added option for hourly egs capacity factors

config/config.default.yaml

@@ -511,6 +511,8 @@ sector:
   enhanced_geothermal: true
   enhanced_geothermal_optimism: false # if true, egs costs are reducing towards 2050 according to Aghahosseini et al., (2020)
   enhanced_geothermal_performant: true # if true, adds only the cheapest patch of EGS potential to each region
+  enhanced_geothermal_flexible: true # if true, adds a storage unit simulating flexible operation of EGS, see Ricks et al. 2023
+  enhanced_geothermal_var_cf: true # if true, adds time-dependent capacity factor to EGS, see Ricks et al. 2023
 
 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#industry
 industry:

rules/build_sector.smk

@@ -705,12 +705,20 @@ rule build_transport_demand:
 
 
 rule build_egs_potentials:
+    params:
+        snapshots=config["snapshots"],
     input:
         egs_cost="data/egs_costs.json",
-        shapes=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
+        regions=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
+        air_temperature=RESOURCES + "temp_air_total_elec_s{simpl}_{clusters}.nc" 
+        if config["sector"]["enhanced_geothermal_var_cf"]
+        else [],
     output:
         egs_potentials=RESOURCES + "egs_potentials_s{simpl}_{clusters}.csv",
         egs_overlap=RESOURCES + "egs_overlap_s{simpl}_{clusters}.csv",
+        egs_capacity_factors=RESOURCES + "egs_capacity_factors_s{simpl}_{clusters}.csv",
+        if config["sector"]["enhanced_geothermal_var_cf"]
+        else [],
     threads: 1
     resources:
         mem_mb=2000,
@@ -790,6 +798,9 @@ rule prepare_sector_network:
         cop_air_urban=RESOURCES + "cop_air_urban_elec_s{simpl}_{clusters}.nc",
         egs_potentials=RESOURCES + "egs_potentials_s{simpl}_{clusters}.csv",
         egs_overlap=RESOURCES + "egs_overlap_s{simpl}_{clusters}.csv",
+        egs_capacity_factors=RESOURCES + "egs_capacity_factors_s{simpl}_{clusters}.csv",
+        if config["sector"]["enhanced_geothermal_var_cf"]
+        else [],
         solar_thermal_total=RESOURCES
         + "solar_thermal_total_elec_s{simpl}_{clusters}.nc"
         if config["sector"]["solar_thermal"]

scripts/build_egs_potentials.py

@@ -22,8 +22,10 @@ logger = logging.getLogger(__name__)
 
 import json
 
-import geopandas as gpd
+import numpy as np
 import pandas as pd
+import xarray as xr
+import geopandas as gpd
 from shapely.geometry import Polygon
 
 
@@ -84,6 +86,48 @@ def prepare_egs_data(egs_file):
     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
+    """ 
+
+    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)
+
+    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
@@ -117,23 +161,23 @@ if __name__ == "__main__":
         )
 
     egs_data = egs_data.loc[egs_data["PowerSust"] > 0].reset_index(drop=True)
-    egs_shapes = egs_data.geometry
+    egs_regions = egs_data.geometry
 
-    network_shapes = (
+    network_regions = (
-        gpd.read_file(snakemake.input.shapes)
+        gpd.read_file(snakemake.input.regions)
         .set_index("name", drop=True)
         .set_crs(epsg=4326)
     )
 
     overlap_matrix = pd.DataFrame(
-        index=network_shapes.index,
+        index=network_regions.index,
         columns=egs_data.index,
     )
 
-    for name, polygon in network_shapes.geometry.items():
+    for name, polygon in network_regions.geometry.items():
         overlap_matrix.loc[name] = (
-            egs_shapes.intersection(polygon).area
+            egs_regions.intersection(polygon).area
-        ) / egs_shapes.area
+        ) / egs_regions.area
 
     overlap_matrix.to_csv(snakemake.output["egs_overlap"])
 
@@ -141,3 +185,10 @@ if __name__ == "__main__":
     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"])