move building of distribution of existing heating to own script
This makes the distribution of existing heating to urban/rural, residential/services and spatially more transparent.
This commit is contained in:
Tom Brown
Fabian Neumann
parent
9897cd6f05
commit
d98ad95332
@ -1,8 +1,40 @@
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# SPDX-FileCopyrightText: : 2023 The PyPSA-Eur Authors
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# SPDX-FileCopyrightText: : 2023-4 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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rule build_existing_heating_distribution:
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params:
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baseyear=config["scenario"]["planning_horizons"][0],
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sector=config["sector"],
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existing_capacities=config["existing_capacities"],
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input:
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existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
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clustered_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}_{clusters}.csv",
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clustered_pop_energy_layout=RESOURCES + "pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
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district_heat_share=RESOURCES + "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
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output:
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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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wildcard_constraints:
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planning_horizons=config["scenario"]["planning_horizons"][0], #only applies to baseyear
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threads: 1
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resources:
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mem_mb=2000,
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log:
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LOGS
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+ "build_existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.log",
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benchmark:
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(
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BENCHMARKS
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+ "build_existing_heating_distribution/elec_s{simpl}_{clusters}_{planning_horizons}"
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)
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conda:
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"../envs/environment.yaml"
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script:
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"../scripts/build_existing_heating_distribution.py"
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rule add_existing_baseyear:
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params:
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baseyear=config["scenario"]["planning_horizons"][0],
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@ -19,7 +51,8 @@ rule add_existing_baseyear:
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costs="data/costs_{}.csv".format(config["scenario"]["planning_horizons"][0]),
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cop_soil_total=RESOURCES + "cop_soil_total_elec_s{simpl}_{clusters}.nc",
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cop_air_total=RESOURCES + "cop_air_total_elec_s{simpl}_{clusters}.nc",
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existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
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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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existing_solar="data/existing_infrastructure/solar_capacity_IRENA.csv",
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existing_onwind="data/existing_infrastructure/onwind_capacity_IRENA.csv",
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existing_offwind="data/existing_infrastructure/offwind_capacity_IRENA.csv",
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@ -409,97 +409,20 @@ def add_heating_capacities_installed_before_baseyear(
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# file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
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# TODO start from original file
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# retrieve existing heating capacities
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techs = [
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"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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]
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df = pd.read_csv(snakemake.input.existing_heating, index_col=0, header=0)
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existing_heating = pd.read_csv(snakemake.input.existing_heating_distribution,
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header=[0,1],
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index_col=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(0.0, inplace=True)
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techs = existing_heating.columns.get_level_values(1).unique()
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# convert GW to MW
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df *= 1e3
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for name in existing_heating.columns.get_level_values(0).unique():
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df.index = cc.convert(df.index, to="iso2")
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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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# distribute technologies to nodes by population
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
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nodal_df = df.loc[pop_layout.ct]
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nodal_df.index = pop_layout.index
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nodal_df = nodal_df.multiply(pop_layout.fraction, axis=0)
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# split existing capacities between residential and services
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# proportional to energy demand
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p_set_sum = n.loads_t.p_set.sum()
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ratio_residential = pd.Series(
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[
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(
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p_set_sum[f"{node} residential rural heat"]
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/ (
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p_set_sum[f"{node} residential rural heat"]
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+ p_set_sum[f"{node} services rural heat"]
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)
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)
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# if rural heating demand for one of the nodes doesn't exist,
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# then columns were dropped before and heating demand share should be 0.0
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if all(
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f"{node} {service} rural heat" in p_set_sum.index
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for service in ["residential", "services"]
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)
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else 0.0
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for node in nodal_df.index
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],
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index=nodal_df.index,
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)
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for tech in techs:
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nodal_df["residential " + tech] = nodal_df[tech] * ratio_residential
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nodal_df["services " + tech] = nodal_df[tech] * (1 - ratio_residential)
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names = [
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"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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]
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nodes = {}
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p_nom = {}
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for name in names:
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name_type = "central" if name == "urban central" else "decentral"
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nodes[name] = pd.Index(
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[
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n.buses.at[index, "location"]
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for index in n.buses.index[
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n.buses.index.str.contains(name)
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& n.buses.index.str.contains("heat")
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]
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]
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)
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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] = nodal_df["air heat pump"][nodes[name]]
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else:
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p_nom[name] = nodal_df[f"{heat_type} {heat_pump_type} heat pump"][
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nodes[name]
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]
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nodes = pd.Index(n.buses.location[n.buses.index.str.contains(f"{name} heat")])
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heat_pump_type = "air" if "urban" in name else "ground"
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# Add heat pumps
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costs_name = f"decentral {heat_pump_type}-sourced heat pump"
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@ -507,7 +430,7 @@ def add_heating_capacities_installed_before_baseyear(
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cop = {"air": ashp_cop, "ground": gshp_cop}
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if time_dep_hp_cop:
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efficiency = cop[heat_pump_type][nodes[name]]
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efficiency = cop[heat_pump_type][nodes]
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else:
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efficiency = costs.at[costs_name, "efficiency"]
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@ -520,27 +443,26 @@ def add_heating_capacities_installed_before_baseyear(
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n.madd(
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"Link",
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nodes[name],
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nodes,
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suffix=f" {name} {heat_pump_type} heat pump-{grouping_year}",
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bus0=nodes[name],
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bus1=nodes[name] + " " + name + " heat",
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bus0=nodes,
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bus1=nodes + " " + name + " heat",
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carrier=f"{name} {heat_pump_type} heat pump",
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efficiency=efficiency,
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capital_cost=costs.at[costs_name, "efficiency"]
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* costs.at[costs_name, "fixed"],
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p_nom=p_nom[name] * ratio / costs.at[costs_name, "efficiency"],
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p_nom=existing_heating[(name, f"{heat_pump_type} heat pump")][nodes] * ratio / costs.at[costs_name, "efficiency"],
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build_year=int(grouping_year),
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lifetime=costs.at[costs_name, "lifetime"],
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)
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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(
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"Link",
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nodes[name],
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nodes,
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suffix=f" {name} resistive heater-{grouping_year}",
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bus0=nodes[name],
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bus1=nodes[name] + " " + name + " heat",
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bus0=nodes,
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bus1=nodes + " " + name + " heat",
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carrier=name + " resistive heater",
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efficiency=costs.at[f"{name_type} resistive heater", "efficiency"],
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capital_cost=(
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@ -548,21 +470,20 @@ def add_heating_capacities_installed_before_baseyear(
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* costs.at[f"{name_type} resistive heater", "fixed"]
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),
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p_nom=(
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0.5
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* nodal_df[f"{heat_type} resistive heater"][nodes[name]]
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existing_heating[(name, "resistive heater")][nodes]
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* ratio
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/ costs.at[f"{name_type} resistive heater", "efficiency"]
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),
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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[f"{name_type} resistive heater", "lifetime"],
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)
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n.madd(
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"Link",
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nodes[name],
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nodes,
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suffix=f" {name} gas boiler-{grouping_year}",
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bus0=spatial.gas.nodes,
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bus1=nodes[name] + " " + name + " heat",
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bus1=nodes + " " + name + " heat",
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bus2="co2 atmosphere",
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carrier=name + " gas boiler",
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efficiency=costs.at[f"{name_type} gas boiler", "efficiency"],
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@ -572,8 +493,7 @@ def add_heating_capacities_installed_before_baseyear(
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* costs.at[f"{name_type} gas boiler", "fixed"]
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),
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p_nom=(
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0.5
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* nodal_df[f"{heat_type} gas boiler"][nodes[name]]
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existing_heating[(name, "gas boiler")][nodes]
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* ratio
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/ costs.at[f"{name_type} gas boiler", "efficiency"]
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),
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@ -583,20 +503,20 @@ def add_heating_capacities_installed_before_baseyear(
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n.madd(
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"Link",
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nodes[name],
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nodes,
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suffix=f" {name} oil boiler-{grouping_year}",
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bus0=spatial.oil.nodes,
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bus1=nodes[name] + " " + name + " heat",
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bus1=nodes + " " + 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"]
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* costs.at["decentral oil boiler", "fixed"],
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p_nom=0.5
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* nodal_df[f"{heat_type} oil boiler"][nodes[name]]
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* ratio
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/ costs.at["decentral oil boiler", "efficiency"],
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p_nom= (
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existing_heating[(name, "oil boiler")][nodes]
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* ratio
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/ costs.at["decentral oil boiler", "efficiency"]),
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build_year=int(grouping_year),
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lifetime=costs.at[f"{name_type} gas boiler", "lifetime"],
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)
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@ -624,6 +544,8 @@ def add_heating_capacities_installed_before_baseyear(
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# drop assets which are at the end of their lifetime
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links_i = n.links[(n.links.build_year + n.links.lifetime <= baseyear)].index
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logger.info("Removing following links because at end of their lifetime:")
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logger.info(links_i)
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n.mremove("Link", links_i)
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108
scripts/build_existing_heating_distribution.py
Normal file
108
scripts/build_existing_heating_distribution.py
Normal file
@ -0,0 +1,108 @@
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# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Builds table of existing heat generation capacities for initial planning
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horizon.
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"""
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import pandas as pd
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import sys
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from pypsa.descriptors import Dict
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import numpy as np
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import country_converter as coco
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cc = coco.CountryConverter()
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def build_existing_heating():
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# retrieve existing heating capacities
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techs = [
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"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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]
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existing_heating = pd.read_csv(snakemake.input.existing_heating,
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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 existing_heating.loc["Albania"] = np.nan
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existing_heating.loc["Montenegro"] = np.nan
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existing_heating.loc["Macedonia"] = np.nan
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existing_heating.fillna(0.0, inplace=True)
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# convert GW to MW
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existing_heating *= 1e3
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existing_heating.index = cc.convert(existing_heating.index, to="iso2")
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# coal and oil boilers are assimilated to oil boilers
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existing_heating["oil boiler"] = existing_heating["oil boiler"] + existing_heating["coal boiler"]
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existing_heating.drop(["coal boiler"], axis=1, inplace=True)
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# distribute technologies to nodes by population
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout,
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index_col=0)
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nodal_heating = existing_heating.loc[pop_layout.ct]
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nodal_heating.index = pop_layout.index
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nodal_heating = nodal_heating.multiply(pop_layout.fraction, axis=0)
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district_heat_info = pd.read_csv(snakemake.input.district_heat_share,
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index_col=0)
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dist_fraction = district_heat_info["district fraction of node"]
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urban_fraction = district_heat_info["urban fraction"]
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energy_layout = pd.read_csv(snakemake.input.clustered_pop_energy_layout,
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index_col=0)
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uses = ["space", "water"]
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sectors = ["residential", "services"]
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nodal_sectoral_totals = pd.DataFrame(dtype=float)
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for sector in sectors:
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nodal_sectoral_totals[sector] = energy_layout[[f"total {sector} {use}" for use in uses]].sum(axis=1)
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nodal_sectoral_fraction = nodal_sectoral_totals.div(nodal_sectoral_totals.sum(axis=1),
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axis=0)
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nodal_heat_name_fraction = pd.DataFrame(dtype=float)
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nodal_heat_name_fraction["urban central"] = dist_fraction
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for sector in sectors:
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nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[sector]*(1 - urban_fraction)
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nodal_heat_name_fraction[f"{sector} urban decentral"] = nodal_sectoral_fraction[sector]*(urban_fraction - dist_fraction)
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nodal_heat_name_tech = pd.concat({name : nodal_heating .multiply(nodal_heat_name_fraction[name],
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axis=0) for name in nodal_heat_name_fraction.columns},
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axis=1,
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names=["heat name","technology"])
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#move all ground HPs to rural, all air to urban
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for sector in sectors:
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nodal_heat_name_tech[(f"{sector} rural","ground heat pump")] += (nodal_heat_name_tech[("urban central","ground heat pump")]*nodal_sectoral_fraction[sector]
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+ nodal_heat_name_tech[(f"{sector} urban decentral","ground heat pump")])
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nodal_heat_name_tech[(f"{sector} urban decentral","ground heat pump")] = 0.
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nodal_heat_name_tech[(f"{sector} urban decentral","air heat pump")] += nodal_heat_name_tech[(f"{sector} rural","air heat pump")]
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nodal_heat_name_tech[(f"{sector} rural","air heat pump")] = 0.
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nodal_heat_name_tech[("urban central","ground heat pump")] = 0.
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nodal_heat_name_tech.to_csv(snakemake.output.existing_heating_distribution)
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if __name__ == "__main__":
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build_existing_heating()
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