Merge pull request #1255 from PyPSA/fix_building_fec

Use JRC-IDEES thermal energy service instead of FE for buildings heating demand

doc/release_notes.rst

@@ -10,6 +10,8 @@ Release Notes
 
 .. Upcoming Release
 
+* 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.
+
 * 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.
 * 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.
 

rules/build_sector.smk

@@ -366,6 +366,7 @@ rule build_energy_totals:
         co2_name=resources("co2_totals.csv"),
         transport_name=resources("transport_data.csv"),
         district_heat_share=resources("district_heat_share.csv"),
+        heating_efficiencies=resources("heating_efficiencies.csv"),
     threads: 16
     resources:
         mem_mb=10000,
@@ -1015,6 +1016,7 @@ rule prepare_sector_network:
         RDIR=RDIR,
         heat_pump_sources=config_provider("sector", "heat_pump_sources"),
         heat_systems=config_provider("sector", "heat_systems"),
+        energy_totals_year=config_provider("energy", "energy_totals_year"),
     input:
         unpack(input_profile_offwind),
         **rules.cluster_gas_network.output,
@@ -1089,6 +1091,7 @@ rule prepare_sector_network:
         district_heat_share=resources(
             "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
         ),
+        heating_efficiencies=resources("heating_efficiencies.csv"),
         temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
         temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
         cop_profiles=resources("cop_profiles_elec_s{simpl}_{clusters}.nc"),

rules/solve_myopic.smk

@@ -10,6 +10,7 @@ rule add_existing_baseyear:
         existing_capacities=config_provider("existing_capacities"),
         costs=config_provider("costs"),
         heat_pump_sources=config_provider("sector", "heat_pump_sources"),
+        energy_totals_year=config_provider("energy", "energy_totals_year"),
     input:
         network=RESULTS
         + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
@@ -26,6 +27,7 @@ rule add_existing_baseyear:
         existing_heating_distribution=resources(
             "existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
         ),
+        heating_efficiencies=resources("heating_efficiencies.csv"),
     output:
         RESULTS
         + "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",

rules/solve_perfect.smk

@@ -8,6 +8,7 @@ rule add_existing_baseyear:
         existing_capacities=config_provider("existing_capacities"),
         costs=config_provider("costs"),
         heat_pump_sources=config_provider("sector", "heat_pump_sources"),
+        energy_totals_year=config_provider("energy", "energy_totals_year"),
     input:
         network=RESULTS
         + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
@@ -25,6 +26,7 @@ rule add_existing_baseyear:
             "existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
         ),
         existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
+        heating_efficiencies=resources("heating_efficiencies.csv"),
     output:
         RESULTS
         + "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",

scripts/add_existing_baseyear.py

@@ -418,6 +418,50 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
             ]
 
 
+def get_efficiency(heat_system, carrier, nodes, heating_efficiencies, costs):
+    """
+    Computes the heating system efficiency based on the sector and carrier
+    type.
+
+    Parameters:
+    -----------
+    heat_system : object
+    carrier : str
+        The type of fuel or energy carrier (e.g., 'gas', 'oil').
+    nodes : pandas.Series
+        A pandas Series containing node information used to match the heating efficiency data.
+    heating_efficiencies : dict
+        A dictionary containing efficiency values for different carriers and sectors.
+    costs : pandas.DataFrame
+        A DataFrame containing boiler cost and efficiency data for different heating systems.
+
+    Returns:
+    --------
+    efficiency : pandas.Series or float
+        A pandas Series mapping the efficiencies based on nodes for residential and services sectors, or a single
+        efficiency value for other heating systems (e.g., urban central).
+
+    Notes:
+    ------
+    - For residential and services sectors, efficiency is mapped based on the nodes.
+    - For other sectors, the default boiler efficiency is retrieved from the `costs` database.
+    """
+
+    if heat_system.value == "urban central":
+        boiler_costs_name = getattr(heat_system, f"{carrier}_boiler_costs_name")
+        efficiency = costs.at[boiler_costs_name, "efficiency"]
+    elif heat_system.sector.value == "residential":
+        key = f"{carrier} residential space efficiency"
+        efficiency = nodes.str[:2].map(heating_efficiencies[key])
+    elif heat_system.sector.value == "services":
+        key = f"{carrier} services space efficiency"
+        efficiency = nodes.str[:2].map(heating_efficiencies[key])
+    else:
+        logger.warning(f"{heat_system} not defined.")
+
+    return efficiency
+
+
 def add_heating_capacities_installed_before_baseyear(
     n: pypsa.Network,
     baseyear: int,
@@ -546,6 +590,10 @@ def add_heating_capacities_installed_before_baseyear(
                 lifetime=costs.at[heat_system.resistive_heater_costs_name, "lifetime"],
             )
 
+            efficiency = get_efficiency(
+                heat_system, "gas", nodes, heating_efficiencies, costs
+            )
+
             n.madd(
                 "Link",
                 nodes,
@@ -554,7 +602,7 @@ def add_heating_capacities_installed_before_baseyear(
                 bus1=nodes + " " + heat_system.value + " heat",
                 bus2="co2 atmosphere",
                 carrier=heat_system.value + " gas boiler",
-                efficiency=costs.at[heat_system.gas_boiler_costs_name, "efficiency"],
+                efficiency=efficiency,
                 efficiency2=costs.at["gas", "CO2 intensity"],
                 capital_cost=(
                     costs.at[heat_system.gas_boiler_costs_name, "efficiency"]
@@ -569,6 +617,10 @@ def add_heating_capacities_installed_before_baseyear(
                 lifetime=costs.at[heat_system.gas_boiler_costs_name, "lifetime"],
             )
 
+            efficiency = get_efficiency(
+                heat_system, "oil", nodes, heating_efficiencies, costs
+            )
+
             n.madd(
                 "Link",
                 nodes,
@@ -577,7 +629,7 @@ def add_heating_capacities_installed_before_baseyear(
                 bus1=nodes + " " + heat_system.value + " heat",
                 bus2="co2 atmosphere",
                 carrier=heat_system.value + " oil boiler",
-                efficiency=costs.at[heat_system.oil_boiler_costs_name, "efficiency"],
+                efficiency=efficiency,
                 efficiency2=costs.at["oil", "CO2 intensity"],
                 capital_cost=costs.at[heat_system.oil_boiler_costs_name, "efficiency"]
                 * costs.at[heat_system.oil_boiler_costs_name, "fixed"],
@@ -621,12 +673,12 @@ def add_heating_capacities_installed_before_baseyear(
 
         snakemake = mock_snakemake(
             "add_existing_baseyear",
-            configfiles="config/config.yaml",
+            configfiles="config/test/config.myopic.yaml",
             simpl="",
-            clusters="20",
+            clusters="5",
             ll="v1.5",
             opts="",
-            sector_opts="none",
+            sector_opts="",
             planning_horizons=2030,
         )
 
@@ -660,6 +712,11 @@ def add_heating_capacities_installed_before_baseyear(
 
     if options["heating"]:
 
+        # one could use baseyear here instead (but dangerous if no data)
+        fn = snakemake.input.heating_efficiencies
+        year = int(snakemake.params["energy_totals_year"])
+        heating_efficiencies = pd.read_csv(fn, index_col=[1, 0]).loc[year]
+
         add_heating_capacities_installed_before_baseyear(
             n=n,
             baseyear=baseyear,

scripts/build_energy_totals.py

@@ -367,6 +367,24 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
     assert df.index[43] == "Thermal uses"
     ct_totals["thermal uses residential"] = df.iloc[43]
 
+    df = pd.read_excel(fn_residential, "RES_hh_eff", index_col=0)
+
+    ct_totals["total residential space efficiency"] = df.loc["Space heating"]
+
+    assert df.index[5] == "Diesel oil"
+    ct_totals["oil residential space efficiency"] = df.iloc[5]
+
+    assert df.index[6] == "Natural gas"
+    ct_totals["gas residential space efficiency"] = df.iloc[6]
+
+    ct_totals["total residential water efficiency"] = df.loc["Water heating"]
+
+    assert df.index[18] == "Diesel oil"
+    ct_totals["oil residential water efficiency"] = df.iloc[18]
+
+    assert df.index[19] == "Natural gas"
+    ct_totals["gas residential water efficiency"] = df.iloc[19]
+
     # services
 
     df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)
@@ -400,6 +418,24 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
     assert df.index[46] == "Thermal uses"
     ct_totals["thermal uses services"] = df.iloc[46]
 
+    df = pd.read_excel(fn_tertiary, "SER_hh_eff", index_col=0)
+
+    ct_totals["total services space efficiency"] = df.loc["Space heating"]
+
+    assert df.index[5] == "Diesel oil"
+    ct_totals["oil services space efficiency"] = df.iloc[5]
+
+    assert df.index[7] == "Conventional gas heaters"
+    ct_totals["gas services space efficiency"] = df.iloc[7]
+
+    ct_totals["total services water efficiency"] = df.loc["Hot water"]
+
+    assert df.index[20] == "Diesel oil"
+    ct_totals["oil services water efficiency"] = df.iloc[20]
+
+    assert df.index[21] == "Natural gas"
+    ct_totals["gas services water efficiency"] = df.iloc[21]
+
     # agriculture, forestry and fishing
 
     start = "Detailed split of energy consumption (ktoe)"
@@ -576,7 +612,8 @@ def build_idees(countries: List[str]) -> pd.DataFrame:
     # efficiency kgoe/100km -> ktoe/100km so that after conversion TWh/100km
     totals.loc[:, "passenger car efficiency"] /= 1e6
     # convert ktoe to TWh
-    exclude = totals.columns.str.fullmatch("passenger cars")
+    patterns = ["passenger cars", ".*space efficiency", ".*water efficiency"]
+    exclude = totals.columns.str.fullmatch("|".join(patterns))
     totals = totals.copy()
     totals.loc[:, ~exclude] *= 11.63 / 1e3
 
@@ -654,11 +691,14 @@ def build_energy_totals(
     eurostat_countries = eurostat.index.unique(0)
     eurostat_years = eurostat.index.unique(1)
 
-    to_drop = ["passenger cars", "passenger car efficiency"]
     new_index = pd.MultiIndex.from_product(
         [countries, eurostat_years], names=["country", "year"]
     )
 
+    efficiency_keywords = ["space efficiency", "water efficiency"]
+    to_drop = idees.columns[idees.columns.str.contains("|".join(efficiency_keywords))]
+    to_drop = to_drop.append(pd.Index(["passenger cars", "passenger car efficiency"]))
+
     df = idees.reindex(new_index).drop(to_drop, axis=1)
 
     in_eurostat = df.index.levels[0].intersection(eurostat_countries)
@@ -1501,6 +1541,59 @@ def build_transformation_output_coke(eurostat, fn):
     df.to_csv(fn)
 
 
+def build_heating_efficiencies(
+    countries: List[str], idees: pd.DataFrame
+) -> pd.DataFrame:
+    """
+    Build heating efficiencies for a set of countries based on IDEES data.
+
+    Parameters
+    ----------
+    countries : List[str]
+        List of country codes.
+    idees : pd.DataFrame
+        DataFrame with IDEES data.
+
+    Returns
+    -------
+    pd.DataFrame
+        DataFrame with heating efficiencies.
+
+
+    Notes
+    -----
+    - It fills missing data with average data.
+    """
+
+    years = np.arange(2000, 2022)
+
+    cols = idees.columns[
+        idees.columns.str.contains("space efficiency")
+        ^ idees.columns.str.contains("water efficiency")
+    ]
+
+    heating_efficiencies = pd.DataFrame(idees[cols])
+
+    new_index = pd.MultiIndex.from_product(
+        [countries, heating_efficiencies.index.unique(1)],
+        names=["country", "year"],
+    )
+
+    heating_efficiencies = heating_efficiencies.reindex(index=new_index)
+
+    for col in cols:
+        unstacked = heating_efficiencies[col].unstack()
+
+        fillvalue = unstacked.mean()
+
+        for ct in unstacked.index:
+            mask = unstacked.loc[ct].isna()
+            unstacked.loc[ct, mask] = fillvalue[mask]
+        heating_efficiencies[col] = unstacked.stack()
+
+    return heating_efficiencies
+
+
 # %%
 if __name__ == "__main__":
     if "snakemake" not in globals():
@@ -1556,3 +1649,6 @@ def build_transformation_output_coke(eurostat, fn):
 
     transport = build_transport_data(countries, population, idees)
     transport.to_csv(snakemake.output.transport_name)
+
+    heating_efficiencies = build_heating_efficiencies(countries, idees)
+    heating_efficiencies.to_csv(snakemake.output.heating_efficiencies)

scripts/prepare_sector_network.py

@@ -1839,9 +1839,14 @@ def build_heat_demand(n):
     for sector, use in product(sectors, uses):
         name = f"{sector} {use}"
 
+        # efficiency for final energy to thermal energy service
+        eff = pop_weighted_energy_totals.index.str[:2].map(
+            heating_efficiencies[f"total {sector} {use} efficiency"]
+        )
+
         heat_demand[name] = (
             heat_demand_shape[name] / heat_demand_shape[name].sum()
-        ).multiply(pop_weighted_energy_totals[f"total {sector} {use}"]) * 1e6
+        ).multiply(pop_weighted_energy_totals[f"total {sector} {use}"] * eff) * 1e6
         electric_heat_supply[name] = (
             heat_demand_shape[name] / heat_demand_shape[name].sum()
         ).multiply(pop_weighted_energy_totals[f"electricity {sector} {use}"]) * 1e6
@@ -4373,6 +4378,10 @@ def add_enhanced_geothermal(n, egs_potentials, egs_overlap, costs):
     )
     pop_weighted_energy_totals.update(pop_weighted_heat_totals)
 
+    fn = snakemake.input.heating_efficiencies
+    year = int(snakemake.params["energy_totals_year"])
+    heating_efficiencies = pd.read_csv(fn, index_col=[1, 0]).loc[year]
+
     patch_electricity_network(n)
 
     spatial = define_spatial(pop_layout.index, options)