config.default.yaml

# SPDX-FileCopyrightText:  PyPSA-Earth and PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0

version: 0.4.1
tutorial: false

logging:
  level: INFO
  format: "%(levelname)s:%(name)s:%(message)s"

results_dir: results/
summary_dir: results/
costs_dir: data/ # TODO change to the equivalent of technology data

foresight: overnight


countries: ["NG", "BJ"]
# Can be replaced by country ["NG", "BJ"], continent ["Africa"] or user-specific region, see more at https://pypsa-earth.readthedocs.io/en/latest/configuration.html#top-level-configuration

enable:
  retrieve_databundle: true #  Recommended 'true', for the first run. Otherwise data might be missing.
  retrieve_databundle_sector: true
  retrieve_cost_data: true # true: retrieves cost data from technology data and saves in resources/costs.csv, false: uses cost data in data/costs.csv
  download_osm_data: true # If 'true', OpenStreetMap data will be downloaded for the above given countries
  build_natura_raster: false # If True, then an exclusion raster will be build
  build_cutout: false
  # If "build_cutout" : true, then environmental data is extracted according to `snapshots` date range and `countries`
  # requires cds API key https://cds.climate.copernicus.eu/api-how-to
  # More information https://atlite.readthedocs.io/en/latest/introduction.html#datasets
  progress_bar: true # show progress bar during downloading routines and other long-running tasks



custom_rules: [] # Default empty [] or link to custom rule file e.g. ["my_folder/my_rules.smk"] that add rules to Snakefile

run:
  name: "" # use this to keep track of runs with different settings
  shared_cutouts: true # set to true to share the default cutout(s) across runs
  # Note: value false requires build_cutout to be enabled
  allow_scenario_failure: false # If True, the workflow will continue even if a scenario in run_scnenario fails

scenario:
  simpl: [""]
  ll: ["copt"]
  clusters: [10]
  opts: [Co2L-3H]
  planning_horizons: # investment years for myopic and perfect; or costs year for overnight
  - 2030
  sopts:
  - "144H"
  demand:
  - "AB"

snapshots:
  start: "2013-01-01"
  end: "2014-01-01"
  inclusive: "left" # end is not inclusive

# definition of the Coordinate Reference Systems
crs:
  geo_crs: EPSG:4326 # general geographic projection, not used for metric measures. "EPSG:4326" is the standard used by OSM and google maps
  distance_crs: EPSG:3857 # projection for distance measurements only. Possible recommended values are "EPSG:3857" (used by OSM and Google Maps)
  area_crs: ESRI:54009 # projection for area measurements only. Possible recommended values are Global Mollweide "ESRI:54009"

# download_osm_data_nprocesses: 10  # (optional) number of threads used to download osm data

augmented_line_connection:
  add_to_snakefile: false # If True, includes this rule to the workflow
  connectivity_upgrade: 2 # Min. lines connection per node,
  # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
  new_line_type: ["HVAC"] # Expanded lines can be either ["HVAC"] or ["HVDC"] or both ["HVAC", "HVDC"]
  min_expansion: 1 # [MW] New created line expands by float/int input
  min_DC_length: 600 # [km] Minimum line length of DC line

cluster_options:
  simplify_network:
    to_substations: false # network is simplified to nodes with positive or negative power injection (i.e. substations or offwind connections)
    algorithm: kmeans # choose from: [hac, kmeans]
    feature: solar+onwind-time # only for hac. choose from: [solar+onwind-time, solar+onwind-cap, solar-time, solar-cap, solar+offwind-cap] etc.
    exclude_carriers: []
    remove_stubs: true
    remove_stubs_across_borders: true
    p_threshold_drop_isolated: 20 # [MW] isolated buses are being discarded if bus mean power is below the specified threshold
    p_threshold_merge_isolated: 300 # [MW] isolated buses are being merged into a single isolated bus if a bus mean power is below the specified threshold
    s_threshold_fetch_isolated: 0.05 # [-] a share of the national load for merging an isolated network into a backbone network
  cluster_network:
    algorithm: kmeans
    feature: solar+onwind-time
    exclude_carriers: []
  alternative_clustering: false # "False" use Voronoi shapes, "True" use GADM shapes
  distribute_cluster: ["load"] # Distributes cluster nodes per country according to ['load'],['pop'] or ['gdp']
  out_logging: true # When "True", logging is printed to console
  aggregation_strategies:
    generators: # use "min" for more conservative assumptions
      p_nom: sum
      p_nom_max: sum
      p_nom_min: sum
      p_min_pu: mean
      marginal_cost: mean
      committable: any
      ramp_limit_up: max
      ramp_limit_down: max
      efficiency: mean

build_shape_options:
  gadm_layer_id: 1 # GADM level area used for the gadm_shapes. Codes are country-dependent but roughly: 0: country, 1: region/county-like, 2: municipality-like
  update_file: false # When true, all the input files are downloaded again and replace the existing files
  out_logging: true # When true, logging is printed to console
  year: 2020 # reference year used to derive shapes, info on population and info on GDP
  nprocesses: 3 # number of processes to be used in build_shapes
  worldpop_method: "standard" # "standard" pulls from web 1kmx1km raster, "api" pulls from API 100mx100m raster,
  # false (not "false") no pop addition to shape which is useful when generating only cutout
  gdp_method: "standard" # "standard" pulls from web 1x1km raster, false (not "false") no gdp addition to shape which useful when generating only cutout
  contended_flag: "set_by_country" # "set_by_country" assigns the contended areas to the countries according to the GADM database, "drop" drops these contended areas from the model
  gadm_file_prefix: "gadm41_"
  gadm_url_prefix: "https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/"

clean_osm_data_options: # osm = OpenStreetMap
  names_by_shapes: true # Set the country name based on the extended country shapes
  threshold_voltage: 51000 # [V] minimum voltage threshold to keep the asset (cable, line, generator, etc.) [V]
  tag_substation: "transmission" # Filters only substations with 'transmission' tag, ('distribution' also available)
  add_line_endings: true # When "True", then line endings are added to the dataset of the substations
  generator_name_method: OSM # Methodology to specify the name to the generator. Options: OSM (name as by OSM dataset), closest_city (name by the closest city)
  use_custom_lines: "OSM_only" # Use OSM (OSM_only), customized (custom_only), or both data sets (add_custom)
  path_custom_lines: false # If exists, provide the specific absolute path of the custom file e.g. (...\data\custom_lines.geojson)
  use_custom_substations: "OSM_only" # Use OSM (OSM_only), customized (custom_only), or both data sets (add_custom)
  path_custom_substations: false # If exists, provide the specific absolute path of the custom file e.g. (...\data\custom_substations.geojson)
  use_custom_cables: "OSM_only" # Use OSM (OSM_only), customized (custom_only), or both data sets (add_custom)
  path_custom_cables: false # If exists, provide the specific absolute path of the custom file e.g. (...\data\custom_cables.geojson)

build_osm_network: # Options of the build_osm_network script; osm = OpenStreetMap
  group_close_buses: true # When "True", close buses are merged and guarantee the voltage matching among line endings
  group_tolerance_buses: 5000 # [m] (default 5000) Tolerance in meters of the close buses to merge
  split_overpassing_lines: true # When True, lines overpassing buses are splitted and connected to the bueses
  overpassing_lines_tolerance: 1 # [m] (default 1) Tolerance to identify lines overpassing buses
  force_ac: false # When true, it forces all components (lines and substation) to be AC-only. To be used if DC assets create problem.

base_network:
  min_voltage_substation_offshore: 51000 # [V] minimum voltage of the offshore substations
  min_voltage_rebase_voltage: 51000 # [V] minimum voltage in base network

load_options:
  ssp: "ssp2-2.6" # shared socio-economic pathway (GDP and population growth) scenario to consider
  weather_year: 2013 # Load scenarios available with different weather year (different renewable potentials)
  prediction_year: 2030 # Load scenarios available with different prediction year (GDP, population)
  scale: 1 # scales all load time-series, i.e. 2 = doubles load

electricity:
  base_voltage: 380.
  voltages: [132., 220., 300., 380., 500., 750.]
  co2limit: 7.75e+7 # European default, 0.05 * 3.1e9*0.5, needs to be adjusted for Africa
  co2base: 1.487e+9 # European default, adjustment to Africa necessary
  agg_p_nom_limits: data/agg_p_nom_minmax.csv
  hvdc_as_lines: false # should HVDC lines be modeled as `Line` or as `Link` component?
  automatic_emission: false
  automatic_emission_base_year: 1990 # 1990 is taken as default. Any year from 1970 to 2018 can be selected.

  operational_reserve: # like https://genxproject.github.io/GenX/dev/core/#Reserves
    activate: false
    epsilon_load: 0.02 # share of total load
    epsilon_vres: 0.02 # share of total renewable supply
    contingency: 0 # fixed capacity in MW

  max_hours:
    battery: 6
    H2: 168

  extendable_carriers:
    Generator: [solar, onwind, offwind-ac, offwind-dc, OCGT]
    StorageUnit: [] # battery, H2
    Store: [battery, H2]
    Link: [] # H2 pipeline

  powerplants_filter: (DateOut >= 2022 or DateOut != DateOut)
  custom_powerplants: false #  "false" use only powerplantmatching (ppm) data, "merge" combines ppm and custom powerplants, "replace" use only custom powerplants

  conventional_carriers: [nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass]
  renewable_carriers: [solar, onwind, offwind-ac, offwind-dc, hydro]

  estimate_renewable_capacities:
    stats: "irena" # False, = greenfield expansion, 'irena' uses IRENA stats to add expansion limits
    year: 2023 # Reference year, available years for IRENA stats are 2000 to 2023
    p_nom_min: 1 # any float, scales the minimum expansion acquired from stats, i.e. 110% of <years>'s capacities => p_nom_min: 1.1
    p_nom_max: false # sets the expansion constraint, False to deactivate this option and use estimated renewable potentials determine by the workflow, float scales the p_nom_min factor accordingly
    technology_mapping:
      # Wind is the Fueltype in ppm.data.Capacity_stats, onwind, offwind-{ac,dc} the carrier in PyPSA-Earth
      Offshore: [offwind-ac, offwind-dc]
      Onshore: [onwind]
      PV: [solar]

lines:
  ac_types:
    132.: "243-AL1/39-ST1A 20.0"
    220.: "Al/St 240/40 2-bundle 220.0"
    300.: "Al/St 240/40 3-bundle 300.0"
    380.: "Al/St 240/40 4-bundle 380.0"
    500.: "Al/St 240/40 4-bundle 380.0"
    750.: "Al/St 560/50 4-bundle 750.0"
  dc_types:
    500.: "HVDC XLPE 1000"
  s_max_pu: 0.7
  s_nom_max: .inf
  length_factor: 1.25
  under_construction: "zero" # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity

links:
  p_max_pu: 1.0
  p_nom_max: .inf
  under_construction: "zero" # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity

transformers:
  x: 0.1
  s_nom: 2000.
  type: ""

atlite:
  nprocesses: 4
  cutouts:
    cutout-2013-era5:
      module: era5
      dx: 0.3 # cutout resolution
      dy: 0.3 # cutout resolution
      # The cutout time is automatically set by the snapshot range. See `snapshot:` option above and 'build_cutout.py'.
      # time: ["2013-01-01", "2014-01-01"]  # to manually specify a different weather year (~70 years available)
      # The cutout spatial extent [x,y] is automatically set by country selection. See `countires:` option above and 'build_cutout.py'.
      # x: [-12., 35.]  # set cutout range manual, instead of automatic by boundaries of country
      # y: [33., 72]    # manual set cutout range

renewable:
  onwind:
    cutout: cutout-2013-era5
    resource:
      method: wind
      turbine: Vestas_V112_3MW
    capacity_per_sqkm: 3 # conservative, ScholzPhd Tab 4.3.1: 10MW/km^2
    # correction_factor: 0.93
    copernicus:
      # Scholz, Y. (2012). Renewable energy based electricity supply at low costs:
      #  development of the REMix model and application for Europe. ( p.42 / p.28)
      # CLC grid codes:
      # 11X/12X - Various forest types
      # 20  - Shrubs
      # 30  - Herbaceus vegetation
      # 40  - Cropland
      # 50  - Urban
      # 60  - Bare / Sparse vegetation
      # 80  - Permanent water bodies
      # 100 - Moss and lichen
      # 200 - Open sea
      grid_codes: [20, 30, 40, 60, 100, 111, 112, 113, 114, 115, 116, 121, 122, 123, 124, 125, 126]
      distance: 1000
      distance_grid_codes: [50]
    natura: true
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
    extendable: true
  offwind-ac:
    cutout: cutout-2013-era5
    resource:
      method: wind
      turbine: NREL_ReferenceTurbine_5MW_offshore
    capacity_per_sqkm: 2
    # correction_factor: 0.8855
    # proxy for wake losses
    # from 10.1016/j.energy.2018.08.153
    # until done more rigorously in #153
    copernicus:
      grid_codes: [80, 200]
    natura: true
    max_depth: 50
    max_shore_distance: 30000
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
    extendable: true
  offwind-dc:
    cutout: cutout-2013-era5
    resource:
      method: wind
      turbine: NREL_ReferenceTurbine_5MW_offshore
    # ScholzPhd Tab 4.3.1: 10MW/km^2
    capacity_per_sqkm: 3
    # correction_factor: 0.8855
    # proxy for wake losses
    # from 10.1016/j.energy.2018.08.153
    # until done more rigorously in #153
    copernicus:
      grid_codes: [80, 200]
    natura: true
    max_depth: 50
    min_shore_distance: 30000
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
    extendable: true
  solar:
    cutout: cutout-2013-era5
    resource:
      method: pv
      panel: CSi
      orientation: latitude_optimal # will lead into optimal design
      # slope: 0.  # slope: 0 represent a flat panel
      # azimuth: 180.  # azimuth: 180 south orientation
    capacity_per_sqkm: 4.6 # From 1.7 to 4.6 addresses issue #361
    # Determined by comparing uncorrected area-weighted full-load hours to those
    # published in Supplementary Data to
    # Pietzcker, Robert Carl, et al. "Using the sun to decarbonize the power
    # sector: The economic potential of photovoltaics and concentrating solar
    # power." Applied Energy 135 (2014): 704-720.
    correction_factor: 0.854337
    copernicus:
      grid_codes: [20, 30, 40, 50, 60, 90, 100]
    natura: true
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
    extendable: true
  hydro:
    cutout: cutout-2013-era5
    hydrobasins_level: 6
    resource:
      method: hydro
      hydrobasins: data/hydrobasins/hybas_world.shp
      flowspeed: 1.0 # m/s
      # weight_with_height: false
      # show_progress: true
    carriers: [ror, PHS, hydro]
    PHS_max_hours: 6
    hydro_max_hours: "energy_capacity_totals_by_country" # not active
    hydro_max_hours_default: 6.0 # (optional, default 6) Default value of max_hours for hydro when NaN values are found
    clip_min_inflow: 1.0
    extendable: true
    normalization:
      method: hydro_capacities # 'hydro_capacities' to rescale country hydro production by using hydro_capacities, 'eia' to rescale by eia data, false for no rescaling
      year: 2013 # (optional) year of statistics used to rescale the runoff time series. When not provided, the cutout weather year is used
    multiplier: 1.1 # multiplier applied after the normalization of the hydro production; default 1.0
  csp:
    cutout: cutout-2013-era5
    resource:
      method: csp
      installation: SAM_solar_tower
    capacity_per_sqkm: 2.392 # From 1.7 to 4.6 addresses issue #361
    # Determined by comparing uncorrected area-weighted full-load hours to those
    # published in Supplementary Data to
    # Pietzcker, Robert Carl, et al. "Using the sun to decarbonize the power
    # sector: The economic potential of photovoltaics and concentrating solar
    # power." Applied Energy 135 (2014): 704-720.
    copernicus:
      grid_codes: [20, 30, 40, 60, 90]
      distancing_codes: [50]
      distance_to_codes: 3000
    natura: true
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
    extendable: true
    csp_model: advanced # simple or advanced
  enhanced_geothermal:
    enable: true
    max_levels: 4

# TODO: Needs to be adjusted for Africa.
costs:
  year: 2030
  version: v0.6.2
  discountrate: [0.071] #, 0.086, 0.111]
  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
  USD2013_to_EUR2013: 0.7532 # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html
  rooftop_share: 0.14 # based on the potentials, assuming  (0.1 kW/m2 and 10 m2/person)
  fill_values:
    FOM: 0
    VOM: 0
    efficiency: 1
    fuel: 0
    investment: 0
    lifetime: 25
    CO2 intensity: 0
    discount rate: 0.07
  marginal_cost: # EUR/MWh
    solar: 0.01
    onwind: 0.015
    offwind: 0.015
    hydro: 0.
    H2: 0.
    electrolysis: 0.
    fuel cell: 0.
    battery: 0.
    battery inverter: 0.
  emission_prices: # in currency per tonne emission, only used with the option Ep
    co2: 0.
  # investment: # EUR/MW
  #   CCGT: 830000
  # FOM: # %/year
  #   CCGT: 3.35
  # VOM: # EUR/MWh
  #   CCGT: 4.2
  # fuel: # EUR/MWh
  #   gas: 10.1
  # lifetime: # years
  #   CCGT: 25.0
  # efficiency: # per unit
  #   CCGT: 0.58
  lines:
    length_factor: 1.25 #to estimate offwind connection costs


monte_carlo:
  # Description: Specify Monte Carlo sampling options for uncertainty analysis.
  # Define the option list for Monte Carlo sampling.
  # Make sure add_to_snakefile is set to true to enable Monte-Carlo
  options:
    add_to_snakefile: false # When set to true, enables Monte Carlo sampling
    samples: 9 # number of optimizations. Note that number of samples when using scipy has to be the square of a prime number
    sampling_strategy: "chaospy" # "pydoe2", "chaospy", "scipy", packages that are supported
    seed: 42 # set seedling for reproducibilty
  # Uncertanties on any PyPSA object are specified by declaring the specific PyPSA object under the key 'uncertainties'.
  # For each PyPSA object, the 'type' and 'args' keys represent the type of distribution and its argument, respectively.
  # Supported distributions types are uniform, normal, lognormal, triangle, beta and gamma.
  # The arguments of the distribution are passed using the key 'args'  as follows, tailored by distribution type
  # normal: [mean, std], lognormal: [mean, std], uniform: [lower_bound, upper_bound],
  # triangle: [mid_point (between 0 - 1)], beta: [alpha, beta], gamma: [shape, scale]
  # More info on the distributions are documented in the Chaospy reference guide...
  # https://chaospy.readthedocs.io/en/master/reference/distribution/index.html
  # An abstract example is as follows:
  # {pypsa network object, e.g. "loads_t.p_set"}:
  # type: {any supported distribution among the previous: "uniform", "normal", ...}
  # args: {arguments passed as a list depending on the distribution, see the above and more at https://pypsa.readthedocs.io/}
  uncertainties:
    loads_t.p_set:
      type: uniform
      args: [0, 1]
    generators_t.p_max_pu.loc[:, n.generators.carrier == "onwind"]:
      type: lognormal
      args: [1.5]
    generators_t.p_max_pu.loc[:, n.generators.carrier == "solar"]:
      type: beta
      args: [0.5, 2]

# ------------------- SECTOR OPTIONS -------------------

policy_config:
  hydrogen:
    temporal_matching: "no_res_matching" # either "h2_yearly_matching", "h2_monthly_matching", "no_res_matching"
    spatial_matching: false
    additionality: false # RE electricity is equal to the amount required for additional hydrogen export compared to the 0 export case ("reference_case")
    allowed_excess: 1.0
    is_reference: false # Whether or not this network is a reference case network, relevant only if additionality is _true_
    remove_h2_load: false #Whether or not to remove the h2 load from the network, relevant only if is_reference is _true_
    path_to_ref: "" # Path to the reference case network for additionality calculation, relevant only if additionality is _true_ and is_reference is _false_
    re_country_load: false # Set to "True" to force the RE electricity to be equal to the electricity required for hydrogen export and the country electricity load. "False" excludes the country electricity load from the constraint.


demand_data:
  update_data: true # if true, the workflow downloads the energy balances data saved in data/demand/unsd/data again. Turn on for the first run.
  base_year: 2019

  other_industries: false # Whether or not to include industries that are not specified. some countries have has exaggerated numbers, check carefully.
  aluminium_year: 2019 # Year of the aluminium demand data specified in `data/AL_production.csv`


fossil_reserves:
  oil: 100 #TWh Maybe redundant

export:
  h2export: [10] # Yearly export demand in TWh
  store: true # [True, False] # specifies whether an export store to balance demand is implemented
  store_capital_costs: "no_costs" # ["standard_costs", "no_costs"] # specifies the costs of the export store. "standard_costs" takes CAPEX of "hydrogen storage tank type 1 including compressor"
  export_profile: "ship" # use "ship" or "constant"
  ship:
    ship_capacity: 0.4 # TWh # 0.05 TWh for new ones, 0.003 TWh for Susio Frontier, 0.4 TWh according to Hampp2021: "Corresponds to 11360 t H2 (l) with LHV of 33.3333 Mwh/t_H2. Cihlar et al 2020 based on IEA 2019, Table 3-B"
    travel_time: 288 # hours # From Agadir to Rotterdam and back (12*24)
    fill_time: 24 # hours, for 48h see Hampp2021
    unload_time: 24 # hours for 48h see Hampp2021

custom_data:
  renewables: [] # ['csp', 'rooftop-solar', 'solar']
  elec_demand: false
  heat_demand: false
  industry_demand: false
  industry_database: false
  transport_demand: false
  water_costs: false
  h2_underground: false
  add_existing: false
  custom_sectors: false
  gas_network: false # If "True" then a custom .csv file must be placed in "resources/custom_data/pipelines.csv" , If "False" the user can choose btw "greenfield" or Model built-in datasets. Please refer to ["sector"] below.

industry:
  reference_year: 2015

solar_thermal:
  clearsky_model: simple
  orientation:
    slope: 45.
    azimuth: 180.

existing_capacities:
  grouping_years_power: [1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]
  grouping_years_heat: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019] # these should not extend 2020
  threshold_capacity: 10
  default_heating_lifetime: 20
  conventional_carriers:
  - lignite
  - coal
  - oil
  - uranium

sector:
  gas:
    spatial_gas: true # ALWAYS TRUE
    network: false # ALWAYS FALSE for now (NOT USED)
    network_data: GGIT # Global dataset -> 'GGIT' , European dataset -> 'IGGIELGN'
    network_data_GGIT_status: ["Construction", "Operating", "Idle", "Shelved", "Mothballed", "Proposed"]
  hydrogen:
    network: true
    H2_retrofit_capacity_per_CH4: 0.6
    network_limit: 2000 #GWkm
    network_routes: gas # "gas or "greenfield". If "gas"  ->  the network data are fetched from ["sector"]["gas"]["network_data"]. If "greenfield"  -> the network follows the topology of electrical transmission lines
    gas_network_repurposing: true # If true -> ["sector"]["gas"]["network"] is automatically false
    underground_storage: false
    hydrogen_colors: false
    set_color_shares: false
    blue_share: 0.40
    pink_share: 0.05
  coal:
    shift_to_elec: true # If true, residential and services demand of coal is shifted to electricity. If false, the final energy demand of coal is disregarded

  international_bunkers: false #Whether or not to count the emissions of international aviation and navigation

  oil:
    spatial_oil: true

  district_heating:
    potential: 0.3 #maximum fraction of urban demand which can be supplied by district heating
    #increase of today's district heating demand to potential maximum district heating share
    #progress = 0 means today's district heating share, progress=-1 means maximum fraction of urban demand is supplied by district heating
    progress: 1
    # 2020: 0.0
    # 2030: 0.3
    # 2040: 0.6
    # 2050: 1.0
    district_heating_loss: 0.15
  reduce_space_heat_exogenously: true # reduces space heat demand by a given factor (applied before losses in DH)
  # this can represent e.g. building renovation, building demolition, or if
  # the factor is negative: increasing floor area, increased thermal comfort, population growth
  reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
  # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
  # 2020: 0.10  # this results in a space heat demand reduction of 10%
  # 2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
  # 2030: 0.09
  # 2035: 0.11
  # 2040: 0.16
  # 2045: 0.21
  # 2050: 0.29

  tes: true
  tes_tau: # 180 day time constant for centralised, 3 day for decentralised
    decentral: 3
    central: 180
  boilers: true
  oil_boilers: false
  chp: true
  micro_chp: false
  solar_thermal: true
  heat_pump_sink_T: 55 #Celsius, based on DTU / large area radiators; used un build_cop_profiles.py
  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
  solar_cf_correction: 0.788457 # = >>>1/1.2683
  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
  bev_charge_efficiency: 0.9 #BEV (dis-)charging efficiency
  transport_heating_deadband_upper: 20.
  transport_heating_deadband_lower: 15.
  ICE_lower_degree_factor: 0.375 #in per cent increase in fuel consumption per degree above deadband
  ICE_upper_degree_factor: 1.6
  EV_lower_degree_factor: 0.98
  EV_upper_degree_factor: 0.63
  bev_avail_max: 0.95
  bev_avail_mean: 0.8
  bev_dsm_restriction_value: 0.75 #Set to 0 for no restriction on BEV DSM
  bev_dsm_restriction_time: 7 #Time at which SOC of BEV has to be dsm_restriction_value
  v2g: true #allows feed-in to grid from EV battery
  bev_dsm: true #turns on EV battery
  bev_energy: 0.05 #average battery size in MWh
  bev_availability: 0.5 #How many cars do smart charging
  transport_fuel_cell_efficiency: 0.5
  transport_internal_combustion_efficiency: 0.3
  industry_util_factor: 0.7

  biomass_transport: true # biomass transport between nodes
  biomass_transport_default_cost: 0.1 #EUR/km/MWh
  solid_biomass_potential: 40 # TWh/a, Potential of whole modelled area
  biogas_potential: 0.5 # TWh/a, Potential of whole modelled area

  efficiency_heat_oil_to_elec: 0.9
  efficiency_heat_biomass_to_elec: 0.9
  efficiency_heat_gas_to_elec: 0.9

  dynamic_transport:
    enable: false # If "True", then the BEV and FCEV shares are obtained depending on the "Co2L"-wildcard (e.g. "Co2L0.70: 0.10"). If "False", then the shares are obtained depending on the "demand" wildcard and "planning_horizons" wildcard as listed below (e.g. "DF_2050: 0.08")
    land_transport_electric_share:
      Co2L2.0: 0.00
      Co2L1.0: 0.01
      Co2L0.90: 0.03
      Co2L0.80: 0.06
      Co2L0.70: 0.10
      Co2L0.60: 0.17
      Co2L0.50: 0.27
      Co2L0.40: 0.40
      Co2L0.30: 0.55
      Co2L0.20: 0.69
      Co2L0.10: 0.80
      Co2L0.00: 0.88
    land_transport_fuel_cell_share:
      Co2L2.0: 0.01
      Co2L1.0: 0.01
      Co2L0.90: 0.01
      Co2L0.80: 0.01
      Co2L0.70: 0.01
      Co2L0.60: 0.01
      Co2L0.50: 0.01
      Co2L0.40: 0.01
      Co2L0.30: 0.01
      Co2L0.20: 0.01
      Co2L0.10: 0.01
      Co2L0.00: 0.01

  land_transport_fuel_cell_share: # 1 means all FCEVs HERE
    BU_2030: 0.00
    AP_2030: 0.004
    NZ_2030: 0.02
    DF_2030: 0.01
    AB_2030: 0.01
    BU_2050: 0.00
    AP_2050: 0.06
    NZ_2050: 0.28
    DF_2050: 0.08

  land_transport_electric_share: # 1 means all EVs  # This leads to problems when non-zero HERE
    BU_2030: 0.00
    AP_2030: 0.075
    NZ_2030: 0.13
    DF_2030: 0.01
    AB_2030: 0.01
    BU_2050: 0.00
    AP_2050: 0.42
    NZ_2050: 0.68
    DF_2050: 0.011

  co2_network: true
  co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
  co2_sequestration_cost: 10 #EUR/tCO2 for sequestration of CO2
  hydrogen_underground_storage: true
  shipping_hydrogen_liquefaction: false
  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011

  shipping_hydrogen_share: #1.0
    BU_2030: 0.00
    AP_2030: 0.00
    NZ_2030: 0.10
    DF_2030: 0.05
    AB_2030: 0.05
    BU_2050: 0.00
    AP_2050: 0.25
    NZ_2050: 0.36
    DF_2050: 0.12

  gadm_level: 1
  h2_cavern: true
  marginal_cost_storage: 0
  methanation: true
  helmeth: true
  dac: true
  SMR: true
  SMR CC: true
  cc_fraction: 0.9
  cc: true
  space_heat_share: 0.6 # the share of space heating from all heating. Remainder goes to water heating.
  airport_sizing_factor: 3

  min_part_load_fischer_tropsch: 0.9

  conventional_generation: # generator : carrier
    OCGT: gas
    #Gen_Test: oil # Just for testing purposes


solving:
  options:
    formulation: kirchhoff
    load_shedding: true
    noisy_costs: true
    min_iterations: 4
    max_iterations: 6
    clip_p_max_pu: 0.01
    skip_iterations: true
    track_iterations: false
    # nhours: 10

  solver:
    name: gurobi
    options: gurobi-default

  solver_options:
    highs-default:
      # refer to https://ergo-code.github.io/HiGHS/dev/options/definitions/
      threads: 4
      solver: "ipm"
      run_crossover: "off"
      small_matrix_value: 1e-6
      large_matrix_value: 1e9
      primal_feasibility_tolerance: 1e-5
      dual_feasibility_tolerance: 1e-5
      ipm_optimality_tolerance: 1e-4
      parallel: "on"
      random_seed: 123
    gurobi-default:
      threads: 4
      method: 2 # barrier
      crossover: 0
      BarConvTol: 1.e-6
      Seed: 123
      AggFill: 0
      PreDual: 0
      GURO_PAR_BARDENSETHRESH: 200
    gurobi-numeric-focus:
      NumericFocus: 3 # Favour numeric stability over speed
      method: 2 # barrier
      crossover: 0 # do not use crossover
      BarHomogeneous: 1 # Use homogeneous barrier if standard does not converge
      BarConvTol: 1.e-5
      FeasibilityTol: 1.e-4
      OptimalityTol: 1.e-4
      ObjScale: -0.5
      threads: 8
      Seed: 123
    gurobi-fallback: # Use gurobi defaults
      crossover: 0
      method: 2 # barrier
      BarHomogeneous: 1 # Use homogeneous barrier if standard does not converge
      BarConvTol: 1.e-5
      FeasibilityTol: 1.e-5
      OptimalityTol: 1.e-5
      Seed: 123
      threads: 8
    cplex-default:
      threads: 4
      lpmethod: 4 # barrier
      solutiontype: 2 # non basic solution, ie no crossover
      barrier.convergetol: 1.e-5
      feasopt.tolerance: 1.e-6
    copt-default:
      Threads: 8
      LpMethod: 2
      Crossover: 0
    cbc-default: {} # Used in CI
    glpk-default: {} # Used in CI

  mem: 30000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2


plotting:
  map:
    figsize: [7, 7]
    boundaries: [-10.2, 29, 35, 72]
    p_nom:
      bus_size_factor: 5.e+4
      linewidth_factor: 3.e+3
    color_geomap:
      ocean: white
      land: whitesmoke

  costs_max: 10
  costs_threshold: 0.2

  energy_max: 20000
  energy_min: -20000
  energy_threshold: 15

  vre_techs:
  - onwind
  - offwind-ac
  - offwind-dc
  - solar
  - ror
  conv_techs:
  - OCGT
  - CCGT
  - nuclear
  - Nuclear
  - coal
  - oil
  storage_techs:
  - hydro+PHS
  - battery
  - H2
  renewable_storage_techs:
  - PHS
  - hydro
  load_carriers:
  - AC load
  AC_carriers:
  - AC line
  - AC transformer
  link_carriers:
  - DC line
  - Converter AC-DC
  heat_links:
  - heat pump
  - resistive heater
  - CHP heat
  - CHP electric
  - gas boiler
  - central heat pump
  - central resistive heater
  - central CHP heat
  - central CHP electric
  - central gas boiler
  heat_generators:
  - gas boiler
  - central gas boiler
  - solar thermal collector
  - central solar thermal collector

  tech_colors:
    onwind: #235ebc
    onshore wind: #235ebc
    offwind: #6895dd
    offwind-ac: #6895dd
    offshore wind: #6895dd
    offshore wind ac: #6895dd
    offshore wind (AC): #6895dd
    offwind-dc: #74c6f2
    offshore wind dc: #74c6f2
    offshore wind (DC): #74c6f2
    wave: #004444
    hydro: #08ad97
    hydro+PHS: #08ad97
    PHS: #08ad97
    hydro reservoir: #08ad97
    hydroelectricity: #08ad97
    ror: #4adbc8
    run of river: #4adbc8
    solar: #f9d002
    solar PV: #f9d002
    solar thermal: #ffef60
    solar rooftop: #ffef60
    biomass: #0c6013
    solid biomass: #06540d
    solid biomass for industry co2 from atmosphere: #654321
    solid biomass for industry co2 to stored: #654321
    solid biomass for industry CC: #654321
    biogas: #23932d
    waste: #68896b
    geothermal: #ba91b1
    OCGT: #d35050
    OCGT marginal: sandybrown
    OCGT-heat: #ee8340
    gas: #d35050
    natural gas: #d35050
    gas boiler: #ee8340
    gas boilers: #ee8340
    gas boiler marginal: #ee8340
    gas-to-power/heat: brown
    SMR: #4F4F2F
    SMR CC: darkblue
    oil: #262626
    oil boiler: #B5A642
    oil emissions: #666666
    gas for industry: #333333
    gas for industry CC: brown
    gas for industry co2 to atmosphere: #654321
    gas for industry co2 to stored: #654321
    nuclear: #ff9000
    Nuclear: r
    Nuclear marginal: r
    uranium: r
    coal: #707070
    Coal: k
    Coal marginal: k
    lignite: #9e5a01
    Lignite: grey
    Lignite marginal: grey
    H2: #ea048a
    H2 for industry: #222222
    H2 for shipping: #6495ED
    H2 liquefaction: m
    hydrogen storage: #ea048a
    battery: slategray
    battery discharger: slategray
    battery charger: slategray
    battery storage: slategray
    home battery: #614700
    home battery storage: #614700
    lines: #70af1d
    transmission lines: #70af1d
    AC: #70af1d
    AC-AC: #70af1d
    AC line: #70af1d
    links: #8a1caf
    HVDC links: #8a1caf
    DC: #8a1caf
    DC-DC: #8a1caf
    DC link: #8a1caf
    load: #ff0000
    load shedding: #ff0000
    Electric load: b
    electricity: k
    electric demand: k
    electricity distribution grid: y
    heat: darkred
    Heat load: r
    heat pumps: #76EE00
    heat pump: #76EE00
    air heat pump: #76EE00
    ground heat pump: #40AA00
    CHP: r
    CHP heat: r
    CHP electric: r
    heat demand: darkred
    rural heat: #880000
    central heat: #b22222
    decentral heat: #800000
    low-temperature heat for industry: #991111
    process heat: #FF3333
    power-to-heat: red
    resistive heater: pink
    Sabatier: #FF1493
    methanation: #FF1493
    power-to-gas: purple
    power-to-liquid: darkgreen
    helmeth: #7D0552
    DAC: deeppink
    co2 stored: #123456
    CO2 pipeline: gray
    CO2 sequestration: #123456
    co2: #123456
    co2 vent: #654321
    process emissions: #222222
    process emissions CC: gray
    process emissions to stored: #444444
    process emissions to atmosphere: #888888
    agriculture heat: #D07A7A
    agriculture machinery oil: #1e1e1e
    agriculture machinery oil emissions: #111111
    agriculture electricity: #222222
    Fischer-Tropsch: #44DD33
    kerosene for aviation: #44BB11
    naphtha for industry: #44FF55
    land transport oil: #44DD33
    land transport oil emissions: #666666
    land transport fuel cell: #AAAAAA
    land transport EV: grey
    V2G: grey
    BEV charger: grey
    shipping: #6495ED
    shipping oil: #6495ED
    shipping oil emissions: #6495ED
    water tanks: #BBBBBB
    hot water storage: #BBBBBB
    hot water charging: #BBBBBB
    hot water discharging: #999999
    Li ion: grey
    district heating: #CC4E5C
    retrofitting: purple
    building retrofitting: purple
    solid biomass transport: green
    biomass EOP: green
    high-temp electrolysis: magenta
    today: #D2691E
    Ambient: k

  nice_names:
    OCGT: Open-Cycle Gas
    CCGT: Combined-Cycle Gas
    offwind-ac: Offshore Wind (AC)
    offwind-dc: Offshore Wind (DC)
    onwind: Onshore Wind
    solar: Solar
    PHS: Pumped Hydro Storage
    hydro: Reservoir & Dam
    battery: Battery Storage
    H2: Hydrogen Storage
    lines: Transmission Lines
    ror: Run of River