658f8ad00c
Previously they were distributed only by country to the first node in the country. Now conventional power plants are assigned to the correct node using the bus map from PyPSA-Eur. Wind and solar are distributed in each country by capacity factor. The code has been refactored and a bug was fixed whereby total capacities of wind and solar in each country were not correct. Now the years in the config.yaml for myopic are integers not strings.
423 lines
19 KiB
Python
Executable File
423 lines
19 KiB
Python
Executable File
# coding: utf-8
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import logging
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logger = logging.getLogger(__name__)
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import pandas as pd
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idx = pd.IndexSlice
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import numpy as np
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import scipy as sp
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import xarray as xr
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import re, os
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from six import iteritems, string_types
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import pypsa
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import yaml
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import pytz
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from vresutils.costdata import annuity
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from prepare_sector_network import prepare_costs
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#First tell PyPSA that links can have multiple outputs by
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#overriding the component_attrs. This can be done for
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#as many buses as you need with format busi for i = 2,3,4,5,....
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#See https://pypsa.org/doc/components.html#link-with-multiple-outputs-or-inputs
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override_component_attrs = pypsa.descriptors.Dict({k : v.copy() for k,v in pypsa.components.component_attrs.items()})
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override_component_attrs["Link"].loc["bus2"] = ["string",np.nan,np.nan,"2nd bus","Input (optional)"]
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override_component_attrs["Link"].loc["bus3"] = ["string",np.nan,np.nan,"3rd bus","Input (optional)"]
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override_component_attrs["Link"].loc["efficiency2"] = ["static or series","per unit",1.,"2nd bus efficiency","Input (optional)"]
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override_component_attrs["Link"].loc["efficiency3"] = ["static or series","per unit",1.,"3rd bus efficiency","Input (optional)"]
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override_component_attrs["Link"].loc["p2"] = ["series","MW",0.,"2nd bus output","Output"]
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override_component_attrs["Link"].loc["p3"] = ["series","MW",0.,"3rd bus output","Output"]
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override_component_attrs["Link"].loc["build_year"] = ["integer","year",np.nan,"build year","Input (optional)"]
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override_component_attrs["Link"].loc["lifetime"] = ["float","years",np.nan,"build year","Input (optional)"]
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override_component_attrs["Generator"].loc["build_year"] = ["integer","year",np.nan,"build year","Input (optional)"]
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override_component_attrs["Generator"].loc["lifetime"] = ["float","years",np.nan,"build year","Input (optional)"]
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override_component_attrs["Store"].loc["build_year"] = ["integer","year",np.nan,"build year","Input (optional)"]
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override_component_attrs["Store"].loc["lifetime"] = ["float","years",np.nan,"build year","Input (optional)"]
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def add_existing_renewables(df_agg):
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cc = pd.read_csv('data/Country_codes.csv',
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index_col=0)
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carriers = {"solar" : "solar",
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"onwind" : "onwind",
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"offwind" : "offwind-ac"}
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for tech in ['solar', 'onwind', 'offwind']:
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carrier = carriers[tech]
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df = pd.read_csv('data/existing_infrastructure/{}_capacity_IRENA.csv'.format(tech),
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index_col=0)
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df = df.fillna(0.)
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df.columns = df.columns.astype(int)
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df.rename(index={'Czechia':'Czech Republic',
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'UK':'United Kingdom',
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'Bosnia Herzg':'Bosnia Herzegovina',
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'North Macedonia': 'Macedonia'}, inplace=True)
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df.rename(index=cc["2 letter code (ISO-3166-2)"], inplace=True)
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# calculate yearly differences
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df.insert(loc=0, value=.0, column='1999')
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df = df.diff(axis=1).drop('1999', axis=1)
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df = df.clip(lower=0)
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#distribute capacities among nodes according to capacity factor
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#weighting with nodal_fraction
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elec_buses = n.buses.index[n.buses.carrier == "AC"]
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nodal_fraction = pd.Series(0.,elec_buses)
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for country in n.buses.loc[elec_buses,"country"].unique():
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gens = [c for c in n.generators_t.p_max_pu.columns if c[:2] == country and c[-len(carrier):] == carrier]
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cfs = n.generators_t.p_max_pu[gens].mean()
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cfs_key = cfs/cfs.sum()
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nodal_fraction.loc[n.generators.loc[gens,"bus"]] = cfs_key.values
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nodal_df = df.loc[n.buses.loc[elec_buses,"country"]]
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nodal_df.index = elec_buses
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nodal_df = nodal_df.multiply(nodal_fraction,axis=0)
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for year in nodal_df.columns:
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for node in nodal_df.index:
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name = f"{node}-{tech}-{year}"
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capacity = nodal_df.loc[node,year]
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if capacity > 0.:
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df_agg.at[name,"Fueltype"] = tech
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df_agg.at[name,"Capacity"] = capacity
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df_agg.at[name,"YearCommissioned"] = year
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df_agg.at[name,"cluster_bus"] = node
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def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
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"""
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Parameters
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----------
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n : network
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grouping_years : intervals to group existing capacities
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costs : to read lifetime to estimate YearDecomissioning
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"""
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print("adding power capacities installed before baseyear")
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### add conventional capacities using 'powerplants.csv'
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df_agg = pd.read_csv(snakemake.input.powerplants, index_col=0)
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rename_fuel = {'Hard Coal':'coal',
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'Lignite':'lignite',
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'Nuclear':'nuclear',
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'Oil':'oil',
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'OCGT':'OCGT',
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'CCGT':'CCGT',
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'Natural Gas':'gas',}
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fueltype_to_drop = ['Hydro',
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'Wind',
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'Solar',
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'Geothermal',
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'Bioenergy',
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'Waste',
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'Other',
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'CCGT, Thermal']
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technology_to_drop = ['Pv',
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'Storage Technologies']
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df_agg.drop(df_agg.index[df_agg.Fueltype.isin(fueltype_to_drop)],inplace=True)
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df_agg.drop(df_agg.index[df_agg.Technology.isin(technology_to_drop)],inplace=True)
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df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel)
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#assign clustered bus
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busmap_s = pd.read_hdf(snakemake.input.clustermaps,
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key="/busmap_s")
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busmap = pd.read_hdf(snakemake.input.clustermaps,
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key="/busmap")
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clustermaps = busmap_s.map(busmap)
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clustermaps.index = clustermaps.index.astype(int)
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df_agg["cluster_bus"] = df_agg.bus.map(clustermaps)
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#include renewables in df_agg
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add_existing_renewables(df_agg)
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df_agg["grouping_year"] = np.take(grouping_years,
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np.digitize(df_agg.YearCommissioned,
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grouping_years,
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right=True))
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df = df_agg.pivot_table(index=["grouping_year",'Fueltype'], columns='cluster_bus',
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values='Capacity', aggfunc='sum')
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print(df)
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carrier = {"OCGT" : "gas",
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"CCGT" : "gas",
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"coal" : "coal",
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"oil" : "oil",
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"lignite" : "lignite",
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"nuclear" : "uranium"}
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for grouping_year, generator in df.index:
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#capacity is the capacity in MW at each node for this
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capacity = df.loc[grouping_year, generator]
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capacity = capacity[~capacity.isna()]
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capacity = capacity[capacity > snakemake.config['existing_capacities']['threshold_capacity']]
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#print(grouping_year,generator,capacity)
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if generator in ['solar', 'onwind', 'offwind']:
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print("adding generators for",grouping_year,generator,capacity)
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if generator =='offwind':
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p_max_pu=n.generators_t.p_max_pu[capacity.index + ' offwind-ac']
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else:
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p_max_pu=n.generators_t.p_max_pu[capacity.index + ' ' + generator]
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n.madd("Generator",
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capacity.index,
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suffix=' ' + generator +"-"+ str(grouping_year),
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bus=capacity.index,
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carrier=generator,
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p_nom=capacity,
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marginal_cost=costs.at[generator,'VOM'],
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capital_cost=costs.at[generator,'fixed'],
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efficiency=costs.at[generator, 'efficiency'],
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p_max_pu=p_max_pu.rename(columns=n.generators.bus),
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build_year=grouping_year,
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lifetime=costs.at[generator,'lifetime'])
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else:
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print("adding links for",grouping_year,generator,capacity)
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n.madd("Link",
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capacity.index,
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suffix= " " + generator +"-" + str(grouping_year),
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bus0="EU " + carrier[generator],
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bus1=capacity.index,
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bus2="co2 atmosphere",
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carrier=generator,
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marginal_cost=costs.at[generator,'efficiency']*costs.at[generator,'VOM'], #NB: VOM is per MWel
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capital_cost=costs.at[generator,'efficiency']*costs.at[generator,'fixed'], #NB: fixed cost is per MWel
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p_nom=capacity,
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efficiency=costs.at[generator,'efficiency'],
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efficiency2=costs.at[carrier[generator],'CO2 intensity'],
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build_year=grouping_year,
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lifetime=costs.at[generator,'lifetime'])
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def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime):
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"""
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Parameters
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----------
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n : network
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baseyear: last year covered in the existing capacities database
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grouping_years : intervals to group existing capacities
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linear decomissioning of heating capacities from 2020 to 2045 is
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currently assumed
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heating capacities split between residential and services proportional
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to heating load in both
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50% capacities in rural busess 50% in urban buses
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"""
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print("adding heating capacities installed before baseyear")
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# Add existing heating capacities, data comes from the study
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# "Mapping and analyses of the current and future (2020 - 2030)
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# heating/cooling fuel deployment (fossil/renewables) "
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# https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
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# file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
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# retrieve existing heating capacities
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techs = ['gas boiler',
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'oil boiler',
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'resistive heater',
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'air heat pump',
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'ground heat pump']
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df = pd.read_csv('data/existing_infrastructure/existing_heating_raw.csv',
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index_col=0,
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header=0)
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# data for Albania, Montenegro and Macedonia not included in database
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df.loc['Albania']=np.nan
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df.loc['Montenegro']=np.nan
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df.loc['Macedonia']=np.nan
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df.fillna(0, inplace=True)
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df *= 1e3 # GW to MW
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cc = pd.read_csv('data/Country_codes.csv', sep=',', index_col=-1)
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name_to_2code = dict(zip(cc['Country'].tolist(),
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cc['2 letter code (ISO-3166-2)'].tolist()))
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df.rename(index=lambda country : name_to_2code[country], inplace=True)
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# coal and oil boilers are assimilated to oil boilers
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df['oil boiler'] =df['oil boiler'] + df['coal boiler']
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df.drop(['coal boiler'], axis=1, inplace=True)
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# rename countries with network buses names
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nodes_elec=[node for node in n.buses.index[n.buses.carrier == "AC"]]
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name_to_busname={ index : [node for node in nodes_elec if index in node][0] for index in df.index}
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df.rename(index=lambda country : name_to_busname[country], inplace=True)
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# split existing capacities between residential and services
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# proportional to energy demand
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ratio_residential=pd.Series([(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] /
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(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] +
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n.loads_t.p_set.sum()['{} services rural heat'.format(node)] ))
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for node in df.index], index=df.index)
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for tech in techs:
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df['residential ' + tech] = df[tech]*ratio_residential
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df['services ' + tech] = df[tech]*(1-ratio_residential)
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nodes={}
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p_nom={}
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for name in ["residential rural",
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"services rural",
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"residential urban decentral",
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"services urban decentral",
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"urban central"]:
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name_type = "central" if name == "urban central" else "decentral"
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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')]])
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heat_pump_type = "air" if "urban" in name else "ground"
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heat_type= "residential" if "residential" in name else "services"
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if name == "urban central":
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p_nom[name]=df['air heat pump'][nodes[name]]
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else:
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p_nom[name] = df['{} {} heat pump'.format(heat_type, heat_pump_type)][nodes[name]]
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# Add heat pumps
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costs_name = "{} {}-sourced heat pump".format("decentral", heat_pump_type)
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cop = {"air" : ashp_cop, "ground" : gshp_cop}
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efficiency = cop[heat_pump_type][nodes[name]] if time_dep_hp_cop else costs.at[costs_name,'efficiency']
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for i,grouping_year in enumerate(grouping_years):
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if int(grouping_year) + default_lifetime <= int(baseyear):
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ratio=0
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else:
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#installation is assumed to be linear for the past 25 years (default lifetime)
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ratio = (int(grouping_year)-int(grouping_years[i-1]))/default_lifetime
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print(str(grouping_year) + ' ratio ' + str(ratio))
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n.madd("Link",
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nodes[name],
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suffix=" {} {} heat pump-{}".format(name,heat_pump_type, grouping_year),
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bus0=nodes[name],
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bus1=nodes[name] + " " + name + " heat",
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carrier="{} {} heat pump".format(name,heat_pump_type),
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efficiency=efficiency,
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capital_cost=costs.at[costs_name,'efficiency']*costs.at[costs_name,'fixed'],
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p_nom=p_nom[name]*ratio,
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build_year=int(grouping_year),
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lifetime=costs.at[costs_name,'lifetime'])
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# add resistive heater, gas boilers and oil boilers
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# (50% capacities to rural buses, 50% to urban buses)
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n.madd("Link",
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nodes[name],
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suffix= " " + name + " resistive heater-{}".format(grouping_year),
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bus0=nodes[name],
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bus1=nodes[name] + " " + name + " heat",
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carrier=name + " resistive heater",
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efficiency=costs.at[name_type + ' resistive heater','efficiency'],
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capital_cost=costs.at[name_type + ' resistive heater','efficiency']*costs.at[name_type + ' resistive heater','fixed'],
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p_nom=0.5*df['{} resistive heater'.format(heat_type)][nodes[name]]*ratio,
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build_year=int(grouping_year),
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lifetime=costs.at[costs_name,'lifetime'])
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n.madd("Link",
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nodes[name],
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suffix= " " + name + " gas boiler-{}".format(grouping_year),
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bus0=["EU gas"]*len(nodes[name]),
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bus1=nodes[name] + " " + name + " heat",
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bus2="co2 atmosphere",
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carrier=name + " gas boiler",
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efficiency=costs.at[name_type + ' gas boiler','efficiency'],
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efficiency2=costs.at['gas','CO2 intensity'],
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capital_cost=costs.at[name_type + ' gas boiler','efficiency']*costs.at[name_type + ' gas boiler','fixed'],
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p_nom=0.5*df['{} gas boiler'.format(heat_type)][nodes[name]]*ratio,
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build_year=int(grouping_year),
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lifetime=costs.at[name_type + ' gas boiler','lifetime'])
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n.madd("Link",
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nodes[name],
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suffix=" " + name + " oil boiler-{}".format(grouping_year),
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bus0=["EU oil"]*len(nodes[name]),
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bus1=nodes[name] + " " + name + " heat",
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bus2="co2 atmosphere",
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carrier=name + " oil boiler",
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efficiency=costs.at['decentral oil boiler','efficiency'],
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efficiency2=costs.at['oil','CO2 intensity'],
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capital_cost=costs.at['decentral oil boiler','efficiency']*costs.at['decentral oil boiler','fixed'],
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p_nom=0.5*df['{} oil boiler'.format(heat_type)][nodes[name]]*ratio,
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build_year=int(grouping_year),
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lifetime=costs.at[name_type + ' gas boiler','lifetime'])
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# delete links with p_nom=nan corresponding to extra nodes in country
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n.mremove("Link", [index for index in n.links.index.to_list() if str(grouping_year) in index and np.isnan(n.links.p_nom[index])])
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# delete links if their lifetime is over and p_nom=0
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n.mremove("Link", [index for index in n.links.index.to_list() if str(grouping_year) in index and n.links.p_nom[index]<snakemake.config['existing_capacities']['threshold_capacity']])
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if __name__ == "__main__":
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# Detect running outside of snakemake and mock snakemake for testing
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if 'snakemake' not in globals():
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from vresutils.snakemake import MockSnakemake
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snakemake = MockSnakemake(
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wildcards=dict(network='elec', simpl='', clusters='37', lv='1.0',
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sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
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co2_budget_name='go',
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planning_horizons='2020'),
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input=dict(network='pypsa-eur-sec/results/test/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{co2_budget_name}_{planning_horizons}.nc',
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costs='pypsa-eur-sec/data/costs/costs_{planning_horizons}.csv',
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cop_air_total="pypsa-eur-sec/resources/cop_air_total_{network}_s{simpl}_{clusters}.nc",
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cop_soil_total="pypsa-eur-sec/resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc"),
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output=['pypsa-eur-sec/results/test/prenetworks_brownfield/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc'],
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)
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import yaml
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with open('config.yaml') as f:
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snakemake.config = yaml.load(f)
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logging.basicConfig(level=snakemake.config['logging_level'])
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options = snakemake.config["sector"]
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opts = snakemake.wildcards.sector_opts.split('-')
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baseyear= snakemake.config['scenario']["planning_horizons"][0]
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n = pypsa.Network(snakemake.input.network,
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override_component_attrs=override_component_attrs)
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Nyears = n.snapshot_weightings.sum()/8760.
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costs = prepare_costs(snakemake.input.costs,
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snakemake.config['costs']['USD2013_to_EUR2013'],
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snakemake.config['costs']['discountrate'],
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Nyears)
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grouping_years=snakemake.config['existing_capacities']['grouping_years']
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add_power_capacities_installed_before_baseyear(n, grouping_years, costs)
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if "H" in opts:
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time_dep_hp_cop = options["time_dep_hp_cop"]
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ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).T.to_pandas().reindex(index=n.snapshots)
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gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).T.to_pandas().reindex(index=n.snapshots)
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default_lifetime = snakemake.config['costs']['lifetime']
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add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
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n.export_to_netcdf(snakemake.output[0])
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