202 lines
6.7 KiB
Python
202 lines
6.7 KiB
Python
# -*- coding: utf-8 -*-
|
|
# SPDX-FileCopyrightText: : 2023 @LukasFranken, The PyPSA-Eur Authors
|
|
#
|
|
# SPDX-License-Identifier: MIT
|
|
"""
|
|
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'
|
|
Note that we input data used here is not the same as in the paper, but was passed on by the authors.
|
|
|
|
The data provides a lon-lat gridded map of Europe (1° x 1°), with each grid cell assigned
|
|
a heat potential (in GWh) and a cost (in EUR/MW).
|
|
|
|
This scripts overlays that map with the network's regions, and builds a csv with CAPEX, OPEX and p_nom_max
|
|
"""
|
|
|
|
import logging
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
import json
|
|
|
|
import geopandas as gpd
|
|
import numpy as np
|
|
import pandas as pd
|
|
import xarray as xr
|
|
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=1.0, 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],
|
|
]
|
|
)
|
|
|
|
years = [2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050]
|
|
lcoes = ["LCOE50", "LCOE100", "LCOE150"]
|
|
|
|
egs_data = dict()
|
|
|
|
for year in years:
|
|
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"])),
|
|
)
|
|
except TypeError:
|
|
country_df = pd.DataFrame(columns=df.columns, index=range(0))
|
|
|
|
for col in df.columns:
|
|
country_df[col] = country_data[0][years.index(year)][col]
|
|
|
|
if country_df.dropna().empty:
|
|
continue
|
|
elif df.empty:
|
|
df = country_df.dropna()
|
|
else:
|
|
df = pd.concat((df, country_df.dropna()), ignore_index=True)
|
|
|
|
gdf = gpd.GeoDataFrame(
|
|
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
|
|
|
|
|
|
def get_capacity_factors(network_regions_file, air_temperatures_file):
|
|
"""
|
|
Performance of EGS is higher for lower temperatures, due to more efficient
|
|
air cooling Data from Ricks et al.: The Role of Flexible Geothermal Power
|
|
in Decarbonized Elec Systems.
|
|
"""
|
|
|
|
# these values are taken from the paper's
|
|
# Supplementary Figure 20 from https://zenodo.org/records/7093330
|
|
# and relate deviations of the ambient temperature from the year-average
|
|
# ambient temperature to EGS capacity factors.
|
|
delta_T = [-15, -10, -5, 0, 5, 10, 15, 20]
|
|
cf = [1.17, 1.13, 1.07, 1, 0.925, 0.84, 0.75, 0.65]
|
|
|
|
x = np.linspace(-15, 20, 200)
|
|
y = np.interp(x, delta_T, cf)
|
|
|
|
upper_x = np.linspace(20, 25, 50)
|
|
m_upper = (y[-1] - y[-2]) / (x[-1] - x[-2])
|
|
upper_y = upper_x * m_upper - x[-1] * m_upper + y[-1]
|
|
|
|
lower_x = np.linspace(-20, -15, 50)
|
|
m_lower = (y[1] - y[0]) / (x[1] - x[0])
|
|
lower_y = lower_x * m_lower - x[0] * m_lower + y[0]
|
|
|
|
x = np.hstack((lower_x, x, upper_x))
|
|
y = np.hstack((lower_y, y, upper_y))
|
|
|
|
network_regions = gpd.read_file(network_regions_file).set_crs(epsg=4326)
|
|
index = network_regions["name"]
|
|
|
|
air_temp = xr.open_dataset(air_temperatures_file)
|
|
|
|
snapshots = pd.date_range(freq="h", **snakemake.params.snapshots)
|
|
capacity_factors = pd.DataFrame(index=snapshots)
|
|
|
|
# bespoke computation of capacity factors for each bus.
|
|
# Considering the respective temperatures, we compute
|
|
# the deviation from the average temperature and relate it
|
|
# to capacity factors based on the data from above.
|
|
for bus in index:
|
|
temp = air_temp.sel(name=bus).to_dataframe()["temperature"]
|
|
capacity_factors[bus] = np.interp((temp - temp.mean()).values, x, y)
|
|
|
|
return capacity_factors
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if "snakemake" not in globals():
|
|
from _helpers import mock_snakemake
|
|
|
|
snakemake = mock_snakemake(
|
|
"build_egs_potentials",
|
|
simpl="",
|
|
clusters=37,
|
|
)
|
|
|
|
egs_config = snakemake.params["sector"]["enhanced_geothermal"]
|
|
costs_config = snakemake.params["costs"]
|
|
|
|
sustainability_factor = egs_config["sustainability_factor"]
|
|
# the share of heat that is replenished from the earth's core.
|
|
# we are not constraining ourselves to the sustainable share, but
|
|
# inversely apply it to our underlying data, which refers to the
|
|
# sustainable heat. Source: Relative magnitude of sustainable heat vs
|
|
# nonsustainable heat in the paper "From hot rock to useful energy..."
|
|
|
|
egs_data = prepare_egs_data(snakemake.input.egs_cost)
|
|
|
|
if egs_config["optimism"]:
|
|
egs_data = egs_data[(year := costs_config["year"])]
|
|
logger.info(
|
|
f"EGS optimism! Building 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."
|
|
)
|
|
|
|
egs_data = egs_data.loc[egs_data["PowerSust"] > 0].reset_index(drop=True)
|
|
egs_regions = egs_data.geometry
|
|
|
|
network_regions = (
|
|
gpd.read_file(snakemake.input.regions)
|
|
.set_index("name", drop=True)
|
|
.set_crs(epsg=4326)
|
|
)
|
|
|
|
overlap_matrix = pd.DataFrame(
|
|
index=network_regions.index,
|
|
columns=egs_data.index,
|
|
)
|
|
|
|
for name, polygon in network_regions.geometry.items():
|
|
overlap_matrix.loc[name] = (
|
|
egs_regions.intersection(polygon).area
|
|
) / egs_regions.area
|
|
|
|
overlap_matrix.to_csv(snakemake.output["egs_overlap"])
|
|
|
|
# consider not only replenished heat
|
|
egs_data["p_nom_max"] = egs_data["PowerSust"] / sustainability_factor
|
|
|
|
egs_data[["p_nom_max", "CAPEX"]].to_csv(snakemake.output["egs_potentials"])
|
|
|
|
capacity_factors = get_capacity_factors(
|
|
snakemake.input["regions"],
|
|
snakemake.input["air_temperature"],
|
|
)
|
|
|
|
capacity_factors.to_csv(snakemake.output["egs_capacity_factors"])
|