add_existing_baseyear.py: Add carrier to conventional generators
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Tom Brown
parent
7abe928337
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ece21bfdfa
@ -48,7 +48,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
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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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@ -56,8 +56,8 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
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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('../pypsa-eur/resources/powerplants.csv', index_col=0)
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@ -89,7 +89,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
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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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for tech in ['solar', 'onwind', 'offwind']:
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df = pd.read_csv('data/existing_infrastructure/{}_capacity_IRENA.csv'.format(tech),
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df = pd.read_csv('data/existing_infrastructure/{}_capacity_IRENA.csv'.format(tech),
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sep=',', index_col=0)
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df.rename(index={'Czechia':'Czech Republic',
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@ -114,10 +114,10 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
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'YearCommissioned':int(year),
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'YearDecommissioning':int(float(year)+costs.at[tech, 'lifetime'])},
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ignore_index=True)
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nodes=set([node[0:2] for node in n.buses.index[n.buses.carrier == "AC"]])
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#TODO: Check if we want to change YearCommisioned into YearRetrofited
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for i,grouping_year in enumerate(grouping_years):
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if i==0:
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@ -127,45 +127,47 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
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df = df_agg[index].pivot_table(index='Country', columns='Fueltype',
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values='Capacity', aggfunc='sum')
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for node in nodes:
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#if a country has more than one node, selects the first one
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bus_selected=[bus for bus in n.buses.index[n.buses.carrier == "AC"] if bus[0:2]==node][0]
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for generator,carrier in [("OCGT","gas"),
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("CCGT", "gas"),
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("coal", "coal"),
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("oil","oil"),
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("nuclear","uranium")]:
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bus_selected=[bus for bus in n.buses.index[n.buses.carrier == "AC"] if bus[0:2]==node][0]
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for generator,carrier in [("OCGT","gas"),
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("CCGT", "gas"),
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("coal", "coal"),
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("oil","oil"),
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("nuclear","uranium")]:
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try:
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if node in df.index and not np.isnan(df.loc[node, generator]):
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n.add("Link",
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bus_selected + " " + generator +"-" + grouping_year,
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bus0="EU " + carrier,
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bus1=bus_selected,
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bus2="co2 atmosphere",
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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=df.loc[node, generator],
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efficiency=costs.at[generator,'efficiency'],
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efficiency2=costs.at[carrier,'CO2 intensity'],
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build_year=int(grouping_year),
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lifetime=costs.at[generator,'lifetime'])
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if node in df.index and not np.isnan(df.loc[node, generator]):
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#use madd so that we can insert the carrier attribute
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n.madd("Link",
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[bus_selected + " " + generator +"-" + grouping_year],
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bus0="EU " + carrier,
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bus1=bus_selected,
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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=df.loc[node, generator],
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efficiency=costs.at[generator,'efficiency'],
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efficiency2=costs.at[carrier,'CO2 intensity'],
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build_year=int(grouping_year),
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lifetime=costs.at[generator,'lifetime'])
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except:
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print("No capacity installed around " + grouping_year + " of " + generator + " in node " + node)
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for generator in ['solar', 'onwind', 'offwind']:
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try:
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try:
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if not np.isnan(df.loc[node, generator]):
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if generator =='offwind':
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p_max_pu=n.generators_t.p_max_pu[bus_selected + ' offwind-ac']
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else:
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p_max_pu=n.generators_t.p_max_pu[bus_selected + ' ' + generator]
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n.add("Generator",
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n.add("Generator",
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bus_selected + ' ' + generator +"-"+ grouping_year,
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bus=bus_selected,
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carrier=generator,
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carrier=generator,
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p_nom=df.loc[node, generator],
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marginal_cost=costs.at[generator,'VOM'],
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capital_cost=costs.at[generator,'fixed'],
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@ -179,7 +181,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
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# delete generators if their lifetime is over and p_nom=0
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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']])
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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']])
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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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@ -187,26 +189,26 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
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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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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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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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"""
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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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# "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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@ -216,58 +218,58 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
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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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# 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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# 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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# 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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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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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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"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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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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# Add heat pumps
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costs_name = "{} {}-sourced heat pump".format("decentral", heat_pump_type)
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@ -277,7 +279,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, 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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#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(grouping_year + ' ratio ' + str(ratio))
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n.madd("Link",
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@ -288,11 +290,11 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
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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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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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# 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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@ -302,10 +304,10 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
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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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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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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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@ -316,9 +318,9 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
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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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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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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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@ -329,30 +331,30 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
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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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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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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 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 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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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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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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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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@ -366,10 +368,10 @@ if __name__ == "__main__":
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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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@ -378,19 +380,12 @@ if __name__ == "__main__":
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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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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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