merge post_integration to validation

climateeconomics/core/core_emissions/ghg_emissions_model.py

@@ -31,6 +31,7 @@ def __init__(self, param):
         """
         Constructor
         """
+        self.emissions_after_2050_df = None
         self.energy_emission_households_df = None
         self.residential_energy_consumption = None
         self.dict_sector_sections_energy_emissions = {}
@@ -361,6 +362,8 @@ def compute(self, inputs_dict):
         self.compute_total_gwp()
         self.compute_gwp_per_sector()
         self.compute_CO2_emissions_objective()
+        self.compute_net_zero_2050_constraint_df()
+
 
         # compute emission households
         self.compute_energy_emission_households()
@@ -439,3 +442,10 @@ def compute_energy_emission_households(self):
             GlossaryCore.TotalEmissions: energy_emission_households
         })
 
+    def compute_net_zero_2050_constraint_df(self):
+
+        self.emissions_after_2050_df = self.ghg_emissions_df.loc[self.years_range >= 2050][
+            [GlossaryCore.Years, GlossaryCore.insertGHGTotalEmissions.format(GlossaryCore.CO2)]]
+
+    def d_2050_carbon_negative_constraint(self, d_total_co2_emissions):
+        return d_total_co2_emissions[self.years_range >= 2050]

climateeconomics/core/core_witness/climateeco_discipline.py

@@ -36,7 +36,7 @@ class ClimateEcoDiscipline(SoSWrapp):
                                                 }
 
     YEAR_START_DESC_IN = {'type': 'int', 'default': GlossaryCore.YearStartDefault,
-                          'unit': 'year', 'visibility': 'Shared', 'namespace': 'ns_public', 'range': [1950,2040]}
+                          'unit': 'year', 'visibility': 'Shared', 'namespace': 'ns_public', 'range': [1950, 2080]}
     TIMESTEP_DESC_IN = {'type': 'int', 'default': 1, 'unit': 'year per period',
                         'visibility': 'Shared', 'namespace': 'ns_public', 'user_level': 2}
     ALPHA_DESC_IN = {'type': 'float', 'range': [0., 1.], 'default': 0.5, 'visibility': 'Shared', 'namespace': GlossaryCore.NS_WITNESS,

climateeconomics/glossarycore.py

@@ -99,6 +99,7 @@ class GlossaryCore:
     ConstraintLowerBoundUsableCapital = "Lower bound usable capital constraint"
     ConstraintUpperBoundUsableCapital = "upper_bound_usable_capital_constraint"
     ConstraintEnergyNonUseCapital = "constraint_non_use_capital_energy"
+    ConstraintCarbonNegative2050 = "constraint_carbon_negative_2050"
     CleanEnergySimpleTechno = "CleanEnergySimpleTechno"
     clean_energy = "clean_energy"
     ConsumptionObjective = "consumption_objective"
@@ -165,6 +166,7 @@ class GlossaryCore:
     TechnoConsumptionWithoutRatioValue = "techno_consumption_woratio"
     ConstructionDelay = "construction_delay"
     LifetimeName = "lifetime"
+    IsTechnoMainstream = "is_mainstream"
     InitialPlantsAgeDistribFactor = "initial_plants_age_distrib_factor"
 
     # namespaces

climateeconomics/sos_processes/iam/witness/witness_coarse_dev_ms_optim_ku_process/usecase_ms_ku.py

@@ -20,6 +20,7 @@
 from climateeconomics.core.tools.ClimateEconomicsStudyManager import (
     ClimateEconomicsStudyManager,
 )
+from climateeconomics.glossarycore import GlossaryCore
 from climateeconomics.sos_processes.iam.witness.witness_coarse_dev_ms_story_telling.usecase_witness_ms_mda import (
     Study as uc_ms_mda,
 )
@@ -39,11 +40,12 @@
 
 class Study(ClimateEconomicsStudyManager):
 
-    def __init__(self, filename=__file__, bspline=False, run_usecase=False, execution_engine=None):
+    def __init__(self, year_start=GlossaryCore.YearStartDefault,filename=__file__, bspline=False, run_usecase=False, execution_engine=None):
         super().__init__(filename, run_usecase=run_usecase, execution_engine=execution_engine)
         self.bspline = bspline
         self.data_dir = join(dirname(__file__), 'data')
         self.test_post_procs = False
+        self.year_start = year_start
 
         self.scatter_scenario = 'optimization scenarios'
 
@@ -62,7 +64,7 @@ def setup_usecase(self, study_folder_path=None):
         }
 
         for scenario_name, studyClass in self.scenario_dict.items():
-            scenarioUseCase = studyClass(execution_engine=self.execution_engine)
+            scenarioUseCase = studyClass(execution_engine=self.execution_engine, year_start=self.year_start)
             scenarioUseCase.study_name = f'{self.study_name}.{self.scatter_scenario}.{scenario_name}'
             scenarioData = scenarioUseCase.setup_usecase()
             scenarioDatadict = {}

climateeconomics/sos_processes/iam/witness/witness_coarse_dev_ms_optim_ku_process/usecase_ms_ku_2023.py

@@ -13,6 +13,8 @@
 limitations under the License.
 
 '''
+import pandas as pd
+
 from climateeconomics.glossarycore import GlossaryCore
 from climateeconomics.sos_processes.iam.witness.witness_coarse_dev_ms_optim_ku_process.usecase_ms_ku import (
     Study as Study2020,
@@ -22,15 +24,20 @@
 class Study(Study2020):
 
     def __init__(self, run_usecase=False, execution_engine=None):
-        super().__init__(filename=__file__, run_usecase=run_usecase, execution_engine=execution_engine)
+        super().__init__(year_start=2023, filename=__file__, run_usecase=run_usecase, execution_engine=execution_engine)
         self.test_post_procs = True
 
     def setup_usecase(self, study_folder_path=None):
         values_dict = super().setup_usecase(study_folder_path)
-        values_dict.update(
-            {f"{self.study_name}.{self.scatter_scenario}.{scenario_name}.{GlossaryCore.YearStart}": 2023 for
-             scenario_name
-             in self.scenario_dict.keys()})
+        year_start_varnames = list(filter(lambda x: f".{GlossaryCore.YearStart}" in x, values_dict.keys()))
+        values_dict.update({varname: 2023 for varname in year_start_varnames})
+
+        values_dict_2023 = {}
+        for key,value in values_dict.items():
+            if isinstance(value, pd.DataFrame) and GlossaryCore.Years in value.columns:
+                new_value = value.loc[value[GlossaryCore.Years] >= 2023]
+                values_dict_2023[key] = new_value
+        values_dict.update(values_dict_2023)
 
         return values_dict
 

climateeconomics/sos_processes/iam/witness/witness_coarse_story_telling_optim_process/usecase_7_optim_story_telling.py

@@ -13,7 +13,10 @@
 See the License for the specific language governing permissions and
 limitations under the License.
 '''
-
+import pandas as pd
+from sostrades_optimization_plugins.models.func_manager.func_manager import (
+    FunctionManager,
+)
 from sostrades_optimization_plugins.models.func_manager.func_manager_disc import (
     FunctionManagerDisc,
 )
@@ -59,6 +62,22 @@ def setup_usecase(self, study_folder_path=None):
         for dict_data in witness_uc_data:
             values_dict.update(dict_data)
 
+        function_df_key = list(filter(lambda x: 'function_df' in x, values_dict.keys()))[0]
+        function_df = values_dict[function_df_key]
+        func_df = pd.DataFrame({
+            'variable': [GlossaryCore.ConstraintCarbonNegative2050,],
+            'parent': [
+                'constraint_carbon_negative_2050'
+            ],
+            'ftype': FunctionManager.INEQ_CONSTRAINT,
+            'weight': 1.,
+            FunctionManager.AGGR: FunctionManager.INEQ_NEGATIVE_WHEN_SATIFIED_AND_SQUARE_IT,
+            'namespace': [GlossaryCore.NS_FUNCTIONS]
+        })
+        function_df = pd.concat([function_df, func_df])
+
+        values_dict[function_df_key] = function_df
+
         # design space WITNESS
 
         # optimization functions:

climateeconomics/sos_wrapping/sos_wrapping_emissions/ghgemissions/ghgemissions_discipline.py

@@ -109,7 +109,8 @@ class GHGemissionsDiscipline(ClimateEcoDiscipline):
         GlossaryCore.EnergyCarbonIntensityDfValue: GlossaryCore.EnergyCarbonIntensityDf,
         GlossaryCore.EconomicsEmissionDfValue: GlossaryCore.EmissionDf,
         GlossaryCore.ResidentialEmissionsDfValue: GlossaryCore.ResidentialEmissionsDf,
-        GlossaryCore.AllSectionsEmissionsDfValue: GlossaryCore.AllSectionsEmissionsDf
+        GlossaryCore.AllSectionsEmissionsDfValue: GlossaryCore.AllSectionsEmissionsDf,
+        GlossaryCore.ConstraintCarbonNegative2050: {'type': 'dataframe', 'unit': '-', 'visibility': ClimateEcoDiscipline.SHARED_VISIBILITY, 'namespace': GlossaryCore.NS_FUNCTIONS}
     }
 
     def setup_sos_disciplines(self):
@@ -189,7 +190,8 @@ def run(self):
             GlossaryCore.TotalEnergyEmissions: self.emissions_model.total_energy_co2eq_emissions,
             GlossaryCore.EnergyCarbonIntensityDfValue: self.emissions_model.carbon_intensity_of_energy_mix,
             GlossaryCore.EconomicsEmissionDfValue: self.emissions_model.total_economics_emisssions[GlossaryCore.EmissionDf['dataframe_descriptor'].keys()],
-            GlossaryCore.ResidentialEmissionsDfValue: self.emissions_model.energy_emission_households_df
+            GlossaryCore.ResidentialEmissionsDfValue: self.emissions_model.energy_emission_households_df,
+            GlossaryCore.ConstraintCarbonNegative2050: self.emissions_model.emissions_after_2050_df
         }
 
         for sector in self.emissions_model.new_sector_list:
@@ -225,12 +227,25 @@ def compute_sos_jacobian(self):
                         (GlossaryCore.GHGEmissionsDfValue, GlossaryCore.insertGHGTotalEmissions.format(ghg)),
                         (GlossaryCore.insertGHGAgriLandEmissions.format(ghg), column),
                         np.identity(len(years)))
+                    if ghg == GlossaryCore.CO2:
+                        d_constraint_2050 = self.emissions_model.d_2050_carbon_negative_constraint(np.identity(len(years)))
+                        self.set_partial_derivative_for_other_types(
+                            (GlossaryCore.ConstraintCarbonNegative2050, GlossaryCore.insertGHGTotalEmissions.format(GlossaryCore.CO2)),
+                            (GlossaryCore.insertGHGAgriLandEmissions.format(ghg), column),
+                            d_constraint_2050)
 
             self.set_partial_derivative_for_other_types(
                 (GlossaryCore.GHGEmissionsDfValue, GlossaryCore.insertGHGTotalEmissions.format(ghg)),
                 ('GHG_total_energy_emissions', GlossaryCore.insertGHGTotalEmissions.format(ghg)),
                 np.identity(len(years)))
 
+            if ghg == GlossaryCore.CO2:
+                d_constraint_2050 = self.emissions_model.d_2050_carbon_negative_constraint(np.identity(len(years)))
+                self.set_partial_derivative_for_other_types(
+                    (GlossaryCore.ConstraintCarbonNegative2050, GlossaryCore.insertGHGTotalEmissions.format(GlossaryCore.CO2)),
+                    ('GHG_total_energy_emissions', GlossaryCore.insertGHGTotalEmissions.format(ghg)),
+                    d_constraint_2050)
+
             self.set_partial_derivative_for_other_types(
                 (GlossaryCore.TotalEnergyEmissions, GlossaryCore.TotalEnergyEmissions),
                 ('GHG_total_energy_emissions', GlossaryCore.insertGHGTotalEmissions.format(ghg)),
@@ -289,6 +304,10 @@ def compute_sos_jacobian(self):
                     (GlossaryCore.GHGEmissionsDfValue, GlossaryCore.insertGHGTotalEmissions.format(GlossaryCore.CO2)),
                     (f"{sector}.{GlossaryCore.SectionGdpDfValue}", section),
                     d_sector_section_non_energy_emissions_d_section_gdp)
+                self.set_partial_derivative_for_other_types(
+                    (GlossaryCore.ConstraintCarbonNegative2050, GlossaryCore.insertGHGTotalEmissions.format(GlossaryCore.CO2)),
+                    (f"{sector}.{GlossaryCore.SectionGdpDfValue}", section),
+                    self.emissions_model.d_2050_carbon_negative_constraint(d_sector_section_non_energy_emissions_d_section_gdp))
                 self.set_partial_derivative_for_other_types(
                     (GlossaryCore.EconomicsEmissionDfValue, GlossaryCore.TotalEmissions),
                     (f"{sector}.{GlossaryCore.SectionGdpDfValue}", section),

climateeconomics/sos_wrapping/sos_wrapping_sectors/demand/documentation/consumption_discipline.md

@@ -6,8 +6,8 @@ Generation of the consumption in $ for each sector based on :
 - population
 - a value of reference for yearly need in each sector, in $/person. For example, GDP Agriculture 2020 / GDP 2020. 
 
-For each sector, consumption, expressed in $, is calculated as : 
-$$ consumption\ sector\ S = population \times Value\ of \reference \sector S $$
+For each sector, consumption, expressed in $, is calculated as :
+$$consumption\ sector\ S = population \times Value\ of \reference \sector S$$
 
 ## Redistribution of resources
 
@@ -20,11 +20,11 @@ Inputs :
 Output:
 - for each sector :
 
-$$ allocated\ energy\ for\ sector\ S = Share\ energy\ sector\ S \times Total\ energy\ production\ S$$
+$$allocated\ energy\ for\ sector\ S = Share\ energy\ sector\ S \times Total\ energy\ production\ S$$
 
 - Total :
 
-$$ Total\ allocated\ energy\ = Sum\ of\ allocated\ energy\ on\ sectors$$
+$$Total\ allocated\ energy\ = Sum\ of\ allocated\ energy\ on\ sectors$$
 
 #### Investments
 
@@ -34,5 +34,5 @@ Inputs :
 Output:
 - Total investments 
 
-$$ Total\ investments\ = Sum\ of\ investments\ on\ sectors\ $$
+$$Total\ investments\ = Sum\ of\ investments\ on\ sectors\$$
 

climateeconomics/sos_wrapping/sos_wrapping_sectors/sectors_redistribution_energy/documentation/sector_redidistribution.md

@@ -35,5 +35,5 @@ Inputs :
 Output:
 - Total investments 
 
-$$ Total\ investments\ = Sum\ of\ investments\ on\ sectors\ $$
+$$ Total\ investments\ = Sum\ of\ investments\ on\ sectors\$$
 

climateeconomics/sos_wrapping/sos_wrapping_sectors/sectors_redistribution_invests/documentation/sector_redidistribution.md

@@ -29,5 +29,5 @@ Inputs :
 Output:
 - Total investments 
 
-$$ Total\ investments\ = Sum\ of\ investments\ on\ sectors\ $$
+$$ Total\ investments\ = Sum\ of\ investments\ on\ sectors\$$
 

climateeconomics/sos_wrapping/sos_wrapping_witness/population/documentation/population_discipline.md

@@ -57,7 +57,7 @@ $$\beta_i := \sum_{\mathclap{j \in J}} \alpha_{i,j}$$
 Finally [^10], death rate is impacted by average calorie intake and deviating from a parametrable reference value will have a significant impact on death rate in both ways. Death rate age range is impacted differently whether the average calorie intake rises or decreases: younger people will be more impacted by undernutrition whereas older one by overnutrition due to cardiovascular deseases [^11]. It is modelized such as:
 $$DR\_i = \widetilde{DR}\_i + \overline{DR}\_i$$
 with $\widetilde{DR}\_i$ death rate related to economy and temperature, $\overline{DR}\_i$ related to calorie intake.
-$$\(\overline{DR}\_i = \alpha\_{i,j} \left| \frac{kcal - kcal\_{\text{ref}}}{\theta \cdot kcal\_{\text{ref}}} \right|\)$$
+$$\overline{DR}_i = \alpha_{i,j} \left| \frac{\text{kcal} - \text{kcal}_{\text{ref}}}{\theta \cdot \text{kcal}_{\text{ref}}} \right|$$
 with $kcal$ average food intake per capita per day, $kcal\_{ref}$ food intake recommended per capita per day, $\alpha$ relative increase in the probability of dying due to risk j for age-group i, $\theta$ fitting value.
 
 The following table shows the relative increase in mortality $\alpha$:

climateeconomics/sos_wrapping/sos_wrapping_witness/tempchange/documentation/tempchange_discipline.md

@@ -40,8 +40,8 @@ $$RF_{CH4,N2O,t} = 0.036 (\sqrt{C_{CH4}^t}-\sqrt{C_{CH4}^{1750}}) - MN(C_{CH4}^t
 
 with $MN$ a function defined as: 
 
-$$ MN(c1,c2) =0.47 \log(1 + 2.01.10^{-5}  (c_1  c_2)^{0.75} +
-                                 5.31.10^{-15}  c_1  (c_1 c_2)^{1.52}) $$
+$$MN(c1,c2) =0.47 \log(1 + 2.01.10^{-5}  (c_1  c_2)^{0.75} +
+                                 5.31.10^{-15}  c_1  (c_1 c_2)^{1.52})$$
 
 
 The Myhre equation is mainly used in climate change models such as in MAGICC [^4], FUND [^5] or FAIR [^6] models.

climateeconomics/sos_wrapping/sos_wrapping_witness/tempchange_v2/documentation/tempchange_discipline.md

@@ -40,8 +40,8 @@ $$RF_{CH4,N2O,t} = 0.036 (\sqrt{C_{CH4}^t}-\sqrt{C_{CH4}^{1750}}) - MN(C_{CH4}^t
 
 with $MN$ a function defined as : 
 
-$$ MN(c1,c2) =0.47 \log(1 + 2.01.10^{-5}  (c_1  c_2)^{0.75} +
-                                 5.31.10^{-15}  c_1  (c_1 c_2)^{1.52}) $$
+$$MN(c1,c2) =0.47 \log(1 + 2.01.10^{-5}  (c_1  c_2)^{0.75} +
+5.31.10^{-15} c_1  (c_1 c_2)^{1.52})$$
 
 
 The Myhre equation is mainly used in climate change models such as in MAGICC [^4], FUND [^5] or FAIR [^6] models.
@@ -53,8 +53,8 @@ The model of Etminan [^7] is an improved version of the Myhre model especially f
 $$RF_{CO2,t} = \eta_{CO2,Etminan}(\ln(C_{CO2}^t) - ln(C_{CO2}^{1750}))$$
 
 with :
- 
-$$ \eta_{CO2,Etminan} = -2.4.10^{-7} (C_{CO2}^t - C_{CO2}^{1750})^2 + 7.2.10^{-4} abs(C_{CO2}^t - C_{CO2}^{1750}) \\ -2.1.10^{-4} * n2o_{mean} + \frac{\eta}{np.log(2)} $$
+
+$$\eta_{CO2,Etminan} = -2.4.10^{-7} (C_{CO2}^t - C_{CO2}^{1750})^2 + 7.2.10^{-4} abs(C_{CO2}^t - C_{CO2}^{1750}) \\ -2.1.10^{-4} * n2o_{mean} + \frac{\eta}{np.log(2)}$$
 
 $$n2o_{mean}= \frac{1}{2}(C_{N2O}^t + C_{N2O}^{1750})$$