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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 geoalchemy2.types import Geometry |
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from shapely.geometry import LineString, MultiLineString, Point |
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import geopandas as gpd |
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import pandas as pd |
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import os |
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from urllib.request import urlretrieve |
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from pathlib import Path |
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from fuzzywuzzy import process |
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from shapely import wkb |
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import math |
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import re |
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from itertools import count |
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import numpy as np |
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from scipy.spatial import cKDTree |
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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 annualize_capital_costs |
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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 = read_h2_excel_sheets() |
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h2_bus_location = pd.read_csv(Path(".")/"h2_grid_nodes.csv") |
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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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# 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(columns={'Planerische \nInbetriebnahme': 'Planerische Inbetriebnahme'}, inplace=True) |
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df.loc[df['Endpunkt\n(Ort)'] == 'AQD Anlandung', 'Endpunkt\n(Ort)'] = 'Schillig' |
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df.loc[df['Endpunkt\n(Ort)'] == 'Hallendorf', 'Endpunkt\n(Ort)'] = 'Salzgitter' |
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if df is H2_grid_Erweiterung: |
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df.rename(columns={'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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inplace=True) |
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df = df[df['Berücksichtigung im Kernnetz \n[ja/nein/zurückgezogen]'].str.strip().str.lower() == 'ja'] |
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df.loc[df['Endpunkt\n(Ort)'] == 'Osdorfer Straße', 'Endpunkt\n(Ort)'] = '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)'] = df['Anfangspunkt\n(Ort)'].astype(str).str.strip() |
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df['Endpunkt\n(Ort)'] = df['Endpunkt\n(Ort)'].astype(str).str.strip() |
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df = df[['Anfangspunkt\n(Ort)', 'Endpunkt\n(Ort)', 'Nenndurchmesser \n(DN)', 'Druckstufe (DP)\n[mind. 30 barg]', |
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'Investitionskosten*\n(Mio. Euro)', 'Planerische Inbetriebnahme', 'Länge \n(km)']] |
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# matching start- and endpoint of each pipeline with georeferenced data |
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df['Anfangspunkt_matched'] = fuzzy_match(df, h2_bus_location, 'Anfangspunkt\n(Ort)') |
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df['Endpunkt_matched'] = fuzzy_match(df, h2_bus_location, 'Endpunkt\n(Ort)') |
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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(df, h2_bus_location[['Ort', 'geom', 'x', 'y']], |
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how='left', left_on='Anfangspunkt_matched', right_on='Ort').rename( |
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columns={'geom': 'geom_start', 'x': 'x_start', 'y': 'y_start'}) |
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df_merged = pd.merge(df_merged, h2_bus_location[['Ort', 'geom', 'x', 'y']], |
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how='left', left_on='Endpunkt_matched', right_on='Ort').rename( |
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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[H2_grid_df['geom_start'] != H2_grid_df['geom_end']] |
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H2_grid_df = pd.merge(H2_grid_df, h2_buses_df, how='left', left_on=['x_start', 'y_start'], right_on=['x','y']).rename( |
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columns={'bus_id': 'bus0'}) |
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H2_grid_df = pd.merge(H2_grid_df, h2_buses_df, how='left', left_on=['x_end', 'y_end'], right_on=['x','y']).rename( |
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columns={'bus_id': 'bus1'}) |
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H2_grid_df[['bus0', 'bus1']] = H2_grid_df[['bus0', 'bus1']].astype('Int64') |
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H2_grid_df['geom_start'] = H2_grid_df['geom_start'].apply(lambda x: wkb.loads(bytes.fromhex(x))) |
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H2_grid_df['geom_end'] = H2_grid_df['geom_end'].apply(lambda x: wkb.loads(bytes.fromhex(x))) |
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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']]), axis=1) |
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H2_grid_df['geom'] = H2_grid_df.apply( |
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lambda row: MultiLineString([LineString([row['geom_start'], row['geom_end']])]), axis=1) |
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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(next_link_id, next_link_id + len(H2_grid_gdf)) |
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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'] = H2_grid_gdf['Planerische Inbetriebnahme'].astype(str).apply( |
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lambda x: int(re.findall(r'\d{4}', x)[-1]) if re.findall(r'\d{4}', x) else |
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int(re.findall(r'\d{2}\.\d{2}\.(\d{4})', x)[-1]) if re.findall(r'\d{2}\.\d{2}\.(\d{4})', x) else None) |
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H2_grid_gdf['build_year'] = H2_grid_gdf['Planerische Inbetriebnahme'].astype('Int64') |
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H2_grid_gdf['p_nom'] = H2_grid_gdf.apply(lambda row:calculate_H2_capacity(row['Druckstufe (DP)\n[mind. 30 barg]'], row['Nenndurchmesser \n(DN)']), axis=1 ) |
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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(lambda row: annualize_capital_costs( |
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(float(row["Investitionskosten*\n(Mio. Euro)"]) * 10**6 / row['p_nom']) |
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if pd.notna(row["Investitionskosten*\n(Mio. Euro)"]) |
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and str(row["Investitionskosten*\n(Mio. Euro)"]).replace(",", "").replace(".", "").isdigit() |
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and float(row["Investitionskosten*\n(Mio. Euro)"]) != 0 |
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else scn_params["overnight_cost"]["H2_pipeline"] * row['Länge \n(km)'], |
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row['lifetime'], 0.05), axis=1) |
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H2_grid_gdf['p_min_pu'] = -1 |
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selected_columns = [ |
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'scn_name', 'link_id', 'bus0', 'bus1', 'build_year', 'p_nom', 'p_nom_min', |
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'p_nom_extendable', 'capital_cost', 'geom', 'topo', 'carrier', 'p_nom_max', 'p_min_pu', |
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] |
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H2_grid_final=H2_grid_gdf[selected_columns] |
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# Insert data to db |
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H2_grid_final.to_postgis( |
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target["table"], |
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con, |
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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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#connect saltcaverns to H2_grid |
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connect_saltcavern_to_h2_grid(scn_name) |
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#connect neighbour countries to H2_grid |
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connect_h2_grid_to_neighbour_countries(scn_name) |
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def replace_pipeline(df, start, end, intermediate): |
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""" |
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Method for adjusting pipelines manually by splittiing pipeline with an intermediate point. |
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Parameters |
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---------- |
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df : pandas.core.frame.DataFrame |
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dataframe to be adjusted |
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start: str |
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startpoint of pipeline |
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end: str |
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endpoint of pipeline |
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intermediate: str |
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new intermediate point for splitting given pipeline |
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Returns |
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--------- |
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df : <class 'pandas.core.frame.DataFrame'> |
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adjusted dataframe |
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""" |
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# Find rows where the start and end points match |
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mask = ((df['Anfangspunkt_matched'] == start) & (df['Endpunkt_matched'] == end)) | \ |
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((df['Anfangspunkt_matched'] == end) & (df['Endpunkt_matched'] == start)) |
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# Separate the rows to replace |
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if mask.any(): |
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df_replacement = df[~mask].copy() |
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row_replaced = df[mask].iloc[0] |
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# Add new rows for the split pipelines |
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new_rows = pd.DataFrame({ |
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'Anfangspunkt_matched': [start, intermediate], |
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'Endpunkt_matched': [intermediate, end], |
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'Nenndurchmesser \n(DN)': [row_replaced['Nenndurchmesser \n(DN)'], row_replaced['Nenndurchmesser \n(DN)']], |
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'Druckstufe (DP)\n[mind. 30 barg]': [row_replaced['Druckstufe (DP)\n[mind. 30 barg]'], row_replaced['Druckstufe (DP)\n[mind. 30 barg]']], |
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'Investitionskosten*\n(Mio. Euro)': [row_replaced['Investitionskosten*\n(Mio. Euro)'], row_replaced['Investitionskosten*\n(Mio. Euro)']], |
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'Planerische Inbetriebnahme': [row_replaced['Planerische Inbetriebnahme'],row_replaced['Planerische Inbetriebnahme']], |
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'Länge \n(km)': [row_replaced['Länge \n(km)'], row_replaced['Länge \n(km)']] |
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}) |
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df_replacement = pd.concat([df_replacement, new_rows], ignore_index=True) |
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return df_replacement |
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else: |
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return df |
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def fuzzy_match(df1, df2, column_to_match, threshold=80): |
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''' |
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Method for matching input data of H2_grid with georeferenced data (even if the strings are not exact the same) |
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Parameters |
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---------- |
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df1 : pandas.core.frame.DataFrame |
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Input dataframe |
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df2 : pandas.core.frame.DataFrame |
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georeferenced dataframe with h2_buses |
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column_to_match: str |
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matching column |
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treshhold: float |
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matching percentage for succesfull comparison |
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Returns |
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--------- |
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matched : list |
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list with all matched location names |
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''' |
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options = df2['Ort'].unique() |
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matched = [] |
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# Compare every locationname in df1 with locationnames in df2 |
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for value in df1[column_to_match]: |
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match, score = process.extractOne(value, options) |
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if score >= threshold: |
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matched.append(match) |
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else: |
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matched.append(None) |
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return matched |
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242
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243
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def calculate_H2_capacity(pressure, diameter): |
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''' |
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245
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Method for calculagting capacity of pipelines based on data input from FNB Gas |
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246
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247
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Parameters |
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248
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---------- |
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249
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pressure : float |
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250
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input for pressure of pipeline |
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diameter: float |
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input for diameter of pipeline |
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column_to_match: str |
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254
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matching column |
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255
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treshhold: float |
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256
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matching percentage for succesfull comparison |
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257
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258
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Returns |
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--------- |
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energy_flow: float |
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transmission capacity of pipeline |
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262
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|
263
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''' |
|
264
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265
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pressure = str(pressure).replace(",", ".") |
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266
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diameter =str(diameter) |
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268
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def convert_to_float(value): |
|
269
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try: |
|
270
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return float(value) |
|
271
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except ValueError: |
|
272
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return 400 #average value from data-source cause capacities of some lines are not fixed yet |
|
273
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274
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# in case of given range for pipeline-capacity calculate average value |
|
275
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if '-' in diameter: |
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276
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diameters = diameter.split('-') |
|
277
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diameter = (convert_to_float(diameters[0]) + convert_to_float(diameters[1])) / 2 |
|
278
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elif '/' in diameter: |
|
279
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diameters = diameter.split('/') |
|
280
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diameter = (convert_to_float(diameters[0]) + convert_to_float(diameters[1])) / 2 |
|
281
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else: |
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282
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try: |
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283
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diameter = float(diameter) |
|
284
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except ValueError: |
|
285
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diameter = 400 #average value from data-source |
|
286
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|
287
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if '-' in pressure: |
|
288
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pressures = pressure.split('-') |
|
289
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pressure = (float(pressures[0]) + float(pressures[1])) / 2 |
|
290
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elif '/' in pressure: |
|
291
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|
|
pressures = pressure.split('/') |
|
292
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pressure = (float(pressures[0]) + float(pressures[1])) / 2 |
|
293
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else: |
|
294
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try: |
|
295
|
|
|
pressure = float(diameter) |
|
296
|
|
|
except ValueError: |
|
297
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|
pressure = 70 #averaqge value from data-source |
|
298
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|
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|
299
|
|
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|
300
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|
|
velocity = 40 #source: L.Koops (2023): GAS PIPELINE VERSUS LIQUID HYDROGEN TRANSPORT – PERSPECTIVES FOR TECHNOLOGIES, ENERGY DEMAND ANDv TRANSPORT CAPACITY, AND IMPLICATIONS FOR AVIATION |
|
301
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|
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temperature = 10+273.15 #source: L.Koops (2023): GAS PIPELINE VERSUS LIQUID HYDROGEN TRANSPORT – PERSPECTIVES FOR TECHNOLOGIES, ENERGY DEMAND ANDv TRANSPORT CAPACITY, AND IMPLICATIONS FOR AVIATION |
|
302
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|
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density = pressure*10**5/(4.1243*10**3*temperature) #gasconstant H2 = 4.1243 [kJ/kgK] |
|
303
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|
|
mass_flow = density*math.pi*((diameter/10**3)/2)**2*velocity |
|
304
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energy_flow = mass_flow * 119.988 #low_heating_value H2 = 119.988 [MJ/kg] |
|
305
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|
306
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|
|
return energy_flow |
|
307
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|
308
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|
309
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|
def download_h2_grid_data(): |
|
310
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|
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""" |
|
311
|
|
|
Download Input data for H2_grid from FNB-Gas (https://fnb-gas.de/wasserstoffnetz-wasserstoff-kernnetz/) |
|
312
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|
|
|
|
313
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|
|
The following data for H2 are downloaded into the folder |
|
314
|
|
|
./datasets/h2_data: |
|
315
|
|
|
* Links (file Anlage_3_Wasserstoffkernnetz_Neubau.xlsx, |
|
316
|
|
|
Anlage_4_Wasserstoffkernnetz_Umstellung.xlsx, |
|
317
|
|
|
Anlage_2_Wasserstoffkernetz_weitere_Leitungen.xlsx) |
|
318
|
|
|
|
|
319
|
|
|
Returns |
|
320
|
|
|
------- |
|
321
|
|
|
None |
|
322
|
|
|
|
|
323
|
|
|
""" |
|
324
|
|
|
path = Path("datasets/h2_data") |
|
325
|
|
|
os.makedirs(path, exist_ok=True) |
|
326
|
|
|
|
|
327
|
|
|
download_config = config.datasets()["etrago_hydrogen"]["sources"]["H2_grid"] |
|
328
|
|
|
target_file_Um = path / download_config["converted_ch4_pipes"]["path"] |
|
329
|
|
|
target_file_Neu = path / download_config["new_constructed_pipes"]["path"] |
|
330
|
|
|
target_file_Erw = path / download_config["pipes_of_further_h2_grid_operators"]["path"] |
|
331
|
|
|
|
|
332
|
|
|
for target_file in [target_file_Neu, target_file_Um, target_file_Erw]: |
|
333
|
|
|
if target_file is target_file_Um: |
|
334
|
|
|
url = download_config["converted_ch4_pipes"]["url"] |
|
335
|
|
|
elif target_file is target_file_Neu: |
|
336
|
|
|
url = download_config["new_constructed_pipes"]['url'] |
|
337
|
|
|
else: |
|
338
|
|
|
url = download_config["pipes_of_further_h2_grid_operators"]['url'] |
|
339
|
|
|
|
|
340
|
|
|
if not os.path.isfile(target_file): |
|
341
|
|
|
urlretrieve(url, target_file) |
|
342
|
|
|
|
|
343
|
|
|
|
|
344
|
|
|
def read_h2_excel_sheets(): |
|
345
|
|
|
""" |
|
346
|
|
|
Read downloaded excel files with location names for future h2-pipelines |
|
347
|
|
|
|
|
348
|
|
|
Returns |
|
349
|
|
|
------- |
|
350
|
|
|
df_Neu : <class 'pandas.core.frame.DataFrame'> |
|
351
|
|
|
df_Um : <class 'pandas.core.frame.DataFrame'> |
|
352
|
|
|
df_Erw : <class 'pandas.core.frame.DataFrame'> |
|
353
|
|
|
|
|
354
|
|
|
|
|
355
|
|
|
""" |
|
356
|
|
|
|
|
357
|
|
|
path = Path(".") / "datasets" / "h2_data" |
|
358
|
|
|
download_config = config.datasets()["etrago_hydrogen"]["sources"]["H2_grid"] |
|
359
|
|
|
excel_file_Um = pd.ExcelFile(f'{path}/{download_config["converted_ch4_pipes"]["path"]}') |
|
360
|
|
|
excel_file_Neu = pd.ExcelFile(f'{path}/{download_config["new_constructed_pipes"]["path"]}') |
|
361
|
|
|
excel_file_Erw = pd.ExcelFile(f'{path}/{download_config["pipes_of_further_h2_grid_operators"]["path"]}') |
|
362
|
|
|
|
|
363
|
|
|
df_Um= pd.read_excel(excel_file_Um, header=3) |
|
364
|
|
|
df_Neu = pd.read_excel(excel_file_Neu, header=3) |
|
365
|
|
|
df_Erw = pd.read_excel(excel_file_Erw, header=2) |
|
366
|
|
|
|
|
367
|
|
|
return df_Neu, df_Um, df_Erw |
|
368
|
|
|
|
|
369
|
|
|
def fix_h2_grid_infrastructure(df): |
|
370
|
|
|
""" |
|
371
|
|
|
Manuell adjustments for more accurate grid topology based on Detailmaßnahmenkarte der |
|
372
|
|
|
FNB-Gas [https://fnb-gas.de/wasserstoffnetz-wasserstoff-kernnetz/] |
|
373
|
|
|
|
|
374
|
|
|
Returns |
|
375
|
|
|
------- |
|
376
|
|
|
df : <class 'pandas.core.frame.DataFrame'> |
|
377
|
|
|
|
|
378
|
|
|
""" |
|
379
|
|
|
|
|
380
|
|
|
df = replace_pipeline(df, 'Lubmin', 'Uckermark', 'Wrangelsburg') |
|
381
|
|
|
df = replace_pipeline(df, 'Wrangelsburg', 'Uckermark', 'Schönermark') |
|
382
|
|
|
df = replace_pipeline(df, 'Hemmingstedt', 'Ascheberg (Holstein)', 'Remmels Nord') |
|
383
|
|
|
df = replace_pipeline(df, 'Heidenau', 'Elbe-Süd', 'Weißenfelde') |
|
384
|
|
|
df = replace_pipeline(df, 'Weißenfelde', 'Elbe-Süd', 'Stade') |
|
385
|
|
|
df = replace_pipeline(df, 'Stade AOS', 'KW Schilling', 'Abzweig Stade') |
|
386
|
|
|
df = replace_pipeline(df, 'Rosengarten (Sottorf)', 'Moorburg', 'Leversen') |
|
387
|
|
|
df = replace_pipeline(df, 'Leversen', 'Moorburg', 'Hamburg Süd') |
|
388
|
|
|
df = replace_pipeline(df, 'Achim', 'Folmhusen', 'Wardenburg') |
|
389
|
|
|
df = replace_pipeline(df, 'Achim', 'Wardenburg', 'Sandkrug') |
|
390
|
|
|
df = replace_pipeline(df, 'Dykhausen', 'Bunde', 'Emden') |
|
391
|
|
|
df = replace_pipeline(df, 'Emden', 'Nüttermoor', 'Jemgum') |
|
392
|
|
|
df = replace_pipeline(df, 'Rostock', 'Glasewitz', 'Fliegerhorst Laage') |
|
393
|
|
|
df = replace_pipeline(df, 'Wilhelmshaven', 'Dykhausen', 'Sande') |
|
394
|
|
|
df = replace_pipeline(df, 'Wilhelmshaven Süd', 'Wilhelmshaven Nord', 'Wilhelmshaven') |
|
395
|
|
|
df = replace_pipeline(df, 'Sande', 'Jemgum', 'Westerstede') |
|
396
|
|
|
df = replace_pipeline(df, 'Kalle', 'Ochtrup', 'Frensdorfer Bruchgraben') |
|
397
|
|
|
df = replace_pipeline(df, 'Frensdorfer Bruchgraben', 'Ochtrup', 'Bad Bentheim') |
|
398
|
|
|
df = replace_pipeline(df, 'Bunde', 'Wettringen', 'Emsbüren') |
|
399
|
|
|
df = replace_pipeline(df, 'Emsbüren', 'Dorsten', 'Ochtrup') |
|
400
|
|
|
df = replace_pipeline(df, 'Ochtrup', 'Dorsten', 'Heek') |
|
401
|
|
|
df = replace_pipeline(df, 'Lemförde', 'Drohne', 'Reiningen') |
|
402
|
|
|
df = replace_pipeline(df, 'Edesbüttel', 'Bobbau', 'Uhrsleben') |
|
403
|
|
|
df = replace_pipeline(df, 'Sixdorf', 'Wiederitzsch', 'Cörmigk') |
|
404
|
|
|
df = replace_pipeline(df, 'Schkeuditz', 'Plaußig', 'Wiederitzsch') |
|
405
|
|
|
df = replace_pipeline(df, 'Wiederitzsch', 'Plaußig', 'Mockau Nord') |
|
406
|
|
|
df = replace_pipeline(df, 'Bobbau', 'Rückersdorf', 'Nempitz') |
|
407
|
|
|
df = replace_pipeline(df, 'Räpitz', 'Böhlen', 'Kleindalzig') |
|
408
|
|
|
df = replace_pipeline(df, 'Buchholz', 'Friedersdorf', 'Werben') |
|
409
|
|
|
df = replace_pipeline(df, 'Radeland', 'Uckermark', 'Friedersdorf') |
|
410
|
|
|
df = replace_pipeline(df, 'Friedersdorf', 'Uckermark', 'Herzfelde') |
|
411
|
|
|
df = replace_pipeline(df, 'Blumberg', 'Berlin-Mitte', 'Berlin-Marzahn') |
|
412
|
|
|
df = replace_pipeline(df, 'Radeland', 'Zethau', 'Coswig') |
|
413
|
|
|
df = replace_pipeline(df, 'Leuna', 'Böhlen', 'Räpitz') |
|
414
|
|
|
df = replace_pipeline(df, 'Dürrengleina', 'Stadtroda', 'Zöllnitz') |
|
415
|
|
|
df = replace_pipeline(df, 'Mailing', 'Kötz', 'Wertingen') |
|
416
|
|
|
df = replace_pipeline(df, 'Lampertheim', 'Rüsselsheim', 'Gernsheim-Nord') |
|
417
|
|
|
df = replace_pipeline(df, 'Birlinghoven', 'Rüsselsheim', 'Wiesbaden') |
|
418
|
|
|
df = replace_pipeline(df, 'Medelsheim', 'Mittelbrunn', 'Seyweiler') |
|
419
|
|
|
df = replace_pipeline(df, 'Seyweiler', 'Dillingen', 'Fürstenhausen') |
|
420
|
|
|
df = replace_pipeline(df, 'Reckrod', 'Wolfsbehringen', 'Eisenach') |
|
421
|
|
|
df = replace_pipeline(df, 'Elten', 'St. Hubert', 'Hüthum') |
|
422
|
|
|
df = replace_pipeline(df, 'St. Hubert', 'Hüthum', 'Uedener Bruch') |
|
423
|
|
|
df = replace_pipeline(df, 'Wallach', 'Möllen', 'Spellen') |
|
424
|
|
|
df = replace_pipeline(df, 'St. Hubert', 'Glehn', 'Krefeld') |
|
425
|
|
|
df = replace_pipeline(df, 'Neumühl', 'Werne', 'Bottrop') |
|
426
|
|
|
df = replace_pipeline(df, 'Bottrop', 'Werne', 'Recklinghausen') |
|
427
|
|
|
df = replace_pipeline(df, 'Werne', 'Eisenach', 'Arnsberg-Bruchhausen') |
|
428
|
|
|
df = replace_pipeline(df, 'Dorsten', 'Gescher', 'Gescher Süd') |
|
429
|
|
|
df = replace_pipeline(df, 'Dorsten', 'Hamborn', 'Averbruch') |
|
430
|
|
|
df = replace_pipeline(df, 'Neumühl', 'Bruckhausen', 'Hamborn') |
|
431
|
|
|
df = replace_pipeline(df, 'Werne', 'Paffrath', 'Westhofen') |
|
432
|
|
|
df = replace_pipeline(df, 'Glehn', 'Voigtslach', 'Dormagen') |
|
433
|
|
|
df = replace_pipeline(df, 'Voigtslach', 'Paffrath','Leverkusen') |
|
434
|
|
|
df = replace_pipeline(df, 'Glehn', 'Ludwigshafen','Wesseling') |
|
435
|
|
|
df = replace_pipeline(df, 'Rothenstadt', 'Rimpar','Reutles') |
|
436
|
|
|
|
|
437
|
|
|
return df |
|
438
|
|
|
|
|
439
|
|
|
def connect_saltcavern_to_h2_grid(scn_name): |
|
440
|
|
|
""" |
|
441
|
|
|
Connect each saltcavern with nearest H2-Bus of the H2-Grid and insert the links into the database |
|
442
|
|
|
|
|
443
|
|
|
Returns |
|
444
|
|
|
------- |
|
445
|
|
|
None |
|
446
|
|
|
|
|
447
|
|
|
""" |
|
448
|
|
|
|
|
449
|
|
|
targets = config.datasets()["etrago_hydrogen"]["targets"] |
|
450
|
|
|
sources = config.datasets()["etrago_hydrogen"]["sources"] |
|
451
|
|
|
engine = db.engine() |
|
452
|
|
|
|
|
453
|
|
|
db.execute_sql(f""" |
|
454
|
|
|
DELETE FROM {targets["hydrogen_links"]["schema"]}.{targets["hydrogen_links"]["table"]} |
|
455
|
|
|
WHERE "carrier" in ('H2_saltcavern') |
|
456
|
|
|
AND scn_name = '{scn_name}'; |
|
457
|
|
|
""") |
|
458
|
|
|
h2_buses_query = f"""SELECT bus_id, x, y,ST_Transform(geom, 32632) as geom |
|
459
|
|
|
FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]} |
|
460
|
|
|
WHERE carrier = 'H2_grid' AND scn_name = '{scn_name}' |
|
461
|
|
|
""" |
|
462
|
|
|
h2_buses = gpd.read_postgis(h2_buses_query, engine) |
|
463
|
|
|
|
|
464
|
|
|
salt_caverns_query = f"""SELECT bus_id, x, y, ST_Transform(geom, 32632) as geom |
|
465
|
|
|
FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]} |
|
466
|
|
|
WHERE carrier = 'H2_saltcavern' AND scn_name = '{scn_name}' |
|
467
|
|
|
""" |
|
468
|
|
|
salt_caverns = gpd.read_postgis(salt_caverns_query, engine) |
|
469
|
|
|
|
|
470
|
|
|
max_link_id = db.next_etrago_id('link') |
|
471
|
|
|
next_link_id = count(start=max_link_id, step=1) |
|
472
|
|
|
scn_params = get_sector_parameters("gas", scn_name) |
|
473
|
|
|
|
|
474
|
|
|
H2_coords = np.array([(point.x, point.y) for point in h2_buses.geometry]) |
|
475
|
|
|
H2_tree = cKDTree(H2_coords) |
|
476
|
|
|
links=[] |
|
477
|
|
|
for idx, bus_saltcavern in salt_caverns.iterrows(): |
|
478
|
|
|
saltcavern_coords = [bus_saltcavern['geom'].x, bus_saltcavern['geom'].y] |
|
479
|
|
|
|
|
480
|
|
|
dist, nearest_idx = H2_tree.query(saltcavern_coords, k=1) |
|
481
|
|
|
nearest_h2_bus = h2_buses.iloc[nearest_idx] |
|
482
|
|
|
|
|
483
|
|
|
link = { |
|
484
|
|
|
'scn_name': scn_name, |
|
485
|
|
|
'bus0': nearest_h2_bus['bus_id'], |
|
486
|
|
|
'bus1': bus_saltcavern['bus_id'], |
|
487
|
|
|
'link_id': next(next_link_id), |
|
488
|
|
|
'carrier': 'H2_saltcavern', |
|
489
|
|
|
'lifetime':25, |
|
490
|
|
|
'p_nom_extendable': True, |
|
491
|
|
|
'p_min_pu': -1, |
|
492
|
|
|
'capital_cost': scn_params["overnight_cost"]["H2_pipeline"]*dist/1000, |
|
493
|
|
|
'geom': MultiLineString([LineString([(nearest_h2_bus['x'], nearest_h2_bus['y']), (bus_saltcavern['x'], bus_saltcavern['y'])])]) |
|
494
|
|
|
} |
|
495
|
|
|
links.append(link) |
|
496
|
|
|
|
|
497
|
|
|
links_df = gpd.GeoDataFrame(links, geometry='geom', crs=4326) |
|
498
|
|
|
|
|
499
|
|
|
links_df.to_postgis( |
|
500
|
|
|
targets["hydrogen_links"]["table"], |
|
501
|
|
|
engine, |
|
502
|
|
|
schema=targets["hydrogen_links"]["schema"], |
|
503
|
|
|
index=False, |
|
504
|
|
|
if_exists="append", |
|
505
|
|
|
dtype={"geom": Geometry()}, |
|
506
|
|
|
) |
|
507
|
|
|
|
|
508
|
|
|
|
|
509
|
|
|
def connect_h2_grid_to_neighbour_countries(scn_name): |
|
510
|
|
|
""" |
|
511
|
|
|
Connect germand H2_grid with neighbour countries. All german H2-Buses wich were planned as connection |
|
512
|
|
|
points for Import/Export of Hydrogen to corresponding neighbours country, are based on Publication |
|
513
|
|
|
of FNB-GAS (https://fnb-gas.de/wasserstoffnetz-wasserstoff-kernnetz/). |
|
514
|
|
|
|
|
515
|
|
|
Returns |
|
516
|
|
|
------- |
|
517
|
|
|
None |
|
518
|
|
|
|
|
519
|
|
|
""" |
|
520
|
|
|
engine = db.engine() |
|
521
|
|
|
targets = config.datasets()["etrago_hydrogen"]["targets"] |
|
522
|
|
|
sources = config.datasets()["etrago_hydrogen"]["sources"] |
|
523
|
|
|
|
|
524
|
|
|
h2_buses_df = gpd.read_postgis( |
|
525
|
|
|
f""" |
|
526
|
|
|
SELECT bus_id, x, y, geom |
|
527
|
|
|
FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]} |
|
528
|
|
|
WHERE carrier in ('H2_grid') |
|
529
|
|
|
AND scn_name = '{scn_name}' |
|
530
|
|
|
|
|
531
|
|
|
""" |
|
532
|
|
|
, engine) |
|
533
|
|
|
|
|
534
|
|
|
h2_links_df = pd.read_sql( |
|
535
|
|
|
f""" |
|
536
|
|
|
SELECT link_id, bus0, bus1, p_nom |
|
537
|
|
|
FROM {sources["links"]["schema"]}.{sources["links"]["table"]} |
|
538
|
|
|
WHERE carrier in ('H2_grid') |
|
539
|
|
|
AND scn_name = '{scn_name}' |
|
540
|
|
|
|
|
541
|
|
|
""" |
|
542
|
|
|
, engine) |
|
543
|
|
|
|
|
544
|
|
|
abroad_buses_df = gpd.read_postgis( |
|
545
|
|
|
f""" |
|
546
|
|
|
SELECT bus_id, x, y, geom, country |
|
547
|
|
|
FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]} |
|
548
|
|
|
WHERE carrier = 'H2' AND scn_name = '{scn_name}' AND country != 'DE' |
|
549
|
|
|
""", |
|
550
|
|
|
engine |
|
551
|
|
|
) |
|
552
|
|
|
|
|
553
|
|
|
abroad_con_buses = [ |
|
554
|
|
|
('Greifenhagen', 'PL'), |
|
555
|
|
|
('Fürstenberg (PL)', 'PL'), |
|
556
|
|
|
('Eynatten', 'BE'), |
|
557
|
|
|
('Überackern', 'AT'), |
|
558
|
|
|
('Vlieghuis', 'NL'), |
|
559
|
|
|
('Oude', 'NL'), |
|
560
|
|
|
('Oude Statenzijl', 'NL'), |
|
561
|
|
|
('Vreden', 'NL'), |
|
562
|
|
|
('Elten', 'NL'), |
|
563
|
|
|
('Leidingen', 'FR'), |
|
564
|
|
|
('Carling', 'FR'), |
|
565
|
|
|
('Medelsheim', 'FR'), |
|
566
|
|
|
('Waidhaus', 'CZ'), |
|
567
|
|
|
('Deutschneudorf', 'CZ'), |
|
568
|
|
|
('Grenzach', 'CH'), |
|
569
|
|
|
('AWZ', 'DK'), |
|
570
|
|
|
('AWZ', 'SE'), |
|
571
|
|
|
('AQD Offshore SEN 1', 'GB'), |
|
572
|
|
|
('AQD Offshore SEN 1', 'NO'), |
|
573
|
|
|
('AQD Offshore SEN 1', 'DK'), |
|
574
|
|
|
('AQD Offshore SEN 1', 'NL'), |
|
575
|
|
|
('Fessenheim', 'FR'), |
|
576
|
|
|
('Ellund', 'DK') |
|
577
|
|
|
] |
|
578
|
|
|
|
|
579
|
|
|
h2_bus_location = pd.read_csv(Path(".")/"h2_grid_nodes.csv") |
|
580
|
|
|
|
|
581
|
|
|
### prepare data for connecting abroad_buses |
|
582
|
|
|
matched_locations = h2_bus_location[h2_bus_location['Ort'].isin([name for name, _ in abroad_con_buses])] |
|
583
|
|
|
matched_buses = matched_locations.merge( |
|
584
|
|
|
h2_buses_df, |
|
585
|
|
|
left_on=['x', 'y'], |
|
586
|
|
|
right_on=['x', 'y'], |
|
587
|
|
|
how='inner' |
|
588
|
|
|
) |
|
589
|
|
|
|
|
590
|
|
|
final_matched_buses = matched_buses[['bus_id', 'Ort', 'x', 'y', 'geom_y']].rename(columns={'geom_y': 'geom'}) |
|
591
|
|
|
|
|
592
|
|
|
abroad_links = h2_links_df[(h2_links_df['bus0'].isin(final_matched_buses['bus_id'])) | (h2_links_df['bus1'].isin(final_matched_buses['bus_id']))] |
|
593
|
|
|
abroad_links_bus0 = abroad_links.merge(final_matched_buses, left_on= 'bus0', right_on= 'bus_id', how='inner') |
|
594
|
|
|
abroad_links_bus1 = abroad_links.merge(final_matched_buses, left_on= 'bus1', right_on= 'bus_id', how='inner') |
|
595
|
|
|
abroad_con_df = pd.concat([abroad_links_bus1, abroad_links_bus0]) |
|
596
|
|
|
|
|
597
|
|
|
|
|
598
|
|
|
connection_links = [] |
|
599
|
|
|
max_link_id = db.next_etrago_id('link') |
|
600
|
|
|
next_max_link_id = count(start=max_link_id, step=1) |
|
601
|
|
|
|
|
602
|
|
|
for inland_name, country_code in abroad_con_buses: |
|
603
|
|
|
# filter out germand h2_buses for connecting neighbour-countries |
|
604
|
|
|
inland_bus = abroad_con_df[abroad_con_df['Ort'] == inland_name] |
|
605
|
|
|
if inland_bus.empty: |
|
606
|
|
|
print(f"Warning: No Inland-Bus found for {inland_name}.") |
|
607
|
|
|
continue |
|
608
|
|
|
|
|
609
|
|
|
# filter out corresponding abroad_bus for connecting neighbour countries |
|
610
|
|
|
abroad_bus = abroad_buses_df[abroad_buses_df['country'] == country_code] |
|
611
|
|
|
if abroad_bus.empty: |
|
612
|
|
|
print(f"Warning: No Abroad-Bus found for {country_code}.") |
|
613
|
|
|
continue |
|
614
|
|
|
|
|
615
|
|
|
for _, i_bus in inland_bus.iterrows(): |
|
616
|
|
|
abroad_bus['distance'] = abroad_bus['geom'].apply( |
|
617
|
|
|
lambda g: i_bus['geom'].distance(g) |
|
|
|
|
|
|
618
|
|
|
) |
|
619
|
|
|
|
|
620
|
|
|
nearest_abroad_bus = abroad_bus.loc[abroad_bus['distance'].idxmin()] |
|
621
|
|
|
relevant_buses = inland_bus[inland_bus['bus_id'] == i_bus['bus_id']] |
|
622
|
|
|
p_nom_value = relevant_buses['p_nom'].sum() |
|
623
|
|
|
|
|
624
|
|
|
connection_links.append({ |
|
625
|
|
|
'scn_name': scn_name, |
|
626
|
|
|
'carrier': 'H2_grid', |
|
627
|
|
|
'link_id': next(next_max_link_id), |
|
628
|
|
|
'bus0': i_bus['bus_id'], |
|
|
|
|
|
|
629
|
|
|
'bus1': nearest_abroad_bus['bus_id'], |
|
|
|
|
|
|
630
|
|
|
'p_nom': p_nom_value, |
|
|
|
|
|
|
631
|
|
|
'p_min_pu': -1, |
|
632
|
|
|
'geom': MultiLineString( |
|
633
|
|
|
[LineString([ |
|
634
|
|
|
(i_bus['geom'].x, i_bus['geom'].y), |
|
635
|
|
|
(nearest_abroad_bus['geom'].x, nearest_abroad_bus['geom'].y) |
|
636
|
|
|
])] |
|
637
|
|
|
) |
|
638
|
|
|
}) |
|
639
|
|
|
connection_links_df = gpd.GeoDataFrame(connection_links, geometry='geom', crs="EPSG:4326") |
|
640
|
|
|
|
|
641
|
|
|
connection_links_df.to_postgis( |
|
642
|
|
|
name=targets["hydrogen_links"]["table"], |
|
643
|
|
|
con=engine, |
|
644
|
|
|
schema=targets["hydrogen_links"]["schema"], |
|
645
|
|
|
if_exists="append", |
|
646
|
|
|
index=False, |
|
647
|
|
|
) |
|
648
|
|
|
print("Neighbour countries are succesfully connected to H2-grid") |
|
649
|
|
|
|
|
650
|
|
|
|
openego /
eGon-data
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