Fix myopic optimization for networks with geothermal district heating

doc/release_notes.rst

@@ -11,6 +11,8 @@ Release Notes
 Upcoming Release
 ================
 
+* Bugfix: Adjusted existing heating data in ``build_existing_heating_distribution`` and the indexing of existing heat pumps for the COP correction in ``add_brownfield`` to make the myopic code work with the geothermal district heating feature.
+
 * Restore share policy "base" functionality to share build renewable profiles
 
 * Feature: Introduce geothermal district heating (direct utilisation and heat pumps). Potentials are based on `Manz et al. 2024: Spatial analysis of renewable and excess heat potentials for climate-neutral district heating in Europe <https://www.sciencedirect.com/science/article/pii/S0960148124001769>`.

scripts/add_brownfield.py

@@ -239,9 +239,14 @@ def update_heat_pump_efficiency(n: pypsa.Network, n_p: pypsa.Network, year: int)
         This function updates the efficiency in place and does not return a value.
     """
 
-    # get names of heat pumps in previous iteration
+    # get names of heat pumps in previous iteration that cannot be replaced by direct utilisation in this iteration
     heat_pump_idx_previous_iteration = n_p.links.index[
         n_p.links.index.str.contains("heat pump")
+        & n_p.links.index.str[:-4].isin(
+            n.links_t.efficiency.columns.str.rstrip(  # sources that can be directly used are no longer represented by heat pumps in the dynamic efficiency dataframe
+                str(year)
+            )
+        )
     ]
     # construct names of same-technology heat pumps in the current iteration
     corresponding_idx_this_iteration = heat_pump_idx_previous_iteration.str[:-4] + str(
@@ -252,18 +257,29 @@ def update_heat_pump_efficiency(n: pypsa.Network, n_p: pypsa.Network, year: int)
         n.links_t["efficiency"].loc[:, corresponding_idx_this_iteration].values
     )
 
+    # Change efficiency2 for heat pumps that use an explicitly modelled heat source
+    previous_iteration_columns = heat_pump_idx_previous_iteration.intersection(
+        n_p.links_t["efficiency2"].columns
+    )
+    current_iteration_columns = corresponding_idx_this_iteration.intersection(
+        n.links_t["efficiency2"].columns
+    )
+    n_p.links_t["efficiency2"].loc[:, previous_iteration_columns] = (
+        n.links_t["efficiency2"].loc[:, current_iteration_columns].values
+    )
+
 
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
 
         snakemake = mock_snakemake(
             "add_brownfield",
-            clusters="37",
+            clusters="39",
             opts="",
-            ll="v1.0",
-            sector_opts="168H-T-H-B-I-dist1",
-            planning_horizons=2030,
+            ll="vopt",
+            sector_opts="",
+            planning_horizons=2050,
         )
 
     configure_logging(snakemake)

scripts/build_existing_heating_distribution.py

@@ -47,14 +47,23 @@
 
 
 def build_existing_heating():
-    # retrieve existing heating capacities
-
-    # Add existing heating capacities, data comes from the study
-    # "Mapping and analyses of the current and future (2020 - 2030)
-    # heating/cooling fuel deployment (fossil/renewables) "
-    # https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en
-    # file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
-    # data is for buildings only (i.e. NOT district heating) and represents the year 2012
+    """
+    Retrieve and clean existing heating capacities for the myopic code.
+    Data comes from the study "Mapping and analyses of the current and
+    future (2020 - 2030) heating/cooling fuel deployment (fossil/renewables)".
+
+    Source
+    ------
+    https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en
+
+    File
+    ----
+    "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
+
+    Notes
+    -----
+    Data is for buildings only (i.e. NOT district heating) and represents the year 2012.
+    """
     # TODO start from original file
 
     existing_heating = pd.read_csv(
@@ -143,7 +152,12 @@ def build_existing_heating():
         )
         nodal_heat_name_tech[(f"{sector} rural", "air heat pump")] = 0.0
 
-    nodal_heat_name_tech[("urban central", "ground heat pump")] = 0.0
+    # add large-scale heat pump sources as columns for district heating with 0 capacity
+
+    for heat_pump_source in snakemake.params.sector["heat_pump_sources"][
+        "urban central"
+    ]:
+        nodal_heat_name_tech[("urban central", f"{heat_pump_source} heat pump")] = 0.0
 
     nodal_heat_name_tech.to_csv(snakemake.output.existing_heating_distribution)