Merge pull request #1255 from PyPSA/fix_building_fec
Use JRC-IDEES thermal energy service instead of FE for buildings heating demand
This commit is contained in:
lisazeyen
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commit
7acda28ee7
@ -10,6 +10,8 @@ Release Notes
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.. Upcoming Release
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* Change the heating demand from final energy which includes losses in legacy equipment to thermal energy service based on JRC-IDEES. Efficiencies of existing heating capacities are lowered according to the conversion of final energy to thermal energy service. For overnight scenarios or future planning horizon this change leads to a reduction in heat supply.
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* Updated district heating supply temperatures based on `Euroheat's DHC Market Outlook 2024<https://api.euroheat.org/uploads/Market_Outlook_2024_beeecd62d4.pdf>`__ and `AGFW-Hauptbericht 2022 <https://www.agfw.de/securedl/sdl-eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE3MjU2MjI2MTUsImV4cCI6MTcyNTcxMjYxNSwidXNlciI6MCwiZ3JvdXBzIjpbMCwtMV0sImZpbGUiOiJmaWxlYWRtaW4vdXNlcl91cGxvYWQvWmFobGVuX3VuZF9TdGF0aXN0aWtlbi9IYXVwdGJlcmljaHRfMjAyMi9BR0ZXX0hhdXB0YmVyaWNodF8yMDIyLnBkZiIsInBhZ2UiOjQzNn0.Bhma3PKg9uJnC57Ixi2p9STW5-II9VXPTDXS544M208/AGFW_Hauptbericht_2022.pdf>`__. `min_forward_temperature` and `return_temperature` (not given by Euroheat) are extrapolated based on German values.
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* Made the overdimensioning factor for heating systems specific for central/decentral heating, defaults to no overdimensionining for central heating and no changes to decentral heating compared to previous version.
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@ -366,6 +366,7 @@ rule build_energy_totals:
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co2_name=resources("co2_totals.csv"),
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transport_name=resources("transport_data.csv"),
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district_heat_share=resources("district_heat_share.csv"),
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heating_efficiencies=resources("heating_efficiencies.csv"),
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threads: 16
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resources:
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mem_mb=10000,
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@ -1015,6 +1016,7 @@ rule prepare_sector_network:
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RDIR=RDIR,
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heat_pump_sources=config_provider("sector", "heat_pump_sources"),
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heat_systems=config_provider("sector", "heat_systems"),
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energy_totals_year=config_provider("energy", "energy_totals_year"),
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input:
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unpack(input_profile_offwind),
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**rules.cluster_gas_network.output,
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@ -1089,6 +1091,7 @@ rule prepare_sector_network:
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district_heat_share=resources(
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"district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
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),
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heating_efficiencies=resources("heating_efficiencies.csv"),
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temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
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temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
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cop_profiles=resources("cop_profiles_elec_s{simpl}_{clusters}.nc"),
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@ -10,6 +10,7 @@ rule add_existing_baseyear:
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existing_capacities=config_provider("existing_capacities"),
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costs=config_provider("costs"),
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heat_pump_sources=config_provider("sector", "heat_pump_sources"),
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energy_totals_year=config_provider("energy", "energy_totals_year"),
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input:
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network=RESULTS
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+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
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@ -26,6 +27,7 @@ rule add_existing_baseyear:
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existing_heating_distribution=resources(
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"existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
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),
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heating_efficiencies=resources("heating_efficiencies.csv"),
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output:
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RESULTS
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+ "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
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@ -8,6 +8,7 @@ rule add_existing_baseyear:
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existing_capacities=config_provider("existing_capacities"),
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costs=config_provider("costs"),
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heat_pump_sources=config_provider("sector", "heat_pump_sources"),
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energy_totals_year=config_provider("energy", "energy_totals_year"),
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input:
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network=RESULTS
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+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
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@ -25,6 +26,7 @@ rule add_existing_baseyear:
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"existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
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),
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existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
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heating_efficiencies=resources("heating_efficiencies.csv"),
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output:
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RESULTS
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+ "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
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@ -418,6 +418,50 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
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]
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def get_efficiency(heat_system, carrier, nodes, heating_efficiencies, costs):
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"""
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Computes the heating system efficiency based on the sector and carrier
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type.
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Parameters:
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-----------
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heat_system : object
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carrier : str
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The type of fuel or energy carrier (e.g., 'gas', 'oil').
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nodes : pandas.Series
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A pandas Series containing node information used to match the heating efficiency data.
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heating_efficiencies : dict
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A dictionary containing efficiency values for different carriers and sectors.
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costs : pandas.DataFrame
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A DataFrame containing boiler cost and efficiency data for different heating systems.
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Returns:
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--------
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efficiency : pandas.Series or float
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A pandas Series mapping the efficiencies based on nodes for residential and services sectors, or a single
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efficiency value for other heating systems (e.g., urban central).
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Notes:
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------
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- For residential and services sectors, efficiency is mapped based on the nodes.
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- For other sectors, the default boiler efficiency is retrieved from the `costs` database.
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"""
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if heat_system.value == "urban central":
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boiler_costs_name = getattr(heat_system, f"{carrier}_boiler_costs_name")
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efficiency = costs.at[boiler_costs_name, "efficiency"]
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elif heat_system.sector.value == "residential":
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key = f"{carrier} residential space efficiency"
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efficiency = nodes.str[:2].map(heating_efficiencies[key])
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elif heat_system.sector.value == "services":
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key = f"{carrier} services space efficiency"
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efficiency = nodes.str[:2].map(heating_efficiencies[key])
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else:
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logger.warning(f"{heat_system} not defined.")
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return efficiency
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def add_heating_capacities_installed_before_baseyear(
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n: pypsa.Network,
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baseyear: int,
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@ -546,6 +590,10 @@ def add_heating_capacities_installed_before_baseyear(
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lifetime=costs.at[heat_system.resistive_heater_costs_name, "lifetime"],
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)
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efficiency = get_efficiency(
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heat_system, "gas", nodes, heating_efficiencies, costs
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)
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n.madd(
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"Link",
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nodes,
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@ -554,7 +602,7 @@ def add_heating_capacities_installed_before_baseyear(
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bus1=nodes + " " + heat_system.value + " heat",
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bus2="co2 atmosphere",
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carrier=heat_system.value + " gas boiler",
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efficiency=costs.at[heat_system.gas_boiler_costs_name, "efficiency"],
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efficiency=efficiency,
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efficiency2=costs.at["gas", "CO2 intensity"],
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capital_cost=(
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costs.at[heat_system.gas_boiler_costs_name, "efficiency"]
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@ -569,6 +617,10 @@ def add_heating_capacities_installed_before_baseyear(
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lifetime=costs.at[heat_system.gas_boiler_costs_name, "lifetime"],
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)
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efficiency = get_efficiency(
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heat_system, "oil", nodes, heating_efficiencies, costs
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)
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n.madd(
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"Link",
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nodes,
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@ -577,7 +629,7 @@ def add_heating_capacities_installed_before_baseyear(
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bus1=nodes + " " + heat_system.value + " heat",
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bus2="co2 atmosphere",
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carrier=heat_system.value + " oil boiler",
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efficiency=costs.at[heat_system.oil_boiler_costs_name, "efficiency"],
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efficiency=efficiency,
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efficiency2=costs.at["oil", "CO2 intensity"],
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capital_cost=costs.at[heat_system.oil_boiler_costs_name, "efficiency"]
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* costs.at[heat_system.oil_boiler_costs_name, "fixed"],
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@ -621,12 +673,12 @@ if __name__ == "__main__":
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snakemake = mock_snakemake(
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"add_existing_baseyear",
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configfiles="config/config.yaml",
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configfiles="config/test/config.myopic.yaml",
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simpl="",
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clusters="20",
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clusters="5",
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ll="v1.5",
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opts="",
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sector_opts="none",
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sector_opts="",
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planning_horizons=2030,
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)
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@ -660,6 +712,11 @@ if __name__ == "__main__":
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if options["heating"]:
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# one could use baseyear here instead (but dangerous if no data)
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fn = snakemake.input.heating_efficiencies
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year = int(snakemake.params["energy_totals_year"])
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heating_efficiencies = pd.read_csv(fn, index_col=[1, 0]).loc[year]
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add_heating_capacities_installed_before_baseyear(
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n=n,
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baseyear=baseyear,
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@ -367,6 +367,24 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
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assert df.index[43] == "Thermal uses"
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ct_totals["thermal uses residential"] = df.iloc[43]
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df = pd.read_excel(fn_residential, "RES_hh_eff", index_col=0)
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ct_totals["total residential space efficiency"] = df.loc["Space heating"]
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assert df.index[5] == "Diesel oil"
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ct_totals["oil residential space efficiency"] = df.iloc[5]
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assert df.index[6] == "Natural gas"
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ct_totals["gas residential space efficiency"] = df.iloc[6]
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ct_totals["total residential water efficiency"] = df.loc["Water heating"]
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assert df.index[18] == "Diesel oil"
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ct_totals["oil residential water efficiency"] = df.iloc[18]
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assert df.index[19] == "Natural gas"
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ct_totals["gas residential water efficiency"] = df.iloc[19]
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# services
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df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)
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@ -400,6 +418,24 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
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assert df.index[46] == "Thermal uses"
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ct_totals["thermal uses services"] = df.iloc[46]
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df = pd.read_excel(fn_tertiary, "SER_hh_eff", index_col=0)
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ct_totals["total services space efficiency"] = df.loc["Space heating"]
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assert df.index[5] == "Diesel oil"
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ct_totals["oil services space efficiency"] = df.iloc[5]
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assert df.index[7] == "Conventional gas heaters"
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ct_totals["gas services space efficiency"] = df.iloc[7]
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ct_totals["total services water efficiency"] = df.loc["Hot water"]
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assert df.index[20] == "Diesel oil"
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ct_totals["oil services water efficiency"] = df.iloc[20]
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assert df.index[21] == "Natural gas"
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ct_totals["gas services water efficiency"] = df.iloc[21]
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# agriculture, forestry and fishing
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start = "Detailed split of energy consumption (ktoe)"
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@ -576,7 +612,8 @@ def build_idees(countries: List[str]) -> pd.DataFrame:
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# efficiency kgoe/100km -> ktoe/100km so that after conversion TWh/100km
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totals.loc[:, "passenger car efficiency"] /= 1e6
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# convert ktoe to TWh
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exclude = totals.columns.str.fullmatch("passenger cars")
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patterns = ["passenger cars", ".*space efficiency", ".*water efficiency"]
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exclude = totals.columns.str.fullmatch("|".join(patterns))
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totals = totals.copy()
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totals.loc[:, ~exclude] *= 11.63 / 1e3
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@ -654,11 +691,14 @@ def build_energy_totals(
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eurostat_countries = eurostat.index.unique(0)
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eurostat_years = eurostat.index.unique(1)
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to_drop = ["passenger cars", "passenger car efficiency"]
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new_index = pd.MultiIndex.from_product(
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[countries, eurostat_years], names=["country", "year"]
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)
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efficiency_keywords = ["space efficiency", "water efficiency"]
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to_drop = idees.columns[idees.columns.str.contains("|".join(efficiency_keywords))]
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to_drop = to_drop.append(pd.Index(["passenger cars", "passenger car efficiency"]))
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df = idees.reindex(new_index).drop(to_drop, axis=1)
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in_eurostat = df.index.levels[0].intersection(eurostat_countries)
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@ -1501,6 +1541,59 @@ def build_transformation_output_coke(eurostat, fn):
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df.to_csv(fn)
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def build_heating_efficiencies(
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countries: List[str], idees: pd.DataFrame
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) -> pd.DataFrame:
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"""
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Build heating efficiencies for a set of countries based on IDEES data.
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Parameters
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----------
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countries : List[str]
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List of country codes.
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idees : pd.DataFrame
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DataFrame with IDEES data.
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Returns
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-------
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pd.DataFrame
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DataFrame with heating efficiencies.
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Notes
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-----
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- It fills missing data with average data.
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"""
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years = np.arange(2000, 2022)
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cols = idees.columns[
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idees.columns.str.contains("space efficiency")
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^ idees.columns.str.contains("water efficiency")
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]
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heating_efficiencies = pd.DataFrame(idees[cols])
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new_index = pd.MultiIndex.from_product(
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[countries, heating_efficiencies.index.unique(1)],
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names=["country", "year"],
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)
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heating_efficiencies = heating_efficiencies.reindex(index=new_index)
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for col in cols:
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unstacked = heating_efficiencies[col].unstack()
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fillvalue = unstacked.mean()
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for ct in unstacked.index:
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mask = unstacked.loc[ct].isna()
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unstacked.loc[ct, mask] = fillvalue[mask]
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heating_efficiencies[col] = unstacked.stack()
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return heating_efficiencies
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# %%
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if __name__ == "__main__":
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if "snakemake" not in globals():
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@ -1556,3 +1649,6 @@ if __name__ == "__main__":
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transport = build_transport_data(countries, population, idees)
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transport.to_csv(snakemake.output.transport_name)
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heating_efficiencies = build_heating_efficiencies(countries, idees)
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heating_efficiencies.to_csv(snakemake.output.heating_efficiencies)
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@ -1839,9 +1839,14 @@ def build_heat_demand(n):
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for sector, use in product(sectors, uses):
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name = f"{sector} {use}"
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# efficiency for final energy to thermal energy service
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eff = pop_weighted_energy_totals.index.str[:2].map(
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heating_efficiencies[f"total {sector} {use} efficiency"]
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)
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heat_demand[name] = (
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heat_demand_shape[name] / heat_demand_shape[name].sum()
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).multiply(pop_weighted_energy_totals[f"total {sector} {use}"]) * 1e6
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).multiply(pop_weighted_energy_totals[f"total {sector} {use}"] * eff) * 1e6
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electric_heat_supply[name] = (
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heat_demand_shape[name] / heat_demand_shape[name].sum()
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).multiply(pop_weighted_energy_totals[f"electricity {sector} {use}"]) * 1e6
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@ -4373,6 +4378,10 @@ if __name__ == "__main__":
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)
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pop_weighted_energy_totals.update(pop_weighted_heat_totals)
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fn = snakemake.input.heating_efficiencies
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year = int(snakemake.params["energy_totals_year"])
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heating_efficiencies = pd.read_csv(fn, index_col=[1, 0]).loc[year]
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patch_electricity_network(n)
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spatial = define_spatial(pop_layout.index, options)
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