added basic egs potential script

rules/build_sector.smk

@@ -268,10 +268,11 @@ rule build_biomass_potentials:
     params:
         biomass=config["biomass"],
     input:
-        enspreso_biomass=HTTP.remote(
+        # enspreso_biomass=HTTP.remote(
-            "https://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx",
+        #     "https://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx",
-            keep_local=True,
+        #     keep_local=True,
-        ),
+        # ),
+        enspreso_biomass="cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx",  # dealing with temporary server issues
         nuts2="data/bundle-sector/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",  # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
         regions_onshore=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
         nuts3_population=ancient("data/bundle/nama_10r_3popgdp.tsv.gz"),
@@ -703,6 +704,24 @@ rule build_transport_demand:
         "../scripts/build_transport_demand.py"
 
 
+rule build_egs_potentials:
+    input:
+        egs_cost="data/egs_costs.json",
+        shapes=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
+    output:
+        egs_potentials=RESOURCES + "egs_potentials_s{simpl}_{clusters}.csv",
+        egs_overlap=RESOURCES + "egs_overlap_s{simpl}_{clusters}.csv",
+    threads: 1
+    resources:
+        mem_mb=2000,
+    log:
+        LOGS + "build_egs_potentials_s{simpl}_{clusters}.log",
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/build_egs_potentials.py"
+
+
 rule prepare_sector_network:
     params:
         co2_budget=config["co2_budget"],

scripts/build_egs_potentials.py

@@ -0,0 +1,122 @@
+# -*- 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 pandas as pd
+import geopandas as gpd
+
+from shapely.geometry import Polygon, Point
+
+
+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.):
+
+        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]
+
+                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)
+    
+        gdf["geometry"] = gdf.geometry.apply(lambda geom: point_to_square(geom))
+        egs_data[year] = gdf
+    
+    return egs_data
+
+
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake(
+            "build_egs_potentials",
+            simpl="",
+            clusters=37,
+        )
+    
+    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}.")
+
+    else:
+        egs_data = egs_data[(default_year := 2020)]
+        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)
+    egs_shapes = egs_data.geometry
+
+    overlap_matrix = pd.DataFrame(index=network_shapes.index, columns=egs_shapes.index)
+    
+    for name, polygon in network_shapes.geometry.items():
+        overlap_matrix.loc[name] = 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", "CAPEX"]].to_csv(snakemake.output["egs_potentials"])