98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
"""
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Build import locations for fossil gas from entry-points, LNG terminals and production sites.
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"""
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import logging
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logger = logging.getLogger(__name__)
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import pandas as pd
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import geopandas as gpd
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from shapely import wkt
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def read_scigrid_gas(fn):
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df = gpd.read_file(fn)
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df = pd.concat([df, df.param.apply(pd.Series)], axis=1)
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df.drop(["param", "uncertainty", "method"], axis=1, inplace=True)
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return df
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def build_gas_input_locations(lng_fn, planned_lng_fn, entry_fn, prod_fn, countries):
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# LNG terminals
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lng = read_scigrid_gas(lng_fn)
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planned_lng = pd.read_csv(planned_lng_fn)
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planned_lng.geometry = planned_lng.geometry.apply(wkt.loads)
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planned_lng = gpd.GeoDataFrame(planned_lng, crs=4326)
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lng = lng.append(planned_lng, ignore_index=True)
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# Entry points from outside the model scope
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entry = read_scigrid_gas(entry_fn)
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entry["from_country"] = entry.from_country.str.rstrip()
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entry = entry.loc[
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~(entry.from_country.isin(countries) & entry.to_country.isin(countries)) & # only take non-EU entries
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~entry.name.str.contains("Tegelen") | # malformed datapoint
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(entry.from_country == "NO") # entries from NO to GB
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]
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# production sites inside the model scope
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prod = read_scigrid_gas(prod_fn)
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prod = prod.loc[
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(prod.geometry.y > 35) &
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(prod.geometry.x < 30) &
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(prod.country_code != "DE")
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]
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conversion_factor = 437.5 # MCM/day to MWh/h
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lng["p_nom"] = lng["max_cap_store2pipe_M_m3_per_d"] * conversion_factor
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entry["p_nom"] = entry["max_cap_from_to_M_m3_per_d"] * conversion_factor
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prod["p_nom"] = prod["max_supply_M_m3_per_d"] * conversion_factor
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lng["type"] = "lng"
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entry["type"] = "pipeline"
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prod["type"] = "production"
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sel = ["geometry", "p_nom", "type"]
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return pd.concat([prod[sel], entry[sel], lng[sel]], ignore_index=True)
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if __name__ == "__main__":
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if 'snakemake' not in globals():
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from helper import mock_snakemake
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snakemake = mock_snakemake(
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'build_gas_import_locations',
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simpl='',
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clusters='37',
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)
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logging.basicConfig(level=snakemake.config['logging_level'])
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onshore_regions = gpd.read_file(snakemake.input.regions_onshore).set_index('name')
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countries = onshore_regions.index.str[:2].unique().str.replace("GB", "UK")
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gas_input_locations = build_gas_input_locations(
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snakemake.input.lng,
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snakemake.input.planned_lng,
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snakemake.input.entry,
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snakemake.input.production,
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countries
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)
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# recommended to use projected CRS rather than geographic CRS
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gas_input_nodes = gpd.sjoin_nearest(
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gas_input_locations.to_crs(3035),
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onshore_regions.to_crs(3035),
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how='left'
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)
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gas_input_nodes.rename(columns={"index_right": "bus"}, inplace=True)
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gas_input_nodes.to_file(snakemake.output.gas_input_nodes, driver='GeoJSON')
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gas_input_nodes_s = gas_input_nodes.groupby(["bus", "type"])["p_nom"].sum().unstack()
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gas_input_nodes_s.columns.name = "p_nom"
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gas_input_nodes_s.to_csv(snakemake.output.gas_input_nodes_simplified) |