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""" |
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The central module containing all code dealing with the H2 grid in eGon100RE |
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""" |
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from itertools import count |
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from pathlib import Path |
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from urllib.request import urlretrieve |
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import math |
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import os |
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import re |
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from fuzzywuzzy import process |
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from geoalchemy2.types import Geometry |
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from scipy.spatial import cKDTree |
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from shapely import wkb |
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from shapely.geometry import LineString, MultiLineString, Point |
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import geopandas as gpd |
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import numpy as np |
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import pandas as pd |
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from egon.data import config, db |
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from egon.data.datasets.scenario_parameters import get_sector_parameters |
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from egon.data.datasets.scenario_parameters.parameters import ( |
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annualize_capital_costs, |
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) |
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def insert_h2_pipelines(scn_name): |
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"Insert H2_grid based on Input Data from FNB-Gas" |
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download_h2_grid_data() |
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H2_grid_Neubau, H2_grid_Umstellung, H2_grid_Erweiterung = ( |
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read_h2_excel_sheets() |
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) |
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h2_bus_location = pd.read_csv( |
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Path(".") |
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/ "data_bundle_egon_data" |
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/ "hydrogen_network" |
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/ "h2_grid_nodes.csv" |
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) |
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con = db.engine() |
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sources = config.datasets()["etrago_hydrogen"]["sources"] |
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target = config.datasets()["etrago_hydrogen"]["targets"]["hydrogen_links"] |
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h2_buses_df = pd.read_sql( |
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f""" |
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SELECT bus_id, x, y FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]} |
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WHERE carrier in ('H2_grid') |
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AND scn_name = '{scn_name}' |
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""", |
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con, |
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) |
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# Delete old entries |
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db.execute_sql( |
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f""" |
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DELETE FROM {target["schema"]}.{target["table"]} |
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WHERE "carrier" = 'H2_grid' |
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AND scn_name = '{scn_name}' AND bus0 IN ( |
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SELECT bus_id |
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FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]} |
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WHERE country = 'DE' |
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) |
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""" |
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) |
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target = config.datasets()["etrago_hydrogen"]["targets"]["hydrogen_links"] |
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for df in [H2_grid_Neubau, H2_grid_Umstellung, H2_grid_Erweiterung]: |
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if df is H2_grid_Neubau: |
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df.rename( |
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columns={ |
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"Planerische \nInbetriebnahme": "Planerische Inbetriebnahme" |
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}, |
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inplace=True, |
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) |
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df.loc[ |
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df["Endpunkt\n(Ort)"] == "AQD Anlandung", "Endpunkt\n(Ort)" |
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] = "Schillig" |
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df.loc[ |
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df["Endpunkt\n(Ort)"] == "Hallendorf", "Endpunkt\n(Ort)" |
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] = "Salzgitter" |
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if df is H2_grid_Erweiterung: |
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df.rename( |
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columns={ |
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"Umstellungsdatum/ Planerische Inbetriebnahme": "Planerische Inbetriebnahme", |
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"Nenndurchmesser (DN)": "Nenndurchmesser \n(DN)", |
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"Investitionskosten\n(Mio. Euro),\nKostenschätzung": "Investitionskosten*\n(Mio. Euro)", |
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}, |
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inplace=True, |
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) |
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df = df[ |
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df["Berücksichtigung im Kernnetz \n[ja/nein/zurückgezogen]"] |
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.str.strip() |
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.str.lower() |
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== "ja" |
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] |
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df.loc[ |
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df["Endpunkt\n(Ort)"] == "Osdorfer Straße", "Endpunkt\n(Ort)" |
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] = "Berlin- Lichterfelde" |
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h2_bus_location["Ort"] = h2_bus_location["Ort"].astype(str).str.strip() |
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df["Anfangspunkt\n(Ort)"] = ( |
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df["Anfangspunkt\n(Ort)"].astype(str).str.strip() |
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) |
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df["Endpunkt\n(Ort)"] = df["Endpunkt\n(Ort)"].astype(str).str.strip() |
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df = df[ |
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[ |
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"Anfangspunkt\n(Ort)", |
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"Endpunkt\n(Ort)", |
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"Nenndurchmesser \n(DN)", |
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"Druckstufe (DP)\n[mind. 30 barg]", |
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"Investitionskosten*\n(Mio. Euro)", |
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"Planerische Inbetriebnahme", |
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"Länge \n(km)", |
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] |
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] |
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# matching start- and endpoint of each pipeline with georeferenced data |
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df["Anfangspunkt_matched"] = fuzzy_match( |
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df, h2_bus_location, "Anfangspunkt\n(Ort)" |
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) |
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df["Endpunkt_matched"] = fuzzy_match( |
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df, h2_bus_location, "Endpunkt\n(Ort)" |
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) |
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# manuell adjustments based on Detailmaßnahmenkarte der FNB-Gas [https://fnb-gas.de/wasserstoffnetz-wasserstoff-kernnetz/] |
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df = fix_h2_grid_infrastructure(df) |
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df_merged = pd.merge( |
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df, |
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h2_bus_location[["Ort", "geom", "x", "y"]], |
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how="left", |
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left_on="Anfangspunkt_matched", |
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right_on="Ort", |
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).rename( |
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columns={"geom": "geom_start", "x": "x_start", "y": "y_start"} |
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) |
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df_merged = pd.merge( |
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df_merged, |
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h2_bus_location[["Ort", "geom", "x", "y"]], |
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how="left", |
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left_on="Endpunkt_matched", |
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right_on="Ort", |
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).rename(columns={"geom": "geom_end", "x": "x_end", "y": "y_end"}) |
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H2_grid_df = df_merged.dropna(subset=["geom_start", "geom_end"]) |
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H2_grid_df = H2_grid_df[ |
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H2_grid_df["geom_start"] != H2_grid_df["geom_end"] |
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] |
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H2_grid_df = pd.merge( |
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H2_grid_df, |
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h2_buses_df, |
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how="left", |
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left_on=["x_start", "y_start"], |
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right_on=["x", "y"], |
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).rename(columns={"bus_id": "bus0"}) |
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H2_grid_df = pd.merge( |
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H2_grid_df, |
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h2_buses_df, |
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how="left", |
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left_on=["x_end", "y_end"], |
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right_on=["x", "y"], |
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).rename(columns={"bus_id": "bus1"}) |
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H2_grid_df[["bus0", "bus1"]] = H2_grid_df[["bus0", "bus1"]].astype( |
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"Int64" |
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) |
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H2_grid_df["geom_start"] = H2_grid_df["geom_start"].apply( |
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lambda x: wkb.loads(bytes.fromhex(x)) |
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) |
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H2_grid_df["geom_end"] = H2_grid_df["geom_end"].apply( |
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lambda x: wkb.loads(bytes.fromhex(x)) |
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) |
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H2_grid_df["topo"] = H2_grid_df.apply( |
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lambda row: LineString([row["geom_start"], row["geom_end"]]), |
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axis=1, |
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) |
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H2_grid_df["geom"] = H2_grid_df.apply( |
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lambda row: MultiLineString( |
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[LineString([row["geom_start"], row["geom_end"]])] |
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), |
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axis=1, |
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) |
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H2_grid_gdf = gpd.GeoDataFrame(H2_grid_df, geometry="geom", crs=4326) |
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scn_params = get_sector_parameters("gas", scn_name) |
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next_link_id = db.next_etrago_id("link") |
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H2_grid_gdf["link_id"] = range( |
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next_link_id, next_link_id + len(H2_grid_gdf) |
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) |
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H2_grid_gdf["scn_name"] = scn_name |
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H2_grid_gdf["carrier"] = "H2_grid" |
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H2_grid_gdf["Planerische Inbetriebnahme"] = ( |
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H2_grid_gdf["Planerische Inbetriebnahme"] |
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.astype(str) |
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.apply( |
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lambda x: ( |
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int(re.findall(r"\d{4}", x)[-1]) |
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if re.findall(r"\d{4}", x) |
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else ( |
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int(re.findall(r"\d{2}\.\d{2}\.(\d{4})", x)[-1]) |
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if re.findall(r"\d{2}\.\d{2}\.(\d{4})", x) |
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else None |
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) |
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) |
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) |
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) |
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H2_grid_gdf["build_year"] = H2_grid_gdf[ |
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"Planerische Inbetriebnahme" |
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].astype("Int64") |
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H2_grid_gdf["p_nom"] = H2_grid_gdf.apply( |
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lambda row: calculate_H2_capacity( |
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row["Druckstufe (DP)\n[mind. 30 barg]"], |
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row["Nenndurchmesser \n(DN)"], |
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), |
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axis=1, |
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) |
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H2_grid_gdf["p_nom_min"] = H2_grid_gdf["p_nom"] |
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H2_grid_gdf["p_nom_max"] = float("Inf") |
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H2_grid_gdf["p_nom_extendable"] = False |
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H2_grid_gdf["lifetime"] = scn_params["lifetime"]["H2_pipeline"] |
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H2_grid_gdf["capital_cost"] = H2_grid_gdf.apply( |
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lambda row: annualize_capital_costs( |
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( |
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( |
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float(row["Investitionskosten*\n(Mio. Euro)"]) |
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* 10**6 |
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/ row["p_nom"] |
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) |
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if pd.notna(row["Investitionskosten*\n(Mio. Euro)"]) |
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and str(row["Investitionskosten*\n(Mio. Euro)"]) |
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.replace(",", "") |
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.replace(".", "") |
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.isdigit() |
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and float(row["Investitionskosten*\n(Mio. Euro)"]) != 0 |
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else scn_params["overnight_cost"]["H2_pipeline"] |
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* row["Länge \n(km)"] |
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), |
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row["lifetime"], |
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0.05, |
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), |
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axis=1, |
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) |
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H2_grid_gdf["p_min_pu"] = -1 |
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253
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selected_columns = [ |
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"scn_name", |
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"link_id", |
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"bus0", |
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"bus1", |
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"build_year", |
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"p_nom", |
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"p_nom_min", |
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"p_nom_extendable", |
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"capital_cost", |
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"geom", |
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"topo", |
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"carrier", |
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"p_nom_max", |
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"p_min_pu", |
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] |
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270
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H2_grid_final = H2_grid_gdf[selected_columns] |
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272
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# Insert data to db |
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273
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H2_grid_final.to_postgis( |
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target["table"], |
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275
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con, |
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276
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schema=target["schema"], |
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if_exists="append", |
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dtype={"geom": Geometry()}, |
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) |
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280
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281
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# connect saltcaverns to H2_grid |
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282
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connect_saltcavern_to_h2_grid(scn_name) |
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283
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284
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# connect neighbour countries to H2_grid |
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285
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connect_h2_grid_to_neighbour_countries(scn_name) |
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286
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287
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288
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def replace_pipeline(df, start, end, intermediate): |
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289
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""" |
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290
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Method for adjusting pipelines manually by splittiing pipeline with an intermediate point. |
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291
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292
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Parameters |
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293
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---------- |
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294
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df : pandas.core.frame.DataFrame |
|
295
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dataframe to be adjusted |
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296
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start: str |
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297
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startpoint of pipeline |
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298
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end: str |
|
299
|
|
|
endpoint of pipeline |
|
300
|
|
|
intermediate: str |
|
301
|
|
|
new intermediate point for splitting given pipeline |
|
302
|
|
|
|
|
303
|
|
|
Returns |
|
304
|
|
|
--------- |
|
305
|
|
|
df : <class 'pandas.core.frame.DataFrame'> |
|
306
|
|
|
adjusted dataframe |
|
307
|
|
|
|
|
308
|
|
|
|
|
309
|
|
|
""" |
|
310
|
|
|
# Find rows where the start and end points match |
|
311
|
|
|
mask = ( |
|
312
|
|
|
(df["Anfangspunkt_matched"] == start) & (df["Endpunkt_matched"] == end) |
|
313
|
|
|
) | ( |
|
314
|
|
|
(df["Anfangspunkt_matched"] == end) & (df["Endpunkt_matched"] == start) |
|
315
|
|
|
) |
|
316
|
|
|
|
|
317
|
|
|
# Separate the rows to replace |
|
318
|
|
|
if mask.any(): |
|
319
|
|
|
df_replacement = df[~mask].copy() |
|
320
|
|
|
row_replaced = df[mask].iloc[0] |
|
321
|
|
|
|
|
322
|
|
|
# Add new rows for the split pipelines |
|
323
|
|
|
new_rows = pd.DataFrame( |
|
324
|
|
|
{ |
|
325
|
|
|
"Anfangspunkt_matched": [start, intermediate], |
|
326
|
|
|
"Endpunkt_matched": [intermediate, end], |
|
327
|
|
|
"Nenndurchmesser \n(DN)": [ |
|
328
|
|
|
row_replaced["Nenndurchmesser \n(DN)"], |
|
329
|
|
|
row_replaced["Nenndurchmesser \n(DN)"], |
|
330
|
|
|
], |
|
331
|
|
|
"Druckstufe (DP)\n[mind. 30 barg]": [ |
|
332
|
|
|
row_replaced["Druckstufe (DP)\n[mind. 30 barg]"], |
|
333
|
|
|
row_replaced["Druckstufe (DP)\n[mind. 30 barg]"], |
|
334
|
|
|
], |
|
335
|
|
|
"Investitionskosten*\n(Mio. Euro)": [ |
|
336
|
|
|
row_replaced["Investitionskosten*\n(Mio. Euro)"], |
|
337
|
|
|
row_replaced["Investitionskosten*\n(Mio. Euro)"], |
|
338
|
|
|
], |
|
339
|
|
|
"Planerische Inbetriebnahme": [ |
|
340
|
|
|
row_replaced["Planerische Inbetriebnahme"], |
|
341
|
|
|
row_replaced["Planerische Inbetriebnahme"], |
|
342
|
|
|
], |
|
343
|
|
|
"Länge \n(km)": [ |
|
344
|
|
|
row_replaced["Länge \n(km)"], |
|
345
|
|
|
row_replaced["Länge \n(km)"], |
|
346
|
|
|
], |
|
347
|
|
|
} |
|
348
|
|
|
) |
|
349
|
|
|
|
|
350
|
|
|
df_replacement = pd.concat( |
|
351
|
|
|
[df_replacement, new_rows], ignore_index=True |
|
352
|
|
|
) |
|
353
|
|
|
return df_replacement |
|
354
|
|
|
else: |
|
355
|
|
|
return df |
|
356
|
|
|
|
|
357
|
|
|
|
|
358
|
|
|
def fuzzy_match(df1, df2, column_to_match, threshold=80): |
|
359
|
|
|
""" |
|
360
|
|
|
Method for matching input data of H2_grid with georeferenced data (even if the strings are not exact the same) |
|
361
|
|
|
|
|
362
|
|
|
Parameters |
|
363
|
|
|
---------- |
|
364
|
|
|
df1 : pandas.core.frame.DataFrame |
|
365
|
|
|
Input dataframe |
|
366
|
|
|
df2 : pandas.core.frame.DataFrame |
|
367
|
|
|
georeferenced dataframe with h2_buses |
|
368
|
|
|
column_to_match: str |
|
369
|
|
|
matching column |
|
370
|
|
|
treshhold: float |
|
371
|
|
|
matching percentage for succesfull comparison |
|
372
|
|
|
|
|
373
|
|
|
Returns |
|
374
|
|
|
--------- |
|
375
|
|
|
matched : list |
|
376
|
|
|
list with all matched location names |
|
377
|
|
|
|
|
378
|
|
|
""" |
|
379
|
|
|
options = df2["Ort"].unique() |
|
380
|
|
|
matched = [] |
|
381
|
|
|
|
|
382
|
|
|
# Compare every locationname in df1 with locationnames in df2 |
|
383
|
|
|
for value in df1[column_to_match]: |
|
384
|
|
|
match, score = process.extractOne(value, options) |
|
385
|
|
|
if score >= threshold: |
|
386
|
|
|
matched.append(match) |
|
387
|
|
|
else: |
|
388
|
|
|
matched.append(None) |
|
389
|
|
|
|
|
390
|
|
|
return matched |
|
391
|
|
|
|
|
392
|
|
|
|
|
393
|
|
|
def calculate_H2_capacity(pressure, diameter): |
|
394
|
|
|
""" |
|
395
|
|
|
Method for calculagting capacity of pipelines based on data input from FNB Gas |
|
396
|
|
|
|
|
397
|
|
|
Parameters |
|
398
|
|
|
---------- |
|
399
|
|
|
pressure : float |
|
400
|
|
|
input for pressure of pipeline |
|
401
|
|
|
diameter: float |
|
402
|
|
|
input for diameter of pipeline |
|
403
|
|
|
column_to_match: str |
|
404
|
|
|
matching column |
|
405
|
|
|
treshhold: float |
|
406
|
|
|
matching percentage for succesfull comparison |
|
407
|
|
|
|
|
408
|
|
|
Returns |
|
409
|
|
|
--------- |
|
410
|
|
|
energy_flow: float |
|
411
|
|
|
transmission capacity of pipeline |
|
412
|
|
|
|
|
413
|
|
|
""" |
|
414
|
|
|
|
|
415
|
|
|
pressure = str(pressure).replace(",", ".") |
|
416
|
|
|
diameter = str(diameter) |
|
417
|
|
|
|
|
418
|
|
|
def convert_to_float(value): |
|
419
|
|
|
try: |
|
420
|
|
|
return float(value) |
|
421
|
|
|
except ValueError: |
|
422
|
|
|
return 400 # average value from data-source cause capacities of some lines are not fixed yet |
|
423
|
|
|
|
|
424
|
|
|
# in case of given range for pipeline-capacity calculate average value |
|
425
|
|
|
if "-" in diameter: |
|
426
|
|
|
diameters = diameter.split("-") |
|
427
|
|
|
diameter = ( |
|
428
|
|
|
convert_to_float(diameters[0]) + convert_to_float(diameters[1]) |
|
429
|
|
|
) / 2 |
|
430
|
|
|
elif "/" in diameter: |
|
431
|
|
|
diameters = diameter.split("/") |
|
432
|
|
|
diameter = ( |
|
433
|
|
|
convert_to_float(diameters[0]) + convert_to_float(diameters[1]) |
|
434
|
|
|
) / 2 |
|
435
|
|
|
else: |
|
436
|
|
|
try: |
|
437
|
|
|
diameter = float(diameter) |
|
438
|
|
|
except ValueError: |
|
439
|
|
|
diameter = 400 # average value from data-source |
|
440
|
|
|
|
|
441
|
|
|
if "-" in pressure: |
|
442
|
|
|
pressures = pressure.split("-") |
|
443
|
|
|
pressure = (float(pressures[0]) + float(pressures[1])) / 2 |
|
444
|
|
|
elif "/" in pressure: |
|
445
|
|
|
pressures = pressure.split("/") |
|
446
|
|
|
pressure = (float(pressures[0]) + float(pressures[1])) / 2 |
|
447
|
|
|
else: |
|
448
|
|
|
try: |
|
449
|
|
|
pressure = float(diameter) |
|
450
|
|
|
except ValueError: |
|
451
|
|
|
pressure = 70 # averaqge value from data-source |
|
452
|
|
|
|
|
453
|
|
|
velocity = 40 # source: L.Koops (2023): GAS PIPELINE VERSUS LIQUID HYDROGEN TRANSPORT – PERSPECTIVES FOR TECHNOLOGIES, ENERGY DEMAND ANDv TRANSPORT CAPACITY, AND IMPLICATIONS FOR AVIATION |
|
454
|
|
|
temperature = ( |
|
455
|
|
|
10 + 273.15 |
|
456
|
|
|
) # source: L.Koops (2023): GAS PIPELINE VERSUS LIQUID HYDROGEN TRANSPORT – PERSPECTIVES FOR TECHNOLOGIES, ENERGY DEMAND ANDv TRANSPORT CAPACITY, AND IMPLICATIONS FOR AVIATION |
|
457
|
|
|
density = ( |
|
458
|
|
|
pressure * 10**5 / (4.1243 * 10**3 * temperature) |
|
459
|
|
|
) # gasconstant H2 = 4.1243 [kJ/kgK] |
|
460
|
|
|
mass_flow = density * math.pi * ((diameter / 10**3) / 2) ** 2 * velocity |
|
461
|
|
|
energy_flow = mass_flow * 119.988 # low_heating_value H2 = 119.988 [MJ/kg] |
|
462
|
|
|
|
|
463
|
|
|
return energy_flow |
|
464
|
|
|
|
|
465
|
|
|
|
|
466
|
|
|
def download_h2_grid_data(): |
|
467
|
|
|
""" |
|
468
|
|
|
Download Input data for H2_grid from FNB-Gas (https://fnb-gas.de/wasserstoffnetz-wasserstoff-kernnetz/) |
|
469
|
|
|
|
|
470
|
|
|
The following data for H2 are downloaded into the folder |
|
471
|
|
|
./datasets/h2_data: |
|
472
|
|
|
* Links (file Anlage_3_Wasserstoffkernnetz_Neubau.xlsx, |
|
473
|
|
|
Anlage_4_Wasserstoffkernnetz_Umstellung.xlsx, |
|
474
|
|
|
Anlage_2_Wasserstoffkernetz_weitere_Leitungen.xlsx) |
|
475
|
|
|
|
|
476
|
|
|
Returns |
|
477
|
|
|
------- |
|
478
|
|
|
None |
|
479
|
|
|
|
|
480
|
|
|
""" |
|
481
|
|
|
path = Path("datasets/h2_data") |
|
482
|
|
|
os.makedirs(path, exist_ok=True) |
|
483
|
|
|
|
|
484
|
|
|
download_config = config.datasets()["etrago_hydrogen"]["sources"][ |
|
485
|
|
|
"H2_grid" |
|
486
|
|
|
] |
|
487
|
|
|
target_file_Um = path / download_config["converted_ch4_pipes"]["path"] |
|
488
|
|
|
target_file_Neu = path / download_config["new_constructed_pipes"]["path"] |
|
489
|
|
|
target_file_Erw = ( |
|
490
|
|
|
path / download_config["pipes_of_further_h2_grid_operators"]["path"] |
|
491
|
|
|
) |
|
492
|
|
|
|
|
493
|
|
|
for target_file in [target_file_Neu, target_file_Um, target_file_Erw]: |
|
494
|
|
|
if target_file is target_file_Um: |
|
495
|
|
|
url = download_config["converted_ch4_pipes"]["url"] |
|
496
|
|
|
elif target_file is target_file_Neu: |
|
497
|
|
|
url = download_config["new_constructed_pipes"]["url"] |
|
498
|
|
|
else: |
|
499
|
|
|
url = download_config["pipes_of_further_h2_grid_operators"]["url"] |
|
500
|
|
|
|
|
501
|
|
|
if not os.path.isfile(target_file): |
|
502
|
|
|
urlretrieve(url, target_file) |
|
503
|
|
|
|
|
504
|
|
|
|
|
505
|
|
|
def read_h2_excel_sheets(): |
|
506
|
|
|
""" |
|
507
|
|
|
Read downloaded excel files with location names for future h2-pipelines |
|
508
|
|
|
|
|
509
|
|
|
Returns |
|
510
|
|
|
------- |
|
511
|
|
|
df_Neu : <class 'pandas.core.frame.DataFrame'> |
|
512
|
|
|
df_Um : <class 'pandas.core.frame.DataFrame'> |
|
513
|
|
|
df_Erw : <class 'pandas.core.frame.DataFrame'> |
|
514
|
|
|
|
|
515
|
|
|
|
|
516
|
|
|
""" |
|
517
|
|
|
|
|
518
|
|
|
path = Path(".") / "datasets" / "h2_data" |
|
519
|
|
|
download_config = config.datasets()["etrago_hydrogen"]["sources"][ |
|
520
|
|
|
"H2_grid" |
|
521
|
|
|
] |
|
522
|
|
|
excel_file_Um = pd.ExcelFile( |
|
523
|
|
|
f'{path}/{download_config["converted_ch4_pipes"]["path"]}' |
|
524
|
|
|
) |
|
525
|
|
|
excel_file_Neu = pd.ExcelFile( |
|
526
|
|
|
f'{path}/{download_config["new_constructed_pipes"]["path"]}' |
|
527
|
|
|
) |
|
528
|
|
|
excel_file_Erw = pd.ExcelFile( |
|
529
|
|
|
f'{path}/{download_config["pipes_of_further_h2_grid_operators"]["path"]}' |
|
530
|
|
|
) |
|
531
|
|
|
|
|
532
|
|
|
df_Um = pd.read_excel(excel_file_Um, header=3) |
|
533
|
|
|
df_Neu = pd.read_excel(excel_file_Neu, header=3) |
|
534
|
|
|
df_Erw = pd.read_excel(excel_file_Erw, header=2) |
|
535
|
|
|
|
|
536
|
|
|
return df_Neu, df_Um, df_Erw |
|
537
|
|
|
|
|
538
|
|
|
|
|
539
|
|
|
def fix_h2_grid_infrastructure(df): |
|
540
|
|
|
""" |
|
541
|
|
|
Manuell adjustments for more accurate grid topology based on Detailmaßnahmenkarte der |
|
542
|
|
|
FNB-Gas [https://fnb-gas.de/wasserstoffnetz-wasserstoff-kernnetz/] |
|
543
|
|
|
|
|
544
|
|
|
Returns |
|
545
|
|
|
------- |
|
546
|
|
|
df : <class 'pandas.core.frame.DataFrame'> |
|
547
|
|
|
|
|
548
|
|
|
""" |
|
549
|
|
|
|
|
550
|
|
|
df = replace_pipeline(df, "Lubmin", "Uckermark", "Wrangelsburg") |
|
551
|
|
|
df = replace_pipeline(df, "Wrangelsburg", "Uckermark", "Schönermark") |
|
552
|
|
|
df = replace_pipeline( |
|
553
|
|
|
df, "Hemmingstedt", "Ascheberg (Holstein)", "Remmels Nord" |
|
554
|
|
|
) |
|
555
|
|
|
df = replace_pipeline(df, "Heidenau", "Elbe-Süd", "Weißenfelde") |
|
556
|
|
|
df = replace_pipeline(df, "Weißenfelde", "Elbe-Süd", "Stade") |
|
557
|
|
|
df = replace_pipeline(df, "Stade AOS", "KW Schilling", "Abzweig Stade") |
|
558
|
|
|
df = replace_pipeline(df, "Rosengarten (Sottorf)", "Moorburg", "Leversen") |
|
559
|
|
|
df = replace_pipeline(df, "Leversen", "Moorburg", "Hamburg Süd") |
|
560
|
|
|
df = replace_pipeline(df, "Achim", "Folmhusen", "Wardenburg") |
|
561
|
|
|
df = replace_pipeline(df, "Achim", "Wardenburg", "Sandkrug") |
|
562
|
|
|
df = replace_pipeline(df, "Dykhausen", "Bunde", "Emden") |
|
563
|
|
|
df = replace_pipeline(df, "Emden", "Nüttermoor", "Jemgum") |
|
564
|
|
|
df = replace_pipeline(df, "Rostock", "Glasewitz", "Fliegerhorst Laage") |
|
565
|
|
|
df = replace_pipeline(df, "Wilhelmshaven", "Dykhausen", "Sande") |
|
566
|
|
|
df = replace_pipeline( |
|
567
|
|
|
df, "Wilhelmshaven Süd", "Wilhelmshaven Nord", "Wilhelmshaven" |
|
568
|
|
|
) |
|
569
|
|
|
df = replace_pipeline(df, "Sande", "Jemgum", "Westerstede") |
|
570
|
|
|
df = replace_pipeline(df, "Kalle", "Ochtrup", "Frensdorfer Bruchgraben") |
|
571
|
|
|
df = replace_pipeline( |
|
572
|
|
|
df, "Frensdorfer Bruchgraben", "Ochtrup", "Bad Bentheim" |
|
573
|
|
|
) |
|
574
|
|
|
df = replace_pipeline(df, "Bunde", "Wettringen", "Emsbüren") |
|
575
|
|
|
df = replace_pipeline(df, "Emsbüren", "Dorsten", "Ochtrup") |
|
576
|
|
|
df = replace_pipeline(df, "Ochtrup", "Dorsten", "Heek") |
|
577
|
|
|
df = replace_pipeline(df, "Lemförde", "Drohne", "Reiningen") |
|
578
|
|
|
df = replace_pipeline(df, "Edesbüttel", "Bobbau", "Uhrsleben") |
|
579
|
|
|
df = replace_pipeline(df, "Sixdorf", "Wiederitzsch", "Cörmigk") |
|
580
|
|
|
df = replace_pipeline(df, "Schkeuditz", "Plaußig", "Wiederitzsch") |
|
581
|
|
|
df = replace_pipeline(df, "Wiederitzsch", "Plaußig", "Mockau Nord") |
|
582
|
|
|
df = replace_pipeline(df, "Bobbau", "Rückersdorf", "Nempitz") |
|
583
|
|
|
df = replace_pipeline(df, "Räpitz", "Böhlen", "Kleindalzig") |
|
584
|
|
|
df = replace_pipeline(df, "Buchholz", "Friedersdorf", "Werben") |
|
585
|
|
|
df = replace_pipeline(df, "Radeland", "Uckermark", "Friedersdorf") |
|
586
|
|
|
df = replace_pipeline(df, "Friedersdorf", "Uckermark", "Herzfelde") |
|
587
|
|
|
df = replace_pipeline(df, "Blumberg", "Berlin-Mitte", "Berlin-Marzahn") |
|
588
|
|
|
df = replace_pipeline(df, "Radeland", "Zethau", "Coswig") |
|
589
|
|
|
df = replace_pipeline(df, "Leuna", "Böhlen", "Räpitz") |
|
590
|
|
|
df = replace_pipeline(df, "Dürrengleina", "Stadtroda", "Zöllnitz") |
|
591
|
|
|
df = replace_pipeline(df, "Mailing", "Kötz", "Wertingen") |
|
592
|
|
|
df = replace_pipeline(df, "Lampertheim", "Rüsselsheim", "Gernsheim-Nord") |
|
593
|
|
|
df = replace_pipeline(df, "Birlinghoven", "Rüsselsheim", "Wiesbaden") |
|
594
|
|
|
df = replace_pipeline(df, "Medelsheim", "Mittelbrunn", "Seyweiler") |
|
595
|
|
|
df = replace_pipeline(df, "Seyweiler", "Dillingen", "Fürstenhausen") |
|
596
|
|
|
df = replace_pipeline(df, "Reckrod", "Wolfsbehringen", "Eisenach") |
|
597
|
|
|
df = replace_pipeline(df, "Elten", "St. Hubert", "Hüthum") |
|
598
|
|
|
df = replace_pipeline(df, "St. Hubert", "Hüthum", "Uedener Bruch") |
|
599
|
|
|
df = replace_pipeline(df, "Wallach", "Möllen", "Spellen") |
|
600
|
|
|
df = replace_pipeline(df, "St. Hubert", "Glehn", "Krefeld") |
|
601
|
|
|
df = replace_pipeline(df, "Neumühl", "Werne", "Bottrop") |
|
602
|
|
|
df = replace_pipeline(df, "Bottrop", "Werne", "Recklinghausen") |
|
603
|
|
|
df = replace_pipeline(df, "Werne", "Eisenach", "Arnsberg-Bruchhausen") |
|
604
|
|
|
df = replace_pipeline(df, "Dorsten", "Gescher", "Gescher Süd") |
|
605
|
|
|
df = replace_pipeline(df, "Dorsten", "Hamborn", "Averbruch") |
|
606
|
|
|
df = replace_pipeline(df, "Neumühl", "Bruckhausen", "Hamborn") |
|
607
|
|
|
df = replace_pipeline(df, "Werne", "Paffrath", "Westhofen") |
|
608
|
|
|
df = replace_pipeline(df, "Glehn", "Voigtslach", "Dormagen") |
|
609
|
|
|
df = replace_pipeline(df, "Voigtslach", "Paffrath", "Leverkusen") |
|
610
|
|
|
df = replace_pipeline(df, "Glehn", "Ludwigshafen", "Wesseling") |
|
611
|
|
|
df = replace_pipeline(df, "Rothenstadt", "Rimpar", "Reutles") |
|
612
|
|
|
|
|
613
|
|
|
return df |
|
614
|
|
|
|
|
615
|
|
|
|
|
616
|
|
|
def connect_saltcavern_to_h2_grid(scn_name): |
|
617
|
|
|
""" |
|
618
|
|
|
Connect each saltcavern with nearest H2-Bus of the H2-Grid and insert the links into the database |
|
619
|
|
|
|
|
620
|
|
|
Returns |
|
621
|
|
|
------- |
|
622
|
|
|
None |
|
623
|
|
|
|
|
624
|
|
|
""" |
|
625
|
|
|
|
|
626
|
|
|
targets = config.datasets()["etrago_hydrogen"]["targets"] |
|
627
|
|
|
sources = config.datasets()["etrago_hydrogen"]["sources"] |
|
628
|
|
|
engine = db.engine() |
|
629
|
|
|
|
|
630
|
|
|
db.execute_sql( |
|
631
|
|
|
f""" |
|
632
|
|
|
DELETE FROM {targets["hydrogen_links"]["schema"]}.{targets["hydrogen_links"]["table"]} |
|
633
|
|
|
WHERE "carrier" in ('H2_saltcavern') |
|
634
|
|
|
AND scn_name = '{scn_name}'; |
|
635
|
|
|
""" |
|
636
|
|
|
) |
|
637
|
|
|
h2_buses_query = f"""SELECT bus_id, x, y,ST_Transform(geom, 32632) as geom |
|
638
|
|
|
FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]} |
|
639
|
|
|
WHERE carrier = 'H2_grid' AND scn_name = '{scn_name}' |
|
640
|
|
|
""" |
|
641
|
|
|
h2_buses = gpd.read_postgis(h2_buses_query, engine) |
|
642
|
|
|
|
|
643
|
|
|
salt_caverns_query = f"""SELECT bus_id, x, y, ST_Transform(geom, 32632) as geom |
|
644
|
|
|
FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]} |
|
645
|
|
|
WHERE carrier = 'H2_saltcavern' AND scn_name = '{scn_name}' |
|
646
|
|
|
""" |
|
647
|
|
|
salt_caverns = gpd.read_postgis(salt_caverns_query, engine) |
|
648
|
|
|
|
|
649
|
|
|
max_link_id = db.next_etrago_id("link") |
|
650
|
|
|
next_link_id = count(start=max_link_id, step=1) |
|
651
|
|
|
scn_params = get_sector_parameters("gas", scn_name) |
|
652
|
|
|
|
|
653
|
|
|
H2_coords = np.array([(point.x, point.y) for point in h2_buses.geometry]) |
|
654
|
|
|
H2_tree = cKDTree(H2_coords) |
|
655
|
|
|
links = [] |
|
656
|
|
|
for idx, bus_saltcavern in salt_caverns.iterrows(): |
|
657
|
|
|
saltcavern_coords = [ |
|
658
|
|
|
bus_saltcavern["geom"].x, |
|
659
|
|
|
bus_saltcavern["geom"].y, |
|
660
|
|
|
] |
|
661
|
|
|
|
|
662
|
|
|
dist, nearest_idx = H2_tree.query(saltcavern_coords, k=1) |
|
663
|
|
|
nearest_h2_bus = h2_buses.iloc[nearest_idx] |
|
664
|
|
|
|
|
665
|
|
|
link = { |
|
666
|
|
|
"scn_name": scn_name, |
|
667
|
|
|
"bus0": nearest_h2_bus["bus_id"], |
|
668
|
|
|
"bus1": bus_saltcavern["bus_id"], |
|
669
|
|
|
"link_id": next(next_link_id), |
|
670
|
|
|
"carrier": "H2_saltcavern", |
|
671
|
|
|
"lifetime": 25, |
|
672
|
|
|
"p_nom_extendable": True, |
|
673
|
|
|
"p_min_pu": -1, |
|
674
|
|
|
"capital_cost": scn_params["overnight_cost"]["H2_pipeline"] |
|
675
|
|
|
* dist |
|
676
|
|
|
/ 1000, |
|
677
|
|
|
"geom": MultiLineString( |
|
678
|
|
|
[ |
|
679
|
|
|
LineString( |
|
680
|
|
|
[ |
|
681
|
|
|
(nearest_h2_bus["x"], nearest_h2_bus["y"]), |
|
682
|
|
|
(bus_saltcavern["x"], bus_saltcavern["y"]), |
|
683
|
|
|
] |
|
684
|
|
|
) |
|
685
|
|
|
] |
|
686
|
|
|
), |
|
687
|
|
|
} |
|
688
|
|
|
links.append(link) |
|
689
|
|
|
|
|
690
|
|
|
links_df = gpd.GeoDataFrame(links, geometry="geom", crs=4326) |
|
691
|
|
|
|
|
692
|
|
|
links_df.to_postgis( |
|
693
|
|
|
targets["hydrogen_links"]["table"], |
|
694
|
|
|
engine, |
|
695
|
|
|
schema=targets["hydrogen_links"]["schema"], |
|
696
|
|
|
index=False, |
|
697
|
|
|
if_exists="append", |
|
698
|
|
|
dtype={"geom": Geometry()}, |
|
699
|
|
|
) |
|
700
|
|
|
|
|
701
|
|
|
|
|
702
|
|
|
def connect_h2_grid_to_neighbour_countries(scn_name): |
|
703
|
|
|
""" |
|
704
|
|
|
Connect germand H2_grid with neighbour countries. All german H2-Buses wich were planned as connection |
|
705
|
|
|
points for Import/Export of Hydrogen to corresponding neighbours country, are based on Publication |
|
706
|
|
|
of FNB-GAS (https://fnb-gas.de/wasserstoffnetz-wasserstoff-kernnetz/). |
|
707
|
|
|
|
|
708
|
|
|
Returns |
|
709
|
|
|
------- |
|
710
|
|
|
None |
|
711
|
|
|
|
|
712
|
|
|
""" |
|
713
|
|
|
engine = db.engine() |
|
714
|
|
|
targets = config.datasets()["etrago_hydrogen"]["targets"] |
|
715
|
|
|
sources = config.datasets()["etrago_hydrogen"]["sources"] |
|
716
|
|
|
|
|
717
|
|
|
h2_buses_df = gpd.read_postgis( |
|
718
|
|
|
f""" |
|
719
|
|
|
SELECT bus_id, x, y, geom |
|
720
|
|
|
FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]} |
|
721
|
|
|
WHERE carrier in ('H2_grid') |
|
722
|
|
|
AND scn_name = '{scn_name}' |
|
723
|
|
|
|
|
724
|
|
|
""", |
|
725
|
|
|
engine, |
|
726
|
|
|
) |
|
727
|
|
|
|
|
728
|
|
|
h2_links_df = pd.read_sql( |
|
729
|
|
|
f""" |
|
730
|
|
|
SELECT link_id, bus0, bus1, p_nom |
|
731
|
|
|
FROM {sources["links"]["schema"]}.{sources["links"]["table"]} |
|
732
|
|
|
WHERE carrier in ('H2_grid') |
|
733
|
|
|
AND scn_name = '{scn_name}' |
|
734
|
|
|
|
|
735
|
|
|
""", |
|
736
|
|
|
engine, |
|
737
|
|
|
) |
|
738
|
|
|
|
|
739
|
|
|
abroad_buses_df = gpd.read_postgis( |
|
740
|
|
|
f""" |
|
741
|
|
|
SELECT bus_id, x, y, geom, country |
|
742
|
|
|
FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]} |
|
743
|
|
|
WHERE carrier = 'H2' AND scn_name = '{scn_name}' AND country != 'DE' |
|
744
|
|
|
""", |
|
745
|
|
|
engine, |
|
746
|
|
|
) |
|
747
|
|
|
|
|
748
|
|
|
abroad_con_buses = [ |
|
749
|
|
|
("Greifenhagen", "PL"), |
|
750
|
|
|
("Fürstenberg (PL)", "PL"), |
|
751
|
|
|
("Eynatten", "BE"), |
|
752
|
|
|
("Überackern", "AT"), |
|
753
|
|
|
("Vlieghuis", "NL"), |
|
754
|
|
|
("Oude", "NL"), |
|
755
|
|
|
("Oude Statenzijl", "NL"), |
|
756
|
|
|
("Vreden", "NL"), |
|
757
|
|
|
("Elten", "NL"), |
|
758
|
|
|
("Leidingen", "FR"), |
|
759
|
|
|
("Carling", "FR"), |
|
760
|
|
|
("Medelsheim", "FR"), |
|
761
|
|
|
("Waidhaus", "CZ"), |
|
762
|
|
|
("Deutschneudorf", "CZ"), |
|
763
|
|
|
("Grenzach", "CH"), |
|
764
|
|
|
("AWZ", "DK"), |
|
765
|
|
|
("AWZ", "SE"), |
|
766
|
|
|
("AQD Offshore SEN 1", "GB"), |
|
767
|
|
|
("AQD Offshore SEN 1", "NO"), |
|
768
|
|
|
("AQD Offshore SEN 1", "DK"), |
|
769
|
|
|
("AQD Offshore SEN 1", "NL"), |
|
770
|
|
|
("Fessenheim", "FR"), |
|
771
|
|
|
("Ellund", "DK"), |
|
772
|
|
|
] |
|
773
|
|
|
|
|
774
|
|
|
h2_bus_location = pd.read_csv( |
|
775
|
|
|
Path(".") |
|
776
|
|
|
/ "data_bundle_egon_data" |
|
777
|
|
|
/ "hydrogen_network" |
|
778
|
|
|
/ "h2_grid_nodes.csv" |
|
779
|
|
|
) |
|
780
|
|
|
|
|
781
|
|
|
### prepare data for connecting abroad_buses |
|
782
|
|
|
matched_locations = h2_bus_location[ |
|
783
|
|
|
h2_bus_location["Ort"].isin([name for name, _ in abroad_con_buses]) |
|
784
|
|
|
] |
|
785
|
|
|
matched_buses = matched_locations.merge( |
|
786
|
|
|
h2_buses_df, left_on=["x", "y"], right_on=["x", "y"], how="inner" |
|
787
|
|
|
) |
|
788
|
|
|
|
|
789
|
|
|
final_matched_buses = matched_buses[ |
|
790
|
|
|
["bus_id", "Ort", "x", "y", "geom_y"] |
|
791
|
|
|
].rename(columns={"geom_y": "geom"}) |
|
792
|
|
|
|
|
793
|
|
|
abroad_links = h2_links_df[ |
|
794
|
|
|
(h2_links_df["bus0"].isin(final_matched_buses["bus_id"])) |
|
795
|
|
|
| (h2_links_df["bus1"].isin(final_matched_buses["bus_id"])) |
|
796
|
|
|
] |
|
797
|
|
|
abroad_links_bus0 = abroad_links.merge( |
|
798
|
|
|
final_matched_buses, left_on="bus0", right_on="bus_id", how="inner" |
|
799
|
|
|
) |
|
800
|
|
|
abroad_links_bus1 = abroad_links.merge( |
|
801
|
|
|
final_matched_buses, left_on="bus1", right_on="bus_id", how="inner" |
|
802
|
|
|
) |
|
803
|
|
|
abroad_con_df = pd.concat([abroad_links_bus1, abroad_links_bus0]) |
|
804
|
|
|
|
|
805
|
|
|
connection_links = [] |
|
806
|
|
|
max_link_id = db.next_etrago_id("link") |
|
807
|
|
|
next_max_link_id = count(start=max_link_id, step=1) |
|
808
|
|
|
|
|
809
|
|
|
for inland_name, country_code in abroad_con_buses: |
|
810
|
|
|
# filter out germand h2_buses for connecting neighbour-countries |
|
811
|
|
|
inland_bus = abroad_con_df[abroad_con_df["Ort"] == inland_name] |
|
812
|
|
|
if inland_bus.empty: |
|
813
|
|
|
print(f"Warning: No Inland-Bus found for {inland_name}.") |
|
814
|
|
|
continue |
|
815
|
|
|
|
|
816
|
|
|
# filter out corresponding abroad_bus for connecting neighbour countries |
|
817
|
|
|
abroad_bus = abroad_buses_df[ |
|
818
|
|
|
abroad_buses_df["country"] == country_code |
|
819
|
|
|
] |
|
820
|
|
|
if abroad_bus.empty: |
|
821
|
|
|
print(f"Warning: No Abroad-Bus found for {country_code}.") |
|
822
|
|
|
continue |
|
823
|
|
|
|
|
824
|
|
|
for _, i_bus in inland_bus.iterrows(): |
|
825
|
|
|
abroad_bus["distance"] = abroad_bus["geom"].apply( |
|
826
|
|
|
lambda g: i_bus["geom"].distance(g) |
|
|
|
|
|
|
827
|
|
|
) |
|
828
|
|
|
|
|
829
|
|
|
nearest_abroad_bus = abroad_bus.loc[ |
|
830
|
|
|
abroad_bus["distance"].idxmin() |
|
831
|
|
|
] |
|
832
|
|
|
relevant_buses = inland_bus[ |
|
833
|
|
|
inland_bus["bus_id"] == i_bus["bus_id"] |
|
834
|
|
|
] |
|
835
|
|
|
p_nom_value = relevant_buses["p_nom"].sum() |
|
836
|
|
|
|
|
837
|
|
|
connection_links.append( |
|
838
|
|
|
{ |
|
839
|
|
|
"scn_name": scn_name, |
|
840
|
|
|
"carrier": "H2_grid", |
|
841
|
|
|
"link_id": next(next_max_link_id), |
|
842
|
|
|
"bus0": i_bus["bus_id"], |
|
|
|
|
|
|
843
|
|
|
"bus1": nearest_abroad_bus["bus_id"], |
|
|
|
|
|
|
844
|
|
|
"p_nom": p_nom_value, |
|
|
|
|
|
|
845
|
|
|
"p_min_pu": -1, |
|
846
|
|
|
"geom": MultiLineString( |
|
847
|
|
|
[ |
|
848
|
|
|
LineString( |
|
849
|
|
|
[ |
|
850
|
|
|
(i_bus["geom"].x, i_bus["geom"].y), |
|
851
|
|
|
( |
|
852
|
|
|
nearest_abroad_bus["geom"].x, |
|
853
|
|
|
nearest_abroad_bus["geom"].y, |
|
854
|
|
|
), |
|
855
|
|
|
] |
|
856
|
|
|
) |
|
857
|
|
|
] |
|
858
|
|
|
), |
|
859
|
|
|
} |
|
860
|
|
|
) |
|
861
|
|
|
connection_links_df = gpd.GeoDataFrame( |
|
862
|
|
|
connection_links, geometry="geom", crs="EPSG:4326" |
|
863
|
|
|
) |
|
864
|
|
|
|
|
865
|
|
|
connection_links_df.to_postgis( |
|
866
|
|
|
name=targets["hydrogen_links"]["table"], |
|
867
|
|
|
con=engine, |
|
868
|
|
|
schema=targets["hydrogen_links"]["schema"], |
|
869
|
|
|
if_exists="append", |
|
870
|
|
|
index=False, |
|
871
|
|
|
) |
|
872
|
|
|
print("Neighbour countries are succesfully connected to H2-grid") |
|
873
|
|
|
|
openego /
eGon-data
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