Merge #474 into CI

src/egon/data/airflow/dags/pipeline.py

@@ -11,6 +11,7 @@
 from egon.data.datasets import database
 from egon.data.datasets.calculate_dlr import Calculate_dlr
 from egon.data.datasets.ch4_storages import CH4Storages
+from egon.data.datasets.saltcavern import SaltcavernData
 from egon.data.datasets.chp import Chp
 from egon.data.datasets.chp_etrago import ChpEtrago
 from egon.data.datasets.data_bundle import DataBundle
@@ -544,9 +545,7 @@
         ]
     )
 
-
     # HTS to etrago table
     hts_etrago_table = HtsEtragoTable(
                         dependencies = [heat_time_series,mv_grid_districts,
                                         district_heating_areas,heat_etrago])
-

src/egon/data/datasets/ch4_storages.py

@@ -100,18 +100,20 @@ def import_installed_ch4_storages():
 
 
 def import_ch4_grid_capacity():
-    """Define dataframe containing the gas storage modelling the ch4 grid storage capacity
+    """Define dataframe containing the modelling of the CH4 grid storage 
+    capacity. The whole storage capacity of the grid (130000 MWh, estimation of 
+    the Bundesnetzagentur) is split uniformly between all the german CH4 nodes 
+    of the grid. The capacities of the pipes are not considerated.
 
     Returns
     -------
     Gas_storages_list :
         Dataframe containing the gas stores in Germany modelling the gas grid storage capacity
     
     """ 
-    Gas_grid_capacity = 130000 # G.Volk "Die Herauforderung an die Bundesnetzagentur die Energiewende zu meistern" Berlin, Dec 2012
+    Gas_grid_capacity = 130000 # Storage capacity of the CH4 grid - G.Volk "Die Herauforderung an die Bundesnetzagentur die Energiewende zu meistern" Berlin, Dec 2012
     N_ch4_nodes_G = ch4_nodes_number_G(define_gas_nodes_list()) # Number of nodes in Germany
-    print(N_ch4_nodes_G)
-    Store_capacity = Gas_grid_capacity / N_ch4_nodes_G
+    Store_capacity = Gas_grid_capacity / N_ch4_nodes_G # Storage capacity associated to each CH4 node of the german grid
 
     sql_gas = """SELECT bus_id, scn_name, carrier, geom
                 FROM grid.egon_etrago_bus
@@ -154,7 +156,6 @@ def import_ch4_storages():
     gas_storages_list =  gas_storages_list.reset_index(drop=True)
 
     # Insert data to db
-    print(gas_storages_list)
     gas_storages_list.to_sql('egon_etrago_store',
                               engine,
                               schema ='grid',

src/egon/data/datasets/hydrogen_etrago/storage.py

@@ -150,40 +150,81 @@ def calculate_and_map_saltcavern_storage_potential():
         geom_col="geometry",
     )
 
+    # get saltcavern shapes
+    saltcavern_data = db.select_geodataframe(
+        f"""SELECT * FROM
+                {sources['saltcaverns']['schema']}.
+                {sources['saltcaverns']['table']}
+            """,
+        geom_col="geometry",
+    )
+
     # hydrogen storage potential data from InSpEE-DS report
     hydrogen_storage_potential = pd.DataFrame(
-        columns=["federal_state", "INSPEEDS", "INSPEE"]
+        columns=["INSPEEDS", "INSPEE"]
     )
 
     # values in MWh, modified to fit the saltstructure data
-    hydrogen_storage_potential.loc[0] = ["Brandenburg", 353e6, 159e6]
-    hydrogen_storage_potential.loc[1] = ["Niedersachsen", 253e6, 702e6]
-    hydrogen_storage_potential.loc[2] = ["Schleswig-Holstein", 0, 413e6]
-    hydrogen_storage_potential.loc[3] = ["Mecklenburg-Vorpommern", 25e6, 193e6]
-    hydrogen_storage_potential.loc[4] = ["Nordrhein-Westfalen", 168e6, 0]
-    hydrogen_storage_potential.loc[5] = ["Sachsen-Anhalt", 318e6, 1614e6]
-    hydrogen_storage_potential.loc[6] = ["Thüringen", 595e6, 1614e6]
+    hydrogen_storage_potential.loc["Brandenburg"] = [353e6, 159e6]
+    hydrogen_storage_potential.loc["Mecklenburg-Vorpommern"] = [25e6, 193e6]
+    hydrogen_storage_potential.loc["Nordrhein-Westfalen"] = [168e6, 0]
+    hydrogen_storage_potential.loc["Sachsen-Anhalt"] = [318e6, 147e6]
+    hydrogen_storage_potential.loc["Thüringen"] = [595e6, 0]
+
+    # distribute SH/HH and NDS/HB potentials by area
+    # overlay saltstructures with federal state, calculate respective area
+    # map storage potential per federal state to area fraction of summed area
+    # potential_i = area_i / area_tot * potential_tot
+    pot_nds_hb = [253e6, 702e6]
+    pot_sh_hh = [0, 413e6]
+
+    potential_data_dict = {
+        0: {
+            "federal_states": ["Schleswig-Holstein", "Hamburg"],
+            "INSPEEDS": 0, "INSPEE": 413e6
+        },
+        1: {
+            "federal_states": ["Niedersachsen", "Bremen"],
+            "INSPEEDS": 253e6, "INSPEE": 702e6
+        }
+    }
+
+    # iterate over aggregated state data for SH/HH and NDS/HB
+    for data in potential_data_dict.values():
+        individual_areas = {}
+        # individual state areas
+        for federal_state in data["federal_states"]:
+            individual_areas[federal_state] = saltcavern_data.overlay(
+                vg250_data[vg250_data["gen"] == federal_state],
+                how="intersection"
+            ).to_crs(epsg=25832).area.sum()
+
+        # derives weights from fraction of individual state area to total area
+        total_area = sum(individual_areas.values())
+        weights = {
+            f: individual_areas[f] / total_area if total_area > 0 else 0
+            for f in data["federal_states"]
+        }
+        # write data into potential dataframe
+        for federal_state in data["federal_states"]:
+            hydrogen_storage_potential.loc[federal_state] = (
+                [
+                    data["INSPEEDS"] * weights[federal_state],
+                    data["INSPEE"] * weights[federal_state]
+                ]
+            )
 
+    # calculate total storage potential
     hydrogen_storage_potential["total"] = (
         # currently only InSpEE saltstructure shapefiles are available
         # hydrogen_storage_potential["INSPEEDS"]
         hydrogen_storage_potential["INSPEE"]
     )
 
-    # get saltcavern shapes
-    saltcavern_data = db.select_geodataframe(
-        f"""SELECT * FROM
-                {sources['saltcaverns']['schema']}.
-                {sources['saltcaverns']['table']}
-            """,
-        geom_col="geometry",
-    )
-
     saltcaverns_in_fed_state = gpd.GeoDataFrame()
 
     # intersection of saltstructures with federal state
-    for row in hydrogen_storage_potential.index:
-        federal_state = hydrogen_storage_potential.loc[row, "federal_state"]
+    for federal_state in hydrogen_storage_potential.index:
         federal_state_data = vg250_data[vg250_data["gen"] == federal_state]
 
         # skip if federal state not available (e.g. local testing)
@@ -193,9 +234,10 @@ def calculate_and_map_saltcavern_storage_potential():
             )
             # write total potential in column, will be overwritten by actual
             # value later
-            saltcaverns_in_fed_state[
+            saltcaverns_in_fed_state.loc[
+                saltcaverns_in_fed_state["gen"] == federal_state,
                 "potential"
-            ] = hydrogen_storage_potential.loc[row, "total"]
+            ] = hydrogen_storage_potential.loc[federal_state, "total"]
 
     # drop all federal state data columns except name of the state
     saltcaverns_in_fed_state.drop(
@@ -227,7 +269,7 @@ def calculate_and_map_saltcavern_storage_potential():
         SELECT * FROM grid.egon_hvmv_substation_voronoi
         """,
         index_col="bus_id",
-    ).to_crs(4326)
+    ).to_crs(4326).sort_index()
 
     # get substations
     substations = db.select_geodataframe(
@@ -241,7 +283,7 @@ def calculate_and_map_saltcavern_storage_potential():
     # epsg for buffer in line with original saltstructre data
     substations_inflation = gpd.GeoDataFrame(
         geometry=substations.to_crs(25832).buffer(500).to_crs(4326)
-    )
+    ).sort_index()
 
     # !!row wise!! intersection between the substations inflation and the
     # respective voronoi (overlay only allows for intersection to all