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pre-commit-ci[bot] 2023-09-05 14:01:10 +00:00
parent e486d6bcfe
commit 0e80244b52
2 changed files with 42 additions and 47 deletions
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73
scripts/build_egs_potentials.py
View File

@ -3,7 +3,8 @@
#
# 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)
'https://www.sciencedirect.com/science/article/pii/S0306261920312551'
@ -20,30 +21,30 @@ import logging
logger = logging.getLogger(__name__)
import json
import pandas as pd
import geopandas as gpd
import geopandas as gpd
import pandas as pd
from shapely.geometry import Polygon
def prepare_egs_data(egs_file):
with open(egs_file) as 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:
x, y = p.coords.xy[0][0], p.coords.xy[1][0]
except IndexError:
return p
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],
])
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],
]
)
years = [2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050]
lcoes = ["LCOE50", "LCOE100", "LCOE150"]
@ -54,11 +55,12 @@ def prepare_egs_data(egs_file):
df = pd.DataFrame(columns=["Lon", "Lat", "CAPEX", "HeatSust", "PowerSust"])
for lcoe in lcoes:
for country_data in jsondata[lcoe]:
try:
country_df = pd.DataFrame(columns=df.columns,
index=range(len(country_data[0][years.index(year)]["Lon"])))
country_df = pd.DataFrame(
columns=df.columns,
index=range(len(country_data[0][years.index(year)]["Lon"])),
)
except TypeError:
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)
gdf = gpd.GeoDataFrame(
df.drop(
columns=["Lon", "Lat"]
),
geometry=gpd.points_from_xy(df.Lon, df.Lat)
).reset_index(drop=True)
df.drop(columns=["Lon", "Lat"]), geometry=gpd.points_from_xy(df.Lon, df.Lat)
).reset_index(drop=True)
gdf["geometry"] = gdf.geometry.apply(lambda geom: point_to_square(geom))
egs_data[year] = gdf
return egs_data
return egs_data
if __name__ == "__main__":
@ -90,43 +88,44 @@ if __name__ == "__main__":
simpl="",
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
egs_data = prepare_egs_data(snakemake.input.egs_cost)
if config["sector"]["enhanced_geothermal_optimism"]:
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:
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_shapes = egs_data.geometry
network_shapes = (
gpd.read_file(snakemake.input.shapes)
.set_index("name", drop=True)
.set_crs(epsg=4326)
)
overlap_matrix = (
pd.DataFrame(
index=network_shapes.index,
columns=(egs_shapes := egs_data.geometry).astype(str).values)
overlap_matrix = pd.DataFrame(
index=network_shapes.index,
columns=(egs_shapes := egs_data.geometry).astype(str).values,
)
for name, polygon in network_shapes.geometry.items():
overlap_matrix.loc[name] = (
egs_shapes
.intersection(polygon).area
egs_shapes.intersection(polygon).area
) / egs_shapes.area
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"])

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