33,7 +33,7 @@ Technico-economic information (Capex, Opex, Production levels, past investments, ## Heat Heat production/consumption is neglected in coal-gasification process. - + - [Tao, L., Milbrandt, A., Zhang, Y. et al. Techno-economic and resource analysis of hydroprocessed renewable jet fuel. Biotechnol Biofuels 10, 261 (2017).](https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3) - [Noah, Matthew. A Techno-Economic and Environmental Assessment of Hydroprocessed Renewable Distillate Fuels. (2011)](https://dspace.mit.edu/bitstream/handle/1721.1/65508/746766700-MIT.pdf?sequence=2&isAllowed=y) @@ -42,3 +42,6 @@ Heat production/consumption is neglected in coal-gasification process. /Sustainable Aviation Fuels (SAF) (2021)](https://www.ieabioenergy.com/wp-content/uploads/2021/06/IEA-Bioenergy-Task-39-Progress-in-the-commercialisation-of-biojet-fuels-May-2021-1.pdf), License: CC BY 4.0. - [ETIP Bioenergy. HVO/HEFA](https://www.etipbioenergy.eu/value-chains/products-end-use/products/hvo-hefa) - [De Jong, S., Antonissen, K., Hoefnagels, R. et al. Life-cycle analysis of greenhouse gas emissions from renewable jet fuel production. Biotechnol Biofuels 10, 64 (2017).](https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0739-7) + + + diff --git a/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation.py b/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation.py index ea189ec2..400eb19f 100644 --- a/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation.py +++ b/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation.py @@ -47,9 +47,10 @@ def compute_resources_needs(self): self.cost_details[f'{NaturalOil.name}_needs'] = self.get_theoretical_natural_oil_needs() / naturaloil_data['calorific_value'] def compute_other_streams_needs(self): - self.cost_details[f'{GaseousHydrogen.name}_needs'] = self.get_theoretical_hydrogen_needs() / self.cost_details['efficiency'] + self.cost_details[f'{GaseousHydrogen.name}_needs'] = self.get_theoretical_hydrogen_needs() / self.cost_details['efficiency'] self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.elec_consumption_factor + def compute_byproducts_production(self): carbon_production_factor = self.get_theoretical_co2_prod() self.production_detailed[f'{GlossaryEnergy.carbon_capture} ({GlossaryEnergy.mass_unit})'] = carbon_production_factor * \ diff --git a/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation_disc.py b/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation_disc.py index 771c7beb..51256229 100644 --- a/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation_disc.py +++ b/energy_models/models/hydrotreated_oil_fuel/hefa_decarboxylation/hefa_decarboxylation_disc.py @@ -48,6 +48,7 @@ class HefaDecarboxylationDiscipline(HydrotreatedOilFuelTechnoDiscipline): # Source: # https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3/tables/2 + # conversion factors dollar_per_gallon_to_dollar_per_m3 = 264.17 gallon_to_mc = 0.00378541 diff --git a/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/documentation/hefa_deoxygenation_disc.markdown b/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/documentation/hefa_deoxygenation_disc.markdown index 79b7625b..1a8d20a1 100644 --- a/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/documentation/hefa_deoxygenation_disc.markdown +++ b/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/documentation/hefa_deoxygenation_disc.markdown @@ -4,8 +4,8 @@ ## **Hydroprocessed Esters and Fatty Acids** Hydroprocessing uses hydrogen to convert unsaturated compounds such as alkenes and aromatics into saturated alkanes (paraffins) and cycloalkanes, which are more stable and less reactive. The conversion is usually a two-staged process. -The first stage, the so-called hydrotreatment, takes place at reaction temperatures between 300 °C and 390 °C. -First hydrogen is added to saturate the double bonds of the unsaturated vegetable oil triglycerides. +The first stage, the so-called hydrotreatment, takes place at reaction temperatures between 300 °C and 390 °C. +First hydrogen is added to saturate the double bonds of the unsaturated vegetable oil triglycerides. Then more hydrogen is added to remove the propane backbone, hereby cleaving the saturated vegetable oil triglycerides to fatty acids. Finally, the fatty acids either undergo hydrooxygenation (by addition of more hydrogen the oxygen leaves as H2O) or decarboxylation (oxygen leaves as CO2 without further addition of hydrogen), or a combination of these two. The result is a mixture of straight chain, branched chain, and cyclic paraffinic hydrocarbons. (see [Hydrotreatment](https://www.etipbioenergy.eu/value-chains/conversion-technologies/conventional-technologies/hydrotreatment-to-hvo)) @@ -33,7 +33,7 @@ Technico-economic information (Capex, Opex, Production levels, past investments, ## Heat Heat production/consumption is neglected in coal-gasification process. - + - [Tao, L., Milbrandt, A., Zhang, Y. et al. Techno-economic and resource analysis of hydroprocessed renewable jet fuel. Biotechnol Biofuels 10, 261 (2017).](https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3) - [Noah, Matthew. A Techno-Economic and Environmental Assessment of Hydroprocessed Renewable Distillate Fuels. (2011)](https://dspace.mit.edu/bitstream/handle/1721.1/65508/746766700-MIT.pdf?sequence=2&isAllowed=y) @@ -42,3 +42,6 @@ Heat production/consumption is neglected in coal-gasification process. /Sustainable Aviation Fuels (SAF) (2021)](https://www.ieabioenergy.com/wp-content/uploads/2021/06/IEA-Bioenergy-Task-39-Progress-in-the-commercialisation-of-biojet-fuels-May-2021-1.pdf), License: CC BY 4.0. - [ETIP Bioenergy. HVO/HEFA](https://www.etipbioenergy.eu/value-chains/products-end-use/products/hvo-hefa) - [De Jong, S., Antonissen, K., Hoefnagels, R. et al. Life-cycle analysis of greenhouse gas emissions from renewable jet fuel production. Biotechnol Biofuels 10, 64 (2017).](https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0739-7) + + + diff --git a/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation.py b/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation.py index 0146f7c2..8828d5fc 100644 --- a/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation.py +++ b/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation.py @@ -42,15 +42,17 @@ class HefaDeoxygenation(HydrotreatedOilFuelTechno): elec_consumption_factor = .185 + def compute_resources_needs(self): naturaloil_data = NaturalOil.data_energy_dict self.cost_details[f'{NaturalOil.name}_needs'] = self.get_theoretical_natural_oil_needs( ) / naturaloil_data['calorific_value'] def compute_other_streams_needs(self): - self.cost_details[f'{GaseousHydrogen.name}_needs'] = self.get_theoretical_hydrogen_needs() / self.cost_details['efficiency'] + self.cost_details[f'{GaseousHydrogen.name}_needs'] = self.get_theoretical_hydrogen_needs() / self.cost_details['efficiency'] self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.elec_consumption_factor + def compute_byproducts_production(self): # Theoretical C02 production in kg water_calorific_value = Water.data_energy_dict['calorific_value'] diff --git a/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation_disc.py b/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation_disc.py index 00f9344e..6611e957 100644 --- a/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation_disc.py +++ b/energy_models/models/hydrotreated_oil_fuel/hefa_deoxygenation/hefa_deoxygenation_disc.py @@ -50,6 +50,7 @@ class HefaDeoxygenationDiscipline(HydrotreatedOilFuelTechnoDiscipline): # Biotechnology for biofuels, 10(1), pp.1-16. # https://biotechnologyforbiofuels.biomedcentral.com/articles/10.1186/s13068-017-0945-3/tables/2 + # conversion factors dollar_per_gallon_to_dollar_per_m3 = 264.17 gallon_to_mc = 0.00378541 diff --git a/energy_models/models/liquid_fuel/fischer_tropsch/documentation/fischer_tropsch_disc.markdown b/energy_models/models/liquid_fuel/fischer_tropsch/documentation/fischer_tropsch_disc.markdown index 752d3aab..8f9baf09 100644 --- a/energy_models/models/liquid_fuel/fischer_tropsch/documentation/fischer_tropsch_disc.markdown +++ b/energy_models/models/liquid_fuel/fischer_tropsch/documentation/fischer_tropsch_disc.markdown @@ -9,16 +9,16 @@ $$(2n + 1) H_2 + n CO --> C_nH_{2n+2} + nH_2O$$(2) These reactions occur in the presence of metal catalysts, typically at temperatures of 150–300 °C (302–572 °F) and pressures of one to several tens of atmospheres. The process was first developed by Franz Fischer and Hans Tropsch in 1925.[^1] -For industrial use, the Fischer Tropsch reaction does not guarantee a single fuel as output of the synthesis. The output stream is called syncrude and is a mixture of different synthetic fuel such as kerosene diesel or naphtas. -For the purpose of the model, we suppose first that the Fischer Tropsch model products only kerosene and we take into account in its cost the hydrocraking process used to extract kerosene from the producted syncrude. +For industrial use, the Fischer Tropsch reaction does not guarantee a single fuel as output of the synthesis. The output stream is called syncrude and is a mixture of different synthetic fuel such as kerosene diesel or naphtas. +For the purpose of the model, we suppose first that the Fischer Tropsch model products only kerosene and we take into account in its cost the hydrocraking process used to extract kerosene from the producted syncrude. ## Usage for XtL industries -The fischer Tropsch synthesis is the main reaction to produce synthesis fuel from power or any sources of energy. The process of producing synfuels through indirect conversion is often referred to as CtL (Coal-to-Liquids Coal Gasification for syngas production) GtL (Gas-to-Liquids, SMR for syngas production) or PBtL (Power-Biomass-to-Liquids, Biomass gasification for syngas production) depending on the initial feedstock. +The fischer Tropsch synthesis is the main reaction to produce synthesis fuel from power or any sources of energy. The process of producing synfuels through indirect conversion is often referred to as CtL (Coal-to-Liquids Coal Gasification for syngas production) GtL (Gas-to-Liquids, SMR for syngas production) or PBtL (Power-Biomass-to-Liquids, Biomass gasification for syngas production) depending on the initial feedstock. The most known synthesis fuel production is the Power-to_Liquids conversion where syngas is produced through renewable electricity via Electrolysis or Co-electrolysis technologies. -### Difference of fuel synthesis pathways depending on the syngas supply [^2] +### Difference of fuel synthesis pathways depending on the syngas supply [^2] ![](Xtl.PNG) (Image Credit: [^2]) @@ -27,34 +27,34 @@ On the Figure above, all concepts are influencing the process of syngas upgradin ### Existing and projected Fischer-Tropsch production -The commercialisation of X-to-liquids processes emerges in some countries. The world's largest scale implementation of Fischer–Tropsch technology is a series of plants operated by Sasol in South Africa with a production of 201000 BPD (Barrel Per Day) based on Coal Gasification [^6] (CtL). China and its large coal production uses also coal gasification for its FT industries and produces around 112000 BPD [^7]. Qatar, is the second largest FT plant in the world based on SMR (Steam Methane Reforming) with a production of 174000 BPD (GtL). Malaysia has also a Fischer Tropsch Plant based on GtL with 12000 BPD of synthetic fuel. In the United States, India, Finland or Russia some states have invested in Fischer–Tropsch plants but not at commercial scale for now. +The commercialisation of X-to-liquids processes emerges in some countries. The world's largest scale implementation of Fischer–Tropsch technology is a series of plants operated by Sasol in South Africa with a production of 201000 BPD (Barrel Per Day) based on Coal Gasification [^6] (CtL). China and its large coal production uses also coal gasification for its FT industries and produces around 112000 BPD [^7]. Qatar, is the second largest FT plant in the world based on SMR (Steam Methane Reforming) with a production of 174000 BPD (GtL). Malaysia has also a Fischer Tropsch Plant based on GtL with 12000 BPD of synthetic fuel. In the United States, India, Finland or Russia some states have invested in Fischer–Tropsch plants but not at commercial scale for now. The first PtL (Power to Liquid with Electrolysis) demo plant at industrial scale is in construction in Norway [^3], capable of producing 10 million litres of fuel a year before scaling up the facility to commercially produce 100 million litres by 2026. The consortium has four main partners: German PtL technology provider Sunfire, Swiss-based CO2 air capture technology specialist Climeworks, Luxembourg-headquartered international engineering company Paul Worth SMS Group and Valinor, a Norwegian family-owned green investment company. ### Sunfire PtL demonstration plant in Dresden, Germany [^4] -![](Sunfire_PtL_Dresden.jpg) +![](Sunfire_PtL_Dresden.jpg) (Image Credit: Sunfire, taken from [^4]) ## Modifying the syngas ratio for the synthesis -The ratio $\frac{CO}{H_2}$ of the needed syngas (gas composed of carbon monoxyde $CO$ and hydrogen $H_2$) must be equal to : +The ratio $\frac{CO}{H_2}$ of the needed syngas (gas composed of carbon monoxyde $CO$ and hydrogen $H_2$) must be equal to : $$r_{syngas} = \frac{n}{2n+1}$$ Depending on the syngas production technology, the syngas ratio of $CO$ over $H_2$ can be different. If the ratio of input syngas is lower than $\frac{n}{2n+1}$ we need to enrich the syngas with carbon monoxyde. If the syngas ratio is higher, some CO in the syngas must be removed. -### The Reverse Water Gas Shift reaction +### The Reverse Water Gas Shift reaction The Reverse Water Gas Shift reaction is able to enrich a syngas mixture using carbon dioxyde ($CO_2$) : $$dCO_2 + e(H_2 +r_1CO) --> (H_2 +r_2 CO) + cH_20$$ -with $r_1 dCO_2 + e(H_2 +r_2CO)$$ with $r_1>r_2$ syngas ratios before and after the reaction : -and with $c$, $d$ and $e$ coefficients of the reaction that can be computed with $r_1$ and $r_2$ to satisfy chemical equilibrium : +and with $c$, $d$ and $e$ coefficients of the reaction that can be computed with $r_1$ and $r_2$ to satisfy chemical equilibrium : $$c = \frac{r1-r2}{1+r2}$$ @@ -83,12 +83,12 @@ $$e = \frac{1+r1}{1+r2}$$ In our context, we know the value of $r_2= \frac{n}{2n+1}$ with $n=12$ which is a valid assumption for kerosene jet fuel (between 10 and 16 carbon atoms by moles). -Then, we are able to determine first which technology do we need to obtain the correct syngas (WGS or RWGS) and secondly, the amount of $CO_2$ (amount of $H_2O$), the production of $H_2O$ (production of $CO_2$) and the total cost of the WGS conversion reaction (RWGS reaction respectively) which will be added to the cost of the Fischer Tropsch synthesis. Note that due to evolving investments, the syngas ratio in input of the model may be different over the years and the choice of the syngas ratio conversion could change between WGS and RWGS. +Then, we are able to determine first which technology do we need to obtain the correct syngas (WGS or RWGS) and secondly, the amount of $CO_2$ (amount of $H_2O$), the production of $H_2O$ (production of $CO_2$) and the total cost of the WGS conversion reaction (RWGS reaction respectively) which will be added to the cost of the Fischer Tropsch synthesis. Note that due to evolving investments, the syngas ratio in input of the model may be different over the years and the choice of the syngas ratio conversion could change between WGS and RWGS. -## Data +## Data -All technical and economical datas are extracted from [^5]. The paper details the techno-Economic assessment of a PtL factory using a hybrid PV-Wind power plant to provide electricity and combined to an electrolyser to produce $H_2$ and a CO2 capture plant. The $CO_2-H_2$ stream is injected into a Reverse Water Gas Shift reactor to enrich the syngas in $CO$ (considering the pure $H_2$ stream as a syngas with a zero $CO$ over $H_2$ ratio.). The economic model includes the FT and Hydrocracker part of the Figure below. +All technical and economical datas are extracted from [^5]. The paper details the techno-Economic assessment of a PtL factory using a hybrid PV-Wind power plant to provide electricity and combined to an electrolyser to produce $H_2$ and a CO2 capture plant. The $CO_2-H_2$ stream is injected into a Reverse Water Gas Shift reactor to enrich the syngas in $CO$ (considering the pure $H_2$ stream as a syngas with a zero $CO$ over $H_2$ ratio.). The economic model includes the FT and Hydrocracker part of the Figure below. ### Ptl flow diagram of Fasihi & al [^5] @@ -98,7 +98,7 @@ All technical and economical datas are extracted from [^5]. The paper details th ## Heat The Fischer-Tropsch reaction is carried out over supported cobalt-based catalysts at 20–30 bar and at temperatures below 240 °C. The FTS is a highly exothermic reaction due to a standard reaction enthalpy of −165kJ/molCO.[^8] -[^1]: [De Klerk , A. (2013) FischerTropsch Process. Kirk Othmer Encyclopedia of Chemical Technology. Weinheim: Wiley-VCH](https://onlinelibrary.wiley.com/doi/abs/10.1002/0471238961.fiscdekl.a01) +[^1]: [De Klerk , A. (2013) FischerTropsch Process. Kirk Othmer Encyclopedia of Chemical Technology. Weinheim: Wiley-VCH](https://onlinelibrary.wiley.com/doi/abs/10.1002/0471238961.fiscdekl.a01) [^2]: [Albrecht, F. (2017) A standardized methodology for the techno-economic evaluation of alternative fuels A case study, Fuel, vol 194, p511-526](https://www.sciencedirect.com/science/article/pii/S0016236116312248) diff --git a/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch.py b/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch.py index 2a2dedb2..94b57386 100644 --- a/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch.py +++ b/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch.py @@ -107,6 +107,7 @@ def select_resources_ratios(self): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() + def compute_specifif_costs_of_technos(self): nb_years = self.year_end - self.year_start + 1 sg_needs_efficiency = [self.get_theoretical_syngas_needs_for_FT( @@ -278,7 +279,7 @@ def compute_specifif_costs_of_technos(self): def grad_price_vs_stream_price(self): ''' - Compute the gradient of global price vs energy prices + Compute the gradient of global price vs energy prices Work also for total CO2_emissions vs energy CO2 emissions ''' elec_needs = self.costs_details_sg_techno[f'{GlossaryEnergy.electricity}_needs'] * \ @@ -472,7 +473,7 @@ def compute_wgs_contribution(self, sg_ratio): GlossaryEnergy.StreamsUsedForProductionValue: GlossaryEnergy.TechnoStreamsUsedDict[GlossaryEnergy.WaterGasShift], GlossaryEnergy.ConstructionDelay: GlossaryEnergy.TechnoConstructionDelayDict[GlossaryEnergy.WaterGasShift], GlossaryEnergy.LifetimeName: GlossaryEnergy.TechnoLifetimeDict[GlossaryEnergy.WaterGasShift], - GlossaryEnergy.InitialPlantsAgeDistribFactor: DatabaseWitnessEnergy.get_techno_age_distrib_factor(techno_name=WaterGasShiftDiscipline.techno_name, year=self.year_start)[0], + GlossaryEnergy.InitialPlantsAgeDistribFactor:DatabaseWitnessEnergy.get_techno_age_distrib_factor(techno_name=WaterGasShiftDiscipline.techno_name, year=self.year_start)[0], } if self.apply_stream_ratio: inputs_dict[GlossaryEnergy.AllStreamsDemandRatioValue] = self.all_streams_demand_ratio @@ -504,12 +505,14 @@ def compute_byproducts_production(self): elif self.sg_transformation_name == GlossaryEnergy.RWGS: self.production_detailed[f'{CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = 0.0 + if self.sg_transformation_name in [GlossaryEnergy.RWGS, 'WGS or RWGS']: water_prod = self.water_prod_RWGS * \ self.cost_details['syngas_needs_for_FT'] / \ self.cost_details['efficiency'] + water_prod += self.get_theoretical_water_prod_from_FT() / \ self.cost_details['efficiency'] @@ -590,7 +593,7 @@ def compute_delec_consumption_dsyngas_ratio(self, dprod_energy_dsyngas_ratio): return delec_consumption def compute_scope_2_emissions(self): - ''' + ''' Need to take into account negative CO2 from biomass_dry and CO2 from electricity (can be 0.0 or positive) ''' @@ -752,7 +755,7 @@ def compute_dco2_emissions_dsyngas_ratio(self): # GlossaryEnergy.syngas: dsyngas_emission_dsyngas_ratio} def get_theoretical_syngas_needs_for_FT(self): - ''' + ''' Get syngas needs in kWh syngas /kWh liquid_fuel H2 + n/(2n+1)CO --> 1/(2n+1) CnH_2n+1 + n/(2n+1)H20 Warning : molar mass is in g/mol but we divide and multiply by one @@ -772,7 +775,7 @@ def get_theoretical_syngas_needs_for_FT(self): return syngas_needs_for_FT def get_theoretical_water_prod_from_FT(self): - ''' + ''' Get water prod in kg H20 /kWh liquid_fuel H2 + n/(2n+1)CO --> 1/(2n+1) CnH_2n+1 + n/(2n+1)H20 Warning : molar mass is in g/mol but we divide and multiply by one diff --git a/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch_disc.py b/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch_disc.py index 371a3b5c..9a7e6564 100644 --- a/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch_disc.py +++ b/energy_models/models/liquid_fuel/fischer_tropsch/fischer_tropsch_disc.py @@ -99,6 +99,7 @@ class FischerTropschDiscipline(LiquidFuelTechnoDiscipline): # liquid_fuel 'carbon_number': 12} # To review + # FischerTropsch Wikipedia : # 140000+34000 BPD in Qatar GtL # 12000 BPD in Malaysia GtL @@ -113,7 +114,7 @@ class FischerTropschDiscipline(LiquidFuelTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'syngas_ratio': {'type': 'array', 'unit': '%', 'visibility': LiquidFuelTechnoDiscipline.SHARED_VISIBILITY, 'namespace': 'ns_syngas'}, diff --git a/energy_models/models/liquid_fuel/refinery/documentation/refinery_disc.markdown b/energy_models/models/liquid_fuel/refinery/documentation/refinery_disc.markdown index 903060b6..a58b7ed5 100644 --- a/energy_models/models/liquid_fuel/refinery/documentation/refinery_disc.markdown +++ b/energy_models/models/liquid_fuel/refinery/documentation/refinery_disc.markdown @@ -23,7 +23,7 @@ Density = 750 kg/m^3 The average price of gasoline around the world is 4.21 USD per US gallon in 2021[^3]. However, there is substantial difference in these prices among countries. As a general rule, richer countries have higher prices while poorer countries and the countries that produce and export oil have significantly lower prices. -**Kerosene (~13%)** +**Kerosene (~13%)** Also known as paraffin, it is a combustible hydrocarbon liquid which is widely used as a fuel in aviation as well as households. @@ -82,17 +82,17 @@ We took 22000 USD per barrel and an OPEX percentage of 0.04, extracted from the **GHG emissions** -The GAINS model predicts methane fugitive emissions from crude oil extraction. Methane is emitted during each phase of oil extraction. During flaring, the waste gas out of oil facilities is burned and emits some CH4 emissions. Some other methane emissions occur during venting which consists at releasing methane directly to the atmosphere during the oil development process (wall completion, tank maintenance ...). Finally unintended leakage is also taken into account in this model for methane emissions. Emission factors from oil extraction are adapted from IEA data in the IEA Methane Tracker 2021 [^12] and compared with the Supplement data of the Hoglund Isaksson paper [^10]. +The GAINS model predicts methane fugitive emissions from crude oil extraction. Methane is emitted during each phase of oil extraction. During flaring, the waste gas out of oil facilities is burned and emits some CH4 emissions. Some other methane emissions occur during venting which consists at releasing methane directly to the atmosphere during the oil development process (wall completion, tank maintenance ...). Finally unintended leakage is also taken into account in this model for methane emissions. Emission factors from oil extraction are adapted from IEA data in the IEA Methane Tracker 2021 [^12] and compared with the Supplement data of the Hoglund Isaksson paper [^10]. ![](emissions.jpg) (source[^11]) **Hydrogen Consumption** -Refineries use hydrogen to lower the sulfur content of diesel fuel. Refinery demand for hydrogen has increased as -demand for diesel fuel has risen both domestically and internationally, and as sulfur-content regulations have become -more stringent. Oil refining is the largest consumer of hydrogen today (close to 40 Mt in 2020), and will remain so -in the short to medium term. Hydrogen used in this sector is normally produced onsite by steam methane reforming, -separated from by-product gases from petrochemical processes or sourced externally as merchant hydrogen +Refineries use hydrogen to lower the sulfur content of diesel fuel. Refinery demand for hydrogen has increased as +demand for diesel fuel has risen both domestically and internationally, and as sulfur-content regulations have become +more stringent. Oil refining is the largest consumer of hydrogen today (close to 40 Mt in 2020), and will remain so +in the short to medium term. Hydrogen used in this sector is normally produced onsite by steam methane reforming, +separated from by-product gases from petrochemical processes or sourced externally as merchant hydrogen (typically produced in dedicated plants for hydrogen production using steam methane reforming).[^9] [^1]: Oil Refinery Wikipedia page, https://en.wikipedia.org/wiki/Oil_refinery @@ -110,6 +110,6 @@ Argonne National Laboratory, https://publications.anl.gov/anlpubs/2011/01/69026. [^11]: https://capterio.com/insights/why-flare-capture-projects-make-sound-esg-investments -[^12]:IEA Methane Tracker 2021, (https://www.iea.org/articles/methane-tracker-data-explorer), License: CC BY 4.0. +[^12]:IEA Methane Tracker 2021, (https://www.iea.org/articles/methane-tracker-data-explorer), License: CC BY 4.0. -[^13]: https://www.osti.gov/servlets/purl/7261027, Page No 41 +[^13]: https://www.osti.gov/servlets/purl/7261027, Page No 41 \ No newline at end of file diff --git a/energy_models/models/liquid_fuel/refinery/refinery.py b/energy_models/models/liquid_fuel/refinery/refinery.py index 58c80b43..e573b7c3 100644 --- a/energy_models/models/liquid_fuel/refinery/refinery.py +++ b/energy_models/models/liquid_fuel/refinery/refinery.py @@ -71,6 +71,7 @@ def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() / self.cost_details['efficiency'] self.cost_details[f'{GaseousHydrogen.name}_needs'] = self.techno_infos_dict['hydrogen_demand'] / self.cost_details['efficiency'] + def compute_byproducts_production(self): for energy in self.other_energy_dict: # if it s a dict, so it is a data_energy_dict diff --git a/energy_models/models/liquid_fuel/refinery/refinery_disc.py b/energy_models/models/liquid_fuel/refinery/refinery_disc.py index 71a84d81..c22eb291 100644 --- a/energy_models/models/liquid_fuel/refinery/refinery_disc.py +++ b/energy_models/models/liquid_fuel/refinery/refinery_disc.py @@ -187,7 +187,7 @@ def set_partial_derivatives_techno(self, grad_dict, carbon_emissions, grad_dict_ self.set_partial_derivative_for_other_types( (GlossaryEnergy.TechnoPricesValue, self.techno_name), (GlossaryEnergy.StreamsCO2EmissionsValue, energy), grad_on_co2_tax) - + dCO2_taxes_factory = (self.techno_model.CO2_taxes[GlossaryEnergy.Years] <= self.techno_model.carbon_intensity[GlossaryEnergy.Years].max( )) * self.techno_model.carbon_intensity[self.techno_name].clip(0).values diff --git a/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/documentation/hydrogen_liquefaction_disc.markdown b/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/documentation/hydrogen_liquefaction_disc.markdown index 65ea9ec2..e8b7978b 100644 --- a/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/documentation/hydrogen_liquefaction_disc.markdown +++ b/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/documentation/hydrogen_liquefaction_disc.markdown @@ -1,7 +1,7 @@ # Hydrogen liquefaction -Hydrogen liquefaction is the process of changing the state of hydrogen from gas to liquid (temperatures of around 20 K). -This is performed through thermodynamics cycles. +Hydrogen liquefaction is the process of changing the state of hydrogen from gas to liquid (temperatures of around 20 K). +This is performed through thermodynamics cycles. The data used here are taken from a model based on a four steps process: - the hydrogen feed is first pressurized and pre-cooled, - then it is cooled down with liquid nitrogen in a Claude cycle to be below the inversion point @@ -38,3 +38,4 @@ the model is based on a liquifier with a capacity of 200000 kg/day. [^5]: [Hydrogen liquefaction and liquid hydrogen storage, G Valenti (2016)](https://www.sciencedirect.com/science/article/pii/B978178242362100002X) [^6]: https://www.idealhy.eu/uploads/documents/IDEALHY_Cryogenics_2012_Precooling.pdf + \ No newline at end of file diff --git a/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction.py b/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction.py index 0d61970b..cc1d264f 100644 --- a/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction.py +++ b/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction.py @@ -39,6 +39,7 @@ def compute_other_streams_needs(self): self.cost_details[f'{GaseousHydrogen.name}_needs'] = 1 / \ self.cost_details['efficiency'] + def compute_byproducts_production(self): pass # self.production[f'{lowtemperatureheat.name} ({self.product_unit})'] = (1 - self.techno_infos_dict['efficiency']) * \ diff --git a/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction_disc.py b/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction_disc.py index 08e03728..b0b00159 100644 --- a/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction_disc.py +++ b/energy_models/models/liquid_hydrogen/hydrogen_liquefaction/hydrogen_liquefaction_disc.py @@ -71,7 +71,7 @@ class HydrogenLiquefactionDiscipline(LiquidHydrogenTechnoDiscipline): 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, 'initial_production': {'type': 'float', 'unit': 'TWh', 'default': initial_production}, - + } DESC_IN.update(LiquidHydrogenTechnoDiscipline.DESC_IN) diff --git a/energy_models/models/methane/fossil_gas/documentation/fossil_gas_disc.markdown b/energy_models/models/methane/fossil_gas/documentation/fossil_gas_disc.markdown index 1670071f..1bcedb64 100644 --- a/energy_models/models/methane/fossil_gas/documentation/fossil_gas_disc.markdown +++ b/energy_models/models/methane/fossil_gas/documentation/fossil_gas_disc.markdown @@ -6,14 +6,14 @@ It is most commonly used to produce heat or electricity for buildings or industr Where, production of high temp heat is calculated in TWh. Its production is depends on production of methane in TWh and efficiency. ![](Industry_gaz.png) -(Image Credit EIA: taken from [^2]) +(Image Credit EIA: taken from [^2]) -## GHG emissions +## GHG emissions -The GAINS model predicts methane fugitive emissions from gas energy. Emission factors from gas production are adopted from IPCC guidelines and a mean value has been taken for the world database of 0.142 kt/PJ [^4]. Losses of natural gas during its transport and final use are taken into account in the consumption model. +The GAINS model predicts methane fugitive emissions from gas energy. Emission factors from gas production are adopted from IPCC guidelines and a mean value has been taken for the world database of 0.142 kt/PJ [^4]. Losses of natural gas during its transport and final use are taken into account in the consumption model. -## Data +## Data Economic and technical datas is taken from Environmental performance indicators – 2019 data - IOGP (International Association of Oil and Gas Producers) [^1] and Sarhosis V, Jaya AA, Hosking LJ, Koj A, Thomas HR.Techno-economics for coalbed methane production in the South Wales Coalfield [^3]. @@ -29,3 +29,4 @@ Center](https://eprint.ncl.ac.uk/file_store/production/219105/A20E5895-2DAF-4D6F [^4]: https://previous.iiasa.ac.at/web/home/research/researchPrograms/air/IR54-GAINS-CH4.pdf [^5]: https://www.sciencedirect.com/topics/earth-and-planetary-sciences/combustion-temperature [^6]: Efficiency, https://geospatial.blogs.com/geospatial/2010/01/energy-efficiency-of-fossil-fuel-power-generation.html#:~:text=The%20average%20efficiencies%20of%20power,up%20the%20stack%22%20as%20heat. + \ No newline at end of file diff --git a/energy_models/models/methane/fossil_gas/fossil_gas.py b/energy_models/models/methane/fossil_gas/fossil_gas.py index 3056e868..03fb8ecd 100644 --- a/energy_models/models/methane/fossil_gas/fossil_gas.py +++ b/energy_models/models/methane/fossil_gas/fossil_gas.py @@ -47,6 +47,7 @@ def compute_other_streams_needs(self): # needs in [kWh/kWh] divided by calorific value in [kWh/kg] to have # needs in [kg/kWh] + def compute_byproducts_production(self): # kg/kWh corresponds to Mt/TWh self.production_detailed[f'{CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = self.techno_infos_dict[ diff --git a/energy_models/models/methane/methanation/documentation/methanation_disc.markdown b/energy_models/models/methane/methanation/documentation/methanation_disc.markdown index e5e8ea6a..448f2a38 100644 --- a/energy_models/models/methane/methanation/documentation/methanation_disc.markdown +++ b/energy_models/models/methane/methanation/documentation/methanation_disc.markdown @@ -1,16 +1,16 @@ # E-Methane or Sabatier process or Chemical methanation -The chemical methanation process is mostly used in Power-to-Gas installations where it is used to transform the produced hydrogen (by co-electrolysis) into methane. -The chemical methanation process is a catalytic exothermic gas reaction and therefore, the equilibrium can be influenced by increasing the pressure and shifting it to the product side. The main reaction of the methanation of carbon dioxyde is : +The chemical methanation process is mostly used in Power-to-Gas installations where it is used to transform the produced hydrogen (by co-electrolysis) into methane. +The chemical methanation process is a catalytic exothermic gas reaction and therefore, the equilibrium can be influenced by increasing the pressure and shifting it to the product side. The main reaction of the methanation of carbon dioxyde is : $$CO_2 + 4H_2 --> CH_4 + 2H_20$$ -## Data +## Data Economic and technical datas is taken from Rosenfeld & al [^1]. CAPEX values are similar in this paper and in the paper of Thema & al [^2]. -A full list of world Power-to-Gas plants is available in Thema & al [^2] and has been used to define the initial world production and the age distribution of existing methanation plants. +A full list of world Power-to-Gas plants is available in Thema & al [^2] and has been used to define the initial world production and the age distribution of existing methanation plants. [^1]: Rosenfeld, D. C., Bohm, H., Lindorfer, J. , Lehner, M. (2020). Scenario analysis of implementing a power-to-gas and biomass gasification system in an integrated steel plant: A techno-economic and environmental study. Renewable Energy, 147(2020), 1511-1524. -[^2]: Thema, M., Bauer, F. and Sterner, M. Power-to-Gas: Electrolysis and methanation status review, Renewable and Sustainable Energy Reviews vol 112, 2019, p.775-787 +[^2]: Thema, M., Bauer, F. and Sterner, M. Power-to-Gas: Electrolysis and methanation status review, Renewable and Sustainable Energy Reviews vol 112, 2019, p.775-787 \ No newline at end of file diff --git a/energy_models/models/methane/methanation/methanation.py b/energy_models/models/methane/methanation/methanation.py index f1174984..0055f4d8 100644 --- a/energy_models/models/methane/methanation/methanation.py +++ b/energy_models/models/methane/methanation/methanation.py @@ -28,6 +28,7 @@ class Methanation(MethaneTechno): + def compute_resources_needs(self): # in kg of CO2 for kWh of CH4 self.cost_details[f'{GlossaryEnergy.carbon_capture}_needs'] = self.get_theoretical_co2_needs() / self.cost_details['efficiency'] @@ -62,7 +63,7 @@ def get_h2o_production(self): return production_for_1kg def get_theoretical_hydrogen_needs(self): - ''' + ''' Get hydrogen needs in kWhH2 /kWh CH4 4 mol of H2 for 1 mol of CH4 Warning : molar mass is in g/mol but we divide and multiply by one @@ -78,7 +79,7 @@ def get_theoretical_hydrogen_needs(self): return h2_needs def get_theoretical_co2_needs(self): - ''' + ''' Get hydrogen needs in kWhH2 /kWh CH4 4 mol of H2 for 1 mol of CH4 Warning : molar mass is in g/mol but we divide and multiply by one diff --git a/energy_models/models/methane/methanation/methanation_disc.py b/energy_models/models/methane/methanation/methanation_disc.py index 94c9e481..982f4dcc 100644 --- a/energy_models/models/methane/methanation/methanation_disc.py +++ b/energy_models/models/methane/methanation/methanation_disc.py @@ -41,6 +41,7 @@ class MethanationDiscipline(MethaneTechnoDiscipline): techno_name = GlossaryEnergy.Methanation + techno_infos_dict_default = {'reaction': 'CO2 + 4H2 = CH4 + 2 H20', 'Opex_percentage': 0.02, # Rosenfeld, D.C., Böhm, H., Lindorfer, J. and Lehner, M., 2020. diff --git a/energy_models/models/methane/upgrading_biogas/documentation/upgrading_biogas_disc.markdown b/energy_models/models/methane/upgrading_biogas/documentation/upgrading_biogas_disc.markdown index d6438226..296c4db8 100644 --- a/energy_models/models/methane/upgrading_biogas/documentation/upgrading_biogas_disc.markdown +++ b/energy_models/models/methane/upgrading_biogas/documentation/upgrading_biogas_disc.markdown @@ -3,7 +3,7 @@ The upgrading process for biogas involves the removal of CO2. The most widely commercialized and used upgrading technologies are those that have for a long time been employed by the natural gas industry, pressure swing adsorption (PSA), chemical solvent scrubbing (using amines), and pressurized water scrubbing.[^2] Biogas Water Scrubber System Design, Greenlane Biogas [^3] -![](water_scrubbing.PNG) +![](water_scrubbing.PNG) (Image Credit: Hudde (2010), taken from [^3]) Amine water scrubbing is one traditional chemical solvent scrubbing method of upgrading that is applied due to its low methane slippage and its capability to provide a high purity renewable methane product. @@ -16,19 +16,19 @@ $$2(OH - 2CH_2 - NH_2) + CO_2 <--> (OH) - (CH_2)_2 - NHCOO- + OH - (CH_2)_2 - NH $$2(OH - (CH_2)_2 - NH_2) + H_2S <--> (HOCH_2CH_2NH_3)_2S$$ $$(HOCH_2CH_2NH_3)_2S + H_2S <-->2HOCH_2CH_2NH_3HS$$ -## Data +## Data -Economic and technical datas is taken from Vo & al [^1]. +Economic and technical datas is taken from Vo & al [^1]. Currently around 3.5 Mtoe of biomethane are produced worldwide and 92.3% are from upgrading biogas[^4]. The initial age distribution has been computed with biomethane plant lists from [^5] since most of biogas is converted into biomethane. ## Cooling [^6] Biogas upgrading process by receiving 2.393 kg/s biogas, 663.2 kW cooling energy, and 1650 kW power produces 0.5533 kg/s biomethane. - + [^1]: Vo, T. T.; Wall, D. M.; Ring, D.; Rajendran, K.; Murphy, J. D. (2018). Techno-economic analysis of biogas upgrading via amine scrubber, carbon capture and ex-situ methanation. Applied Energy, 212, pp. 1191-1202. [^2]:https://anaerobic-digestion.com/biogas-upgrading-technologies/ [^3]:https://biomass.ucdavis.edu/files/2015/10/Biogas-Cleanup-Report_FinalDraftv3_12Nov2014-2.pdf [^4]:Abanades, S., Abbaspour, H., Ahmadi, A., Das, B., Ehyaei, M.A., Esmaeilion, F., Assad, M.E.H., Hajilounezhad, T., Jamali, D.H., Hmida, A. and Ozgoli, H.A., 2021. A critical review of biogas production and usage with legislations framework across the globe. International Journal of Environmental Science and Technology, pp.1-24. [^5]:http://task37.ieabioenergy.com/plant-list.html -[^6]:https://www.sciencedirect.com/science/article/abs/pii/S0957582021002469 +[^6]:https://www.sciencedirect.com/science/article/abs/pii/S0957582021002469 \ No newline at end of file diff --git a/energy_models/models/methane/upgrading_biogas/upgrading_biogas.py b/energy_models/models/methane/upgrading_biogas/upgrading_biogas.py index 95089686..3ff05757 100644 --- a/energy_models/models/methane/upgrading_biogas/upgrading_biogas.py +++ b/energy_models/models/methane/upgrading_biogas/upgrading_biogas.py @@ -35,6 +35,7 @@ def compute_other_streams_needs(self): def compute_resources_needs(self): self.cost_details[f"{GlossaryEnergy.MonoEthanolAmineResource}_needs"] = self.get_MEA_loss() + def compute_byproducts_production(self): # kg/kWh corresponds to Mt/TWh co2_prod = self.get_theoretical_co2_prod() @@ -49,7 +50,7 @@ def compute_byproducts_production(self): def get_biogas_needs(self): ''' - COmpute theoretical biogas needs with proportion of CO2 and CH4 given in biogas energy + COmpute theoretical biogas needs with proportion of CO2 and CH4 given in biogas energy Divide by efficiency for realistic demand ''' biogas_data = BioGas.data_energy_dict @@ -71,7 +72,7 @@ def get_MEA_loss(self): return mea_loss def get_theoretical_co2_prod(self, unit='kg/kWh'): - ''' + ''' Get CO2 prod from upgrading biogas With the fraction of CO2 in biogas considered ''' diff --git a/energy_models/models/methanol/co2_hydrogenation/co2_hydrogenation_disc.py b/energy_models/models/methanol/co2_hydrogenation/co2_hydrogenation_disc.py index 472b0d74..9df27ac2 100644 --- a/energy_models/models/methanol/co2_hydrogenation/co2_hydrogenation_disc.py +++ b/energy_models/models/methanol/co2_hydrogenation/co2_hydrogenation_disc.py @@ -48,6 +48,8 @@ class CO2HydrogenationDiscipline(MethanolTechnoDiscipline): methanol_density = Methanol.data_energy_dict['density'] methanol_calorific_value = Methanol.data_energy_dict['calorific_value'] + + techno_infos_dict_default = { 'Capex_init': 35.58 / (20 * 50) / 5.54, # Total capital [M$] / (annual production * lifetime) [kt] / conversion factor [kWh/kg] = [$/kWh] diff --git a/energy_models/models/methanol/co2_hydrogenation/documentation/co2_hydrogenation_disc.markdown b/energy_models/models/methanol/co2_hydrogenation/documentation/co2_hydrogenation_disc.markdown index eb3b2a33..fe76d404 100644 --- a/energy_models/models/methanol/co2_hydrogenation/documentation/co2_hydrogenation_disc.markdown +++ b/energy_models/models/methanol/co2_hydrogenation/documentation/co2_hydrogenation_disc.markdown @@ -9,14 +9,14 @@ It can also be used as a fuel, through combustion reaction as an alternative to A CO2 stream and a dihydrogen stream are combined, and are brought together with a catalyst, to transform the mix into $$CH_{3}OH$$. -A raw methanol stream, containing impurities (less than for syngas reforming) comes out of the catalyzer and into a distillery, +A raw methanol stream, containing impurities (less than for syngas reforming) comes out of the catalyzer and into a distillery, to be cooled down and remove the impurities. ## Sources All the data used to fill the properties of this technology come from the sources below. -The details of the calculation/transformation of the data can be found on an excel sheet in the documentation +The details of the calculation/transformation of the data can be found on an excel sheet in the documentation folder of the technology in the corresponding git repository. [^1][Collodi, G., Azzaro, G., Ferrari, N. and Santos, S., 2017. Demonstrating large scale industrial CCS through CCU–a case study for methanol production. Energy Procedia, 114, pp.122-138.](https://www.sciencedirect.com/science/article/pii/S1876610217313280) @@ -27,9 +27,9 @@ folder of the technology in the corresponding git repository. [^4] [Engineering Toolbox](https://www.engineeringtoolbox.com) -[^5] [Eco-Techno-Economic Analysis of Methanol Production from Biogas and Power-to-X, +[^5] [Eco-Techno-Economic Analysis of Methanol Production from Biogas and Power-to-X, Emanuele Moioli and Tilman Schildhauer, Industrial & Engineering Chemistry Research 2022 61 (21), 7335-7348](https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.1c04682) [^6] [Schröder, J., Müller-Langer, F., Aakko-Saksa, P., Winther, K., Baumgarten, W. and Lindgren, M., 2020. Methanol as motor fuel: Summary Report.](https://www.iea-amf.org/content/fuel_information/methanol#general) -[^7] [Nyári, J., 2018. Techno-economic feasibility study of a methanol plant using carbon dioxide and hydrogen.](http://kth.diva-portal.org/smash/get/diva2:1290829/FULLTEXT01.pdf) +[^7] [Nyári, J., 2018. Techno-economic feasibility study of a methanol plant using carbon dioxide and hydrogen.](http://kth.diva-portal.org/smash/get/diva2:1290829/FULLTEXT01.pdf) \ No newline at end of file diff --git a/energy_models/models/solid_fuel/coal_extraction/coal_extraction_disc.py b/energy_models/models/solid_fuel/coal_extraction/coal_extraction_disc.py index 5cbb2564..5dc83068 100644 --- a/energy_models/models/solid_fuel/coal_extraction/coal_extraction_disc.py +++ b/energy_models/models/solid_fuel/coal_extraction/coal_extraction_disc.py @@ -45,6 +45,7 @@ class CoalExtractionDiscipline(SolidFuelTechnoDiscipline): } techno_name = GlossaryEnergy.CoalExtraction + # Most coal seams are too deep underground for opencast mining and require # underground mining, a method that currently accounts for about 60 # percent of world coal production. Wikipedia source : @@ -107,7 +108,7 @@ class CoalExtractionDiscipline(SolidFuelTechnoDiscipline): # From ourworldindata initial_production = 43752. - energy_own_use # First invest is zero to get exactly the initial production in 2020 - + DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, } diff --git a/energy_models/models/solid_fuel/coal_extraction/documentation/coal_extraction_disc.markdown b/energy_models/models/solid_fuel/coal_extraction/documentation/coal_extraction_disc.markdown index 8d108a71..a9b12166 100644 --- a/energy_models/models/solid_fuel/coal_extraction/documentation/coal_extraction_disc.markdown +++ b/energy_models/models/solid_fuel/coal_extraction/documentation/coal_extraction_disc.markdown @@ -17,7 +17,7 @@ Lifetime : 35 years, Capex : 0.00081 USD/kWh, Opex : 0.2*Capex The exchange rate between AU.D and US.D is assumed as follow : 1AU.D = 0.77 US.D Additional datas can be found in [^3]. -![](mines_data.PNG) +![](mines_data.PNG) @@ -33,7 +33,7 @@ According to [^5], the full combustion of 1 short ton of coal emits 2.86 short t The emission of methane is a big issue for coal extraction mines. A lot of methane gas is immersed in coal seams and is leaking into the atmosphere via mineshafts the whole time the coal is mined. But that is not all. Methane leaks for decades into the atmosphere after mining from abandoned mines. Consequently, the global methane emissions from coal mining could continue groing even with declining coal production. [^4] -The Model for Calculating Coal Mine Methane (MC2M) developed by [^4] and used by IPCC models computed the annual CH4 emissions from coal mines with the equation : +The Model for Calculating Coal Mine Methane (MC2M) developed by [^4] and used by IPCC models computed the annual CH4 emissions from coal mines with the equation : $$CH4\_emissions (m^3) = coal\_production (t) *gas\_content(mine\_depth,coal\_type)*ef\_coefficient$$ @@ -63,4 +63,4 @@ In 2020, the MC2M model estimates that around 70 Mt of CH4 emissions are coming [^8]: Wikipedia page : https://en.wikipedia.org/wiki/Coal_mining [^9]: https://globalenergymonitor.org/wp-content/uploads/2021/03/Coal-Mine-Methane-On-the-Brink.pdf [^10]: https://www.eia.gov/energyexplained/coal/mining-and-transportation.php -[^11]: https://www.sciencedirect.com/science/article/abs/pii/S0360544221003376 +[^11]: https://www.sciencedirect.com/science/article/abs/pii/S0360544221003376 \ No newline at end of file diff --git a/energy_models/models/solid_fuel/pelletizing/documentation/pelletizing_disc.markdown b/energy_models/models/solid_fuel/pelletizing/documentation/pelletizing_disc.markdown index 2aabe23f..8c008e02 100644 --- a/energy_models/models/solid_fuel/pelletizing/documentation/pelletizing_disc.markdown +++ b/energy_models/models/solid_fuel/pelletizing/documentation/pelletizing_disc.markdown @@ -7,7 +7,7 @@ Biomass comes from organic feedstocks as wood, plants or wastes. - herbaceous biomass comes from crops residues or energy crops (providing biomass for energy purpose and not feeding). Algae can also be used as biomass source. - Wastes biomass comes from municipal or industrial wastes. -The heat generated during pelletizing can come from several sources, depending on the type of materials being pelletized and the equipment used. +The heat generated during pelletizing can come from several sources, depending on the type of materials being pelletized and the equipment used. Consumption of heat is calculated in TWh for high temperature. It depends on production of solid fuel(TWh) and efficiency value. @@ -20,7 +20,7 @@ Biomass in SosTrades has been divided following its use. ![](biomass.png) **Biomass problematic[^2] :** -The low energy density of biomass feedstocks tends to limit the transport distance from a biomass power plant. Moreover forests and landfields have to be well managed to stay as sustainable energy source and carbon neutral and therefore large local quantities are not available. This can place a limit on the scale of the biomass power plant, meaning that biomass struggles to take advantage of economies of scale in the generating plant because large quantities of low-cost feedstock are not available. +The low energy density of biomass feedstocks tends to limit the transport distance from a biomass power plant. Moreover forests and landfields have to be well managed to stay as sustainable energy source and carbon neutral and therefore large local quantities are not available. This can place a limit on the scale of the biomass power plant, meaning that biomass struggles to take advantage of economies of scale in the generating plant because large quantities of low-cost feedstock are not available. Transforming biomass into **pellets** offers a solution to this problem. Biomass is dried, crushed and pelletized to be converted into pellets that have standardized size and properties, high energy content and high density. It reduses costs of transports, storage and handling. Pellets can directly be used in firing or co-firing with coal in boilers. diff --git a/energy_models/models/syngas/autothermal_reforming/autothermal_reforming.py b/energy_models/models/syngas/autothermal_reforming/autothermal_reforming.py index 4a54f026..ffc7b429 100644 --- a/energy_models/models/syngas/autothermal_reforming/autothermal_reforming.py +++ b/energy_models/models/syngas/autothermal_reforming/autothermal_reforming.py @@ -31,6 +31,7 @@ def compute_resources_needs(self): # need in kg to produce 1kwh of syngas self.cost_details[f'{GlossaryEnergy.OxygenResource}_needs'] = self.get_theoretical_O2_needs() / self.cost_details['efficiency'] + def compute_other_streams_needs(self): # need in kwh to produce 1kwh of syngas self.cost_details[f'{Methane.name}_needs'] = self.get_theoretical_CH4_needs() / self.cost_details['efficiency'] @@ -53,7 +54,7 @@ def get_theoretical_CH4_needs(self): return methane_needs def get_theoretical_CO2_needs(self): - ''' + ''' Get water needs in kg CO2 /kWh H2 1 mol of CO2 for 3 mol of CO and 3 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -69,7 +70,7 @@ def get_theoretical_CO2_needs(self): return water_needs def get_theoretical_O2_needs(self): - ''' + ''' Get water needs in kg O2 /kWh H2 1 mol of O2 for 3 mol of CO and 3 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one diff --git a/energy_models/models/syngas/autothermal_reforming/documentation/autothermal_reforming_disc.markdown b/energy_models/models/syngas/autothermal_reforming/documentation/autothermal_reforming_disc.markdown index a70701fd..87f7f145 100644 --- a/energy_models/models/syngas/autothermal_reforming/documentation/autothermal_reforming_disc.markdown +++ b/energy_models/models/syngas/autothermal_reforming/documentation/autothermal_reforming_disc.markdown @@ -1,19 +1,19 @@ # Autothermal reforming -Autothermal reforming uses CO_2 and oxygen in a reaction with methane to form syngas. The reaction takes place in a single chamber where the methane is partially oxidized. The reaction is exothermic due to the oxidation. +Autothermal reforming uses CO_2 and oxygen in a reaction with methane to form syngas. The reaction takes place in a single chamber where the methane is partially oxidized. The reaction is exothermic due to the oxidation. $$2CH_4 + O_2 + CO_2 --> 3CO + H_2O + 3H_2$$ -The syngas produced ratio of H2:CO is 1:1. +The syngas produced ratio of H2:CO is 1:1. Economic and technical datas was taken from Ayodele et Al. [^1] & Cormos et Al. [^2]. -![](atr.png) +![](atr.png) ## Heat Heat production/consumption is neglected in coal-gasification process. - + [^1]: Freida Ozavize Ayodele , Siti Indati Mustapa , Bamidele Victor Ayodele and Norsyahida Mohammad (2020) An Overview of Economic Analysis and Environmental Impacts of Natural Gas Conversion Technologies -[^2]: Ana-Maria Cormos et Al. (2018). Economic Assessments of Hydrogen Production Processes Based on Natural Gas Reforming with Carbon Capture +[^2]: Ana-Maria Cormos et Al. (2018). Economic Assessments of Hydrogen Production Processes Based on Natural Gas Reforming with Carbon Capture \ No newline at end of file diff --git a/energy_models/models/syngas/biomass_gasification/biomass_gasification.py b/energy_models/models/syngas/biomass_gasification/biomass_gasification.py index c8cff39f..ebf44aed 100644 --- a/energy_models/models/syngas/biomass_gasification/biomass_gasification.py +++ b/energy_models/models/syngas/biomass_gasification/biomass_gasification.py @@ -34,5 +34,6 @@ def compute_other_streams_needs(self): self.cost_details[f'{BiomassDry.name}_needs'] = self.techno_infos_dict['biomass_demand'] + def compute_byproducts_production(self): self.compute_ghg_emissions(Methane.emission_name) diff --git a/energy_models/models/syngas/biomass_gasification/biomass_gasification_disc.py b/energy_models/models/syngas/biomass_gasification/biomass_gasification_disc.py index 6d69f29e..f224706e 100644 --- a/energy_models/models/syngas/biomass_gasification/biomass_gasification_disc.py +++ b/energy_models/models/syngas/biomass_gasification/biomass_gasification_disc.py @@ -101,7 +101,7 @@ class BiomassGasificationDiscipline(SyngasTechnoDiscipline): 'kgH20_perkgSyngas': 157.75 / (10.99 / 0.42), } # We do not invest on biomass gasification yet - + syngas_ratio = BiomassGasification.syngas_COH2_ratio # 24 plants for liquid fuel production with global production of liquid fuel from biomass-derived syngas diff --git a/energy_models/models/syngas/biomass_gasification/documentation/biomass_gasification_disc.markdown b/energy_models/models/syngas/biomass_gasification/documentation/biomass_gasification_disc.markdown index b05df88e..95aecd74 100644 --- a/energy_models/models/syngas/biomass_gasification/documentation/biomass_gasification_disc.markdown +++ b/energy_models/models/syngas/biomass_gasification/documentation/biomass_gasification_disc.markdown @@ -6,11 +6,11 @@ Biomass resources mainly include agriculture crops, agriculture crop waste, wood, wood waste and animal manure. A biomass analysis reveals a concentration of Carbon, Hydrogen, Oxygen and a bit of Sulfur and Nitrogen. ## Principle of work -The gasifier is the core equipment unit for the hydrogen production process. Fuels (such as coal and biomass) are used in the gasification process, and fuel is converted to syngas primarily containing CO, CO2, H2, H2O and CH4. The composition of syngas derived from gasification mainly depends on the fuel, gasifier type and gasification agent. +The gasifier is the core equipment unit for the hydrogen production process. Fuels (such as coal and biomass) are used in the gasification process, and fuel is converted to syngas primarily containing CO, CO2, H2, H2O and CH4. The composition of syngas derived from gasification mainly depends on the fuel, gasifier type and gasification agent. The selection of the gasifier type depends on the raw material. Various gasification agents, including air, oxygen, oxygen-enriched air and steam, can be employed for the gasification process. -Produced syngas followed the below formula and consist in a primarily mixture of carbon monoxide and hydrogen : +Produced syngas followed the below formula and consist in a primarily mixture of carbon monoxide and hydrogen : $$Biomass(CH_aO_bN_cS_d) + O_2 --> CO_2$$ $$Biomass(CH_aO_bN_cS_d) + CO_2 --> 2CO$$ @@ -22,11 +22,11 @@ $$Biomass(CH_aO_bN_cS_d) + O_2 + H_2O --> CO + CO_2 + H_2 + other species$$ (other species => $N_2$, $H_2S$ less than 0.3 Mole Frac(%)) -## World production +## World production Altalto, a partnership project by British Airways, Shell and Velocys, aims for the development of first commercial production of biojet at a capacity of 60 million liters/year. BioTfueL project34, driven by a group of companies from France (Axens, CEA, IFP Energies Nouvelles, -Avril, ThyssenKrupp Industrial Solutions, Total), aims for the production of 60 t/y FT liquids. +Avril, ThyssenKrupp Industrial Solutions, Total), aims for the production of 60 t/y FT liquids. The UPM Lappeenranta Biorefinery, producing wood-based renewable diesel from forestry residues, has started commercial production in January 2015 with 130000 t/y. To resume, there are 24 plants for liquid fuel production with global production of liquid fuel from biomass-derived syngas (750,000 t/year); 8 plants for gaseous fuel @@ -36,12 +36,12 @@ from biomass-derived syngas of 9000 t/year.[^7] In the beginning of 2021, Fulcrum bioenergy and Essar Oil UK announced Fulcrum NorthPoint project which aims for annual SAF production of 100 million liters at Essar Oil site in Stanlow (UK) with planned production start-up date in 2025. -## Datas +## Datas Economic datas is taken from Rosenfeld et al. [^1], technical datas from Wang et al. [^2], Albara et al. [^3], Sara et al. [^4], Mustafa et. al [^5]. -### GHG emissions +### GHG emissions -The Chapter 4 of the IPCC report predicts methane fugitive emissions from biomass gasification. Emission factor is provided based on the carbon contents of biomass used in a selection of plants and the composition of its syngas and has a value of 18.3 kgCH4/TJ [^8]. +The Chapter 4 of the IPCC report predicts methane fugitive emissions from biomass gasification. Emission factor is provided based on the carbon contents of biomass used in a selection of plants and the composition of its syngas and has a value of 18.3 kgCH4/TJ [^8]. [^1]: Rosenfeld, D. C., Bohm, H., Lindorfer, J. , Lehner, M. (2020). Scenario analysis of implementing a power-to-gas and biomass gasification system in an integrated steel plant: A techno-economic and environmental study. Renewable Energy, 147(2020), 1511-1524. @@ -57,4 +57,4 @@ The Chapter 4 of the IPCC report predicts methane fugitive emissions from biomas [^6]: https://www.etipbioenergy.eu/images/ETIP_B_Factsheet_BtL_2021.pdf [^7]: Molino, A., Larocca, V., Chianese, S. and Musmarra, D., 2018. Biofuels production by biomass gasification: A review. Energies, 11(4), p.811. -[^8]: IPCC report Chapter 4 Fugitive Emissions : https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/2_Volume2/19R_V2_4_Ch04_Fugitive_Emissions.pdf +[^8]: IPCC report Chapter 4 Fugitive Emissions : https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/2_Volume2/19R_V2_4_Ch04_Fugitive_Emissions.pdf \ No newline at end of file diff --git a/energy_models/models/syngas/co_electrolysis/co_electrolysis.py b/energy_models/models/syngas/co_electrolysis/co_electrolysis.py index 32b2973d..a244e3eb 100644 --- a/energy_models/models/syngas/co_electrolysis/co_electrolysis.py +++ b/energy_models/models/syngas/co_electrolysis/co_electrolysis.py @@ -36,8 +36,9 @@ def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() + def get_theoretical_CO2_needs(self): - ''' + ''' Get water needs in kg CO2 /kWh syngas 1 mol of CO2 for 1 mol of CO and 1 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -53,7 +54,7 @@ def get_theoretical_CO2_needs(self): return co2_needs def get_theoretical_water_needs(self): - ''' + ''' Get water needs in kg water /kWh syngas 1 mol of H2O for 1 mol of CO and 1 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one diff --git a/energy_models/models/syngas/co_electrolysis/documentation/co_electrolysis_disc.markdown b/energy_models/models/syngas/co_electrolysis/documentation/co_electrolysis_disc.markdown index d0e94186..97d06c18 100644 --- a/energy_models/models/syngas/co_electrolysis/documentation/co_electrolysis_disc.markdown +++ b/energy_models/models/syngas/co_electrolysis/documentation/co_electrolysis_disc.markdown @@ -1,22 +1,23 @@ # CoElectrolysis -High temperature co-electrolysis is a process which electro-chemically reduces H2O and CO2 using solid oxide electrolyzer cell via the reactions in the equations : +High temperature co-electrolysis is a process which electro-chemically reduces H2O and CO2 using solid oxide electrolyzer cell via the reactions in the equations : $$H_2O -> H2 + 1/2 O_2$$ $$CO_2 -> CO + 1/2 O_2$$ -Total reaction is : +Total reaction is : $$H_2O + CO_2 -> H2 + CO + O_2$$ -![](coelectrolysis.jpg) +![](coelectrolysis.jpg) Economic and technical datas was taken from Becker et Al. [^1] ## Heat Heat production/consumption is neglected in coal-gasification process. - + [^1]: Becker et Al. (2012) Production of FischereTropsch liquid fuels from high temperature solid oxide co-electrolysis units + diff --git a/energy_models/models/syngas/coal_gasification/coal_gasification.py b/energy_models/models/syngas/coal_gasification/coal_gasification.py index fed4c3ae..a9709ddb 100644 --- a/energy_models/models/syngas/coal_gasification/coal_gasification.py +++ b/energy_models/models/syngas/coal_gasification/coal_gasification.py @@ -42,6 +42,7 @@ def compute_other_streams_needs(self): # in kwh of fuel by kwh of syngas self.cost_details[f'{SolidFuel.name}_needs'] = self.get_fuel_needs() + def compute_byproducts_production(self): self.production_detailed[f'{CarbonCapture.flue_gas_name} ({GlossaryEnergy.mass_unit})'] = self.techno_infos_dict[ diff --git a/energy_models/models/syngas/coal_gasification/coal_gasification_disc.py b/energy_models/models/syngas/coal_gasification/coal_gasification_disc.py index 0b79f76e..55129d39 100644 --- a/energy_models/models/syngas/coal_gasification/coal_gasification_disc.py +++ b/energy_models/models/syngas/coal_gasification/coal_gasification_disc.py @@ -64,9 +64,9 @@ class CoalGasificationDiscipline(SyngasTechnoDiscipline): 'Capex_init_unit': '$/kWh', 'euro_dollar': 1.12, 'efficiency': 1.0, - 'techno_evo_eff': 'no', } + 'techno_evo_eff': 'no',} # We do not invest on coal gasification yet - + syngas_ratio = CoalGasification.syngas_COH2_ratio # From Future of hydrogen : Around 70 Mt of dedicated hydrogen are produced today, 76% from natural gas and diff --git a/energy_models/models/syngas/coal_gasification/documentation/coal_gasification_disc.markdown b/energy_models/models/syngas/coal_gasification/documentation/coal_gasification_disc.markdown index 02298f08..3f2696b8 100644 --- a/energy_models/models/syngas/coal_gasification/documentation/coal_gasification_disc.markdown +++ b/energy_models/models/syngas/coal_gasification/documentation/coal_gasification_disc.markdown @@ -1,37 +1,37 @@ # Syngas from Coal Gasification ## Definition -Coal gasification is the process of producing syngas from coal,water and oxygen. During gasification, the coal is blown through with oxygen and water vapor : +Coal gasification is the process of producing syngas from coal,water and oxygen. During gasification, the coal is blown through with oxygen and water vapor : $$3C(coal) + O_2 + H_2O \rightarrow H_2 + 3CO$$ -If the refiner wants to produce liquid fuels, the coal gas is collected at this state and routed to a Fischer–Tropsch reactor. If, however, hydrogen is the desired end-product, the coal gas (primarily the CO product) undergoes the water gas shift reaction. +If the refiner wants to produce liquid fuels, the coal gas is collected at this state and routed to a Fischer–Tropsch reactor. If, however, hydrogen is the desired end-product, the coal gas (primarily the CO product) undergoes the water gas shift reaction. -## World production +## World production The two main applications of coal gasification are to produce energy (hydrogen with Water Gas Shift reaction and liquid fuels with Fischer Tropsch reaction) and to reduced iron from iron ore. - Almost 23 % of Hydrogen is produced with coal gasification mostly in China. The world's largest scale implementation of Fischer–Tropsch technology in South Africa is based on coal gasification. + Almost 23 % of Hydrogen is produced with coal gasification mostly in China. The world's largest scale implementation of Fischer–Tropsch technology in South Africa is based on coal gasification. - In industry, syngas from coal gasification is used to transform iron oxides extracted from iron mines into iron ore using the Direct Reduced Iron (DRI) technology : + In industry, syngas from coal gasification is used to transform iron oxides extracted from iron mines into iron ore using the Direct Reduced Iron (DRI) technology : $$2FeO + (CO + H_2) \rightarrow 2Fe + CO_2 + H_2O$$ - IEA website reports a coal consumption of 3333 TWh for other transformation [^3] (includes Hydrogen and DRI) and 264.72 TWh for liquefaction plants. + IEA website reports a coal consumption of 3333 TWh for other transformation [^3] (includes Hydrogen and DRI) and 264.72 TWh for liquefaction plants. ## Datas Technical and economic datas were taken from Wang et Al. [^1] and ETSAP [^2] -### GHG emissions +### GHG emissions -The Chapter 4 of the IPCC report predicts methane fugitive emissions from biomass gasification. Emission factor is provided based on the volume of CH4 emissions released during the production and treatment of syngas and has a value of 6.1 kgCH4/TJ [^4]. +The Chapter 4 of the IPCC report predicts methane fugitive emissions from biomass gasification. Emission factor is provided based on the volume of CH4 emissions released during the production and treatment of syngas and has a value of 6.1 kgCH4/TJ [^4]. ## Heat Heat production/consumption is neglected in coal-gasification process. - + [^1]: Wang, Y., Li, G., Liu, Z. , Cui, P., Zhu, Z. (2019). Techno-economic analysis of biomass-to-hydrogen process in comparison with coal-to-hydrogen process. Energy, 185(2019), 1063-1075. [^2]: IEA ETSAP - Technology Brief P05 – May 2010 - www.etsap.org -[^3]: [IEA 2022, Data Tables](https://www.iea.org/data-and-statistics/data-tables?country=WORLD&energy=Balances&year=2019), License: CC BY 4.0. +[^3]: [IEA 2022, Data Tables](https://www.iea.org/data-and-statistics/data-tables?country=WORLD&energy=Balances&year=2019), License: CC BY 4.0. \ No newline at end of file diff --git a/energy_models/models/syngas/pyrolysis/documentation/pyrolysis_disc.markdown b/energy_models/models/syngas/pyrolysis/documentation/pyrolysis_disc.markdown index 56ac3e5b..feb649e4 100644 --- a/energy_models/models/syngas/pyrolysis/documentation/pyrolysis_disc.markdown +++ b/energy_models/models/syngas/pyrolysis/documentation/pyrolysis_disc.markdown @@ -1,13 +1,13 @@ # Wood pyrolysis -Pyrolysis is the thermal decomposition of materials at elevated temperatures in an inert atmosphere. It can be applied to wood where it is burned in an oxygen free environment. +Pyrolysis is the thermal decomposition of materials at elevated temperatures in an inert atmosphere. It can be applied to wood where it is burned in an oxygen free environment. Several types of pyrolysis exist : Fast pyrolysis at 500-1000°C or slow pyrolysis at 500°C. The temperature will have a direct effect on the mass balance of the pyrolysis. If the bio-oil is the priority product, it was found that fast pyrolysis at 500°C maximize the yield, while fast pyrolysis at 1000 °C is prefered to maximize syngas production. Economic-data and technical one was taken from Salman et Al. [^1] & Wei et Al. [^2] -![](pyrolysis_out.png) +![](pyrolysis_out.png) ## Heat Pyrolysis is typically defined as the thermochemical decomposition of biomass feedstock at medium (300–800°C) to high temperatures (800–1300°C) in an inert atmosphere. @@ -16,7 +16,7 @@ The overall reaction of biomass feedstock is.. Biofuel + heat → liquid + syngas + solid C + H2O → CO + H2 .... ΔH°= +131 kJ/mol - + [^1]: Salman, & Salman, Chaudhary Awais. (2014). Techno-economic analysis of wood pyrolysis in Sweden. -[^2]: Wei, Lin & Pordesimo, L.O. & To, Filip & Herndon, Cary & Batchelor, William. (2009). Evaluation of Micro-Scale Syngas Production Costs through Modeling. +[^2]: Wei, Lin & Pordesimo, L.O. & To, Filip & Herndon, Cary & Batchelor, William. (2009). Evaluation of Micro-Scale Syngas Production Costs through Modeling. \ No newline at end of file diff --git a/energy_models/models/syngas/pyrolysis/pyrolysis_disc.py b/energy_models/models/syngas/pyrolysis/pyrolysis_disc.py index 264a8cad..11326054 100644 --- a/energy_models/models/syngas/pyrolysis/pyrolysis_disc.py +++ b/energy_models/models/syngas/pyrolysis/pyrolysis_disc.py @@ -79,7 +79,7 @@ class PyrolysisDiscipline(SyngasTechnoDiscipline): DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + } # -- add specific techno outputs to this DESC_IN.update(SyngasTechnoDiscipline.DESC_IN) diff --git a/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift.py b/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift.py index bde1a7c8..0ebbffc1 100644 --- a/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift.py +++ b/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift.py @@ -72,7 +72,7 @@ def configure_parameters_update(self, inputs_dict): def check_capex_unity(self, data_config): ''' - Overload the check_capex_unity for this particular model + Overload the check_capex_unity for this particular model ''' capex_list = np.array(data_config['Capex_init_vs_CO_H2_ratio']) @@ -406,6 +406,7 @@ def compute_other_streams_needs(self): # Cost of methane for 1 kWH of H2 self.cost_details['syngas_needs'] = self.get_theoretical_syngas_needs(self.syngas_ratio) / self.cost_details['efficiency'] + def compute_byproducts_production(self): th_water_prod = self.get_theoretical_water_prod() @@ -416,7 +417,7 @@ def compute_byproducts_production(self): def compute_streams_consumption(self): """ Compute the consumption and the production of the technology for a given investment - Maybe add efficiency in consumption computation ? + Maybe add efficiency in consumption computation ? """ super().compute_streams_consumption() @@ -476,8 +477,8 @@ def compute_dco2_emissions_dsyngas_ratio(self): return dco2_emissions_dsyngas_ratio def get_theoretical_syngas_needs(self, syngas_ratio): - ''' - dCO2 + e(H2 +r1CO)--> (H2 +r2CO) + cH20 + ''' + dCO2 + e(H2 +r1CO)--> (H2 +r2CO) + cH20 e = (1+r2)/(1+r1) c = (r2-r1)/(1+r1) @@ -576,8 +577,8 @@ def compute_dsyngas_needs_dsyngas_ratio(self): return dsyngas_needs_dsyngas_ratio def get_theoretical_water_prod(self): - ''' - dCO2 + e(H2 +r1CO)--> (H2 +r2CO) + cH20 + ''' + dCO2 + e(H2 +r1CO)--> (H2 +r2CO) + cH20 e = (1+r2)/(1+r1) c = (r2-r1)/(1+r1) @@ -630,8 +631,8 @@ def compute_dwater_prod_dsynags_ratio(self): return dwater_needs_dsyngas_ratio def get_theoretical_co2_needs(self, unit='kg/kWh'): - ''' - dCO2 + e(H2 +r1CO)--> (H2 +r2CO) + cH20 + ''' + dCO2 + e(H2 +r1CO)--> (H2 +r2CO) + cH20 e = (1+r2)/(1+r1) c = (r2-r1)/(1+r1) diff --git a/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift_disc.py b/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift_disc.py index a28e1de7..8e7195fb 100644 --- a/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift_disc.py +++ b/energy_models/models/syngas/reversed_water_gas_shift/reversed_water_gas_shift_disc.py @@ -87,7 +87,7 @@ class ReversedWaterGasShiftDiscipline(SyngasTechnoDiscipline): # Fake initial age distrib (not found in the litterature...) DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default, 'unit': 'defined in dict'}, - + 'syngas_ratio': {'type': 'array', 'unit': '%'}, 'needed_syngas_ratio': {'type': 'float', 'unit': '%'} } diff --git a/energy_models/models/syngas/smr/documentation/smr_disc.markdown b/energy_models/models/syngas/smr/documentation/smr_disc.markdown index 44db58af..97659f02 100644 --- a/energy_models/models/syngas/smr/documentation/smr_disc.markdown +++ b/energy_models/models/syngas/smr/documentation/smr_disc.markdown @@ -1,24 +1,24 @@ # Steam Methane Reforming (SMR) -Hydrogen production using steam methane reforming (SMR) is a two-step process. The first step is to inject water vapor at very high temperature (700 to 1000 Celsius degrees) with methane in a pressurized environment (3 to 25 bars), which is the SMR process. The reaction generates hydrogen (H2) and carbon monoxide (CO) : +Hydrogen production using steam methane reforming (SMR) is a two-step process. The first step is to inject water vapor at very high temperature (700 to 1000 Celsius degrees) with methane in a pressurized environment (3 to 25 bars), which is the SMR process. The reaction generates hydrogen (H2) and carbon monoxide (CO) : $$CH_4 + H_20 (+heat) --> CO + 3H_2$$ -The Water Gas Shift process is then used to extract hydrogen from syngas mix. +The Water Gas Shift process is then used to extract hydrogen from syngas mix. -## World production +## World production Annual global hydrogen production is around 70 million tonnes of hydrogen and around 75% comes from natural gas reforming.[^1] Syngas from natural gas is also used in liquefaction plants in the Gas to Liquid process (GtL).In Qatar, the second largest FT plant in the world converts natural gas to petroleum liquids at a rate of 140,000 barrels per day.Another plant in Qatar has a capacity of 34,000 barrels per day.[^4] -## Datas +## Datas Economic and technical datas is taken from Keipi & al [^2], Diglio & al [^3]. ## Heat -[^5] Natural gas and steam are fed to the pre-reformer where the heavies are converted to methane to +[^5] Natural gas and steam are fed to the pre-reformer where the heavies are converted to methane to prevent soot and enhance the efficiency of the process . More steam is added prior to entering the main reactor, where syngas is produced, in an equilibrium-limited endothermic reaction: CH4 + H2O → CO + 3H2 .... ΔH°= 206 kJ/mol @@ -28,4 +28,4 @@ CH4 + H2O → CO + 3H2 .... ΔH°= 206 kJ/mol [^2]: Tiina Keipi, Henrik Tolvanen, Jukka Konttinen,Economic analysis of hydrogen production by methane thermal decomposition: Comparison to competing technologies,Energy Conversion and Management,Volume 159,2018,Pages 264-273,ISSN 0196-8904 [^3]: Diglio, G., Hanak, D.P., Bareschino, P., Mancusi, E., Pepe, F., Montagnaro, F. and Manovic, V., 2017. Techno-economic analysis of sorption-enhanced steam methane reforming in a fixed bed reactor network integrated with fuel cell. Journal of Power Sources, 364, pp.41-51. [^4]: https://en.wikipedia.org/wiki/Fischer%E2%80%93Tropsch_process#Commercialization -[^5]: https://www.sciencedirect.com/science/article/pii/S2666790822001574 +[^5]: https://www.sciencedirect.com/science/article/pii/S2666790822001574 \ No newline at end of file diff --git a/energy_models/models/syngas/smr/smr.py b/energy_models/models/syngas/smr/smr.py index 3eb5ce25..83b3af8c 100644 --- a/energy_models/models/syngas/smr/smr.py +++ b/energy_models/models/syngas/smr/smr.py @@ -34,8 +34,9 @@ def compute_other_streams_needs(self): # need in kg to produce 1kwh of syngas self.cost_details[f'{Methane.name}_needs'] = self.get_theoretical_CH4_needs() / self.cost_details['efficiency'] + def get_theoretical_CH4_needs(self): - ''' + ''' Get CH4 needs in kWh CH4 /kWh syngas 1 mol of CH4 for 1 mol of CO and 1 mol of H2 Warning : molar mass is in g/mol but we divide and multiply by one @@ -51,9 +52,9 @@ def get_theoretical_CH4_needs(self): return methane_needs def get_theoretical_water_needs(self): - ''' + ''' Get water needs in kg water /kWh syngas - 1 mol of H2O for 1 mol of synags + 1 mol of H2O for 1 mol of synags Warning : molar mass is in g/mol but we divide and multiply by one ''' diff --git a/energy_models/models/wet_biomass/animal_manure/animal_manure.py b/energy_models/models/wet_biomass/animal_manure/animal_manure.py index 01a84896..6c7e4989 100644 --- a/energy_models/models/wet_biomass/animal_manure/animal_manure.py +++ b/energy_models/models/wet_biomass/animal_manure/animal_manure.py @@ -28,4 +28,4 @@ def compute_other_streams_needs(self): def compute_byproducts_production(self): self.production_detailed[f'{GlossaryEnergy.carbon_capture} (kg)'] = self.techno_infos_dict['CO2_from_production'] / \ self.data_energy_dict['calorific_value'] * \ - self.production_detailed[f'{WetBiomassTechno.energy_name} (kWh)'] + self.production_detailed[f'{WetBiomassTechno.energy_name} (kWh)'] \ No newline at end of file diff --git a/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues.py b/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues.py index 2db5a602..74f53e5d 100644 --- a/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues.py +++ b/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues.py @@ -25,6 +25,7 @@ class WetCropResidues(WetBiomassTechno): def compute_other_streams_needs(self): self.cost_details[f'{GlossaryEnergy.electricity}_needs'] = self.get_electricity_needs() + def compute_byproducts_production(self): self.production_detailed[f'{GlossaryEnergy.carbon_capture} (kg)'] = self.techno_infos_dict['CO2_from_production'] / \ self.data_energy_dict['calorific_value'] * \ diff --git a/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues_disc.py b/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues_disc.py index 76f885d5..b62745ca 100644 --- a/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues_disc.py +++ b/energy_models/models/wet_biomass/wet_crop_residue/wet_crop_residues_disc.py @@ -89,7 +89,7 @@ class WetCropResiduesDiscipline(WetBiomassTechnoDiscipline): # Age distribution fake DESC_IN = {'techno_infos_dict': {'type': 'dict', 'default': techno_infos_dict_default}, - + } # -- add specific techno inputs to this DESC_IN.update(WetBiomassTechnoDiscipline.DESC_IN) diff --git a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/process.py b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/process.py index 3957b83a..26b4fc39 100644 --- a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/process.py +++ b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/process.py @@ -95,6 +95,7 @@ def get_builders(self): after_name=self.ee.study_name, clean_existing=False) + # --------------------------------------------- mods_dict = { energy_mix: 'energy_models.core.energy_mix.energy_mix_disc.Energy_Mix_Discipline', @@ -197,6 +198,7 @@ def get_builders(self): self.ee.ns_manager.add_ns_def(ns_dict) + # --------------------------------------------- # design variables builder design_var_path = 'sostrades_optimization_plugins.models.design_var.design_var_disc.DesignVarDiscipline' diff --git a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/usecase.py b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/usecase.py index f01f7794..5cdbffb3 100644 --- a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/usecase.py +++ b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process/usecase.py @@ -406,10 +406,10 @@ def make_dspace_utilisation_ratio(self) -> pd.DataFrame: def make_func_df(self): func_df = pd.DataFrame({ "variable": [GlossaryEnergy.ObjectiveEnergyNonUseCapitalByStream, GlossaryEnergy.ObjectiveEnergyNonUseCapital, GlossaryEnergy.CO2EmissionsObjectiveValue, GlossaryEnergy.TargetProductionConstraintValue, GlossaryEnergy.MaxBudgetConstraintValue,], - "parent": ["objectives", "objectives", "objectives", "constraints", "constraints"], - "ftype": [FunctionManagerDisc.OBJECTIVE, FunctionManagerDisc.OBJECTIVE, FunctionManagerDisc.OBJECTIVE, FunctionManagerDisc.INEQ_CONSTRAINT, FunctionManagerDisc.INEQ_CONSTRAINT], + "parent": ["objectives", "objectives","objectives", "constraints", "constraints"], + "ftype": [FunctionManagerDisc.OBJECTIVE, FunctionManagerDisc.OBJECTIVE,FunctionManagerDisc.OBJECTIVE, FunctionManagerDisc.INEQ_CONSTRAINT, FunctionManagerDisc.INEQ_CONSTRAINT], "weight": [0., 0.1, 1.0, 10.0, 10.0,], - FunctionManagerDisc.AGGR_TYPE: [FunctionManager.AGGR_TYPE_SUM, FunctionManager.AGGR_TYPE_SUM, FunctionManager.AGGR_TYPE_SUM, FunctionManager.INEQ_NEGATIVE_WHEN_SATIFIED_AND_SQUARE_IT, FunctionManager.INEQ_NEGATIVE_WHEN_SATIFIED_AND_SQUARE_IT,], + FunctionManagerDisc.AGGR_TYPE: [FunctionManager.AGGR_TYPE_SUM,FunctionManager.AGGR_TYPE_SUM, FunctionManager.AGGR_TYPE_SUM, FunctionManager.INEQ_NEGATIVE_WHEN_SATIFIED_AND_SQUARE_IT, FunctionManager.INEQ_NEGATIVE_WHEN_SATIFIED_AND_SQUARE_IT,], "namespace": [GlossaryEnergy.NS_FUNCTIONS] * 5 }) return func_df diff --git a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/process.py b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/process.py index 0a44e2e2..b35827a4 100644 --- a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/process.py +++ b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/process.py @@ -30,4 +30,4 @@ class ProcessBuilder(EnergyMixFullProcessBuilder): def __init__(self, ee): super().__init__(ee) - self.techno_dict = GlossaryEnergy.DEFAULT_COARSE_TECHNO_DICT + self.techno_dict=GlossaryEnergy.DEFAULT_COARSE_TECHNO_DICT diff --git a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/usecase.py b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/usecase.py index 9ed53de5..dd7dd844 100644 --- a/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/usecase.py +++ b/energy_models/sos_processes/energy/MDA/energy_mix_optim_sub_process_coarse/usecase.py @@ -35,7 +35,6 @@ def __init__( ) self.test_post_procs = False - if "__main__" == __name__: uc_cls = Study() uc_cls.load_data() diff --git a/energy_models/sos_processes/energy/MDA/energy_process_v0/process.py b/energy_models/sos_processes/energy/MDA/energy_process_v0/process.py index 62ab1c81..a17e8057 100644 --- a/energy_models/sos_processes/energy/MDA/energy_process_v0/process.py +++ b/energy_models/sos_processes/energy/MDA/energy_process_v0/process.py @@ -47,7 +47,7 @@ class ProcessBuilder(WITNESSSubProcessBuilder): def __init__(self, ee): super(ProcessBuilder, self).__init__(ee) - self.techno_dict = GlossaryEnergy.DEFAULT_TECHNO_DICT + self.techno_dict=GlossaryEnergy.DEFAULT_TECHNO_DICT def get_builders(self): diff --git a/energy_models/sos_processes/energy/MDO/__init__.py b/energy_models/sos_processes/energy/MDO/__init__.py index 888f8915..18d49eab 100644 --- a/energy_models/sos_processes/energy/MDO/__init__.py +++ b/energy_models/sos_processes/energy/MDO/__init__.py @@ -12,4 +12,4 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -''' +''' \ No newline at end of file diff --git a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/__init__.py b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/__init__.py index 888f8915..18d49eab 100644 --- a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/__init__.py +++ b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/__init__.py @@ -12,4 +12,4 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -''' +''' \ No newline at end of file diff --git a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/process.py b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/process.py index c7f30ec0..98472519 100644 --- a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/process.py +++ b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/process.py @@ -25,7 +25,6 @@ class ProcessBuilder(BaseProcessBuilder): 'category': '', 'version': '', } - def __init__(self, ee): super().__init__(ee) self.sub_process_repo = 'energy_models.sos_processes.energy.MDA' diff --git a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/usecase_with_utilization_ratio.py b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/usecase_with_utilization_ratio.py index 04445975..f9492a2e 100644 --- a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/usecase_with_utilization_ratio.py +++ b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process/usecase_with_utilization_ratio.py @@ -33,7 +33,6 @@ def __init__( ) self.test_post_procs = True - if '__main__' == __name__: uc_cls = Study(run_usecase=True) uc_cls.load_data() diff --git a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/__init__.py b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/__init__.py index 888f8915..18d49eab 100644 --- a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/__init__.py +++ b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/__init__.py @@ -12,4 +12,4 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -''' +''' \ No newline at end of file diff --git a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/process.py b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/process.py index 2a59ca54..c03ca314 100644 --- a/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/process.py +++ b/energy_models/sos_processes/energy/MDO/energy_mix_optim_process_coarse/process.py @@ -27,7 +27,6 @@ class ProcessBuilder(ProccesEnergyMixOptimFull): 'category': '', 'version': '', } - def __init__(self, ee): super().__init__(ee) self.sub_process_name = "energy_mix_optim_sub_process_coarse" diff --git a/energy_models/sos_processes/energy/techno_mix/biodiesel_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/biodiesel_mix/usecase.py index e008c066..ce0ecaff 100644 --- a/energy_models/sos_processes/energy/techno_mix/biodiesel_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/biodiesel_mix/usecase.py @@ -112,7 +112,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.Transesterification}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + ##f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: @@ -144,4 +144,4 @@ def setup_usecase(self, study_folder_path=None): if '__main__' == __name__: uc_cls = Study(main_study=True, technologies_list=DEFAULT_TECHNOLOGIES_LIST) - uc_cls.test() + uc_cls.test() \ No newline at end of file diff --git a/energy_models/sos_processes/energy/techno_mix/biogas_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/biogas_mix/usecase.py index aeb1bea7..e9ab1a80 100644 --- a/energy_models/sos_processes/energy/techno_mix/biogas_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/biogas_mix/usecase.py @@ -112,7 +112,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.AnaerobicDigestion}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: values_dict.update( @@ -142,4 +142,4 @@ def setup_usecase(self, study_folder_path=None): if '__main__' == __name__: uc_cls = Study(main_study=True, technologies_list=DEFAULT_TECHNOLOGIES_LIST) - uc_cls.test() + uc_cls.test() \ No newline at end of file diff --git a/energy_models/sos_processes/energy/techno_mix/biomass_dry_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/biomass_dry_mix/usecase.py index 1f013a13..549f83ee 100644 --- a/energy_models/sos_processes/energy/techno_mix/biomass_dry_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/biomass_dry_mix/usecase.py @@ -119,7 +119,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.CropEnergy}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: values_dict.update( diff --git a/energy_models/sos_processes/energy/techno_mix/carbon_capture_coarse_mix/usecase_coarse.py b/energy_models/sos_processes/energy/techno_mix/carbon_capture_coarse_mix/usecase_coarse.py index 5ce8384b..b48c157f 100644 --- a/energy_models/sos_processes/energy/techno_mix/carbon_capture_coarse_mix/usecase_coarse.py +++ b/energy_models/sos_processes/energy/techno_mix/carbon_capture_coarse_mix/usecase_coarse.py @@ -119,7 +119,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{ccs_name}.{GlossaryEnergy.flue_gas_capture}.FlueGasTechno.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, + #f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, } techno_capital = pd.DataFrame({GlossaryEnergy.Years: years, GlossaryEnergy.Capital: 0.0, GlossaryEnergy.NonUseCapital: 0.}) diff --git a/energy_models/sos_processes/energy/techno_mix/carbon_capture_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/carbon_capture_mix/usecase.py index 665e8131..0f13e127 100644 --- a/energy_models/sos_processes/energy/techno_mix/carbon_capture_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/carbon_capture_mix/usecase.py @@ -196,7 +196,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{ccs_name}.{GlossaryEnergy.flue_gas_capture}.flue_gas_mean': self.flue_gas_mean, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, + #f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, f'{self.study_name}.{GlossaryEnergy.ccs_list}': [GlossaryEnergy.carbon_capture, GlossaryEnergy.carbon_storage] } diff --git a/energy_models/sos_processes/energy/techno_mix/carbon_storage_coarse_mix/usecase_coarse.py b/energy_models/sos_processes/energy/techno_mix/carbon_storage_coarse_mix/usecase_coarse.py index eb543eb1..ee769948 100644 --- a/energy_models/sos_processes/energy/techno_mix/carbon_storage_coarse_mix/usecase_coarse.py +++ b/energy_models/sos_processes/energy/techno_mix/carbon_storage_coarse_mix/usecase_coarse.py @@ -100,7 +100,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{ccs_name}.{GlossaryEnergy.CarbonStorageTechno}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, + #f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, } if self.main_study: diff --git a/energy_models/sos_processes/energy/techno_mix/carbon_storage_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/carbon_storage_mix/usecase.py index fa3f2afb..a3cc114d 100644 --- a/energy_models/sos_processes/energy/techno_mix/carbon_storage_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/carbon_storage_mix/usecase.py @@ -138,7 +138,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{ccs_name}.{GlossaryEnergy.techno_list}': self.technologies_list, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{ccs_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, + #f'{self.study_name}.{ccs_name}.invest_techno_mix': investment_mix, } techno_margin_dict = { f'{self.study_name}.{ccs_name}.{techno}.{GlossaryEnergy.MarginValue}': margin for techno in @@ -174,4 +174,4 @@ def setup_usecase(self, study_folder_path=None): if '__main__' == __name__: uc_cls = Study(main_study=True) uc_cls.load_data() - uc_cls.run() + uc_cls.run() \ No newline at end of file diff --git a/energy_models/sos_processes/energy/techno_mix/clean_energy_mix/__init__.py b/energy_models/sos_processes/energy/techno_mix/clean_energy_mix/__init__.py index fa34a5ef..f18f78a9 100644 --- a/energy_models/sos_processes/energy/techno_mix/clean_energy_mix/__init__.py +++ b/energy_models/sos_processes/energy/techno_mix/clean_energy_mix/__init__.py @@ -12,4 +12,4 @@ See the License for the specific language governing permissions and limitations under the License. -''' +''' \ No newline at end of file diff --git a/energy_models/sos_processes/energy/techno_mix/electricity_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/electricity_mix/usecase.py index e2a9c911..354511c9 100644 --- a/energy_models/sos_processes/energy/techno_mix/electricity_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/electricity_mix/usecase.py @@ -28,15 +28,14 @@ DEFAULT_TECHNOLOGIES_LIST = ['WindOffshore', GlossaryEnergy.WindOnshore, GlossaryEnergy.SolarPv, 'SolarThermal', GlossaryEnergy.Hydropower, 'Nuclear', 'CombinedCycleGasTurbine', GlossaryEnergy.GasTurbine, 'BiogasFired', - # 'Geothermal',todo : removed because was using medium heat temperature + #'Geothermal',todo : removed because was using medium heat temperature GlossaryEnergy.CoalGen, 'OilGen', 'BiomassFired'] TECHNOLOGIES_LIST = ['WindOffshore', GlossaryEnergy.WindOnshore, GlossaryEnergy.SolarPv, 'SolarThermal', GlossaryEnergy.Hydropower, 'Nuclear', 'CombinedCycleGasTurbine', GlossaryEnergy.GasTurbine, 'BiogasFired', - # 'Geothermal',todo : removed because was using medium heat temperature + #'Geothermal',todo : removed because was using medium heat temperature GlossaryEnergy.CoalGen, 'OilGen', 'BiomassFired'] - class Study(EnergyMixStudyManager): def __init__(self, year_start=GlossaryEnergy.YearStartDefault, year_end=GlossaryEnergy.YearEndDefault, technologies_list=DEFAULT_TECHNOLOGIES_LIST, diff --git a/energy_models/sos_processes/energy/techno_mix/ethanol_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/ethanol_mix/usecase.py index c06945a5..80f3d66d 100644 --- a/energy_models/sos_processes/energy/techno_mix/ethanol_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/ethanol_mix/usecase.py @@ -100,7 +100,7 @@ def setup_usecase(self, study_folder_path=None): values_dict = {f'{self.study_name}.{GlossaryEnergy.YearStart}': self.year_start, f'{self.study_name}.{GlossaryEnergy.YearEnd}': self.year_end, f'{self.study_name}.{energy_name}.{GlossaryEnergy.techno_list}': self.technologies_list, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: diff --git a/energy_models/sos_processes/energy/techno_mix/gaseous_hydrogen_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/gaseous_hydrogen_mix/usecase.py index 6773066e..24466cc5 100644 --- a/energy_models/sos_processes/energy/techno_mix/gaseous_hydrogen_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/gaseous_hydrogen_mix/usecase.py @@ -134,7 +134,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.WaterGasShift}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: values_dict.update( diff --git a/energy_models/sos_processes/energy/techno_mix/hightemperatureheat_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/hightemperatureheat_mix/usecase.py index af256d3b..db010e59 100644 --- a/energy_models/sos_processes/energy/techno_mix/hightemperatureheat_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/hightemperatureheat_mix/usecase.py @@ -118,7 +118,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.techno_list}': self.technologies_list, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.ElectricBoiler}.flux_input_dict': land_rate, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.NaturalGasBoiler}.flux_input_dict': land_rate, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.HeatPump}.flux_input_dict': land_rate, diff --git a/energy_models/sos_processes/energy/techno_mix/hydrotreated_oil_fuel_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/hydrotreated_oil_fuel_mix/usecase.py index 70789412..6505a7b5 100644 --- a/energy_models/sos_processes/energy/techno_mix/hydrotreated_oil_fuel_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/hydrotreated_oil_fuel_mix/usecase.py @@ -119,7 +119,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.HefaDeoxygenation}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: diff --git a/energy_models/sos_processes/energy/techno_mix/liquid_fuel_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/liquid_fuel_mix/usecase.py index d4daa36c..72740750 100644 --- a/energy_models/sos_processes/energy/techno_mix/liquid_fuel_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/liquid_fuel_mix/usecase.py @@ -117,7 +117,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.FischerTropsch}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: values_dict.update({ @@ -146,4 +146,4 @@ def setup_usecase(self, study_folder_path=None): if '__main__' == __name__: uc_cls = Study(main_study=True) - uc_cls.test() + uc_cls.test() \ No newline at end of file diff --git a/energy_models/sos_processes/energy/techno_mix/lowtemperatureheat_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/lowtemperatureheat_mix/usecase.py index 9e36c4f7..3b83885a 100644 --- a/energy_models/sos_processes/energy/techno_mix/lowtemperatureheat_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/lowtemperatureheat_mix/usecase.py @@ -115,7 +115,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.techno_list}': self.technologies_list, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.ElectricBoiler}.flux_input_dict': land_rate, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.NaturalGasBoiler}.flux_input_dict': land_rate, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.HeatPump}.flux_input_dict': land_rate, diff --git a/energy_models/sos_processes/energy/techno_mix/mediumtemperatureheat_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/mediumtemperatureheat_mix/usecase.py index 8fe9ead2..9d1810ae 100644 --- a/energy_models/sos_processes/energy/techno_mix/mediumtemperatureheat_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/mediumtemperatureheat_mix/usecase.py @@ -117,7 +117,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.techno_list}': self.technologies_list, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.ElectricBoiler}.flux_input_dict': land_rate, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.NaturalGasBoiler}.flux_input_dict': land_rate, # f'{self.study_name}.{energy_name}.{GlossaryEnergy.HeatPump}.flux_input_dict': land_rate, diff --git a/energy_models/sos_processes/energy/techno_mix/methane_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/methane_mix/usecase.py index 0e3afadb..16d28003 100644 --- a/energy_models/sos_processes/energy/techno_mix/methane_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/methane_mix/usecase.py @@ -113,7 +113,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.UpgradingBiogas}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: diff --git a/energy_models/sos_processes/energy/techno_mix/methanol_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/methanol_mix/usecase.py index 4c0821e8..18b99ed1 100644 --- a/energy_models/sos_processes/energy/techno_mix/methanol_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/methanol_mix/usecase.py @@ -99,7 +99,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.techno_list}': self.technologies_list, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: @@ -129,4 +129,4 @@ def setup_usecase(self, study_folder_path=None): if '__main__' == __name__: uc_cls = Study(main_study=True) - uc_cls.test() + uc_cls.test() \ No newline at end of file diff --git a/energy_models/sos_processes/energy/techno_mix/syngas_mix/usecase.py b/energy_models/sos_processes/energy/techno_mix/syngas_mix/usecase.py index 0120b2e7..2ae94fb5 100644 --- a/energy_models/sos_processes/energy/techno_mix/syngas_mix/usecase.py +++ b/energy_models/sos_processes/energy/techno_mix/syngas_mix/usecase.py @@ -133,7 +133,7 @@ def setup_usecase(self, study_folder_path=None): f'{self.study_name}.{energy_name}.{GlossaryEnergy.CoElectrolysis}.{GlossaryEnergy.MarginValue}': margin, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportCostValue}': transport, f'{self.study_name}.{energy_name}.{GlossaryEnergy.TransportMarginValue}': margin, - # f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, + #f'{self.study_name}.{energy_name}.{GlossaryEnergy.invest_techno_mix}.: investment_mix, } if self.main_study: diff --git a/energy_models/sos_processes/post_processing/post_proc_technology_mix.py b/energy_models/sos_processes/post_processing/post_proc_technology_mix.py index b5d1ca6b..178dc81e 100644 --- a/energy_models/sos_processes/post_processing/post_proc_technology_mix.py +++ b/energy_models/sos_processes/post_processing/post_proc_technology_mix.py @@ -80,7 +80,7 @@ def get_techno_price_filter_data(execution_engine, namespace, title, price_name, else: var_energyproduction_all_energy_df = var_energyproduction_all_energy_df.merge(var_energyproduction_df) var_energyproduction_all_energy_df.columns = var_energyproduction_all_energy_df.columns.str.replace( - energ + " ", energ + ".").str.replace(" (TWh)", "") # (r" \(.*\)", "") + energ + " ", energ + ".").str.replace(" (TWh)", "") #(r" \(.*\)", "") # FIXME: r" is raw string not regex, need to use re.sub y_incre += 1 diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul00_24technos_8streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul00_24technos_8streams.json index 588d459c..44c4d412 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul00_24technos_8streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul00_24technos_8streams.json @@ -1,66 +1,66 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.SOEC", - "Electrolysis.PEM", - "Electrolysis.AWE" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation", - "HefaDeoxygenation" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOffshore", - "WindOnshore", - "SolarPv", - "SolarThermal", - "Hydropower", - "Nuclear" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.CalciumLooping", - "flue_gas_capture.ChilledAmmoniaProcess", - "flue_gas_capture.CO2Membranes", - "flue_gas_capture.MonoEthanolAmine", - "flue_gas_capture.PiperazineProcess", - "flue_gas_capture.PressureSwingAdsorption" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "GeologicMineralization", - "PureCarbonSolidStorage", - "CarbonStorageTechno", - "Reforestation" - ] - }, - "biomass_dry": { - "type": "agriculture", - "value": [ - "CropEnergy" - ] - }, - "heat.hightemperatureheat": { - "type": "energy", - "value": [ - "GeothermalHighHeat" - ] - }, - "renewable": { - "type": "energy", - "value": [ - "RenewableSimpleTechno" - ] - } -} + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.SOEC", + "Electrolysis.PEM", + "Electrolysis.AWE" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation", + "HefaDeoxygenation" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOffshore", + "WindOnshore", + "SolarPv", + "SolarThermal", + "Hydropower", + "Nuclear" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.CalciumLooping", + "flue_gas_capture.ChilledAmmoniaProcess", + "flue_gas_capture.CO2Membranes", + "flue_gas_capture.MonoEthanolAmine", + "flue_gas_capture.PiperazineProcess", + "flue_gas_capture.PressureSwingAdsorption" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "GeologicMineralization", + "PureCarbonSolidStorage", + "CarbonStorageTechno", + "Reforestation" + ] + }, + "biomass_dry": { + "type": "agriculture", + "value": [ + "CropEnergy" + ] + }, + "heat.hightemperatureheat": { + "type": "energy", + "value": [ + "GeothermalHighHeat" + ] + }, + "renewable": { + "type": "energy", + "value": [ + "RenewableSimpleTechno" + ] + } +} \ No newline at end of file diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul01_24technos_12streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul01_24technos_12streams.json index 823d7e3c..64309c83 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul01_24technos_12streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul01_24technos_12streams.json @@ -1,86 +1,86 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.SOEC" - ] - }, - "hydrogen.liquid_hydrogen": { - "type": "energy", - "value": [ - "HydrogenLiquefaction" - ] - }, - "syngas": { - "type": "energy", - "value": [ - "CoElectrolysis" - ] - }, - "fuel.liquid_fuel": { - "type": "energy", - "value": [ - "Refinery", - "FischerTropsch" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation", - "HefaDeoxygenation" - ] - }, - "fuel.biodiesel": { - "type": "energy", - "value": [ - "Transesterification" - ] - }, - "solid_fuel": { - "type": "energy", - "value": [ - "Pelletizing" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOffshore", - "WindOnshore", - "SolarPv", - "SolarThermal", - "Hydropower", - "Nuclear" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.CalciumLooping", - "flue_gas_capture.ChilledAmmoniaProcess", - "flue_gas_capture.CO2Membranes", - "flue_gas_capture.MonoEthanolAmine", - "flue_gas_capture.PiperazineProcess", - "flue_gas_capture.PressureSwingAdsorption" - ] - }, - "biomass_dry": { - "type": "agriculture", - "value": [ - "ManagedWood" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "CarbonStorageTechno" - ] - }, - "heat.hightemperatureheat": { - "type": "energy", - "value": [ - "ElectricBoilerHighHeat" - ] - } -} + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.SOEC" + ] + }, + "hydrogen.liquid_hydrogen": { + "type": "energy", + "value": [ + "HydrogenLiquefaction" + ] + }, + "syngas": { + "type": "energy", + "value": [ + "CoElectrolysis" + ] + }, + "fuel.liquid_fuel": { + "type": "energy", + "value": [ + "Refinery", + "FischerTropsch" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation", + "HefaDeoxygenation" + ] + }, + "fuel.biodiesel": { + "type": "energy", + "value": [ + "Transesterification" + ] + }, + "solid_fuel": { + "type": "energy", + "value": [ + "Pelletizing" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOffshore", + "WindOnshore", + "SolarPv", + "SolarThermal", + "Hydropower", + "Nuclear" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.CalciumLooping", + "flue_gas_capture.ChilledAmmoniaProcess", + "flue_gas_capture.CO2Membranes", + "flue_gas_capture.MonoEthanolAmine", + "flue_gas_capture.PiperazineProcess", + "flue_gas_capture.PressureSwingAdsorption" + ] + }, + "biomass_dry": { + "type": "agriculture", + "value": [ + "ManagedWood" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "CarbonStorageTechno" + ] + }, + "heat.hightemperatureheat": { + "type": "energy", + "value": [ + "ElectricBoilerHighHeat" + ] + } +} \ No newline at end of file diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul02_29technos_11streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul02_29technos_11streams.json index 9e8d396f..7c27c9c7 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul02_29technos_11streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul02_29technos_11streams.json @@ -1,86 +1,86 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.SOEC" - ] - }, - "hydrogen.liquid_hydrogen": { - "type": "energy", - "value": [ - "HydrogenLiquefaction" - ] - }, - "syngas": { - "type": "energy", - "value": [ - "CoElectrolysis" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation", - "HefaDeoxygenation" - ] - }, - "fuel.biodiesel": { - "type": "energy", - "value": [ - "Transesterification" - ] - }, - "solid_fuel": { - "type": "energy", - "value": [ - "Pelletizing" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOffshore", - "WindOnshore", - "SolarPv", - "SolarThermal", - "Hydropower", - "Nuclear" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.CalciumLooping", - "flue_gas_capture.ChilledAmmoniaProcess", - "flue_gas_capture.MonoEthanolAmine", - "flue_gas_capture.PiperazineProcess", - "flue_gas_capture.PressureSwingAdsorption" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "BiomassBuryingFossilization", - "DeepOceanInjection", - "DeepSalineFormation", - "DepletedOilGas", - "EnhancedOilRecovery", - "GeologicMineralization", - "PureCarbonSolidStorage", - "CarbonStorageTechno", - "Reforestation" - ] - }, - "biomass_dry": { - "type": "agriculture", - "value": [ - "CropEnergy" - ] - }, - "heat.hightemperatureheat": { - "type": "energy", - "value": [ - "HeatPumpHighHeat" - ] - } -} + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.SOEC" + ] + }, + "hydrogen.liquid_hydrogen": { + "type": "energy", + "value": [ + "HydrogenLiquefaction" + ] + }, + "syngas": { + "type": "energy", + "value": [ + "CoElectrolysis" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation", + "HefaDeoxygenation" + ] + }, + "fuel.biodiesel": { + "type": "energy", + "value": [ + "Transesterification" + ] + }, + "solid_fuel": { + "type": "energy", + "value": [ + "Pelletizing" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOffshore", + "WindOnshore", + "SolarPv", + "SolarThermal", + "Hydropower", + "Nuclear" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.CalciumLooping", + "flue_gas_capture.ChilledAmmoniaProcess", + "flue_gas_capture.MonoEthanolAmine", + "flue_gas_capture.PiperazineProcess", + "flue_gas_capture.PressureSwingAdsorption" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "BiomassBuryingFossilization", + "DeepOceanInjection", + "DeepSalineFormation", + "DepletedOilGas", + "EnhancedOilRecovery", + "GeologicMineralization", + "PureCarbonSolidStorage", + "CarbonStorageTechno", + "Reforestation" + ] + }, + "biomass_dry": { + "type": "agriculture", + "value": [ + "CropEnergy" + ] + }, + "heat.hightemperatureheat": { + "type": "energy", + "value": [ + "HeatPumpHighHeat" + ] + } +} \ No newline at end of file diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul52_5technos_5streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul52_5technos_5streams.json index 4dd7124d..6e5116ea 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul52_5technos_5streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul52_5technos_5streams.json @@ -1,32 +1,32 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.SOEC" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOnshore" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.PressureSwingAdsorption" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "CarbonStorageTechno" - ] - } -} + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.SOEC" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOnshore" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.PressureSwingAdsorption" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "CarbonStorageTechno" + ] + } +} \ No newline at end of file diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul59_24technos_11streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul59_24technos_11streams.json index 5d2a0f15..0d692400 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul59_24technos_11streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-14 Jul59_24technos_11streams.json @@ -1,81 +1,81 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.SOEC", - "Electrolysis.PEM", - "Electrolysis.AWE" - ] - }, - "hydrogen.liquid_hydrogen": { - "type": "energy", - "value": [ - "HydrogenLiquefaction" - ] - }, - "syngas": { - "type": "energy", - "value": [ - "CoElectrolysis" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation", - "HefaDeoxygenation" - ] - }, - "fuel.biodiesel": { - "type": "energy", - "value": [ - "Transesterification" - ] - }, - "solid_fuel": { - "type": "energy", - "value": [ - "Pelletizing" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOffshore", - "WindOnshore", - "SolarPv", - "SolarThermal", - "Hydropower", - "Nuclear" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.CalciumLooping", - "flue_gas_capture.ChilledAmmoniaProcess", - "flue_gas_capture.CO2Membranes", - "flue_gas_capture.MonoEthanolAmine", - "flue_gas_capture.PiperazineProcess", - "flue_gas_capture.PressureSwingAdsorption" - ] - }, - "biomass_dry": { - "type": "agriculture", - "value": [ - "CropEnergy" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "CarbonStorageTechno" - ] - }, - "heat.hightemperatureheat": { - "type": "energy", - "value": [ - "GeothermalHighHeat" - ] - } -} + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.SOEC", + "Electrolysis.PEM", + "Electrolysis.AWE" + ] + }, + "hydrogen.liquid_hydrogen": { + "type": "energy", + "value": [ + "HydrogenLiquefaction" + ] + }, + "syngas": { + "type": "energy", + "value": [ + "CoElectrolysis" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation", + "HefaDeoxygenation" + ] + }, + "fuel.biodiesel": { + "type": "energy", + "value": [ + "Transesterification" + ] + }, + "solid_fuel": { + "type": "energy", + "value": [ + "Pelletizing" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOffshore", + "WindOnshore", + "SolarPv", + "SolarThermal", + "Hydropower", + "Nuclear" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.CalciumLooping", + "flue_gas_capture.ChilledAmmoniaProcess", + "flue_gas_capture.CO2Membranes", + "flue_gas_capture.MonoEthanolAmine", + "flue_gas_capture.PiperazineProcess", + "flue_gas_capture.PressureSwingAdsorption" + ] + }, + "biomass_dry": { + "type": "agriculture", + "value": [ + "CropEnergy" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "CarbonStorageTechno" + ] + }, + "heat.hightemperatureheat": { + "type": "energy", + "value": [ + "GeothermalHighHeat" + ] + } +} \ No newline at end of file diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul36_5technos_5streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul36_5technos_5streams.json index c3c8b2d7..b1baebf3 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul36_5technos_5streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul36_5technos_5streams.json @@ -1,32 +1,32 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.AWE" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOffshore" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.PiperazineProcess" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "CarbonStorageTechno" - ] - } -} + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.AWE" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOffshore" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.PiperazineProcess" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "CarbonStorageTechno" + ] + } +} \ No newline at end of file diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul39_5technos_5streams.json b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul39_5technos_5streams.json index 2b930e76..d5a24ad0 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul39_5technos_5streams.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_2024-07-15 Jul39_5technos_5streams.json @@ -1,32 +1,32 @@ { - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.AWE" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "Hydropower" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "flue_gas_capture.PressureSwingAdsorption" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "CarbonStorageTechno" - ] - } -} + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.AWE" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "Hydropower" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "flue_gas_capture.PressureSwingAdsorption" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "CarbonStorageTechno" + ] + } +} \ No newline at end of file diff --git a/energy_models/sos_processes/techno_dict/data/techno_dict_test.json b/energy_models/sos_processes/techno_dict/data/techno_dict_test.json index 88fde8bb..487acd3b 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dict_test.json +++ b/energy_models/sos_processes/techno_dict/data/techno_dict_test.json @@ -1,92 +1,92 @@ { - "methane": { - "type": "energy", - "value": [ - "FossilGas" - ] - }, - "hydrogen.gaseous_hydrogen": { - "type": "energy", - "value": [ - "Electrolysis.SOEC", - "Electrolysis.PEM", - "Electrolysis.AWE", - "PlasmaCracking", - "WaterGasShift" - ] - }, - "hydrogen.liquid_hydrogen": { - "type": "energy", - "value": [ - "HydrogenLiquefaction" - ] - }, - "biogas": { - "type": "energy", - "value": [ - "AnaerobicDigestion" - ] - }, - "syngas": { - "type": "energy", - "value": [ - "Pyrolysis" - ] - }, - "fuel.liquid_fuel": { - "type": "energy", - "value": [ - "Refinery", - "FischerTropsch" - ] - }, - "fuel.hydrotreated_oil_fuel": { - "type": "energy", - "value": [ - "HefaDecarboxylation", - "HefaDeoxygenation" - ] - }, - "fuel.biodiesel": { - "type": "energy", - "value": [ - "Transesterification" - ] - }, - "solid_fuel": { - "type": "energy", - "value": [ - "CoalExtraction" - ] - }, - "electricity": { - "type": "energy", - "value": [ - "WindOffshore", - "WindOnshore", - "SolarPv", - "SolarThermal", - "Hydropower", - "Nuclear", - "CombinedCycleGasTurbine", - "BiogasFired", - "CoalGen", - "OilGen" - ] - }, - "carbon_capture": { - "type": "CCUS", - "value": [ - "direct_air_capture.AmineScrubbing", - "direct_air_capture.CalciumPotassiumScrubbing" - ] - }, - "carbon_storage": { - "type": "CCUS", - "value": [ - "DeepOceanInjection", - "DeepSalineFormation", - "GeologicMineralization" - ] - } -} + "methane": { + "type": "energy", + "value": [ + "FossilGas" + ] + }, + "hydrogen.gaseous_hydrogen": { + "type": "energy", + "value": [ + "Electrolysis.SOEC", + "Electrolysis.PEM", + "Electrolysis.AWE", + "PlasmaCracking", + "WaterGasShift" + ] + }, + "hydrogen.liquid_hydrogen": { + "type": "energy", + "value": [ + "HydrogenLiquefaction" + ] + }, + "biogas": { + "type": "energy", + "value": [ + "AnaerobicDigestion" + ] + }, + "syngas": { + "type": "energy", + "value": [ + "Pyrolysis" + ] + }, + "fuel.liquid_fuel": { + "type": "energy", + "value": [ + "Refinery", + "FischerTropsch" + ] + }, + "fuel.hydrotreated_oil_fuel": { + "type": "energy", + "value": [ + "HefaDecarboxylation", + "HefaDeoxygenation" + ] + }, + "fuel.biodiesel": { + "type": "energy", + "value": [ + "Transesterification" + ] + }, + "solid_fuel": { + "type": "energy", + "value": [ + "CoalExtraction" + ] + }, + "electricity": { + "type": "energy", + "value": [ + "WindOffshore", + "WindOnshore", + "SolarPv", + "SolarThermal", + "Hydropower", + "Nuclear", + "CombinedCycleGasTurbine", + "BiogasFired", + "CoalGen", + "OilGen" + ] + }, + "carbon_capture": { + "type": "CCUS", + "value": [ + "direct_air_capture.AmineScrubbing", + "direct_air_capture.CalciumPotassiumScrubbing" + ] + }, + "carbon_storage": { + "type": "CCUS", + "value": [ + "DeepOceanInjection", + "DeepSalineFormation", + "GeologicMineralization" + ] + } +} \ No newline at end of file diff --git a/energy_models/sos_processes/techno_dict/data/techno_dicts.py b/energy_models/sos_processes/techno_dict/data/techno_dicts.py index 54678d61..0bb0bc6a 100644 --- a/energy_models/sos_processes/techno_dict/data/techno_dicts.py +++ b/energy_models/sos_processes/techno_dict/data/techno_dicts.py @@ -20,13 +20,10 @@ filename = "techno_dict_2024-07-14 Jul01_24technos_12streams.json" filename = "techno_dict_test.json" - - def load_dict(filename: str): filepath = join(techno_dict_folder, filename) with open(filepath, 'r') as json_file: loaded_dict = json.load(json_file) return loaded_dict - techno_dict_midway = load_dict(filename) diff --git a/energy_models/sos_processes/techno_dict/techno_dict_builder.py b/energy_models/sos_processes/techno_dict/techno_dict_builder.py index 6a572bb3..eb3a9846 100644 --- a/energy_models/sos_processes/techno_dict/techno_dict_builder.py +++ b/energy_models/sos_processes/techno_dict/techno_dict_builder.py @@ -76,8 +76,11 @@ def techno_dict_builder(techno_infos: dict, initial_selection: list[str], if stream not in energy_to_producing_technos: energy_to_producing_technos[stream] = [] + + # Print the list of unique energy values + # Create the problem prob = pulp.LpProblem("Minimal_Technology_Selection", pulp.LpMinimize) @@ -135,12 +138,14 @@ def techno_dict_builder(techno_infos: dict, initial_selection: list[str], raise ValueError(f"There is a total of {len(techno_infos)} technos available, please lower the minimal_techno_number constraint value") prob += pulp.lpSum([tech_vars[t] for t in techno_infos]) >= minimal_techno_number + # Objective: Minimize the number of additional technologies selected prob += pulp.lpSum([tech_vars[t] for t in techno_infos]) # Solve the problem prob.solve() + def show_infos(): print("Energy to Technologies Dictionary:", energy_to_producing_technos) @@ -157,12 +162,13 @@ def show_infos(): rhs_value = constraint.constant print(f"{name}: LHS = {lhs_value}, RHS = {rhs_value}, Status = {lhs_value == rhs_value}") + # Print the selected technologies selected_technologies = [t for t in techno_infos if pulp.value(tech_vars[t]) == 1] selected_streams = [s for s in all_streams if pulp.value(bool_stream_produced_vars[s]) == 1] + print('='*100) print('=' * 100) - print('=' * 100) - print('=' * 100) + print('='*100) print(f"\n\nInitially Selected Technologies ({len(initial_selection)}):".upper(), initial_selection) for selected_techno in initial_selection: print(selected_techno, techno_infos[selected_techno]) @@ -187,7 +193,7 @@ def show_infos(): return techno_dict_for_witness, n_technos, n_streams -# techno_dict_builder(technologies_test) +#techno_dict_builder(technologies_test) def build_techno_infos(stream_used_by_technos: dict, stream_produced_by_techno: dict): out = {} @@ -209,15 +215,15 @@ def build_techno_infos(stream_used_by_technos: dict, stream_produced_by_techno: inital_selection = [ GlossaryEnergy.HefaDecarboxylation, GlossaryEnergy.FischerTropsch, - # f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.FlueGasTechno}", - # f"{GlossaryEnergy.direct_air_capture}.{GlossaryEnergy.DirectAirCaptureTechno}", + #f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.FlueGasTechno}", + #f"{GlossaryEnergy.direct_air_capture}.{GlossaryEnergy.DirectAirCaptureTechno}", ] technos_to_avoid = [ GlossaryEnergy.BiomassFermentation, -f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.MonoEthanolAmine}", # remove -f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.ChilledAmmoniaProcess}", # remove -f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.CO2Membranes}", # remove +f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.MonoEthanolAmine}", # remove +f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.ChilledAmmoniaProcess}", # remove +f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.CO2Membranes}", # remove f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.PressureSwingAdsorption}", # remove f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.CalciumLooping}", # remove f"{GlossaryEnergy.flue_gas_capture}.{GlossaryEnergy.PiperazineProcess}", # remove @@ -227,15 +233,15 @@ def build_techno_infos(stream_used_by_technos: dict, stream_produced_by_techno: GlossaryEnergy.CleanEnergySimpleTechno, GlossaryEnergy.RWGS, -# GlossaryEnergy.FischerTropsch +#GlossaryEnergy.FischerTropsch ] streams_to_avoid = [ GlossaryEnergy.hightemperatureheat_energyname, GlossaryEnergy.mediumtemperatureheat_energyname, GlossaryEnergy.lowtemperatureheat_energyname, GlossaryEnergy.biomass_dry, -# GlossaryEnergy.syngas, -# f'{GlossaryEnergy.fuel}.{GlossaryEnergy.liquid_fuel}', +#GlossaryEnergy.syngas, +#f'{GlossaryEnergy.fuel}.{GlossaryEnergy.liquid_fuel}', ] streams_to_have = [ GlossaryEnergy.carbon_capture, diff --git a/energy_models/sos_processes/techno_dict/visualistion.py b/energy_models/sos_processes/techno_dict/visualistion.py index 9fac7e69..64385eaf 100644 --- a/energy_models/sos_processes/techno_dict/visualistion.py +++ b/energy_models/sos_processes/techno_dict/visualistion.py @@ -27,6 +27,7 @@ } + def generate_color_mapping(strings): # Get a list of all available colors colors = list(mcolors.CSS4_COLORS.keys()) @@ -52,17 +53,15 @@ def generate_color_mapping(strings): return color_mapping - def visualize(technologies): # Initialize the directed graph import re import networkx as nx - def improve_string(ss): - ss = ss.replace('.', '\n').replace('_', ' ') + ss = ss.replace('.','\n').replace('_',' ') ss = re.sub(r'(? remove the self.override_dump_jacobian in both tests + ''' # FIXME: address this --> remove the self.override_dump_jacobian in both tests os.remove(path_pickle) def test_02_energy_mix_co2_tax(self): diff --git a/energy_models/tests/l1_test_gradient_ethanol.py b/energy_models/tests/l1_test_gradient_ethanol.py index 47fbb52f..55b717e6 100644 --- a/energy_models/tests/l1_test_gradient_ethanol.py +++ b/energy_models/tests/l1_test_gradient_ethanol.py @@ -69,7 +69,7 @@ def setUp(self): self.invest_level = pd.DataFrame({GlossaryEnergy.Years: years, GlossaryEnergy.InvestValue: np.linspace(0.001, 0.0008, len(years)) }) - + self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: years, GlossaryEnergy.CO2Tax: np.linspace(15., 40., len(years))}) self.margin = pd.DataFrame( @@ -118,7 +118,7 @@ def test_01_biomass_fermentation_discipline_analytic_grad(self): np.arange(GlossaryEnergy.YearStartDefault, self.year_end + 1)), f'{self.name}.{GlossaryEnergy.ResourcesPriceValue}': get_default_resources_prices( np.arange(GlossaryEnergy.YearStartDefault, self.year_end + 1)), - f'{self.name}.techno_infos_dict': techno_infos_dict, } + f'{self.name}.techno_infos_dict': techno_infos_dict,} self.ee.load_study_from_input_dict(inputs_dict) diff --git a/energy_models/tests/l1_test_gradient_flue_gas.py b/energy_models/tests/l1_test_gradient_flue_gas.py index fb9eec81..debf8c46 100644 --- a/energy_models/tests/l1_test_gradient_flue_gas.py +++ b/energy_models/tests/l1_test_gradient_flue_gas.py @@ -90,6 +90,7 @@ def setUp(self): self.invest_level = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: np.linspace(22., 31., len(self.years))}) + self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) diff --git a/energy_models/tests/l1_test_gradient_fossil_simple_techno.py b/energy_models/tests/l1_test_gradient_fossil_simple_techno.py index 0684d931..84ffa18a 100644 --- a/energy_models/tests/l1_test_gradient_fossil_simple_techno.py +++ b/energy_models/tests/l1_test_gradient_fossil_simple_techno.py @@ -61,6 +61,7 @@ def setUp(self): self.invest_level = pd.DataFrame( {GlossaryEnergy.Years: years, GlossaryEnergy.InvestValue: 33.0 * 1.10 ** (years - GlossaryEnergy.YearStartDefault)}) + self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: years, GlossaryEnergy.CO2Tax: np.linspace(15., 40., len(years))}) @@ -101,6 +102,7 @@ def test_01_discipline_analytic_grad(self): self.ee.configure() self.ee.display_treeview_nodes() + invest_before_ystart = pd.DataFrame( {'past years': np.arange(-3, 0), GlossaryEnergy.InvestValue: [0.0, 1483.79, 1489.95]}) diff --git a/energy_models/tests/l1_test_gradient_hydrogen.py b/energy_models/tests/l1_test_gradient_hydrogen.py index cc13988e..565b01a7 100644 --- a/energy_models/tests/l1_test_gradient_hydrogen.py +++ b/energy_models/tests/l1_test_gradient_hydrogen.py @@ -72,7 +72,7 @@ def setUp(self): self.plasmacracking_techno_prices = pd.DataFrame({GlossaryEnergy.Years: years, GlossaryEnergy.PlasmaCracking: np.linspace(63., 32., len(years)), - 'PlasmaCracking_wotaxes': np.linspace(63., 32., len(years)) + 'PlasmaCracking_wotaxes' :np.linspace(63., 32., len(years)) }) self.smr_consumption = pd.DataFrame({GlossaryEnergy.Years: self.years, @@ -134,6 +134,7 @@ def setUp(self): self.invest_level_negative = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: np.linspace(5000, -5000, len(self.years))}) + self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) diff --git a/energy_models/tests/l1_test_gradient_hydrotreated_oil_fuel.py b/energy_models/tests/l1_test_gradient_hydrotreated_oil_fuel.py index 057076f1..00cce3eb 100644 --- a/energy_models/tests/l1_test_gradient_hydrotreated_oil_fuel.py +++ b/energy_models/tests/l1_test_gradient_hydrotreated_oil_fuel.py @@ -69,7 +69,7 @@ def setUp(self): self.invest_level = pd.DataFrame({GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: np.linspace(0.001, 0.0008, len(self.years)) }) - + self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) self.margin = pd.DataFrame( diff --git a/energy_models/tests/l1_test_gradient_liquid_fuel.py b/energy_models/tests/l1_test_gradient_liquid_fuel.py index 92cf003f..4ee0a9bd 100644 --- a/energy_models/tests/l1_test_gradient_liquid_fuel.py +++ b/energy_models/tests/l1_test_gradient_liquid_fuel.py @@ -92,7 +92,7 @@ def setUp(self): self.invest_level_negative2 = pd.DataFrame({GlossaryEnergy.Years: years, GlossaryEnergy.InvestValue: np.linspace(4435750000.0, 5093000000.0, len(self.years)) * 1.0e-9}) - + self.invest_level = pd.DataFrame( {GlossaryEnergy.Years: years, GlossaryEnergy.InvestValue: np.linspace(4435750000.0, 5093000000.0, len(self.years)) * 1.0e-9}) @@ -289,7 +289,7 @@ def test_03_transesterification_discipline_analytic_grad_negative_invest(self): self.ee.execute() disc_techno = self.ee.root_process.proxy_disciplines[0].mdo_discipline_wrapp.mdo_discipline - self.override_dump_jacobian = True # Test seems KO in server, regenerate pickle at each test + self.override_dump_jacobian = True # Test seems KO in server, regenerate pickle at each test self.check_jacobian(location=dirname(__file__), filename=f'jacobian_{self.energy_name}_{self.model_name}_negative.pkl', discipline=disc_techno, step=1.0e-16, derr_approx='complex_step', diff --git a/energy_models/tests/l1_test_gradient_liquid_hydrogen.py b/energy_models/tests/l1_test_gradient_liquid_hydrogen.py index f7ca6e0c..841916cc 100644 --- a/energy_models/tests/l1_test_gradient_liquid_hydrogen.py +++ b/energy_models/tests/l1_test_gradient_liquid_hydrogen.py @@ -80,6 +80,8 @@ def setUp(self): self.invest_level = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: 0.1715}) + + self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) self.margin = pd.DataFrame( @@ -231,6 +233,8 @@ def test_02_liquid_hydrogen_discipline_jacobian(self): inputs_dict[f'{namespace}.{self.energy_name}.{GlossaryEnergy.HydrogenLiquefaction}.{GlossaryEnergy.TechnoProductionValue}'][ f'{GlossaryEnergy.hydrogen}.{GlossaryEnergy.liquid_hydrogen} ({GlossaryEnergy.energy_unit})'] *= np.linspace(5.0, 5.0, len(self.years)) + + self.ee.load_study_from_input_dict(inputs_dict) self.ee.execute() diff --git a/energy_models/tests/l1_test_gradient_methane.py b/energy_models/tests/l1_test_gradient_methane.py index c1149fab..f1e63eaa 100644 --- a/energy_models/tests/l1_test_gradient_methane.py +++ b/energy_models/tests/l1_test_gradient_methane.py @@ -63,7 +63,7 @@ def setUp(self): self.stream_co2_emissions = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.electricity: 0.0, - GlossaryEnergy.carbon_capture: -2, f'{GlossaryEnergy.hydrogen}.{GlossaryEnergy.gaseous_hydrogen}': 0.0, GlossaryEnergy.biogas: -0.51}) + GlossaryEnergy.carbon_capture: -2,f'{GlossaryEnergy.hydrogen}.{GlossaryEnergy.gaseous_hydrogen}': 0.0, GlossaryEnergy.biogas: -0.51}) # Use the same inest as SMR techno self.invest_level_methanation = pd.DataFrame({GlossaryEnergy.Years: self.years, @@ -71,6 +71,7 @@ def setUp(self): self.invest_level = pd.DataFrame({GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: np.linspace(4., 5.0, len(self.years))}) + self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) @@ -331,6 +332,7 @@ def test_04_methane_discipline_jacobian(self): if mda_data_output_dict[self.energy_name][key]['is_coupling']: coupled_outputs += [f'{namespace}.{self.energy_name}.{key}'] + self.ee.load_study_from_input_dict(inputs_dict) self.ee.execute() diff --git a/energy_models/tests/l1_test_gradient_methanol.py b/energy_models/tests/l1_test_gradient_methanol.py index d0185984..0947ad68 100644 --- a/energy_models/tests/l1_test_gradient_methanol.py +++ b/energy_models/tests/l1_test_gradient_methanol.py @@ -81,7 +81,7 @@ def setUp(self): self.invest_level = pd.DataFrame({GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: np.linspace(0.001, 0.0008, len(self.years)) }) - + self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) self.margin = pd.DataFrame( diff --git a/energy_models/tests/l1_test_gradient_one_invest.py b/energy_models/tests/l1_test_gradient_one_invest.py index c82be3d5..da224f8b 100644 --- a/energy_models/tests/l1_test_gradient_one_invest.py +++ b/energy_models/tests/l1_test_gradient_one_invest.py @@ -136,5 +136,7 @@ def test_01_one_invest_analytic_grad(self): ) + + if '__main__' == __name__: pass diff --git a/energy_models/tests/l1_test_gradient_ratio.py b/energy_models/tests/l1_test_gradient_ratio.py index f314d0f1..dba7c279 100644 --- a/energy_models/tests/l1_test_gradient_ratio.py +++ b/energy_models/tests/l1_test_gradient_ratio.py @@ -610,6 +610,7 @@ def test_08_gaseous_hydrogen_discipline_jacobian(self): if mda_data_output_dict[self.energy_name][key]['is_coupling']: coupled_outputs += [f'{namespace}.{self.energy_name}.{key}'] + # Overwrite values for ratios with values from setup inputs_dict[f'{namespace}.{GlossaryEnergy.YearEnd}'] = self.year_end inputs_dict[f'{namespace}.is_apply_ratio'] = self.is_apply_ratio diff --git a/energy_models/tests/l1_test_gradient_solid_fuel.py b/energy_models/tests/l1_test_gradient_solid_fuel.py index 21794b02..b0ff7701 100644 --- a/energy_models/tests/l1_test_gradient_solid_fuel.py +++ b/energy_models/tests/l1_test_gradient_solid_fuel.py @@ -70,6 +70,7 @@ def setUp(self): self.invest_level = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.InvestValue: np.linspace(0.001, 0.0008, len(self.years))}) + self.co2_taxes = pd.DataFrame( {GlossaryEnergy.Years: self.years, GlossaryEnergy.CO2Tax: np.linspace(14., 40., len(self.years))}) @@ -279,6 +280,7 @@ def test_03_solid_fuel_discipline_jacobian(self): for techno in technos: coupled_inputs.append(f"{self.name}.{self.energy_name}.{techno}.{GlossaryEnergy.TechnoCapitalValue}") + self.ee.load_study_from_input_dict(inputs_dict) self.ee.execute() diff --git a/energy_models/tests/l2_test_simple.py b/energy_models/tests/l2_test_simple.py index 0ab9cf72..53171602 100644 --- a/energy_models/tests/l2_test_simple.py +++ b/energy_models/tests/l2_test_simple.py @@ -21,8 +21,8 @@ class SimpleTest(AbstractJacobianUnittest): - ''' - Very simple (and quick) test to setup the jenkins jobs for l2 test + ''' + Very simple (and quick) test to setup the jenkins jobs for l2 test and the launch at stable merge ''' diff --git a/energy_models/tests/performances/energy_models_perfos.csv b/energy_models/tests/performances/energy_models_perfos.csv index 5d155d3c..0d5a708b 100644 --- a/energy_models/tests/performances/energy_models_perfos.csv +++ b/energy_models/tests/performances/energy_models_perfos.csv @@ -999,3 +999,5 @@ ncalls,tottime,percall,cumtime,percall,filename:lineno(function) 2890,0.012,0.000,0.037,0.000,C:\Users\NG92D02\Anaconda3\lib\site-packages\pandas\core\array_algos\putmask.py:29(putmask_inplace) 53622,0.037,0.000,0.037,0.000,{built-in method pandas._libs.missing.checknull} 36429,0.037,0.000,0.037,0.000,C:\Users\NG92D02\Anaconda3\lib\site-packages\numpy\core\numerictypes.py:651() + + diff --git a/energy_models/tests/to_fix/CalciumLoopingDiscipline.py b/energy_models/tests/to_fix/CalciumLoopingDiscipline.py index 4c1cf48f..4eee6d1f 100644 --- a/energy_models/tests/to_fix/CalciumLoopingDiscipline.py +++ b/energy_models/tests/to_fix/CalciumLoopingDiscipline.py @@ -57,6 +57,7 @@ def test_execute(self): disc_techno = self.ee.root_process.proxy_disciplines[0].mdo_discipline_wrapp.mdo_discipline self.check_jacobian(location=dirname(__file__), filename='jacobianIsolatedDiscTest_MDO_MDA_CCUS_carbon_capture_flue_gas_capture_CalciumLooping.pkl', - discipline=disc_techno, step=1e-15, derr_approx='complex_step', local_data=disc_techno.local_data, + discipline=disc_techno, step=1e-15, derr_approx='complex_step', local_data = disc_techno.local_data, inputs=coupling_inputs, outputs=coupling_ouputs) + \ No newline at end of file diff --git a/energy_models/tests/to_fix/MonoEthanolAmineDiscipline.py b/energy_models/tests/to_fix/MonoEthanolAmineDiscipline.py index c64002cb..c022ed88 100644 --- a/energy_models/tests/to_fix/MonoEthanolAmineDiscipline.py +++ b/energy_models/tests/to_fix/MonoEthanolAmineDiscipline.py @@ -57,6 +57,7 @@ def test_execute(self): disc_techno = self.ee.root_process.proxy_disciplines[0].mdo_discipline_wrapp.mdo_discipline self.check_jacobian(location=dirname(__file__), filename='jacobianIsolatedDiscTest_MDO_MDA_CCUS_carbon_capture_flue_gas_capture_MonoEthanolAmine.pkl', - discipline=disc_techno, step=1e-15, derr_approx='complex_step', local_data=disc_techno.local_data, + discipline=disc_techno, step=1e-15, derr_approx='complex_step', local_data = disc_techno.local_data, inputs=coupling_inputs, outputs=coupling_ouputs) + \ No newline at end of file diff --git a/energy_models/tests/to_fix/PiperazineProcessDiscipline.py b/energy_models/tests/to_fix/PiperazineProcessDiscipline.py index c6427209..5e7af477 100644 --- a/energy_models/tests/to_fix/PiperazineProcessDiscipline.py +++ b/energy_models/tests/to_fix/PiperazineProcessDiscipline.py @@ -57,6 +57,7 @@ def test_execute(self): disc_techno = self.ee.root_process.proxy_disciplines[0].mdo_discipline_wrapp.mdo_discipline self.check_jacobian(location=dirname(__file__), filename='jacobianIsolatedDiscTest_MDO_MDA_CCUS_carbon_capture_flue_gas_capture_PiperazineProcess.pkl', - discipline=disc_techno, step=1e-15, derr_approx='complex_step', local_data=disc_techno.local_data, + discipline=disc_techno, step=1e-15, derr_approx='complex_step', local_data = disc_techno.local_data, inputs=coupling_inputs, outputs=coupling_ouputs) + \ No newline at end of file diff --git a/headers_ignore_config.json b/headers_ignore_config.json index 954a8d36..c0debe55 100644 --- a/headers_ignore_config.json +++ b/headers_ignore_config.json @@ -1,31 +1,31 @@ { - "extension_to_ignore": [ - "pkl", - "png", - "jpg", - "csv", - "md", - "markdown", - "avif", - "json", - "in", - "gitignore", - "cfg", - "puml", - "pdf", - "txt", - "ipynb", - "zip", - "rst", - "ini", - "coveragerc", - "yaml", - "bat", - "toml" - ], - "files_to_ignore": [ - ".readthedocs", - "docs/Makefile" - ], - "airbus_rev_commit": "37fb4ae" -} + "extension_to_ignore": [ + "pkl", + "png", + "jpg", + "csv", + "md", + "markdown", + "avif", + "json", + "in", + "gitignore", + "cfg", + "puml", + "pdf", + "txt", + "ipynb", + "zip", + "rst", + "ini", + "coveragerc", + "yaml", + "bat", + "toml" + ], + "files_to_ignore": [ + ".readthedocs", + "docs/Makefile" + ], + "airbus_rev_commit": "37fb4ae" +} \ No newline at end of file diff --git a/parameters_glossary.csv b/parameters_glossary.csv index 92a2ae72..67e82b84 100644 --- a/parameters_glossary.csv +++ b/parameters_glossary.csv @@ -130,8 +130,8 @@ invest_objective_ref,Invest objective ref,,Reference used for the investment obj invest_sum_ref,Invest Sum ref,G$,Reference used for the investment sum constraint.,, invest_constraint_ref,Invest constraint ref,,Reference used for the investment constraint.,, invest_limit_ref,Invest Limit Reference,G$,Reference Limit used for the investment constraint.,, -invest_constraint,Investment Constraint,,Constraint applied to difference of energy investment given by macroeconomy and design space.,, -invest_objective_sum,Invest Objective,,Investment in the considered mix.,, +invest_constraint,Investment Constraint,,Constraint applied to difference of energy investment given by macroeconomy and design space.,, +invest_objective_sum,Invest Objective,,Investment in the considered mix.,, invest_sum_cons,Invest constraint ref,,Reference used for the investment constraint.,, invest_techno_mix,Techno Mix Investment Coefficients,,Coefficients of technology mix investments (not normalized) for a specific energy to distribute the energy investments.,, is_apply_ratio,Is apply ratio,,On/Off for the ratio.,, @@ -190,7 +190,7 @@ syngas_CoalGasification_array_mix,CoalGasification array mix,%,Coal gasification syngas_SMR_array_mix,SMR array mix,%,SMR data.,, syngas_array_mix,Syngas array mix,%,Syngas data.,, syngas_prod_constraint_limit,Syngas prod constraint limit,TWh,Syngas production constraint limit.,, -syngas_prod_constraint,Syngas prod constraint,,Syngas production constraint.,, +syngas_prod_constraint,Syngas prod constraint,,Syngas production constraint.,, syngas_prod_objective,Syngas prod objective,TWh,Syngas production objective.,, syngas_prod_ref,Syngas production constraint reference,TWh,Syngas production constraint reference for objective computation.,, syngas_ratio,Molar ratio of CO/H2,%,Molar ratio between monoxyde of carbon and dihydrogene.,, diff --git a/platform_version_required.txt b/platform_version_required.txt index ac8cb4ee..8a58a0dc 100644 --- a/platform_version_required.txt +++ b/platform_version_required.txt @@ -1 +1 @@ -v4.1.3 +v4.1.3 \ No newline at end of file diff --git a/pytest.ini b/pytest.ini index 85772ec7..149dba41 100644 --- a/pytest.ini +++ b/pytest.ini @@ -1,5 +1,5 @@ [pytest] python_files = l1_test*.py -testpaths = +testpaths = energy_models/tests addopts = --numprocesses=auto From 416b3ffb65dea2cfb57d925fa5f51e62389f5003 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sim=C3=A3o=20Rodrigues?= Date: Mon, 25 Nov 2024 16:18:38 +0100 Subject: [PATCH 10/13] Prevent Refinery and Methane production from considering price of Oil and Methane twice --- .../models/liquid_fuel/refinery/refinery.py | 16 ++++++++++++++++ .../models/methane/fossil_gas/fossil_gas.py | 17 +++++++++++++++++ 2 files changed, 33 insertions(+) diff --git a/energy_models/models/liquid_fuel/refinery/refinery.py b/energy_models/models/liquid_fuel/refinery/refinery.py index e573b7c3..74114018 100644 --- a/energy_models/models/liquid_fuel/refinery/refinery.py +++ b/energy_models/models/liquid_fuel/refinery/refinery.py @@ -40,6 +40,22 @@ def __init__(self, name): self.dprod_dinvest = None self.dprod_list_dcapex_list = None + def compute_cost_of_resources_usage(self): + """ + Cost of resource R = need of resource R x price of resource R + """ + cost_of_resource_usage = { + GlossaryEnergy.Years: self.years, + } + for resource in self.resources_used_for_production: + if resource == GlossaryEnergy.OilResource: + # Skip OilResource so not to count it twice + continue + + cost_of_resource_usage[resource] = self.cost_details[f"{resource}_needs"].values * self.resources_prices[resource].values + + self.cost_of_resources_usage = pd.DataFrame(cost_of_resource_usage) + def get_fuel_needs(self): """ Get the fuel needs for 1 kwh of the energy producted by the technology diff --git a/energy_models/models/methane/fossil_gas/fossil_gas.py b/energy_models/models/methane/fossil_gas/fossil_gas.py index 03fb8ecd..7f015700 100644 --- a/energy_models/models/methane/fossil_gas/fossil_gas.py +++ b/energy_models/models/methane/fossil_gas/fossil_gas.py @@ -15,6 +15,7 @@ limitations under the License. ''' +import pandas as pd from energy_models.core.stream_type.carbon_models.carbon_capture import CarbonCapture from energy_models.core.stream_type.energy_models.methane import Methane from energy_models.core.techno_type.base_techno_models.methane_techno import ( @@ -39,6 +40,22 @@ def get_fuel_needs(self): return fuel_need + def compute_cost_of_resources_usage(self): + """ + Cost of resource R = need of resource R x price of resource R + """ + cost_of_resource_usage = { + GlossaryEnergy.Years: self.years, + } + for resource in self.resources_used_for_production: + if resource == GlossaryEnergy.methane: + # Skip Methane so not to count it twice + continue + + cost_of_resource_usage[resource] = self.cost_details[f"{resource}_needs"].values * self.resources_prices[resource].values + + self.cost_of_resources_usage = pd.DataFrame(cost_of_resource_usage) + def compute_resources_needs(self): self.cost_details[f'{self.NATURAL_GAS_RESOURCE_NAME}_needs'] = self.get_fuel_needs() / Methane.data_energy_dict['calorific_value'] # kg/kWh From 1d1a3276e64321ef35e6e2977412f04d641ba640 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sim=C3=A3o=20Rodrigues?= Date: Mon, 25 Nov 2024 17:30:42 +0100 Subject: [PATCH 11/13] Fix error in resources cost from natural gas and oil --- .../models/liquid_fuel/refinery/refinery.py | 8 +++++--- .../models/methane/fossil_gas/fossil_gas.py | 12 +++++++----- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/energy_models/models/liquid_fuel/refinery/refinery.py b/energy_models/models/liquid_fuel/refinery/refinery.py index 74114018..0a871976 100644 --- a/energy_models/models/liquid_fuel/refinery/refinery.py +++ b/energy_models/models/liquid_fuel/refinery/refinery.py @@ -43,6 +43,8 @@ def __init__(self, name): def compute_cost_of_resources_usage(self): """ Cost of resource R = need of resource R x price of resource R + + Does not take oil price into account """ cost_of_resource_usage = { GlossaryEnergy.Years: self.years, @@ -50,9 +52,9 @@ def compute_cost_of_resources_usage(self): for resource in self.resources_used_for_production: if resource == GlossaryEnergy.OilResource: # Skip OilResource so not to count it twice - continue - - cost_of_resource_usage[resource] = self.cost_details[f"{resource}_needs"].values * self.resources_prices[resource].values + cost_of_resource_usage[resource] = 0.0 + else: + cost_of_resource_usage[resource] = self.cost_details[f"{resource}_needs"].values * self.resources_prices[resource].values self.cost_of_resources_usage = pd.DataFrame(cost_of_resource_usage) diff --git a/energy_models/models/methane/fossil_gas/fossil_gas.py b/energy_models/models/methane/fossil_gas/fossil_gas.py index 7f015700..2f954a91 100644 --- a/energy_models/models/methane/fossil_gas/fossil_gas.py +++ b/energy_models/models/methane/fossil_gas/fossil_gas.py @@ -43,16 +43,18 @@ def get_fuel_needs(self): def compute_cost_of_resources_usage(self): """ Cost of resource R = need of resource R x price of resource R + + Does not take natural gas price into account """ cost_of_resource_usage = { GlossaryEnergy.Years: self.years, } for resource in self.resources_used_for_production: - if resource == GlossaryEnergy.methane: - # Skip Methane so not to count it twice - continue - - cost_of_resource_usage[resource] = self.cost_details[f"{resource}_needs"].values * self.resources_prices[resource].values + if resource == GlossaryEnergy.NaturalGasResource: + # Skip NaturalGasResource so not to count it twice + cost_of_resource_usage[resource] = 0.0 + else: + cost_of_resource_usage[resource] = self.cost_details[f"{resource}_needs"].values * self.resources_prices[resource].values self.cost_of_resources_usage = pd.DataFrame(cost_of_resource_usage) From 44ca3d91cdef64be93e812d16a785a0594f942d7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sim=C3=A3o=20Rodrigues?= Date: Mon, 25 Nov 2024 18:04:49 +0100 Subject: [PATCH 12/13] ruff fix --- energy_models/models/methane/fossil_gas/fossil_gas.py | 1 + 1 file changed, 1 insertion(+) diff --git a/energy_models/models/methane/fossil_gas/fossil_gas.py b/energy_models/models/methane/fossil_gas/fossil_gas.py index 2f954a91..e395c384 100644 --- a/energy_models/models/methane/fossil_gas/fossil_gas.py +++ b/energy_models/models/methane/fossil_gas/fossil_gas.py @@ -16,6 +16,7 @@ ''' import pandas as pd + from energy_models.core.stream_type.carbon_models.carbon_capture import CarbonCapture from energy_models.core.stream_type.energy_models.methane import Methane from energy_models.core.techno_type.base_techno_models.methane_techno import ( From e701dc5c15ce421955fe1c3a14576d2f5c4a11f9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sim=C3=A3o=20Rodrigues?= Date: Tue, 26 Nov 2024 09:03:44 +0100 Subject: [PATCH 13/13] ruff fix --- data_energy/fitting/gaseous_bioenergy.py | 13 +++++++------ data_energy/fitting/hydropower.py | 8 ++++---- data_energy/fitting/windpower.py | 16 +++++++--------- 3 files changed, 18 insertions(+), 19 deletions(-) diff --git a/data_energy/fitting/gaseous_bioenergy.py b/data_energy/fitting/gaseous_bioenergy.py index f8b1bd12..b607e6f2 100644 --- a/data_energy/fitting/gaseous_bioenergy.py +++ b/data_energy/fitting/gaseous_bioenergy.py @@ -14,21 +14,22 @@ limitations under the License. ''' import os +import pickle +from copy import deepcopy import numpy as np import pandas as pd -import pickle +from climateeconomics.glossarycore import GlossaryCore from scipy.interpolate import interp1d from scipy.optimize import minimize from sostrades_core.execution_engine.execution_engine import ExecutionEngine +from sostrades_core.tools.bspline.bspline import BSpline from sostrades_core.tools.post_processing.charts.two_axes_instanciated_chart import ( - InstanciatedSeries, - TwoAxesInstanciatedChart, + InstanciatedSeries, + TwoAxesInstanciatedChart, ) -from sostrades_core.tools.bspline.bspline import BSpline + from energy_models.glossaryenergy import GlossaryEnergy -from climateeconomics.glossarycore import GlossaryCore -from copy import deepcopy """ This script is used to calibrate the gaseous bioenergy invest so that the energy production matches the IEA NZE scenario diff --git a/data_energy/fitting/hydropower.py b/data_energy/fitting/hydropower.py index 9d9d38aa..9b202a8f 100644 --- a/data_energy/fitting/hydropower.py +++ b/data_energy/fitting/hydropower.py @@ -14,19 +14,19 @@ limitations under the License. ''' import os +import pickle +from copy import deepcopy import numpy as np import pandas as pd -import pickle -from copy import deepcopy from climateeconomics.glossarycore import GlossaryCore from scipy.interpolate import interp1d from scipy.optimize import minimize from sostrades_core.execution_engine.execution_engine import ExecutionEngine from sostrades_core.tools.bspline.bspline import BSpline from sostrades_core.tools.post_processing.charts.two_axes_instanciated_chart import ( - InstanciatedSeries, - TwoAxesInstanciatedChart, + InstanciatedSeries, + TwoAxesInstanciatedChart, ) from energy_models.glossaryenergy import GlossaryEnergy diff --git a/data_energy/fitting/windpower.py b/data_energy/fitting/windpower.py index 196d9e0c..102cc573 100644 --- a/data_energy/fitting/windpower.py +++ b/data_energy/fitting/windpower.py @@ -14,28 +14,26 @@ limitations under the License. ''' import os -from functools import reduce import pickle +from copy import deepcopy +from functools import reduce + import numpy as np import pandas as pd -from copy import deepcopy from climateeconomics.glossarycore import GlossaryCore from scipy.interpolate import interp1d from scipy.optimize import minimize from sostrades_core.execution_engine.execution_engine import ExecutionEngine +from sostrades_core.tools.bspline.bspline import BSpline from sostrades_core.tools.post_processing.charts.two_axes_instanciated_chart import ( InstanciatedSeries, TwoAxesInstanciatedChart, ) -from sostrades_core.tools.bspline.bspline import BSpline + +from energy_models.glossaryenergy import GlossaryEnergy from energy_models.models.electricity.wind_onshore.wind_onshore_disc import ( WindOnshoreDiscipline, ) -from energy_models.models.electricity.wind_offshore.wind_offshore_disc import ( - WindOffshoreDiscipline, -) -from energy_models.glossaryenergy import GlossaryEnergy - """ This script is used to calibrate the windpower invest so that the electricity production matches the IEA NZE scenario @@ -69,7 +67,7 @@ invest_year_start = 80. #G$ construction_delay = GlossaryEnergy.TechnoConstructionDelayDict['WindOffshore'] # same construction delay for windonshore and windoffshore if construction_delay != GlossaryEnergy.TechnoConstructionDelayDict['WindOnshore']: - raise ValueError(f"must adapt script as construction delay for windOnshore and windOffshore differ") + raise ValueError("must adapt script as construction delay for windOnshore and windOffshore differ") name = 'usecase_witness_optim_nze_eval' model_name_onshore = f"WITNESS_MDO.WITNESS_Eval.WITNESS.EnergyMix.electricity.{GlossaryEnergy.WindOnshore}"

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