cherry-pick from #282

Co-authored-by: Fabian Hofmann <hofmann@fias.uni-frankfurt.de>

.github/workflows/ci.yaml

@@ -102,7 +102,5 @@ jobs:
         run: |
           conda activate pypsa-eur
           conda list
-          cp test/config.overnight.yaml config.yaml
+          snakemake -call --configfile test/config.overnight.yaml
-          snakemake -call
+          snakemake -call --configfile test/config.myopic.yaml
-          cp test/config.myopic.yaml config.yaml
-          snakemake -call

.gitignore

@@ -27,6 +27,10 @@ gurobi.log
 /data/retro/tabula-calculator-calcsetbuilding.csv
 /data/nuts*
 data/gas_network/scigrid-gas/
+data/costs_*.csv
+
+dask-worker-space
+
 dask-worker-space/
 publications.jrc.ec.europa.eu/
 

Snakefile

@@ -259,9 +259,9 @@ rule build_biomass_potentials:
         enspreso_biomass=HTTP.remote("https://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx", keep_local=True),
         nuts2="data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson", # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
         regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
-        nuts3_population="../pypsa-eur/data/bundle/nama_10r_3popgdp.tsv.gz",
+        nuts3_population=pypsaeur("data/bundle/nama_10r_3popgdp.tsv.gz"),
-        swiss_cantons="../pypsa-eur/data/bundle/ch_cantons.csv",
+        swiss_cantons=pypsaeur("data/bundle/ch_cantons.csv"),
-        swiss_population="../pypsa-eur/data/bundle/je-e-21.03.02.xls",
+        swiss_population=pypsaeur("data/bundle/je-e-21.03.02.xls"),
         country_shapes=pypsaeur('resources/country_shapes.geojson')
     output:
         biomass_potentials_all='resources/biomass_potentials_all_s{simpl}_{clusters}.csv',

scripts/add_brownfield.py

@@ -12,7 +12,6 @@ import numpy as np
 
 from add_existing_baseyear import add_build_year_to_new_assets
 from helper import override_component_attrs, update_config_with_sector_opts
-from solve_network import basename
 
 
 def add_brownfield(n, n_p, year):
@@ -99,7 +98,7 @@ def add_brownfield(n, n_p, year):
             pipe_capacity = n.links.loc[gas_pipes_i, 'p_nom']
             # already retrofitted capacity from gas -> H2
             already_retrofitted = (n.links.loc[h2_retrofitted_fixed_i, 'p_nom']
-                                   .rename(lambda x: basename(x).replace(fr, to)).groupby(level=0).sum())
+                                   .rename(lambda x: x.split("-2")[0].replace(fr, to)).groupby(level=0).sum())
             remaining_capacity = pipe_capacity - CH4_per_H2 * already_retrofitted.reindex(index=pipe_capacity.index).fillna(0)
             n.links.loc[gas_pipes_i, "p_nom"] = remaining_capacity
         else:

scripts/plot_network.py

@@ -62,6 +62,10 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
 
     for comp in components:
         df_c = getattr(n, comp)
+
+        if df_c.empty:
+            continue
+
         df_c["nice_group"] = df_c.carrier.map(rename_techs_tyndp)
 
         attr = "e_nom_opt" if comp == "stores" else "p_nom_opt"
@@ -240,7 +244,7 @@ def group_pipes(df, drop_direction=False):
         axis=1
     )
     # group pipe lines connecting the same buses and rename them for plotting
-    pipe_capacity = df["p_nom_opt"].groupby(level=0).sum()
+    pipe_capacity = df.groupby(level=0).agg({"p_nom_opt": sum, "bus0": "first", "bus1": "first"})
 
     return pipe_capacity
 
@@ -276,13 +280,16 @@ def plot_h2_map(network, regions):
     # drop all links which are not H2 pipelines
     n.links.drop(n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True)
 
-    h2_new = n.links.loc[n.links.carrier=="H2 pipeline"]
+    h2_new = n.links[n.links.carrier=="H2 pipeline"]
-    h2_retro = n.links.loc[n.links.carrier=='H2 pipeline retrofitted']
+    h2_retro = n.links[n.links.carrier=='H2 pipeline retrofitted']
 
     if snakemake.config['foresight'] == 'myopic':
         # sum capacitiy for pipelines from different investment periods
         h2_new = group_pipes(h2_new)
-        h2_retro = group_pipes(h2_retro, drop_direction=True).reindex(h2_new.index).fillna(0)
+
+        if not h2_retro.empty:
+            h2_retro = group_pipes(h2_retro, drop_direction=True).reindex(h2_new.index).fillna(0)
+
 
     if not h2_retro.empty:
 

scripts/prepare_sector_network.py

@@ -458,7 +458,9 @@ def remove_elec_base_techs(n):
     for c in n.iterate_components(snakemake.config["pypsa_eur"]):
         to_keep = snakemake.config["pypsa_eur"][c.name]
         to_remove = pd.Index(c.df.carrier.unique()).symmetric_difference(to_keep)
-        print("Removing", c.list_name, "with carrier", to_remove)
+        if to_remove.empty:
+            continue
+        logger.info(f"Removing {c.list_name} with carrier {list(to_remove)}")
         names = c.df.index[c.df.carrier.isin(to_remove)]
         n.mremove(c.name, names)
         n.carriers.drop(to_remove, inplace=True, errors="ignore")
@@ -469,8 +471,10 @@ def remove_non_electric_buses(n):
     """
     remove buses from pypsa-eur with carriers which are not AC buses
     """
-    print("drop buses from PyPSA-Eur with carrier: ", n.buses[~n.buses.carrier.isin(["AC", "DC"])].carrier.unique())
+    to_drop = list(n.buses.query("carrier not in ['AC', 'DC']").carrier.unique())
-    n.buses = n.buses[n.buses.carrier.isin(["AC", "DC"])]
+    if to_drop:
+        logger.info(f"Drop buses from PyPSA-Eur with carrier: {to_drop}")
+        n.buses = n.buses[n.buses.carrier.isin(["AC", "DC"])]
 
 
 def patch_electricity_network(n):
@@ -533,6 +537,8 @@ def add_co2_tracking(n, options):
         bus=spatial.co2.nodes
     )
 
+    n.add("Carrier", "co2 stored")
+
     if options['co2_vent']:
 
         n.madd("Link",
@@ -821,12 +827,12 @@ def insert_electricity_distribution_grid(n, costs):
     # TODO pop_layout?
     # TODO options?
 
-    print("Inserting electricity distribution grid with investment cost factor of",
+    cost_factor = options['electricity_distribution_grid_cost_factor']
-          options['electricity_distribution_grid_cost_factor'])
+
+    logger.info(f"Inserting electricity distribution grid with investment cost factor of {cost_factor:.2f}")
 
     nodes = pop_layout.index
 
-    cost_factor = options['electricity_distribution_grid_cost_factor']
 
     n.madd("Bus",
         nodes + " low voltage",
@@ -941,7 +947,7 @@ def insert_gas_distribution_costs(n, costs):
 
     f_costs = options['gas_distribution_grid_cost_factor']
 
-    print("Inserting gas distribution grid with investment cost factor of", f_costs)
+    logger.info(f"Inserting gas distribution grid with investment cost factor of {f_costs}")
 
     capital_cost = costs.loc['electricity distribution grid']["fixed"] * f_costs
 
@@ -1317,7 +1323,7 @@ def add_land_transport(n, costs):
     
     total_share = fuel_cell_share + electric_share + ice_share
     if total_share != 1:
-        logger.warning(f"Total land transport shares sum up to {total_share*100}%, corresponding to increased or decreased demand assumptions.")
+        logger.warning(f"Total land transport shares sum up to {total_share:.2%}, corresponding to increased or decreased demand assumptions.")
 
     logger.info(f"FCEV share: {fuel_cell_share*100}%")
     logger.info(f"EV share: {electric_share*100}%")
@@ -1487,7 +1493,7 @@ def add_heat(n, costs):
     # exogenously reduce space heat demand
     if options["reduce_space_heat_exogenously"]:
         dE = get(options["reduce_space_heat_exogenously_factor"], investment_year)
-        print(f"assumed space heat reduction of {dE*100} %")
+        logger.info(f"assumed space heat reduction of {dE:.2%}")
         for sector in sectors:
             heat_demand[sector + " space"] = (1 - dE) * heat_demand[sector + " space"]
 
@@ -1847,10 +1853,9 @@ def create_nodes_for_heat_sector():
     diff = (urban_fraction * central_fraction) - dist_fraction_node
     progress = get(options["district_heating"]["progress"], investment_year)
     dist_fraction_node += diff * progress
-    print(
+    logger.info(
-        "The current district heating share compared to the maximum",
+        f"Increase district heating share by a progress factor of {progress:.2%} "
-        f"possible is increased by a progress factor of\n{progress}",
+        f"resulting in new average share of {dist_fraction_node.mean():.2%}"
-        f"resulting in a district heating share of\n{dist_fraction_node}"
     )
 
     return nodes, dist_fraction_node, urban_fraction
@@ -2188,7 +2193,7 @@ def add_industry(n, costs):
 
     total_share = shipping_hydrogen_share + shipping_methanol_share + shipping_oil_share
     if total_share != 1:
-        logger.warning(f"Total shipping shares sum up to {total_share*100}%, corresponding to increased or decreased demand assumptions.")
+        logger.warning(f"Total shipping shares sum up to {total_share:.2%}, corresponding to increased or decreased demand assumptions.")
 
     domestic_navigation = pop_weighted_energy_totals.loc[nodes, "total domestic navigation"].squeeze()
     international_navigation = pd.read_csv(snakemake.input.shipping_demand, index_col=0).squeeze()
@@ -2557,7 +2562,7 @@ def add_agriculture(n, costs):
 
     total_share = electric_share + oil_share
     if total_share != 1:
-        logger.warning(f"Total agriculture machinery shares sum up to {total_share*100}%, corresponding to increased or decreased demand assumptions.")
+        logger.warning(f"Total agriculture machinery shares sum up to {total_share:.2%}, corresponding to increased or decreased demand assumptions.")
 
     machinery_nodal_energy = pop_weighted_energy_totals.loc[nodes, "total agriculture machinery"]
 
@@ -2635,7 +2640,7 @@ def maybe_adjust_costs_and_potentials(n, opts):
                     else:
                         sel = c.df.carrier.str.contains(carrier)
                     c.df.loc[sel,attr] *= factor
-            print("changing", attr , "for", carrier, "by factor", factor)
+            logger.info(f"changing {attr} for {carrier} by factor {factor}")
 
 
 # TODO this should rather be a config no wildcard
@@ -2880,7 +2885,7 @@ if __name__ == "__main__":
     for o in opts:
         if o[:4] == "wave":
             wave_cost_factor = float(o[4:].replace("p", ".").replace("m", "-"))
-            print("Including wave generators with cost factor of", wave_cost_factor)
+            logger.info(f"Including wave generators with cost factor of {wave_cost_factor}")
             add_wave(n, wave_cost_factor)
         if o[:4] == "dist":
             options['electricity_distribution_grid'] = True
@@ -2949,7 +2954,7 @@ if __name__ == "__main__":
         limit = o[o.find("Co2L")+4:]
         limit = float(limit.replace("p", ".").replace("m", "-"))
         break
-    print("Add CO2 limit from", limit_type)
+    logger.info(f"Add CO2 limit from {limit_type}")
     add_co2limit(n, Nyears, limit)
 
     for o in opts:

test/config.myopic.yaml

@@ -1,62 +1,18 @@
-version: 0.6.0
-
-logging_level: INFO
-
-retrieve_sector_databundle: true
-retrieve_cost_data: true
-
-results_dir: results/
-summary_dir: results
 run: test-myopic  # use this to keep track of runs with different settings
 foresight: myopic # options are overnight, myopic, perfect (perfect is not yet implemented)
-# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
 
 scenario:
-  simpl: # only relevant for PyPSA-Eur
-    - ''
   lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
     - 1.5
   clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
     - 5
-  opts: # only relevant for PyPSA-Eur
-    - ''
   sector_opts: # this is where the main scenario settings are
     - 191H-T-H-B-I-A-solar+p3-dist1
-  # to really understand the options here, look in scripts/prepare_sector_network.py
-  # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
-  # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
-  # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
-  # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation,
-  # A for agriculture, forestry and fishing
-  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
-  # emissions throughout the transition path in the timeframe determined by the
-  # planning_horizons), be:beta decay; ex:exponential decay
-  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
-  # decay with initial growth rate 0
   planning_horizons: # investment years for myopic and perfect; or costs year for overnight
     - 2030
     - 2040
     - 2050
-  # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
 
-# CO2 budget as a fraction of 1990 emissions
-# this is over-ridden if CO2Lx is set in sector_opts
-# this is also over-ridden if cb is set in sector_opts
-co2_budget:
-  2020: 0.7011648746
-  2025: 0.5241935484
-  2030: 0.2970430108
-  2035: 0.1500896057
-  2040: 0.0712365591
-  2045: 0.0322580645
-  2050: 0
-
-# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
 snapshots:
   # arguments to pd.date_range
   start: "2013-03-01"
@@ -66,173 +22,10 @@ snapshots:
 atlite:
   cutout: ../pypsa-eur/cutouts/be-03-2013-era5.nc
 
-# this information is NOT used but needed as an argument for
-# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
-electricity:
-  max_hours:
-    battery: 6
-    H2: 168
-
-# regulate what components with which carriers are kept from PyPSA-Eur;
-# some technologies are removed because they are implemented differently
-# (e.g. battery or H2 storage) or have different year-dependent costs
-# in PyPSA-Eur-Sec
-pypsa_eur:
-  Bus:
-    - AC
-  Link:
-    - DC
-  Generator:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  StorageUnit:
-    - PHS
-    - hydro
-  Store: []
-
-
-energy:
-  energy_totals_year: 2011
-  base_emissions_year: 1990
-  eurostat_report_year: 2016
-  emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
-
-biomass:
-  year: 2030
-  scenario: ENS_Med
-  classes:
-    solid biomass:
-      - Agricultural waste
-      - Fuelwood residues
-      - Secondary Forestry residues - woodchips
-      - Sawdust
-      - Residues from landscape care
-      - Municipal waste
-    not included:
-      - Sugar from sugar beet
-      - Rape seed
-      - "Sunflower, soya seed "
-      - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
-      - Miscanthus, switchgrass, RCG
-      - Willow
-      - Poplar
-      - FuelwoodRW
-      - C&P_RW
-    biogas:
-      - Manure solid, liquid
-      - Sludge
-
-
-solar_thermal:
-  clearsky_model: simple  # should be "simple" or "enhanced"?
-  orientation:
-    slope: 45.
-    azimuth: 180.
-
-# only relevant for foresight = myopic or perfect
 existing_capacities:
   grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
-  threshold_capacity: 10
-  conventional_carriers:
-    - lignite
-    - coal
-    - oil
-    - uranium
-
 
 sector:
-  district_heating:
-    potential: 0.6  # 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:
-      2020: 0.0
-      2030: 0.3
-      2040: 0.6
-      2050: 1.0
-    district_heating_loss: 0.15
-  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
-  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_dsm: true #turns on EV battery
-  bev_availability: 0.5  #How many cars do smart charging
-  bev_energy: 0.05  #average battery size in MWh
-  bev_charge_efficiency: 0.9  #BEV (dis-)charging efficiency
-  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
-  bev_charge_rate: 0.011 #3-phase charger with 11 kW
-  bev_avail_max: 0.95
-  bev_avail_mean: 0.8
-  v2g: true #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share:
-    2020: 0
-    2030: 0.05
-    2040: 0.1
-    2050: 0.15
-  land_transport_electric_share:
-    2020: 0
-    2030: 0.25
-    2040: 0.6
-    2050: 0.85
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
-  agriculture_machinery_electric_share: 0
-  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
-  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
-  shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share:
-    2020: 0
-    2025: 0
-    2030: 0.05
-    2035: 0.15
-    2040: 0.3
-    2045: 0.6
-    2050: 1
-  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
-  heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
-   # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  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
-  retrofitting :  # co-optimises building renovation to reduce space heat demand
-    retro_endogen: false  # co-optimise space heat savings
-    cost_factor: 1.0   # weight costs for building renovation
-    interest_rate: 0.04  # for investment in building components
-    annualise_cost: true  # annualise the investment costs
-    tax_weighting: false   # weight costs depending on taxes in countries
-    construction_index: true   # weight costs depending on labour/material costs per country
-  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
-  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
-  marginal_cost_storage: 0. #1e-4
-  methanation: true
-  helmeth: true
-  dac: true
   co2_vent: true
   SMR: true
   regional_co2_sequestration_potential:
@@ -348,267 +141,8 @@ costs:
 
 
 solving:
-  #tmpdir: "path/to/tmp"
-  options:
-    formulation: kirchhoff
-    clip_p_max_pu: 1.e-2
-    load_shedding: false
-    noisy_costs: true
-    skip_iterations: true
-    track_iterations: false
-    min_iterations: 4
-    max_iterations: 6
-    keep_shadowprices:
-      - Bus
-      - Line
-      - Link
-      - Transformer
-      - GlobalConstraint
-      - Generator
-      - Store
-      - StorageUnit
-
   solver:
     name: cbc
-    # threads: 4
+    options: cbc-default
-    # method: 2 # barrier
-    # crossover: 0
-    # BarConvTol: 1.e-6
-    # Seed: 123
-    # AggFill: 0
-    # PreDual: 0
-    # GURO_PAR_BARDENSETHRESH: 200
-    #FeasibilityTol: 1.e-6
-
-    #name: cplex
-    #threads: 4
-    #lpmethod: 4 # barrier
-    #solutiontype: 2 # non basic solution, ie no crossover
-    #barrier_convergetol: 1.e-5
-    #feasopt_tolerance: 1.e-6
   mem: 4000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
 
-
-plotting:
-  map:
-    boundaries: [-11, 30, 34, 71]
-    color_geomap:
-      ocean: white
-      land: whitesmoke
-  costs_max: 1000
-  costs_threshold: 1
-  energy_max: 20000
-  energy_min: -20000
-  energy_threshold: 50
-  vre_techs:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  renewable_storage_techs:
-    - PHS
-    - hydro
-  conv_techs:
-    - OCGT
-    - CCGT
-    - Nuclear
-    - Coal
-  storage_techs:
-    - hydro+PHS
-    - battery
-    - H2
-  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:
-    # wind
-    onwind: "#235ebc"
-    onshore wind: "#235ebc"
-    offwind: "#6895dd"
-    offshore wind: "#6895dd"
-    offwind-ac: "#6895dd"
-    offshore wind (AC): "#6895dd"
-    offwind-dc: "#74c6f2"
-    offshore wind (DC): "#74c6f2"
-    # water
-    hydro: '#298c81'
-    hydro reservoir: '#298c81'
-    ror: '#3dbfb0'
-    run of river: '#3dbfb0'
-    hydroelectricity: '#298c81'
-    PHS: '#51dbcc'
-    wave: '#a7d4cf'
-    # solar
-    solar: "#f9d002"
-    solar PV: "#f9d002"
-    solar thermal: '#ffbf2b'
-    solar rooftop: '#ffea80'
-    # gas
-    OCGT: '#e0986c'
-    OCGT marginal: '#e0986c'
-    OCGT-heat: '#e0986c'
-    gas boiler: '#db6a25'
-    gas boilers: '#db6a25'
-    gas boiler marginal: '#db6a25'
-    gas: '#e05b09'
-    fossil gas: '#e05b09'
-    natural gas: '#e05b09'
-    CCGT: '#a85522'
-    CCGT marginal: '#a85522'
-    gas for industry co2 to atmosphere: '#692e0a'
-    gas for industry co2 to stored: '#8a3400'
-    gas for industry: '#853403'
-    gas for industry CC: '#692e0a'
-    gas pipeline: '#ebbca0'
-    gas pipeline new: '#a87c62'
-    # oil
-    oil: '#c9c9c9'
-    oil boiler: '#adadad'
-    agriculture machinery oil: '#949494'
-    shipping oil: "#808080"
-    land transport oil: '#afafaf'
-    # nuclear
-    Nuclear: '#ff8c00'
-    Nuclear marginal: '#ff8c00'
-    nuclear: '#ff8c00'
-    uranium: '#ff8c00'
-    # coal
-    Coal: '#545454'
-    coal: '#545454'
-    Coal marginal: '#545454'
-    solid: '#545454'
-    Lignite: '#826837'
-    lignite: '#826837'
-    Lignite marginal: '#826837'
-    # biomass
-    biogas: '#e3d37d'
-    biomass: '#baa741'
-    solid biomass: '#baa741'
-    solid biomass transport: '#baa741'
-    solid biomass for industry: '#7a6d26'
-    solid biomass for industry CC: '#47411c'
-    solid biomass for industry co2 from atmosphere: '#736412'
-    solid biomass for industry co2 to stored: '#47411c'
-    # power transmission
-    lines: '#6c9459'
-    transmission lines: '#6c9459'
-    electricity distribution grid: '#97ad8c'
-    # electricity demand
-    Electric load: '#110d63'
-    electric demand: '#110d63'
-    electricity: '#110d63'
-    industry electricity: '#2d2a66'
-    industry new electricity: '#2d2a66'
-    agriculture electricity: '#494778'
-    # battery + EVs
-    battery: '#ace37f'
-    battery storage: '#ace37f'
-    home battery: '#80c944'
-    home battery storage: '#80c944'
-    BEV charger: '#baf238'
-    V2G: '#e5ffa8'
-    land transport EV: '#baf238'
-    Li ion: '#baf238'
-    # hot water storage
-    water tanks: '#e69487'
-    hot water storage: '#e69487'
-    hot water charging: '#e69487'
-    hot water discharging: '#e69487'
-    # heat demand
-    Heat load: '#cc1f1f'
-    heat: '#cc1f1f'
-    heat demand: '#cc1f1f'
-    rural heat: '#ff5c5c'
-    central heat: '#cc1f1f'
-    decentral heat: '#750606'
-    low-temperature heat for industry: '#8f2727'
-    process heat: '#ff0000'
-    agriculture heat: '#d9a5a5'
-    # heat supply
-    heat pumps: '#2fb537'
-    heat pump: '#2fb537'
-    air heat pump: '#36eb41'
-    ground heat pump: '#2fb537'
-    Ambient: '#98eb9d'
-    CHP: '#8a5751'
-    CHP CC: '#634643'
-    CHP heat: '#8a5751'
-    CHP electric: '#8a5751'
-    district heating: '#e8beac'
-    resistive heater: '#d8f9b8'
-    retrofitting: '#8487e8'
-    building retrofitting: '#8487e8'
-    # hydrogen
-    H2 for industry: "#f073da"
-    H2 for shipping: "#ebaee0"
-    H2: '#bf13a0'
-    hydrogen: '#bf13a0'
-    SMR: '#870c71'
-    SMR CC: '#4f1745'
-    H2 liquefaction: '#d647bd'
-    hydrogen storage: '#bf13a0'
-    H2 storage: '#bf13a0'
-    land transport fuel cell: '#6b3161'
-    H2 pipeline: '#f081dc'
-    H2 pipeline retrofitted: '#ba99b5'
-    H2 Fuel Cell: '#c251ae'
-    H2 Electrolysis: '#ff29d9'
-    # syngas
-    Sabatier: '#9850ad'
-    methanation: '#c44ce6'
-    methane: '#c44ce6'
-    helmeth: '#e899ff'
-    # synfuels
-    Fischer-Tropsch: '#25c49a'
-    liquid: '#25c49a'
-    kerosene for aviation: '#a1ffe6'
-    naphtha for industry: '#57ebc4'
-    # co2
-    CC: '#f29dae'
-    CCS: '#f29dae'
-    CO2 sequestration: '#f29dae'
-    DAC: '#ff5270'
-    co2 stored: '#f2385a'
-    co2: '#f29dae'
-    co2 vent: '#ffd4dc'
-    CO2 pipeline: '#f5627f'
-    # emissions
-    process emissions CC: '#000000'
-    process emissions: '#222222'
-    process emissions to stored: '#444444'
-    process emissions to atmosphere: '#888888'
-    oil emissions: '#aaaaaa'
-    shipping oil emissions: "#555555"
-    land transport oil emissions: '#777777'
-    agriculture machinery oil emissions: '#333333'
-    # other
-    shipping: '#03a2ff'
-    power-to-heat: '#2fb537'
-    power-to-gas: '#c44ce6'
-    power-to-H2: '#ff29d9'
-    power-to-liquid: '#25c49a'
-    gas-to-power/heat: '#ee8340'
-    waste: '#e3d37d'
-    other: '#000000'

test/config.overnight.yaml

@@ -1,60 +1,17 @@
-version: 0.6.0
-
-logging_level: INFO
-
-retrieve_sector_databundle: true
-retrieve_cost_data: true
-
-results_dir: results/
-summary_dir: results
 run: test-overnight  # use this to keep track of runs with different settings
 foresight: overnight # options are overnight, myopic, perfect (perfect is not yet implemented)
-# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
 
 scenario:
-  simpl: # only relevant for PyPSA-Eur
-    - ''
   lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
     - 1.5
   clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
     - 5
-  opts: # only relevant for PyPSA-Eur
-    - ''
   sector_opts: # this is where the main scenario settings are
     - CO2L0-191H-T-H-B-I-A-solar+p3-dist1
-  # to really understand the options here, look in scripts/prepare_sector_network.py
-  # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
-  # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
-  # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
-  # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation,
-  # A for agriculture, forestry and fishing
-  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
-  # emissions throughout the transition path in the timeframe determined by the
-  # planning_horizons), be:beta decay; ex:exponential decay
-  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
-  # decay with initial growth rate 0
   planning_horizons: # investment years for myopic and perfect; or costs year for overnight
     - 2030
   # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
 
-# CO2 budget as a fraction of 1990 emissions
-# this is over-ridden if CO2Lx is set in sector_opts
-# this is also over-ridden if cb is set in sector_opts
-co2_budget:
-  2020: 0.7011648746
-  2025: 0.5241935484
-  2030: 0.2970430108
-  2035: 0.1500896057
-  2040: 0.0712365591
-  2045: 0.0322580645
-  2050: 0
-
-# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
 snapshots:
   # arguments to pd.date_range
   start: "2013-03-01"
@@ -64,173 +21,7 @@ snapshots:
 atlite:
   cutout: ../pypsa-eur/cutouts/be-03-2013-era5.nc
 
-# this information is NOT used but needed as an argument for
-# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
-electricity:
-  max_hours:
-    battery: 6
-    H2: 168
-
-# regulate what components with which carriers are kept from PyPSA-Eur;
-# some technologies are removed because they are implemented differently
-# (e.g. battery or H2 storage) or have different year-dependent costs
-# in PyPSA-Eur-Sec
-pypsa_eur:
-  Bus:
-    - AC
-  Link:
-    - DC
-  Generator:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  StorageUnit:
-    - PHS
-    - hydro
-  Store: []
-
-
-energy:
-  energy_totals_year: 2011
-  base_emissions_year: 1990
-  eurostat_report_year: 2016
-  emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
-
-biomass:
-  year: 2030
-  scenario: ENS_Med
-  classes:
-    solid biomass:
-      - Agricultural waste
-      - Fuelwood residues
-      - Secondary Forestry residues - woodchips
-      - Sawdust
-      - Residues from landscape care
-      - Municipal waste
-    not included:
-      - Sugar from sugar beet
-      - Rape seed
-      - "Sunflower, soya seed "
-      - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
-      - Miscanthus, switchgrass, RCG
-      - Willow
-      - Poplar
-      - FuelwoodRW
-      - C&P_RW
-    biogas:
-      - Manure solid, liquid
-      - Sludge
-
-
-solar_thermal:
-  clearsky_model: simple  # should be "simple" or "enhanced"?
-  orientation:
-    slope: 45.
-    azimuth: 180.
-
-# only relevant for foresight = myopic or perfect
-existing_capacities:
-  grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
-  threshold_capacity: 10
-  conventional_carriers:
-    - lignite
-    - coal
-    - oil
-    - uranium
-
-
 sector:
-  district_heating:
-    potential: 0.6  # 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
-  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
-  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_dsm: true #turns on EV battery
-  bev_availability: 0.5  #How many cars do smart charging
-  bev_energy: 0.05  #average battery size in MWh
-  bev_charge_efficiency: 0.9  #BEV (dis-)charging efficiency
-  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
-  bev_charge_rate: 0.011 #3-phase charger with 11 kW
-  bev_avail_max: 0.95
-  bev_avail_mean: 0.8
-  v2g: true #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
-    # 2020: 0
-    # 2030: 0.05
-    # 2040: 0.1
-    # 2050: 0.15
-  land_transport_electric_share: 0.85 # 1 means all EVs
-    # 2020: 0
-    # 2030: 0.25
-    # 2040: 0.6
-    # 2050: 0.85
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
-  agriculture_machinery_electric_share: 0
-  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
-  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
-  shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share: 1 # 1 means all hydrogen FC
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.15
-    # 2040: 0.3
-    # 2045: 0.6
-    # 2050: 1
-  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
-  heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
-   # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  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
-  retrofitting :  # co-optimises building renovation to reduce space heat demand
-    retro_endogen: false  # co-optimise space heat savings
-    cost_factor: 1.0   # weight costs for building renovation
-    interest_rate: 0.04  # for investment in building components
-    annualise_cost: true  # annualise the investment costs
-    tax_weighting: false   # weight costs depending on taxes in countries
-    construction_index: true   # weight costs depending on labour/material costs per country
-  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
-  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
-  marginal_cost_storage: 0. #1e-4
-  methanation: true
-  helmeth: true
-  dac: true
   co2_vent: true
   SMR: true
   regional_co2_sequestration_potential:
@@ -251,362 +42,12 @@ sector:
   electricity_grid_connection: true  # only applies to onshore wind and utility PV
   H2_network: true
   gas_network: true
-  H2_retrofit: true  # if set to True existing gas pipes can be retrofitted to H2 pipes
+  H2_retrofit: true # if set to True existing gas pipes can be retrofitted to H2 pipes
-  # according to hydrogen backbone strategy (April, 2020) p.15
-  # https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
-  # 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
-  H2_retrofit_capacity_per_CH4: 0.6  # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
-  gas_network_connectivity_upgrade: 1 # 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
-  gas_distribution_grid: true
-  gas_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  biomass_transport: false  # biomass transport between nodes
-  conventional_generation: # generator : carrier
-    OCGT: gas
   biomass_boiler: false
-  biomass_to_liquid: false
-  biosng: false
-
-
-industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
-    # 2020: 0.6
-    # 2025: 0.55
-    # 2030: 0.5
-    # 2035: 0.45
-    # 2040: 0.4
-    # 2045: 0.35
-    # 2050: 0.3
-  DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.2
-    # 2040: 0.4
-    # 2045: 0.7
-    # 2050: 1
-  H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
-  elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
-    # 2020: 0.4
-    # 2025: 0.375
-    # 2030: 0.35
-    # 2035: 0.325
-    # 2040: 0.3
-    # 2045: 0.25
-    # 2050: 0.2
-  MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
-  MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
-  MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
-  MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
-  NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
-  petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
-  HVC_primary_fraction: 1. # fraction of today's HVC produced via primary route
-  HVC_mechanical_recycling_fraction: 0. # fraction of today's HVC produced via mechanical recycling
-  HVC_chemical_recycling_fraction: 0. # fraction of today's HVC produced via chemical recycling
-  HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
-  MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
-  MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
-  chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
-  MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
-  MWh_H2_per_tCl: -0.9372  # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
-  methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
-  MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
-  MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
-  hotmaps_locate_missing: false
-  reference_year: 2015
-  # references:
-  # DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
-  # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
-
-costs:
-  year: 2030
-  version: v0.5.0
-  lifetime: 25 #default lifetime
-  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
-  discountrate: 0.07
-  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
-  USD2013_to_EUR2013: 0.7532
-
-  # Marginal and capital costs can be overwritten
-  # capital_cost:
-  #   onwind: 500
-  marginal_cost:
-    solar: 0.01
-    onwind: 0.015
-    offwind: 0.015
-    hydro: 0.
-    H2: 0.
-    battery: 0.
-
-  emission_prices: # only used with the option Ep (emission prices)
-    co2: 0.
-
-  lines:
-    length_factor: 1.25 #to estimate offwind connection costs
-
 
 solving:
-  #tmpdir: "path/to/tmp"
-  options:
-    formulation: kirchhoff
-    clip_p_max_pu: 1.e-2
-    load_shedding: false
-    noisy_costs: true
-    skip_iterations: true
-    track_iterations: false
-    min_iterations: 4
-    max_iterations: 6
-    keep_shadowprices:
-      - Bus
-      - Line
-      - Link
-      - Transformer
-      - GlobalConstraint
-      - Generator
-      - Store
-      - StorageUnit
-
   solver:
     name: cbc
-    # threads: 4
+    options: cbc-default
-    # method: 2 # barrier
-    # crossover: 0
-    # BarConvTol: 1.e-6
-    # Seed: 123
-    # AggFill: 0
-    # PreDual: 0
-    # GURO_PAR_BARDENSETHRESH: 200
-    #FeasibilityTol: 1.e-6
-
-    #name: cplex
-    #threads: 4
-    #lpmethod: 4 # barrier
-    #solutiontype: 2 # non basic solution, ie no crossover
-    #barrier_convergetol: 1.e-5
-    #feasopt_tolerance: 1.e-6
   mem: 4000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
 
-
-plotting:
-  map:
-    boundaries: [-11, 30, 34, 71]
-    color_geomap:
-      ocean: white
-      land: whitesmoke
-  costs_max: 1000
-  costs_threshold: 1
-  energy_max: 20000
-  energy_min: -20000
-  energy_threshold: 50
-  vre_techs:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  renewable_storage_techs:
-    - PHS
-    - hydro
-  conv_techs:
-    - OCGT
-    - CCGT
-    - Nuclear
-    - Coal
-  storage_techs:
-    - hydro+PHS
-    - battery
-    - H2
-  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:
-    # wind
-    onwind: "#235ebc"
-    onshore wind: "#235ebc"
-    offwind: "#6895dd"
-    offshore wind: "#6895dd"
-    offwind-ac: "#6895dd"
-    offshore wind (AC): "#6895dd"
-    offwind-dc: "#74c6f2"
-    offshore wind (DC): "#74c6f2"
-    # water
-    hydro: '#298c81'
-    hydro reservoir: '#298c81'
-    ror: '#3dbfb0'
-    run of river: '#3dbfb0'
-    hydroelectricity: '#298c81'
-    PHS: '#51dbcc'
-    wave: '#a7d4cf'
-    # solar
-    solar: "#f9d002"
-    solar PV: "#f9d002"
-    solar thermal: '#ffbf2b'
-    solar rooftop: '#ffea80'
-    # gas
-    OCGT: '#e0986c'
-    OCGT marginal: '#e0986c'
-    OCGT-heat: '#e0986c'
-    gas boiler: '#db6a25'
-    gas boilers: '#db6a25'
-    gas boiler marginal: '#db6a25'
-    gas: '#e05b09'
-    fossil gas: '#e05b09'
-    natural gas: '#e05b09'
-    CCGT: '#a85522'
-    CCGT marginal: '#a85522'
-    gas for industry co2 to atmosphere: '#692e0a'
-    gas for industry co2 to stored: '#8a3400'
-    gas for industry: '#853403'
-    gas for industry CC: '#692e0a'
-    gas pipeline: '#ebbca0'
-    gas pipeline new: '#a87c62'
-    # oil
-    oil: '#c9c9c9'
-    oil boiler: '#adadad'
-    agriculture machinery oil: '#949494'
-    shipping oil: "#808080"
-    land transport oil: '#afafaf'
-    # nuclear
-    Nuclear: '#ff8c00'
-    Nuclear marginal: '#ff8c00'
-    nuclear: '#ff8c00'
-    uranium: '#ff8c00'
-    # coal
-    Coal: '#545454'
-    coal: '#545454'
-    Coal marginal: '#545454'
-    solid: '#545454'
-    Lignite: '#826837'
-    lignite: '#826837'
-    Lignite marginal: '#826837'
-    # biomass
-    biogas: '#e3d37d'
-    biomass: '#baa741'
-    solid biomass: '#baa741'
-    solid biomass transport: '#baa741'
-    solid biomass for industry: '#7a6d26'
-    solid biomass for industry CC: '#47411c'
-    solid biomass for industry co2 from atmosphere: '#736412'
-    solid biomass for industry co2 to stored: '#47411c'
-    # power transmission
-    lines: '#6c9459'
-    transmission lines: '#6c9459'
-    electricity distribution grid: '#97ad8c'
-    # electricity demand
-    Electric load: '#110d63'
-    electric demand: '#110d63'
-    electricity: '#110d63'
-    industry electricity: '#2d2a66'
-    industry new electricity: '#2d2a66'
-    agriculture electricity: '#494778'
-    # battery + EVs
-    battery: '#ace37f'
-    battery storage: '#ace37f'
-    home battery: '#80c944'
-    home battery storage: '#80c944'
-    BEV charger: '#baf238'
-    V2G: '#e5ffa8'
-    land transport EV: '#baf238'
-    Li ion: '#baf238'
-    # hot water storage
-    water tanks: '#e69487'
-    hot water storage: '#e69487'
-    hot water charging: '#e69487'
-    hot water discharging: '#e69487'
-    # heat demand
-    Heat load: '#cc1f1f'
-    heat: '#cc1f1f'
-    heat demand: '#cc1f1f'
-    rural heat: '#ff5c5c'
-    central heat: '#cc1f1f'
-    decentral heat: '#750606'
-    low-temperature heat for industry: '#8f2727'
-    process heat: '#ff0000'
-    agriculture heat: '#d9a5a5'
-    # heat supply
-    heat pumps: '#2fb537'
-    heat pump: '#2fb537'
-    air heat pump: '#36eb41'
-    ground heat pump: '#2fb537'
-    Ambient: '#98eb9d'
-    CHP: '#8a5751'
-    CHP CC: '#634643'
-    CHP heat: '#8a5751'
-    CHP electric: '#8a5751'
-    district heating: '#e8beac'
-    resistive heater: '#d8f9b8'
-    retrofitting: '#8487e8'
-    building retrofitting: '#8487e8'
-    # hydrogen
-    H2 for industry: "#f073da"
-    H2 for shipping: "#ebaee0"
-    H2: '#bf13a0'
-    hydrogen: '#bf13a0'
-    SMR: '#870c71'
-    SMR CC: '#4f1745'
-    H2 liquefaction: '#d647bd'
-    hydrogen storage: '#bf13a0'
-    H2 storage: '#bf13a0'
-    land transport fuel cell: '#6b3161'
-    H2 pipeline: '#f081dc'
-    H2 pipeline retrofitted: '#ba99b5'
-    H2 Fuel Cell: '#c251ae'
-    H2 Electrolysis: '#ff29d9'
-    # syngas
-    Sabatier: '#9850ad'
-    methanation: '#c44ce6'
-    methane: '#c44ce6'
-    helmeth: '#e899ff'
-    # synfuels
-    Fischer-Tropsch: '#25c49a'
-    liquid: '#25c49a'
-    kerosene for aviation: '#a1ffe6'
-    naphtha for industry: '#57ebc4'
-    # co2
-    CC: '#f29dae'
-    CCS: '#f29dae'
-    CO2 sequestration: '#f29dae'
-    DAC: '#ff5270'
-    co2 stored: '#f2385a'
-    co2: '#f29dae'
-    co2 vent: '#ffd4dc'
-    CO2 pipeline: '#f5627f'
-    # emissions
-    process emissions CC: '#000000'
-    process emissions: '#222222'
-    process emissions to stored: '#444444'
-    process emissions to atmosphere: '#888888'
-    oil emissions: '#aaaaaa'
-    shipping oil emissions: "#555555"
-    land transport oil emissions: '#777777'
-    agriculture machinery oil emissions: '#333333'
-    # other
-    shipping: '#03a2ff'
-    power-to-heat: '#2fb537'
-    power-to-gas: '#c44ce6'
-    power-to-H2: '#ff29d9'
-    power-to-liquid: '#25c49a'
-    gas-to-power/heat: '#ee8340'
-    waste: '#e3d37d'
-    other: '#000000'