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add_existing_baseyear.py: Add carrier to conventional generators

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Tom Brown 2020-07-30 08:22:27 +02:00
parent 7abe928337
commit ece21bfdfa
1 changed files with 88 additions and 93 deletions
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181
scripts/add_existing_baseyear.py
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@ -48,7 +48,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
Parameters Parameters
---------- ----------
n : network n : network
grouping_years : intervals to group existing capacities grouping_years : intervals to group existing capacities
costs : to read lifetime to estimate YearDecomissioning costs : to read lifetime to estimate YearDecomissioning
@ -56,8 +56,8 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
""" """
print("adding power capacities installed before baseyear") print("adding power capacities installed before baseyear")
### add conventional capacities using 'powerplants.csv' ### add conventional capacities using 'powerplants.csv'
df_agg = pd.read_csv('../pypsa-eur/resources/powerplants.csv', index_col=0) df_agg = pd.read_csv('../pypsa-eur/resources/powerplants.csv', index_col=0)
@ -89,7 +89,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
name_to_2code = dict(zip(cc['Country'].tolist(), name_to_2code = dict(zip(cc['Country'].tolist(),
cc['2 letter code (ISO-3166-2)'].tolist())) cc['2 letter code (ISO-3166-2)'].tolist()))
for tech in ['solar', 'onwind', 'offwind']: for tech in ['solar', 'onwind', 'offwind']:
df = pd.read_csv('data/existing_infrastructure/{}_capacity_IRENA.csv'.format(tech), df = pd.read_csv('data/existing_infrastructure/{}_capacity_IRENA.csv'.format(tech),
sep=',', index_col=0) sep=',', index_col=0)
df.rename(index={'Czechia':'Czech Republic', df.rename(index={'Czechia':'Czech Republic',
@ -114,10 +114,10 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
'YearCommissioned':int(year), 'YearCommissioned':int(year),
'YearDecommissioning':int(float(year)+costs.at[tech, 'lifetime'])}, 'YearDecommissioning':int(float(year)+costs.at[tech, 'lifetime'])},
ignore_index=True) ignore_index=True)
nodes=set([node[0:2] for node in n.buses.index[n.buses.carrier == "AC"]]) nodes=set([node[0:2] for node in n.buses.index[n.buses.carrier == "AC"]])
#TODO: Check if we want to change YearCommisioned into YearRetrofited #TODO: Check if we want to change YearCommisioned into YearRetrofited
for i,grouping_year in enumerate(grouping_years): for i,grouping_year in enumerate(grouping_years):
if i==0: if i==0:
@ -127,45 +127,47 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
df = df_agg[index].pivot_table(index='Country', columns='Fueltype', df = df_agg[index].pivot_table(index='Country', columns='Fueltype',
values='Capacity', aggfunc='sum') values='Capacity', aggfunc='sum')
for node in nodes: for node in nodes:
#if a country has more than one node, selects the first one #if a country has more than one node, selects the first one
bus_selected=[bus for bus in n.buses.index[n.buses.carrier == "AC"] if bus[0:2]==node][0] bus_selected=[bus for bus in n.buses.index[n.buses.carrier == "AC"] if bus[0:2]==node][0]
for generator,carrier in [("OCGT","gas"), for generator,carrier in [("OCGT","gas"),
("CCGT", "gas"), ("CCGT", "gas"),
("coal", "coal"), ("coal", "coal"),
("oil","oil"), ("oil","oil"),
("nuclear","uranium")]: ("nuclear","uranium")]:
try: try:
if node in df.index and not np.isnan(df.loc[node, generator]): if node in df.index and not np.isnan(df.loc[node, generator]):
n.add("Link", #use madd so that we can insert the carrier attribute
bus_selected + " " + generator +"-" + grouping_year, n.madd("Link",
bus0="EU " + carrier, [bus_selected + " " + generator +"-" + grouping_year],
bus1=bus_selected, bus0="EU " + carrier,
bus2="co2 atmosphere", bus1=bus_selected,
marginal_cost=costs.at[generator,'efficiency']*costs.at[generator,'VOM'], #NB: VOM is per MWel bus2="co2 atmosphere",
capital_cost=costs.at[generator,'efficiency']*costs.at[generator,'fixed'], #NB: fixed cost is per MWel carrier=generator,
p_nom=df.loc[node, generator], marginal_cost=costs.at[generator,'efficiency']*costs.at[generator,'VOM'], #NB: VOM is per MWel
efficiency=costs.at[generator,'efficiency'], capital_cost=costs.at[generator,'efficiency']*costs.at[generator,'fixed'], #NB: fixed cost is per MWel
efficiency2=costs.at[carrier,'CO2 intensity'], p_nom=df.loc[node, generator],
build_year=int(grouping_year), efficiency=costs.at[generator,'efficiency'],
lifetime=costs.at[generator,'lifetime']) efficiency2=costs.at[carrier,'CO2 intensity'],
build_year=int(grouping_year),
lifetime=costs.at[generator,'lifetime'])
except: except:
print("No capacity installed around " + grouping_year + " of " + generator + " in node " + node) print("No capacity installed around " + grouping_year + " of " + generator + " in node " + node)
for generator in ['solar', 'onwind', 'offwind']: for generator in ['solar', 'onwind', 'offwind']:
try: try:
if not np.isnan(df.loc[node, generator]): if not np.isnan(df.loc[node, generator]):
if generator =='offwind': if generator =='offwind':
p_max_pu=n.generators_t.p_max_pu[bus_selected + ' offwind-ac'] p_max_pu=n.generators_t.p_max_pu[bus_selected + ' offwind-ac']
else: else:
p_max_pu=n.generators_t.p_max_pu[bus_selected + ' ' + generator] p_max_pu=n.generators_t.p_max_pu[bus_selected + ' ' + generator]
n.add("Generator", n.add("Generator",
bus_selected + ' ' + generator +"-"+ grouping_year, bus_selected + ' ' + generator +"-"+ grouping_year,
bus=bus_selected, bus=bus_selected,
carrier=generator, carrier=generator,
p_nom=df.loc[node, generator], p_nom=df.loc[node, generator],
marginal_cost=costs.at[generator,'VOM'], marginal_cost=costs.at[generator,'VOM'],
capital_cost=costs.at[generator,'fixed'], capital_cost=costs.at[generator,'fixed'],
@ -179,7 +181,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
# delete generators if their lifetime is over and p_nom=0 # delete generators if their lifetime is over and p_nom=0
n.mremove("Generator", [index for index in n.generators.index.to_list() if grouping_year in index and n.generators.p_nom[index] < snakemake.config['existing_capacities']['threshold_capacity']]) n.mremove("Generator", [index for index in n.generators.index.to_list() if grouping_year in index and n.generators.p_nom[index] < snakemake.config['existing_capacities']['threshold_capacity']])
n.mremove("Link", [index for index in n.links.index.to_list() if grouping_year in index and n.links.p_nom[index] < snakemake.config['existing_capacities']['threshold_capacity']]) n.mremove("Link", [index for index in n.links.index.to_list() if grouping_year in index and n.links.p_nom[index] < snakemake.config['existing_capacities']['threshold_capacity']])
def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime): def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime):
""" """
@ -187,26 +189,26 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
Parameters Parameters
---------- ----------
n : network n : network
baseyear: last year covered in the existing capacities database baseyear: last year covered in the existing capacities database
grouping_years : intervals to group existing capacities grouping_years : intervals to group existing capacities
linear decomissioning of heating capacities from 2020 to 2045 is linear decomissioning of heating capacities from 2020 to 2045 is
currently assumed currently assumed
heating capacities split between residential and services proportional heating capacities split between residential and services proportional
to heating load in both to heating load in both
50% capacities in rural busess 50% in urban buses 50% capacities in rural busess 50% in urban buses
""" """
print("adding heating capacities installed before baseyear") print("adding heating capacities installed before baseyear")
# Add existing heating capacities, data comes from the study # Add existing heating capacities, data comes from the study
# "Mapping and analyses of the current and future (2020 - 2030) # "Mapping and analyses of the current and future (2020 - 2030)
# heating/cooling fuel deployment (fossil/renewables) " # heating/cooling fuel deployment (fossil/renewables) "
# https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1 # https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
# file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv". # file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
# retrieve existing heating capacities # retrieve existing heating capacities
techs = ['gas boiler', techs = ['gas boiler',
'oil boiler', 'oil boiler',
@ -216,58 +218,58 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
df = pd.read_csv('data/existing_infrastructure/existing_heating_raw.csv', df = pd.read_csv('data/existing_infrastructure/existing_heating_raw.csv',
index_col=0, index_col=0,
header=0) header=0)
# data for Albania, Montenegro and Macedonia not included in database # data for Albania, Montenegro and Macedonia not included in database
df.loc['Albania']=np.nan df.loc['Albania']=np.nan
df.loc['Montenegro']=np.nan df.loc['Montenegro']=np.nan
df.loc['Macedonia']=np.nan df.loc['Macedonia']=np.nan
df.fillna(0, inplace=True) df.fillna(0, inplace=True)
df *= 1e3 # GW to MW df *= 1e3 # GW to MW
cc = pd.read_csv('data/Country_codes.csv', sep=',', index_col=-1) cc = pd.read_csv('data/Country_codes.csv', sep=',', index_col=-1)
name_to_2code = dict(zip(cc['Country'].tolist(), name_to_2code = dict(zip(cc['Country'].tolist(),
cc['2 letter code (ISO-3166-2)'].tolist())) cc['2 letter code (ISO-3166-2)'].tolist()))
df.rename(index=lambda country : name_to_2code[country], inplace=True) df.rename(index=lambda country : name_to_2code[country], inplace=True)
# coal and oil boilers are assimilated to oil boilers # coal and oil boilers are assimilated to oil boilers
df['oil boiler'] =df['oil boiler'] + df['coal boiler'] df['oil boiler'] =df['oil boiler'] + df['coal boiler']
df.drop(['coal boiler'], axis=1, inplace=True) df.drop(['coal boiler'], axis=1, inplace=True)
# rename countries with network buses names # rename countries with network buses names
nodes_elec=[node for node in n.buses.index[n.buses.carrier == "AC"]] nodes_elec=[node for node in n.buses.index[n.buses.carrier == "AC"]]
name_to_busname={ index : [node for node in nodes_elec if index in node][0] for index in df.index} name_to_busname={ index : [node for node in nodes_elec if index in node][0] for index in df.index}
df.rename(index=lambda country : name_to_busname[country], inplace=True) df.rename(index=lambda country : name_to_busname[country], inplace=True)
# split existing capacities between residential and services # split existing capacities between residential and services
# proportional to energy demand # proportional to energy demand
ratio_residential=pd.Series([(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] / ratio_residential=pd.Series([(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] /
(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] + (n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] +
n.loads_t.p_set.sum()['{} services rural heat'.format(node)] )) n.loads_t.p_set.sum()['{} services rural heat'.format(node)] ))
for node in df.index], index=df.index) for node in df.index], index=df.index)
for tech in techs: for tech in techs:
df['residential ' + tech] = df[tech]*ratio_residential df['residential ' + tech] = df[tech]*ratio_residential
df['services ' + tech] = df[tech]*(1-ratio_residential) df['services ' + tech] = df[tech]*(1-ratio_residential)
nodes={} nodes={}
p_nom={} p_nom={}
for name in ["residential rural", for name in ["residential rural",
"services rural", "services rural",
"residential urban decentral", "residential urban decentral",
"services urban decentral", "services urban decentral",
"urban central"]: "urban central"]:
name_type = "central" if name == "urban central" else "decentral" name_type = "central" if name == "urban central" else "decentral"
nodes[name] = pd.Index([index[0:5] for index in n.buses.index[n.buses.index.str.contains(name) & n.buses.index.str.contains('heat')]]) nodes[name] = pd.Index([index[0:5] for index in n.buses.index[n.buses.index.str.contains(name) & n.buses.index.str.contains('heat')]])
heat_pump_type = "air" if "urban" in name else "ground" heat_pump_type = "air" if "urban" in name else "ground"
heat_type= "residential" if "residential" in name else "services" heat_type= "residential" if "residential" in name else "services"
if name == "urban central": if name == "urban central":
p_nom[name]=df['air heat pump'][nodes[name]] p_nom[name]=df['air heat pump'][nodes[name]]
else: else:
p_nom[name] = df['{} {} heat pump'.format(heat_type, heat_pump_type)][nodes[name]] p_nom[name] = df['{} {} heat pump'.format(heat_type, heat_pump_type)][nodes[name]]
# Add heat pumps # Add heat pumps
costs_name = "{} {}-sourced heat pump".format("decentral", heat_pump_type) costs_name = "{} {}-sourced heat pump".format("decentral", heat_pump_type)
@ -277,7 +279,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
if int(grouping_year) + default_lifetime <= int(baseyear): if int(grouping_year) + default_lifetime <= int(baseyear):
ratio=0 ratio=0
else: else:
#installation is assumed to be linear for the past 25 years (default lifetime) #installation is assumed to be linear for the past 25 years (default lifetime)
ratio = (int(grouping_year)-int(grouping_years[i-1]))/default_lifetime ratio = (int(grouping_year)-int(grouping_years[i-1]))/default_lifetime
print(grouping_year + ' ratio ' + str(ratio)) print(grouping_year + ' ratio ' + str(ratio))
n.madd("Link", n.madd("Link",
@ -288,11 +290,11 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
carrier="{} {} heat pump".format(name,heat_pump_type), carrier="{} {} heat pump".format(name,heat_pump_type),
efficiency=efficiency, efficiency=efficiency,
capital_cost=costs.at[costs_name,'efficiency']*costs.at[costs_name,'fixed'], capital_cost=costs.at[costs_name,'efficiency']*costs.at[costs_name,'fixed'],
p_nom=p_nom[name]*ratio, p_nom=p_nom[name]*ratio,
build_year=int(grouping_year), build_year=int(grouping_year),
lifetime=costs.at[costs_name,'lifetime']) lifetime=costs.at[costs_name,'lifetime'])
# add resistive heater, gas boilers and oil boilers # add resistive heater, gas boilers and oil boilers
# (50% capacities to rural buses, 50% to urban buses) # (50% capacities to rural buses, 50% to urban buses)
n.madd("Link", n.madd("Link",
nodes[name], nodes[name],
@ -302,10 +304,10 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
carrier=name + " resistive heater", carrier=name + " resistive heater",
efficiency=costs.at[name_type + ' resistive heater','efficiency'], efficiency=costs.at[name_type + ' resistive heater','efficiency'],
capital_cost=costs.at[name_type + ' resistive heater','efficiency']*costs.at[name_type + ' resistive heater','fixed'], capital_cost=costs.at[name_type + ' resistive heater','efficiency']*costs.at[name_type + ' resistive heater','fixed'],
p_nom=0.5*df['{} resistive heater'.format(heat_type)][nodes[name]]*ratio, p_nom=0.5*df['{} resistive heater'.format(heat_type)][nodes[name]]*ratio,
build_year=int(grouping_year), build_year=int(grouping_year),
lifetime=costs.at[costs_name,'lifetime']) lifetime=costs.at[costs_name,'lifetime'])
n.madd("Link", n.madd("Link",
nodes[name], nodes[name],
suffix= " " + name + " gas boiler-{}".format(grouping_year), suffix= " " + name + " gas boiler-{}".format(grouping_year),
@ -316,9 +318,9 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
efficiency=costs.at[name_type + ' gas boiler','efficiency'], efficiency=costs.at[name_type + ' gas boiler','efficiency'],
efficiency2=costs.at['gas','CO2 intensity'], efficiency2=costs.at['gas','CO2 intensity'],
capital_cost=costs.at[name_type + ' gas boiler','efficiency']*costs.at[name_type + ' gas boiler','fixed'], capital_cost=costs.at[name_type + ' gas boiler','efficiency']*costs.at[name_type + ' gas boiler','fixed'],
p_nom=0.5*df['{} gas boiler'.format(heat_type)][nodes[name]]*ratio, p_nom=0.5*df['{} gas boiler'.format(heat_type)][nodes[name]]*ratio,
build_year=int(grouping_year), build_year=int(grouping_year),
lifetime=costs.at[name_type + ' gas boiler','lifetime']) lifetime=costs.at[name_type + ' gas boiler','lifetime'])
n.madd("Link", n.madd("Link",
nodes[name], nodes[name],
suffix=" " + name + " oil boiler-{}".format(grouping_year), suffix=" " + name + " oil boiler-{}".format(grouping_year),
@ -329,30 +331,30 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
efficiency=costs.at['decentral oil boiler','efficiency'], efficiency=costs.at['decentral oil boiler','efficiency'],
efficiency2=costs.at['oil','CO2 intensity'], efficiency2=costs.at['oil','CO2 intensity'],
capital_cost=costs.at['decentral oil boiler','efficiency']*costs.at['decentral oil boiler','fixed'], capital_cost=costs.at['decentral oil boiler','efficiency']*costs.at['decentral oil boiler','fixed'],
p_nom=0.5*df['{} oil boiler'.format(heat_type)][nodes[name]]*ratio, p_nom=0.5*df['{} oil boiler'.format(heat_type)][nodes[name]]*ratio,
build_year=int(grouping_year), build_year=int(grouping_year),
lifetime=costs.at[name_type + ' gas boiler','lifetime']) lifetime=costs.at[name_type + ' gas boiler','lifetime'])
# delete links with p_nom=nan corresponding to extra nodes in country # delete links with p_nom=nan corresponding to extra nodes in country
n.mremove("Link", [index for index in n.links.index.to_list() if grouping_year in index and np.isnan(n.links.p_nom[index])]) n.mremove("Link", [index for index in n.links.index.to_list() if grouping_year in index and np.isnan(n.links.p_nom[index])])
# delete links if their lifetime is over and p_nom=0 # delete links if their lifetime is over and p_nom=0
n.mremove("Link", [index for index in n.links.index.to_list() if grouping_year in index and n.links.p_nom[index]<snakemake.config['existing_capacities']['threshold_capacity']]) n.mremove("Link", [index for index in n.links.index.to_list() if grouping_year in index and n.links.p_nom[index]<snakemake.config['existing_capacities']['threshold_capacity']])
if __name__ == "__main__": if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing # Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' not in globals(): if 'snakemake' not in globals():
from vresutils.snakemake import MockSnakemake from vresutils.snakemake import MockSnakemake
snakemake = MockSnakemake( snakemake = MockSnakemake(
wildcards=dict(network='elec', simpl='', clusters='37', lv='1.0', wildcards=dict(network='elec', simpl='', clusters='37', lv='1.0',
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1', sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
co2_budget_name='go', co2_budget_name='go',
planning_horizons='2020'), planning_horizons='2020'),
input=dict(network='pypsa-eur-sec/results/test/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{co2_budget_name}_{planning_horizons}.nc', input=dict(network='pypsa-eur-sec/results/test/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{co2_budget_name}_{planning_horizons}.nc',
costs='pypsa-eur-sec/data/costs/costs_{planning_horizons}.csv', costs='pypsa-eur-sec/data/costs/costs_{planning_horizons}.csv',
cop_air_total="pypsa-eur-sec/resources/cop_air_total_{network}_s{simpl}_{clusters}.nc", cop_air_total="pypsa-eur-sec/resources/cop_air_total_{network}_s{simpl}_{clusters}.nc",
cop_soil_total="pypsa-eur-sec/resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc"), cop_soil_total="pypsa-eur-sec/resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc"),
output=['pypsa-eur-sec/results/test/prenetworks_brownfield/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc'], output=['pypsa-eur-sec/results/test/prenetworks_brownfield/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc'],
) )
import yaml import yaml
@ -366,10 +368,10 @@ if __name__ == "__main__":
opts = snakemake.wildcards.sector_opts.split('-') opts = snakemake.wildcards.sector_opts.split('-')
baseyear= snakemake.config['scenario']["planning_horizons"][0] baseyear= snakemake.config['scenario']["planning_horizons"][0]
n = pypsa.Network(snakemake.input.network, n = pypsa.Network(snakemake.input.network,
override_component_attrs=override_component_attrs) override_component_attrs=override_component_attrs)
Nyears = n.snapshot_weightings.sum()/8760. Nyears = n.snapshot_weightings.sum()/8760.
costs = prepare_costs(snakemake.input.costs, costs = prepare_costs(snakemake.input.costs,
snakemake.config['costs']['USD2013_to_EUR2013'], snakemake.config['costs']['USD2013_to_EUR2013'],
@ -378,19 +380,12 @@ if __name__ == "__main__":
grouping_years=snakemake.config['existing_capacities']['grouping_years'] grouping_years=snakemake.config['existing_capacities']['grouping_years']
add_power_capacities_installed_before_baseyear(n, grouping_years, costs) add_power_capacities_installed_before_baseyear(n, grouping_years, costs)
if "H" in opts: if "H" in opts:
time_dep_hp_cop = options["time_dep_hp_cop"] time_dep_hp_cop = options["time_dep_hp_cop"]
ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).T.to_pandas().reindex(index=n.snapshots) ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).T.to_pandas().reindex(index=n.snapshots)
gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).T.to_pandas().reindex(index=n.snapshots) gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).T.to_pandas().reindex(index=n.snapshots)
default_lifetime = snakemake.config['costs']['lifetime'] default_lifetime = snakemake.config['costs']['lifetime']
add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime) add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
n.export_to_netcdf(snakemake.output[0]) n.export_to_netcdf(snakemake.output[0])