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73
scripts/build_egs_potentials.py
View File

@ -3,7 +3,8 @@
# #
# SPDX-License-Identifier: MIT # SPDX-License-Identifier: MIT
""" """
This rule extracts potential and cost for electricity generation through enhanced geothermal systems This rule extracts potential and cost for electricity generation through
enhanced geothermal systems.
For this, we use data from "From hot rock to useful energy..." by Aghahosseini, Breyer (2020) For this, we use data from "From hot rock to useful energy..." by Aghahosseini, Breyer (2020)
'https://www.sciencedirect.com/science/article/pii/S0306261920312551' 'https://www.sciencedirect.com/science/article/pii/S0306261920312551'
@ -20,30 +21,30 @@ import logging
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
import json import json
import pandas as pd
import geopandas as gpd
import geopandas as gpd
import pandas as pd
from shapely.geometry import Polygon from shapely.geometry import Polygon
def prepare_egs_data(egs_file): def prepare_egs_data(egs_file):
with open(egs_file) as f: with open(egs_file) as f:
jsondata = json.load(f) jsondata = json.load(f)
def point_to_square(p, lon_extent=1., lat_extent=1.): def point_to_square(p, lon_extent=1.0, lat_extent=1.0):
try: try:
x, y = p.coords.xy[0][0], p.coords.xy[1][0] x, y = p.coords.xy[0][0], p.coords.xy[1][0]
except IndexError: except IndexError:
return p return p
return Polygon([ return Polygon(
[x-lon_extent/2, y-lat_extent/2], [
[x-lon_extent/2, y+lat_extent/2], [x - lon_extent / 2, y - lat_extent / 2],
[x+lon_extent/2, y+lat_extent/2], [x - lon_extent / 2, y + lat_extent / 2],
[x+lon_extent/2, y-lat_extent/2], [x + lon_extent / 2, y + lat_extent / 2],
]) [x + lon_extent / 2, y - lat_extent / 2],
]
)
years = [2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050] years = [2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050]
lcoes = ["LCOE50", "LCOE100", "LCOE150"] lcoes = ["LCOE50", "LCOE100", "LCOE150"]
@ -54,11 +55,12 @@ def prepare_egs_data(egs_file):
df = pd.DataFrame(columns=["Lon", "Lat", "CAPEX", "HeatSust", "PowerSust"]) df = pd.DataFrame(columns=["Lon", "Lat", "CAPEX", "HeatSust", "PowerSust"])
for lcoe in lcoes: for lcoe in lcoes:
for country_data in jsondata[lcoe]: for country_data in jsondata[lcoe]:
try: try:
country_df = pd.DataFrame(columns=df.columns, country_df = pd.DataFrame(
index=range(len(country_data[0][years.index(year)]["Lon"]))) columns=df.columns,
index=range(len(country_data[0][years.index(year)]["Lon"])),
)
except TypeError: except TypeError:
country_df = pd.DataFrame(columns=df.columns, index=range(0)) country_df = pd.DataFrame(columns=df.columns, index=range(0))
@ -68,17 +70,13 @@ def prepare_egs_data(egs_file):
df = pd.concat((df, country_df.dropna()), axis=0, ignore_index=True) df = pd.concat((df, country_df.dropna()), axis=0, ignore_index=True)
gdf = gpd.GeoDataFrame( gdf = gpd.GeoDataFrame(
df.drop( df.drop(columns=["Lon", "Lat"]), geometry=gpd.points_from_xy(df.Lon, df.Lat)
columns=["Lon", "Lat"] ).reset_index(drop=True)
),
geometry=gpd.points_from_xy(df.Lon, df.Lat)
).reset_index(drop=True)
gdf["geometry"] = gdf.geometry.apply(lambda geom: point_to_square(geom)) gdf["geometry"] = gdf.geometry.apply(lambda geom: point_to_square(geom))
egs_data[year] = gdf egs_data[year] = gdf
return egs_data
return egs_data
if __name__ == "__main__": if __name__ == "__main__":
@ -90,43 +88,44 @@ if __name__ == "__main__":
simpl="", simpl="",
clusters=37, clusters=37,
) )
sustainability_factor = 0.0025 # factor sustainable p_nom vs p_nom sustainability_factor = 0.0025 # factor sustainable p_nom vs p_nom
config = snakemake.config config = snakemake.config
egs_data = prepare_egs_data(snakemake.input.egs_cost) egs_data = prepare_egs_data(snakemake.input.egs_cost)
if config["sector"]["enhanced_geothermal_optimism"]: if config["sector"]["enhanced_geothermal_optimism"]:
egs_data = egs_data[(year := config["costs"]["year"])] egs_data = egs_data[(year := config["costs"]["year"])]
logger.info(f"EGS optimism! Builing EGS potentials with costs estimated for {year}.") logger.info(
f"EGS optimism! Builing EGS potentials with costs estimated for {year}."
)
else: else:
egs_data = egs_data[(default_year := 2020)] egs_data = egs_data[(default_year := 2020)]
logger.info(f"No EGS optimism! Building EGS potentials with {default_year} costs.") logger.info(
f"No EGS optimism! Building EGS potentials with {default_year} costs."
)
egs_data.index = egs_data.geometry.astype(str) egs_data.index = egs_data.geometry.astype(str)
egs_shapes = egs_data.geometry egs_shapes = egs_data.geometry
network_shapes = ( network_shapes = (
gpd.read_file(snakemake.input.shapes) gpd.read_file(snakemake.input.shapes)
.set_index("name", drop=True) .set_index("name", drop=True)
.set_crs(epsg=4326) .set_crs(epsg=4326)
) )
overlap_matrix = ( overlap_matrix = pd.DataFrame(
pd.DataFrame( index=network_shapes.index,
index=network_shapes.index, columns=(egs_shapes := egs_data.geometry).astype(str).values,
columns=(egs_shapes := egs_data.geometry).astype(str).values)
) )
for name, polygon in network_shapes.geometry.items(): for name, polygon in network_shapes.geometry.items():
overlap_matrix.loc[name] = ( overlap_matrix.loc[name] = (
egs_shapes egs_shapes.intersection(polygon).area
.intersection(polygon).area
) / egs_shapes.area ) / egs_shapes.area
overlap_matrix.to_csv(snakemake.output["egs_overlap"]) overlap_matrix.to_csv(snakemake.output["egs_overlap"])
egs_data["p_nom_max"] = egs_data["PowerSust"] / sustainability_factor egs_data["p_nom_max"] = egs_data["PowerSust"] / sustainability_factor
egs_data[["p_nom_max", "CAPEX"]].to_csv(snakemake.output["egs_potentials"]) egs_data[["p_nom_max", "CAPEX"]].to_csv(snakemake.output["egs_potentials"])

16
scripts/prepare_sector_network.py
View File

@ -3304,7 +3304,7 @@ def add_enhanced_geothermal(
overlap = pd.read_csv(egs_overlap, index_col=0) overlap = pd.read_csv(egs_overlap, index_col=0)
egs_potentials = pd.read_csv(egs_potentials, index_col=0) egs_potentials = pd.read_csv(egs_potentials, index_col=0)
Nyears = n.snapshot_weightings.generators.sum() / 8760 Nyears = n.snapshot_weightings.generators.sum() / 8760
dr = config["costs"]["fill_values"]["discount rate"] dr = config["costs"]["fill_values"]["discount rate"]
lt = costs.at["geothermal", "lifetime"] lt = costs.at["geothermal", "lifetime"]
@ -3312,9 +3312,7 @@ def add_enhanced_geothermal(
egs_annuity = calculate_annuity(lt, dr) egs_annuity = calculate_annuity(lt, dr)
egs_potentials["capital_cost"] = ( egs_potentials["capital_cost"] = (
(egs_annuity + 0.02 / 1.02) * (egs_annuity + 0.02 / 1.02) * egs_potentials["CAPEX"] * Nyears
egs_potentials["CAPEX"] *
Nyears
) )
# Uncommented for causing a crash for runs with default config. # Uncommented for causing a crash for runs with default config.
@ -3352,15 +3350,15 @@ def add_enhanced_geothermal(
if not bus_overlap.sum(): if not bus_overlap.sum():
continue continue
overlap = bus_overlap.loc[bus_overlap > 0.] overlap = bus_overlap.loc[bus_overlap > 0.0]
bus_egs = egs_potentials.loc[bus_overlap.loc[bus_overlap > 0.].index] bus_egs = egs_potentials.loc[bus_overlap.loc[bus_overlap > 0.0].index]
if not len(bus_egs): if not len(bus_egs):
continue continue
bus_egs["p_nom_max"] = bus_egs["p_nom_max"].multiply(bus_overlap) bus_egs["p_nom_max"] = bus_egs["p_nom_max"].multiply(bus_overlap)
bus_egs = bus_egs.loc[bus_egs.p_nom_max > 0.] bus_egs = bus_egs.loc[bus_egs.p_nom_max > 0.0]
if config["sector"]["enhanced_geothermal_best_only"]: if config["sector"]["enhanced_geothermal_best_only"]:
bus_egs = bus_egs.sort_values(by="capital_cost").iloc[:1] bus_egs = bus_egs.sort_values(by="capital_cost").iloc[:1]
@ -3391,7 +3389,6 @@ def add_enhanced_geothermal(
) )
if __name__ == "__main__": if __name__ == "__main__":
if "snakemake" not in globals(): if "snakemake" not in globals():
from _helpers import mock_snakemake from _helpers import mock_snakemake
@ -3567,7 +3564,6 @@ if __name__ == "__main__":
if options.get("enhanced_geothermal"): if options.get("enhanced_geothermal"):
logger.info("Adding Enhanced Geothermal Potential.") logger.info("Adding Enhanced Geothermal Potential.")
""" """
add_enhanced_geothermal( add_enhanced_geothermal(
n, n,