changed implementation to always use 2020 cost
This commit is contained in:
LukasFrankenQ
parent
97acf2f12e
commit
46cd9a8274
@ -20,7 +20,7 @@ dependencies:
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- openpyxl!=3.1.1
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- pycountry
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- seaborn
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- snakemake-minimal>=8.5,<8.6
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- snakemake-minimal>=8.5
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- memory_profiler
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- yaml
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- pytables
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@ -248,8 +248,8 @@ rule build_solar_thermal_profiles:
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output:
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solar_thermal=resources("solar_thermal_{scope}_elec_s{simpl}_{clusters}.nc"),
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resources:
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mem_mb=20000,
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threads: 16
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mem_mb=8000,
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threads: 1
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log:
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logs("build_solar_thermal_profiles_{scope}_s{simpl}_{clusters}.log"),
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benchmark:
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@ -816,6 +816,34 @@ def input_profile_offwind(w):
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}
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rule build_egs_potentials:
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params:
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snapshots=config_provider("snapshots"),
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sector=config_provider("sector"),
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costs=config_provider("costs"),
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input:
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egs_cost="data/egs_costs.json",
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regions=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
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air_temperature=(
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resources("temp_air_total_elec_s{simpl}_{clusters}.nc")
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if config_provider("sector", "enhanced_geothermal", "var_cf")
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else []
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),
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output:
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egs_potentials=resources("egs_potentials_s{simpl}_{clusters}.csv"),
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egs_overlap=resources("egs_overlap_s{simpl}_{clusters}.csv"),
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egs_capacity_factors=resources("egs_capacity_factors_s{simpl}_{clusters}.csv"),
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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("build_egs_potentials_s{simpl}_{clusters}.log"),
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conda:
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"../envs/environment.yaml"
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script:
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"../scripts/build_egs_potentials.py"
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rule prepare_sector_network:
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params:
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time_resolution=config_provider("clustering", "temporal", "resolution_sector"),
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@ -931,6 +959,21 @@ rule prepare_sector_network:
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if config_provider("sector", "solar_thermal")(w)
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else []
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),
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egs_potentials=lambda w: (
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resources("egs_potentials_s{simpl}_{clusters}.csv")
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if config_provider("sector", "enhanced_geothermal", "enable")(w)
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else []
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),
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egs_overlap=lambda w: (
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resources("egs_overlap_s{simpl}_{clusters}.csv")
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if config_provider("sector", "enhanced_geothermal", "enable")(w)
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else []
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),
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egs_capacity_factors=lambda w: (
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resources("egs_capacity_factors_s{simpl}_{clusters}.csv")
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if config_provider("sector", "enhanced_geothermal", "enable")(w)
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else []
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),
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output:
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RESULTS
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+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
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@ -30,6 +30,9 @@ from shapely.geometry import Polygon
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def prepare_egs_data(egs_file):
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"""
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Processes the original .json file EGS data to a more human-readable format.
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"""
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with open(egs_file) as f:
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jsondata = json.load(f)
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@ -86,6 +89,65 @@ def prepare_egs_data(egs_file):
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return egs_data
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def prepare_capex(prepared_data):
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"""
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The source paper provides only data for year and regions where LCOE <
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100Euro/MWh. However, this implementations starts with the costs for 2020
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for all regions and then adjusts the costs according to the user's chosen
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setting in the config file.
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As such, for regions where cost data is available only from, say,
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2035, we need to reverse-engineer the costs for 2020. This is done
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in the following (unfortunately verbose) function.
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"""
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default_year = 2020
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# obtains all available CAPEX data
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capex_df = pd.DataFrame(columns=prepared_data.keys())
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for year in capex_df.columns:
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year_data = prepared_data[year].groupby("geometry").mean().reset_index()
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for g in year_data.geometry:
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if not g in year_data.geometry.tolist():
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# weird but apparently necessary
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continue
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capex_df.loc[g, year] = year_data.loc[
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year_data.geometry == g, "CAPEX"
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].values[0]
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capex_df = capex_df.loc[:, default_year:]
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# fill up missing values assuming cost reduction factors similar to existing values
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for sooner, later in zip(capex_df.columns[::-1][1:], capex_df.columns[::-1]):
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missing_mask = capex_df[sooner].isna()
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cr_factor = (
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capex_df.loc[~missing_mask, later] / capex_df.loc[~missing_mask, sooner]
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)
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capex_df.loc[missing_mask, sooner] = (
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capex_df.loc[missing_mask, later] / cr_factor.mean()
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)
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# harmonice capacity and CAPEX
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p_nom_max = prepared_data[2050].groupby("geometry")["PowerSust"].mean()
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p_nom_max = p_nom_max.loc[p_nom_max > 0]
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capex_df = capex_df.loc[p_nom_max.index]
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data = (
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pd.concat((capex_df[default_year], p_nom_max), axis=1)
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.reset_index()
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.rename(columns={2020: "CAPEX"})
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)
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return gpd.GeoDataFrame(data, geometry=data.geometry)
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def get_capacity_factors(network_regions_file, air_temperatures_file):
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"""
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Performance of EGS is higher for lower temperatures, due to more efficient
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@ -146,28 +208,9 @@ if __name__ == "__main__":
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egs_config = snakemake.params["sector"]["enhanced_geothermal"]
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costs_config = snakemake.params["costs"]
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sustainability_factor = egs_config["sustainability_factor"]
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# the share of heat that is replenished from the earth's core.
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# we are not constraining ourselves to the sustainable share, but
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# inversely apply it to our underlying data, which refers to the
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# sustainable heat. Source: Relative magnitude of sustainable heat vs
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# nonsustainable heat in the paper "From hot rock to useful energy..."
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egs_data = prepare_egs_data(snakemake.input.egs_cost)
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egs_data = prepare_capex(egs_data)
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if egs_config["optimism"]:
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egs_data = egs_data[(year := costs_config["year"])]
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logger.info(
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f"EGS optimism! Building EGS potentials with costs estimated for {year}."
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)
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else:
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egs_data = egs_data[(default_year := 2020)]
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logger.info(
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f"No EGS optimism! Building EGS potentials with {default_year} costs."
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)
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egs_data = egs_data.loc[egs_data["PowerSust"] > 0].reset_index(drop=True)
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egs_regions = egs_data.geometry
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network_regions = (
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@ -188,7 +231,12 @@ if __name__ == "__main__":
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overlap_matrix.to_csv(snakemake.output["egs_overlap"])
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# consider not only replenished heat
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# the share of heat that is replenished from the earth's core.
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# we are not constraining ourselves to the sustainable share, but
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# inversely apply it to our underlying data, which refers to the
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# sustainable heat. Source: Relative magnitude of sustainable heat vs
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# nonsustainable heat in the paper "From hot rock to useful energy..."
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sustainability_factor = egs_config["sustainability_factor"]
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egs_data["p_nom_max"] = egs_data["PowerSust"] / sustainability_factor
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egs_data[["p_nom_max", "CAPEX"]].to_csv(snakemake.output["egs_potentials"])
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@ -190,7 +190,7 @@ def define_spatial(nodes, options):
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# deep geothermal
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spatial.geothermal_heat = SimpleNamespace()
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spatial.geothermal_heat.nodes = ["EU deep geothermal"]
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spatial.geothermal_heat.nodes = ["EU enhanced geothermal systems"]
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spatial.geothermal_heat.locations = ["EU"]
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return spatial
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@ -3599,15 +3599,11 @@ def add_enhanced_geothermal(
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# under egs optimism, the expected cost reductions also cover costs for ORC
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# hence, the ORC costs are no longer taken from technology-data
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orc_capex = (
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costs.at["organic rankine cycle", "investment"]
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if not snakemake.params.sector["enhanced_geothermal_optimism"]
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else 0.0
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)
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orc_capex = costs.at["organic rankine cycle", "investment"]
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# cost for ORC is subtracted, as it is already included in the geothermal cost.
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# The orc cost are attributed to a separate link representing the ORC.
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# also capital_cost conversion Eurofalse/kW -> Euro/MW
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# also capital_cost conversion Euro/kW -> Euro/MW
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egs_potentials["capital_cost"] = (
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(egs_annuity + FOM / (1.0 + FOM))
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@ -3619,8 +3615,8 @@ def add_enhanced_geothermal(
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egs_potentials["capital_cost"] > 0
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).all(), "Error in EGS cost, negative values found."
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plant_annuity = calculate_annuity(costs.at["organic rankine cycle", "lifetime"], dr)
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plant_capital_cost = (plant_annuity + FOM / (1 + FOM)) * orc_capex * Nyears
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orc_annuity = calculate_annuity(costs.at["organic rankine cycle", "lifetime"], dr)
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orc_capital_cost = (orc_annuity + FOM / (1 + FOM)) * orc_capex * Nyears
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efficiency_orc = costs.at["organic rankine cycle", "efficiency"]
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efficiency_dh = costs.at["geothermal", "efficiency residential heat"]
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@ -3647,19 +3643,22 @@ def add_enhanced_geothermal(
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p_nom_extendable=True,
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)
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if snakemake.params.sector["enhanced_geothermal_var_cf"]:
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if egs_config["var_cf"]:
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efficiency = pd.read_csv(
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snakemake.input.egs_capacity_factors, parse_dates=True, index_col=0
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)
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logger.info("Adding Enhanced Geothermal with time-varying capacity factors.")
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else:
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efficiency = pd.Series(1, overlap.index)
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efficiency = 1.0
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# if urban central heat exists, adds geothermal as CHP
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as_chp = "urban central heat" in n.loads.carrier.unique()
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if as_chp:
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logger.info("Adding Enhanced Geothermal as Combined Heat and Power.")
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logger.info("Adding EGS as Combined Heat and Power.")
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else:
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logger.info("Adding EGS for Electricity Only.")
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for bus, bus_overlap in overlap.iterrows():
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if not bus_overlap.sum():
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@ -3674,34 +3673,36 @@ def add_enhanced_geothermal(
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bus_egs["p_nom_max"] = bus_egs["p_nom_max"].multiply(bus_overlap)
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bus_egs = bus_egs.loc[bus_egs.p_nom_max > 0.0]
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if egs_config["performant"]:
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bus_egs = bus_egs.sort_values(by="capital_cost").iloc[:1]
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appendix = pd.Index([""])
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else:
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appendix = " " + pd.Index(np.arange(len(bus_egs)).astype(str))
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appendix = " " + pd.Index(np.arange(len(bus_egs)).astype(str))
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# add surface bus
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n.add(
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n.madd(
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"Bus",
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f"{bus} geothermal heat surface",
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pd.Index([f"{bus} geothermal heat surface"]),
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location=bus,
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unit="MWh_th",
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carrier="geothermal heat",
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)
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bus_egs.index = np.arange(len(bus_egs)).astype(str)
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well_name = f"{bus} enhanced geothermal" + appendix
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bus_eta = pd.concat(
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(efficiency[bus].rename(idx) for idx in well_name),
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axis=1,
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)
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if egs_config["var_cf"]:
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bus_eta = pd.concat(
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(efficiency[bus].rename(idx) for idx in well_name),
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axis=1,
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)
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else:
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bus_eta = efficiency
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p_nom_max = bus_egs["p_nom_max"]
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capital_cost = bus_egs["capital_cost"]
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bus1 = pd.Series(f"{bus} geothermal heat surface", well_name)
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# adding geothermal wells as multiple generators to represent supply curve
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n.madd(
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"Link",
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well_name,
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location=bus,
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bus0=spatial.geothermal_heat.nodes,
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bus1=bus1,
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carrier="geothermal heat",
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@ -3711,6 +3712,7 @@ def add_enhanced_geothermal(
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efficiency=bus_eta,
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)
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# adding Organic Rankine Cycle as a single link
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n.add(
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"Link",
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bus + " geothermal organic rankine cycle",
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@ -3718,7 +3720,7 @@ def add_enhanced_geothermal(
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bus1=bus,
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p_nom_extendable=True,
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carrier="geothermal organic rankine cycle",
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capital_cost=plant_capital_cost * efficiency_orc,
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capital_cost=orc_capital_cost * efficiency_orc,
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efficiency=efficiency_orc,
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)
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@ -3729,7 +3731,7 @@ def add_enhanced_geothermal(
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bus0=f"{bus} geothermal heat surface",
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bus1=bus + " urban central heat",
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carrier="geothermal district heat",
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capital_cost=plant_capital_cost
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capital_cost=orc_capital_cost
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* efficiency_orc
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* costs.at["geothermal", "district heating cost"],
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efficiency=efficiency_dh,
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@ -3745,8 +3747,8 @@ def add_enhanced_geothermal(
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# Hence, it is counter-intuitive to install it at the surface bus,
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# this is however the more lean and computationally efficient solution.
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max_hours = egs_config["reservoir_max_hours"]
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boost = egs_config["reservoir_max_boost"]
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max_hours = egs_config["max_hours"]
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boost = egs_config["max_boost"]
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n.add(
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"StorageUnit",
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@ -3756,6 +3758,7 @@ def add_enhanced_geothermal(
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p_nom_extendable=True,
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p_min_pu=-boost,
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max_hours=max_hours,
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cyclic_state_of_charge=True,
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)
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@ -3884,6 +3887,12 @@ if __name__ == "__main__":
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if options["electricity_distribution_grid"]:
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insert_electricity_distribution_grid(n, costs)
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if options["enhanced_geothermal"].get("enable", False):
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logger.info("Adding Enhanced Geothermal Systems (EGS).")
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add_enhanced_geothermal(
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n, snakemake.input["egs_potentials"], snakemake.input["egs_overlap"], costs
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)
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maybe_adjust_costs_and_potentials(n, snakemake.params["adjustments"])
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if options["gas_distribution_grid"]:
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@ -3911,12 +3920,6 @@ if __name__ == "__main__":
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if options.get("cluster_heat_buses", False) and not first_year_myopic:
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cluster_heat_buses(n)
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if options["enhanced_geothermal"].get("enable", False):
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logger.info("Adding Enhanced Geothermal Potential.")
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add_enhanced_geothermal(
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n, snakemake.input["egs_potentials"], snakemake.input["egs_overlap"], costs
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)
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n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
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sanitize_carriers(n, snakemake.config)
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@ -801,7 +801,7 @@ def add_geothermal_chp_constraint(n):
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elec_index = n.links.loc[
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n.links.carrier == "geothermal organic rankine cycle"
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].index
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heat_index = n.links.loc[n.links.carrier == "geothermal heat district heat"].index
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heat_index = n.links.loc[n.links.carrier == "geothermal district heat"].index
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p_nom_lhs = (
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n.model["Link-p_nom"].loc[heat_index] - n.model["Link-p_nom"].loc[elec_index]
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@ -824,7 +824,7 @@ def add_flexible_egs_constraint(n):
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n.model.add_constraints(
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p_nom_lhs <= p_nom_rhs,
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name="Upper bounds the charging capacity of the storage unit",
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name="Upper bounds the charging capacity of the geothermal reservoir according to the well capacity",
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)
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@ -954,28 +954,6 @@ def solve_network(n, config, solving, **kwargs):
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n.model.print_infeasibilities()
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raise RuntimeError("Solving status 'infeasible'")
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# check if enhanced_geothermal_performant might have changed model results
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if (
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snakemake.config["sector"]["enhanced_geothermal"]
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and snakemake.config["sector"]["enhanced_geothermal_performant"]
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):
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mask = (mask := n.links.carrier == "geothermal heat") & (
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n.links.loc[mask, "p_nom_max"] > 0.0
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)
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saturated = n.links.loc[mask, "p_nom_max"] == n.links.loc[mask, "p_nom_opt"]
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if saturated.any():
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logger.warning(
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(
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"Potential for enhanced geothermal heat is saturated at bus(es):\n"
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f"{', '.join(n.links.loc[saturated.loc[saturated.astype(bool)].index, 'location'].tolist())}.\n"
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"Consider setting config['sector']['enhanced_geothermal_performant'] to False."
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)
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)
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return n
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if __name__ == "__main__":
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if "snakemake" not in globals():
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Block a user