144 lines
4.5 KiB
Python
144 lines
4.5 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2023 @LukasFranken, The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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This rule extracts potential and cost for electricity generation through
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enhanced geothermal systems.
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For this, we use data from "From hot rock to useful energy..." by Aghahosseini, Breyer (2020)
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'https://www.sciencedirect.com/science/article/pii/S0306261920312551'
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Note that we input data used here is not the same as in the paper, but was passed on by the authors.
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The data provides a lon-lat gridded map of Europe (1° x 1°), with each grid cell assigned
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a heat potential (in GWh) and a cost (in EUR/MW).
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This scripts overlays that map with the network's regions, and builds a csv with CAPEX, OPEX and p_nom_max
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"""
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import logging
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logger = logging.getLogger(__name__)
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import json
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import geopandas as gpd
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import pandas as pd
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from shapely.geometry import Polygon
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def prepare_egs_data(egs_file):
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with open(egs_file) as f:
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jsondata = json.load(f)
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def point_to_square(p, lon_extent=1.0, lat_extent=1.0):
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try:
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x, y = p.coords.xy[0][0], p.coords.xy[1][0]
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except IndexError:
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return p
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return Polygon(
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[
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[x - lon_extent / 2, y - lat_extent / 2],
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[x - lon_extent / 2, y + lat_extent / 2],
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[x + lon_extent / 2, y + lat_extent / 2],
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[x + lon_extent / 2, y - lat_extent / 2],
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]
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)
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years = [2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050]
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lcoes = ["LCOE50", "LCOE100", "LCOE150"]
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egs_data = dict()
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for year in years:
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df = pd.DataFrame(columns=["Lon", "Lat", "CAPEX", "HeatSust", "PowerSust"])
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for lcoe in lcoes:
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for country_data in jsondata[lcoe]:
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try:
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country_df = pd.DataFrame(
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columns=df.columns,
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index=range(len(country_data[0][years.index(year)]["Lon"])),
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)
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except TypeError:
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country_df = pd.DataFrame(columns=df.columns, index=range(0))
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for col in df.columns:
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country_df[col] = country_data[0][years.index(year)][col]
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if country_df.dropna().empty:
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continue
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elif df.empty:
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df = country_df.dropna()
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else:
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df = pd.concat((df, country_df.dropna()), ignore_index=True)
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gdf = gpd.GeoDataFrame(
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df.drop(columns=["Lon", "Lat"]), geometry=gpd.points_from_xy(df.Lon, df.Lat)
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).reset_index(drop=True)
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gdf["geometry"] = gdf.geometry.apply(lambda geom: point_to_square(geom))
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egs_data[year] = gdf
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return egs_data
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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snakemake = mock_snakemake(
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"build_egs_potentials",
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simpl="",
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clusters=37,
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)
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sustainability_factor = 0.0025
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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.
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config = snakemake.config
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egs_data = prepare_egs_data(snakemake.input.egs_cost)
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if config["sector"]["enhanced_geothermal_optimism"]:
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egs_data = egs_data[(year := config["costs"]["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_shapes = egs_data.geometry
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network_shapes = (
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gpd.read_file(snakemake.input.shapes)
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.set_index("name", drop=True)
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.set_crs(epsg=4326)
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)
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overlap_matrix = pd.DataFrame(
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index=network_shapes.index,
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columns=egs_data.index,
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)
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for name, polygon in network_shapes.geometry.items():
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overlap_matrix.loc[name] = (
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egs_shapes.intersection(polygon).area
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) / egs_shapes.area
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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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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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