data.datasets.hydrogen_etrago.storage.insert_H2_storage_eGon100RE() - Code Metrics - Inspection of "Features/#864 gas sanity checks eGon2035" - openego/eGon-data - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — dev (#1068)

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created 2022-12-14 19:44 UTC

insert_H2_storage_eGon100RE() A

↳ Parent: data.datasets.hydrogen_etrago.storage

Complexity

Conditions

Size

Total Lines	4
Code Lines	3

Duplication

Lines	0
Ratio	0 %

Importance

Changes

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eloc	3
dl	0
loc	4
rs	10
c	0
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f	0
cc	1
nop	0

"""The central module containing all code dealing with heat sector in etrago
"""
from geoalchemy2 import Geometry
import geopandas as gpd
import pandas as pd

from egon.data import config, db
from egon.data.datasets.etrago_helpers import copy_and_modify_stores
from egon.data.datasets.scenario_parameters import get_sector_parameters


def insert_H2_overground_storage(scn_name="eGon2035"):
    """Insert H2 steel tank storage for every H2 bus."""
    # The targets of etrago_hydrogen also serve as source here ಠ_ಠ
    sources = config.datasets()["etrago_hydrogen"]["sources"]
    targets = config.datasets()["etrago_hydrogen"]["targets"]

    # Place storage at every H2 bus
    storages = db.select_geodataframe(
        f"""
        SELECT bus_id, scn_name, geom
        FROM {sources['buses']['schema']}.
        {sources['buses']['table']} WHERE carrier = 'H2_grid'
        AND scn_name = '{scn_name}' AND country = 'DE'""",
        index_col="bus_id",
    )

    carrier = "H2_overground"
    # Add missing column
    storages["bus"] = storages.index
    storages["carrier"] = carrier

    # Does e_nom_extenable = True render e_nom useless?
    storages["e_nom"] = 0
    storages["e_nom_extendable"] = True

    # read carrier information from scnario parameter data
    scn_params = get_sector_parameters("gas", scn_name)
    storages["capital_cost"] = scn_params["capital_cost"][carrier]
    storages["lifetime"] = scn_params["lifetime"][carrier]

    # Remove useless columns
    storages.drop(columns=["geom"], inplace=True)

    # Clean table
    db.execute_sql(
        f"""
        DELETE FROM grid.egon_etrago_store WHERE carrier = '{carrier}' AND
        scn_name = '{scn_name}' AND bus not IN (
            SELECT bus_id FROM grid.egon_etrago_bus
            WHERE scn_name = '{scn_name}' AND country != 'DE'
        );
        """
    )

    # Select next id value
    new_id = db.next_etrago_id("store")
    storages["store_id"] = range(new_id, new_id + len(storages))
    storages = storages.reset_index(drop=True)

    # Insert data to db
    storages.to_sql(
        targets["hydrogen_stores"]["table"],
        db.engine(),
        schema=targets["hydrogen_stores"]["schema"],
        index=False,
        if_exists="append",
    )


def insert_H2_saltcavern_storage(scn_name="eGon2035"):
    """Insert H2 saltcavern storage for every H2_saltcavern bus in the table."""

    # Datatables sources and targets
    sources = config.datasets()["etrago_hydrogen"]["sources"]
    targets = config.datasets()["etrago_hydrogen"]["targets"]

    storage_potentials = db.select_geodataframe(
        f"""
        SELECT *
        FROM {sources['saltcavern_data']['schema']}.
        {sources['saltcavern_data']['table']}""",
        geom_col="geometry",
    )

    # Place storage at every H2 bus from the H2 AC saltcavern map
    H2_AC_bus_map = db.select_dataframe(
        f"""
        SELECT *
        FROM {sources['H2_AC_map']['schema']}.
        {sources['H2_AC_map']['table']}""",
    )

    storage_potentials["storage_potential"] = (
        storage_potentials["area_fraction"] * storage_potentials["potential"]
    )

    storage_potentials[
        "summed_potential_per_bus"
    ] = storage_potentials.groupby("bus_id")["storage_potential"].transform(
        "sum"
    )

    storages = storage_potentials[
        ["summed_potential_per_bus", "bus_id"]
    ].copy()
    storages.drop_duplicates("bus_id", keep="last", inplace=True)

    # map AC buses in potetial data to respective H2 buses
    storages = storages.merge(
        H2_AC_bus_map, left_on="bus_id", right_on="bus_AC"
    ).reindex(columns=["bus_H2", "summed_potential_per_bus", "scn_name"])

    # rename columns
    storages.rename(
        columns={"bus_H2": "bus", "summed_potential_per_bus": "e_nom_max"},
        inplace=True,
    )

    # add missing columns
    carrier = "H2_underground"
    storages["carrier"] = carrier
    storages["e_nom"] = 0
    storages["e_nom_extendable"] = True

    # read carrier information from scnario parameter data
    scn_params = get_sector_parameters("gas", scn_name)
    storages["capital_cost"] = scn_params["capital_cost"][carrier]
    storages["lifetime"] = scn_params["lifetime"][carrier]

    # Clean table
    db.execute_sql(
        f"""
        DELETE FROM grid.egon_etrago_store WHERE carrier = '{carrier}' AND
        scn_name = '{scn_name}' AND bus not IN (
            SELECT bus_id FROM grid.egon_etrago_bus
            WHERE scn_name = '{scn_name}' AND country != 'DE'
        );
        """
    )

    # Select next id value
    new_id = db.next_etrago_id("store")
    storages["store_id"] = range(new_id, new_id + len(storages))
    storages = storages.reset_index(drop=True)

    # # Insert data to db
    storages.to_sql(
        targets["hydrogen_stores"]["table"],
        db.engine(),
        schema=targets["hydrogen_stores"]["schema"],
        index=False,
        if_exists="append",
    )


def calculate_and_map_saltcavern_storage_potential():
    """Calculate site specific storage potential based on InSpEE-DS report."""

    # select onshore vg250 data
    sources = config.datasets()["bgr"]["sources"]
    vg250_data = db.select_geodataframe(
        f"""SELECT * FROM
                {sources['vg250_federal_states']['schema']}.
                {sources['vg250_federal_states']['table']}
            WHERE gf = '4'""",
        index_col="id",
        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=["INSPEEDS", "INSPEE"])

    # values in MWh, modified to fit the saltstructure data
    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

    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"]:
            try:
                individual_areas[federal_state] = (
                    saltcavern_data.overlay(
                        vg250_data[vg250_data["gen"] == federal_state],
                        how="intersection",
                    )
                    .to_crs(epsg=25832)
                    .area.sum()
                )
            except ValueError:
                individual_areas[federal_state] = 0

        # 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"]
    )

    saltcaverns_in_fed_state = gpd.GeoDataFrame()

    # intersection of saltstructures with 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)
        if federal_state_data.size > 0:
            saltcaverns_in_fed_state = saltcaverns_in_fed_state.append(
                saltcavern_data.overlay(federal_state_data, how="intersection")
            )
            # write total potential in column, will be overwritten by actual
            # value later
            saltcaverns_in_fed_state.loc[
                saltcaverns_in_fed_state["gen"] == federal_state, "potential"
            ] = hydrogen_storage_potential.loc[federal_state, "total"]

    # drop all federal state data columns except name of the state
    saltcaverns_in_fed_state.drop(
        columns=[
            col
            for col in federal_state_data.columns

            if col not in ["gen", "geometry"]
        ],
        inplace=True,
    )

    # this is required for the first loop as no geometry has been set
    # prior to this, also set crs to match original saltcavern_data crs
    saltcaverns_in_fed_state.set_geometry("geometry")
    saltcaverns_in_fed_state.set_crs(saltcavern_data.crs, inplace=True)
    saltcaverns_in_fed_state.to_crs(epsg=4326, inplace=True)

    # recalculate area in case structures have been split at federal
    # state borders in original data epsg
    # mapping of potential to individual H2 storage is in
    # hydrogen_etrago/storage.py
    saltcaverns_in_fed_state["shape_star"] = saltcaverns_in_fed_state.to_crs(
        epsg=25832
    ).area

    # get substation voronois
    substation_voronoi = (
        db.select_geodataframe(
            f"""
        SELECT * FROM grid.egon_hvmv_substation_voronoi
        """,
            index_col="bus_id",
        )
        .to_crs(4326)
        .sort_index()
    )

    # get substations
    substations = db.select_geodataframe(
        f"""
        SELECT * FROM grid.egon_hvmv_substation""",
        geom_col="point",
        index_col="bus_id",
    ).to_crs(4326)

    # create 500 m radius around substations as storage potential area
    # 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
    # voronois)
    voroni_buffer_intersect = substations_inflation["geometry"].intersection(
        substation_voronoi["geom"]
    )

    # make intersection a dataframe to kepp bus_id column in potential area
    # overlay
    voroni_buffer_intersect = gpd.GeoDataFrame(
        {
            "bus_id": voroni_buffer_intersect.index.tolist(),
            "geometry": voroni_buffer_intersect.geometry.tolist(),
        }
    ).set_crs(epsg=4326)

    # overlay saltstructures with substation buffer
    potential_areas = saltcaverns_in_fed_state.overlay(
        voroni_buffer_intersect, how="intersection"
    ).set_crs(epsg=4326)

    # calculate area fraction of individual site over total area within
    # the same federal state
    potential_areas["area_fraction"] = potential_areas.to_crs(
        epsg=25832
    ).area / potential_areas.groupby("gen")["shape_star"].transform("sum")

    return potential_areas

def write_saltcavern_potential():
    """Write saltcavern potentials in the database"""
    potential_areas = calculate_and_map_saltcavern_storage_potential()

    # write information to saltcavern data
    targets = config.datasets()["bgr"]["targets"]
    potential_areas.to_crs(epsg=4326).to_postgis(
        targets["storage_potential"]["table"],
        db.engine(),
        schema=targets["storage_potential"]["schema"],
        index=True,
        if_exists="replace",
        dtype={"geometry": Geometry()},
    )


def insert_H2_storage_eGon100RE():
    """Copy H2 storage from the eGon2035 to the eGon100RE scenario."""
    copy_and_modify_stores(
        "eGon2035", "eGon100RE", ["H2_underground", "H2_overground"], "gas"
    )


1			"""The central module containing all code dealing with heat sector in etrago
2			"""
3			from geoalchemy2 import Geometry
4			import geopandas as gpd
5			import pandas as pd
6
7			from egon.data import config, db
8			from egon.data.datasets.etrago_helpers import copy_and_modify_stores
9			from egon.data.datasets.scenario_parameters import get_sector_parameters
10
11
12			def insert_H2_overground_storage(scn_name="eGon2035"):
13			"""Insert H2 steel tank storage for every H2 bus."""
14			# The targets of etrago_hydrogen also serve as source here ಠ_ಠ
15			sources = config.datasets()["etrago_hydrogen"]["sources"]
16			targets = config.datasets()["etrago_hydrogen"]["targets"]
17
18			# Place storage at every H2 bus
19			storages = db.select_geodataframe(
20			f"""
21			SELECT bus_id, scn_name, geom
22			FROM {sources['buses']['schema']}.
23			{sources['buses']['table']} WHERE carrier = 'H2_grid'
24			AND scn_name = '{scn_name}' AND country = 'DE'""",
25			index_col="bus_id",
26			)
27
28			carrier = "H2_overground"
29			# Add missing column
30			storages["bus"] = storages.index
31			storages["carrier"] = carrier
32
33			# Does e_nom_extenable = True render e_nom useless?
34			storages["e_nom"] = 0
35			storages["e_nom_extendable"] = True
36
37			# read carrier information from scnario parameter data
38			scn_params = get_sector_parameters("gas", scn_name)
39			storages["capital_cost"] = scn_params["capital_cost"][carrier]
40			storages["lifetime"] = scn_params["lifetime"][carrier]
41
42			# Remove useless columns
43			storages.drop(columns=["geom"], inplace=True)
44
45			# Clean table
46			db.execute_sql(
47			f"""
48			DELETE FROM grid.egon_etrago_store WHERE carrier = '{carrier}' AND
49			scn_name = '{scn_name}' AND bus not IN (
50			SELECT bus_id FROM grid.egon_etrago_bus
51			WHERE scn_name = '{scn_name}' AND country != 'DE'
52			);
53			"""
54			)
55
56			# Select next id value
57			new_id = db.next_etrago_id("store")
58			storages["store_id"] = range(new_id, new_id + len(storages))
59			storages = storages.reset_index(drop=True)
60
61			# Insert data to db
62			storages.to_sql(
63			targets["hydrogen_stores"]["table"],
64			db.engine(),
65			schema=targets["hydrogen_stores"]["schema"],
66			index=False,
67			if_exists="append",
68			)
69
70
71			def insert_H2_saltcavern_storage(scn_name="eGon2035"):
72			"""Insert H2 saltcavern storage for every H2_saltcavern bus in the table."""
73
74			# Datatables sources and targets
75			sources = config.datasets()["etrago_hydrogen"]["sources"]
76			targets = config.datasets()["etrago_hydrogen"]["targets"]
77
78			storage_potentials = db.select_geodataframe(
79			f"""
80			SELECT *
81			FROM {sources['saltcavern_data']['schema']}.
82			{sources['saltcavern_data']['table']}""",
83			geom_col="geometry",
84			)
85
86			# Place storage at every H2 bus from the H2 AC saltcavern map
87			H2_AC_bus_map = db.select_dataframe(
88			f"""
89			SELECT *
90			FROM {sources['H2_AC_map']['schema']}.
91			{sources['H2_AC_map']['table']}""",
92			)
93
94			storage_potentials["storage_potential"] = (
95			storage_potentials["area_fraction"] * storage_potentials["potential"]
96			)
97
98			storage_potentials[
99			"summed_potential_per_bus"
100			] = storage_potentials.groupby("bus_id")["storage_potential"].transform(
101			"sum"
102			)
103
104			storages = storage_potentials[
105			["summed_potential_per_bus", "bus_id"]
106			].copy()
107			storages.drop_duplicates("bus_id", keep="last", inplace=True)
108
109			# map AC buses in potetial data to respective H2 buses
110			storages = storages.merge(
111			H2_AC_bus_map, left_on="bus_id", right_on="bus_AC"
112			).reindex(columns=["bus_H2", "summed_potential_per_bus", "scn_name"])
113
114			# rename columns
115			storages.rename(
116			columns={"bus_H2": "bus", "summed_potential_per_bus": "e_nom_max"},
117			inplace=True,
118			)
119
120			# add missing columns
121			carrier = "H2_underground"
122			storages["carrier"] = carrier
123			storages["e_nom"] = 0
124			storages["e_nom_extendable"] = True
125
126			# read carrier information from scnario parameter data
127			scn_params = get_sector_parameters("gas", scn_name)
128			storages["capital_cost"] = scn_params["capital_cost"][carrier]
129			storages["lifetime"] = scn_params["lifetime"][carrier]
130
131			# Clean table
132			db.execute_sql(
133			f"""
134			DELETE FROM grid.egon_etrago_store WHERE carrier = '{carrier}' AND
135			scn_name = '{scn_name}' AND bus not IN (
136			SELECT bus_id FROM grid.egon_etrago_bus
137			WHERE scn_name = '{scn_name}' AND country != 'DE'
138			);
139			"""
140			)
141
142			# Select next id value
143			new_id = db.next_etrago_id("store")
144			storages["store_id"] = range(new_id, new_id + len(storages))
145			storages = storages.reset_index(drop=True)
146
147			# # Insert data to db
148			storages.to_sql(
149			targets["hydrogen_stores"]["table"],
150			db.engine(),
151			schema=targets["hydrogen_stores"]["schema"],
152			index=False,
153			if_exists="append",
154			)
155
156
157			def calculate_and_map_saltcavern_storage_potential():
158			"""Calculate site specific storage potential based on InSpEE-DS report."""
159
160			# select onshore vg250 data
161			sources = config.datasets()["bgr"]["sources"]
162			vg250_data = db.select_geodataframe(
163			f"""SELECT * FROM
164			{sources['vg250_federal_states']['schema']}.
165			{sources['vg250_federal_states']['table']}
166			WHERE gf = '4'""",
167			index_col="id",
168			geom_col="geometry",
169			)
170
171			# get saltcavern shapes
172			saltcavern_data = db.select_geodataframe(
173			f"""SELECT * FROM
174			{sources['saltcaverns']['schema']}.
175			{sources['saltcaverns']['table']}
176			""",
177			geom_col="geometry",
178			)
179
180			# hydrogen storage potential data from InSpEE-DS report
181			hydrogen_storage_potential = pd.DataFrame(columns=["INSPEEDS", "INSPEE"])
182
183			# values in MWh, modified to fit the saltstructure data
184			hydrogen_storage_potential.loc["Brandenburg"] = [353e6, 159e6]
185			hydrogen_storage_potential.loc["Mecklenburg-Vorpommern"] = [25e6, 193e6]
186			hydrogen_storage_potential.loc["Nordrhein-Westfalen"] = [168e6, 0]
187			hydrogen_storage_potential.loc["Sachsen-Anhalt"] = [318e6, 147e6]
188			hydrogen_storage_potential.loc["Thüringen"] = [595e6, 0]
189
190			# distribute SH/HH and NDS/HB potentials by area
191			# overlay saltstructures with federal state, calculate respective area
192			# map storage potential per federal state to area fraction of summed area
193			# potential_i = area_i / area_tot * potential_tot
194
195			potential_data_dict = {
196			0: {
197			"federal_states": ["Schleswig-Holstein", "Hamburg"],
198			"INSPEEDS": 0,
199			"INSPEE": 413e6,
200			},
201			1: {
202			"federal_states": ["Niedersachsen", "Bremen"],
203			"INSPEEDS": 253e6,
204			"INSPEE": 702e6,
205			},
206			}
207
208			# iterate over aggregated state data for SH/HH and NDS/HB
209			for data in potential_data_dict.values():
210			individual_areas = {}
211			# individual state areas
212			for federal_state in data["federal_states"]:
213			try:
214			individual_areas[federal_state] = (
215			saltcavern_data.overlay(
216			vg250_data[vg250_data["gen"] == federal_state],
217			how="intersection",
218			)
219			.to_crs(epsg=25832)
220			.area.sum()
221			)
222			except ValueError:
223			individual_areas[federal_state] = 0
224
225			# derives weights from fraction of individual state area to total area
226			total_area = sum(individual_areas.values())
227			weights = {
228			f: individual_areas[f] / total_area if total_area > 0 else 0
229			for f in data["federal_states"]
230			}
231			# write data into potential dataframe
232			for federal_state in data["federal_states"]:
233			hydrogen_storage_potential.loc[federal_state] = [
234			data["INSPEEDS"] * weights[federal_state],
235			data["INSPEE"] * weights[federal_state],
236			]
237
238			# calculate total storage potential
239			hydrogen_storage_potential["total"] = (
240			# currently only InSpEE saltstructure shapefiles are available
241			hydrogen_storage_potential["INSPEEDS"]
242			+ hydrogen_storage_potential["INSPEE"]
243			)
244
245			saltcaverns_in_fed_state = gpd.GeoDataFrame()
246
247			# intersection of saltstructures with federal state
248			for federal_state in hydrogen_storage_potential.index:
249			federal_state_data = vg250_data[vg250_data["gen"] == federal_state]
250
251			# skip if federal state not available (e.g. local testing)
252			if federal_state_data.size > 0:
253			saltcaverns_in_fed_state = saltcaverns_in_fed_state.append(
254			saltcavern_data.overlay(federal_state_data, how="intersection")
255			)
256			# write total potential in column, will be overwritten by actual
257			# value later
258			saltcaverns_in_fed_state.loc[
259			saltcaverns_in_fed_state["gen"] == federal_state, "potential"
260			] = hydrogen_storage_potential.loc[federal_state, "total"]
261
262			# drop all federal state data columns except name of the state
263			saltcaverns_in_fed_state.drop(
264			columns=[
265			col
266			for col in federal_state_data.columns
			0 ignored issues – show introduced 2021-11-05 13:06 UTC by Report Bug Copy Issue Report The variable `federal_state_data` does not seem to be defined in case the `for` loop on line `248` is not entered. Are you sure this can never be the case? Loading history...
267			if col not in ["gen", "geometry"]
268			],
269			inplace=True,
270			)
271
272			# this is required for the first loop as no geometry has been set
273			# prior to this, also set crs to match original saltcavern_data crs
274			saltcaverns_in_fed_state.set_geometry("geometry")
275			saltcaverns_in_fed_state.set_crs(saltcavern_data.crs, inplace=True)
276			saltcaverns_in_fed_state.to_crs(epsg=4326, inplace=True)
277
278			# recalculate area in case structures have been split at federal
279			# state borders in original data epsg
280			# mapping of potential to individual H2 storage is in
281			# hydrogen_etrago/storage.py
282			saltcaverns_in_fed_state["shape_star"] = saltcaverns_in_fed_state.to_crs(
283			epsg=25832
284			).area
285
286			# get substation voronois
287			substation_voronoi = (
288			db.select_geodataframe(
289			f"""
290			SELECT * FROM grid.egon_hvmv_substation_voronoi
291			""",
292			index_col="bus_id",
293			)
294			.to_crs(4326)
295			.sort_index()
296			)
297
298			# get substations
299			substations = db.select_geodataframe(
300			f"""
301			SELECT * FROM grid.egon_hvmv_substation""",
302			geom_col="point",
303			index_col="bus_id",
304			).to_crs(4326)
305
306			# create 500 m radius around substations as storage potential area
307			# epsg for buffer in line with original saltstructre data
308			substations_inflation = gpd.GeoDataFrame(
309			geometry=substations.to_crs(25832).buffer(500).to_crs(4326)
310			).sort_index()
311
312			# !!row wise!! intersection between the substations inflation and the
313			# respective voronoi (overlay only allows for intersection to all
314			# voronois)
315			voroni_buffer_intersect = substations_inflation["geometry"].intersection(
316			substation_voronoi["geom"]
317			)
318
319			# make intersection a dataframe to kepp bus_id column in potential area
320			# overlay
321			voroni_buffer_intersect = gpd.GeoDataFrame(
322			{
323			"bus_id": voroni_buffer_intersect.index.tolist(),
324			"geometry": voroni_buffer_intersect.geometry.tolist(),
325			}
326			).set_crs(epsg=4326)
327
328			# overlay saltstructures with substation buffer
329			potential_areas = saltcaverns_in_fed_state.overlay(
330			voroni_buffer_intersect, how="intersection"
331			).set_crs(epsg=4326)
332
333			# calculate area fraction of individual site over total area within
334			# the same federal state
335			potential_areas["area_fraction"] = potential_areas.to_crs(
336			epsg=25832
337			).area / potential_areas.groupby("gen")["shape_star"].transform("sum")
338
339			return potential_areas
340
341			def write_saltcavern_potential():
342			"""Write saltcavern potentials in the database"""
343			potential_areas = calculate_and_map_saltcavern_storage_potential()
344
345			# write information to saltcavern data
346			targets = config.datasets()["bgr"]["targets"]
347			potential_areas.to_crs(epsg=4326).to_postgis(
348			targets["storage_potential"]["table"],
349			db.engine(),
350			schema=targets["storage_potential"]["schema"],
351			index=True,
352			if_exists="replace",
353			dtype={"geometry": Geometry()},
354			)
355
356
357			def insert_H2_storage_eGon100RE():
358			"""Copy H2 storage from the eGon2035 to the eGon100RE scenario."""
359			copy_and_modify_stores(
360			"eGon2035", "eGon100RE", ["H2_underground", "H2_overground"], "gas"
361			)
362

openego / eGon-data

Pull Request — dev (#1068)

insert_H2_storage_eGon100RE() A

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