gas_input: switch production data from scigrid to gem
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850e2083a6
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@ -85,12 +85,11 @@ if config["sector"]["gas_network"] or config["sector"]["H2_retrofit"]:
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rule build_gas_input_locations:
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input:
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lng=HTTP.remote(
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gem=HTTP.remote(
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"https://globalenergymonitor.org/wp-content/uploads/2023/07/Europe-Gas-Tracker-2023-03-v3.xlsx",
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keep_local=True,
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),
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entry="data/gas_network/scigrid-gas/data/IGGIELGN_BorderPoints.geojson",
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production="data/gas_network/scigrid-gas/data/IGGIELGN_Productions.geojson",
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regions_onshore=RESOURCES
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+ "regions_onshore_elec_s{simpl}_{clusters}.geojson",
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regions_offshore=RESOURCES
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@ -23,11 +23,10 @@ def read_scigrid_gas(fn):
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return df
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def build_gem_lng_data(lng_fn):
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df = pd.read_excel(lng_fn[0], sheet_name="LNG terminals - data")
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def build_gem_lng_data(fn):
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df = pd.read_excel(fn[0], sheet_name="LNG terminals - data")
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df = df.set_index("ComboID")
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remove_status = ["Cancelled"]
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remove_country = ["Cyprus", "Turkey"]
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remove_terminal = ["Puerto de la Luz LNG Terminal", "Gran Canaria LNG Terminal"]
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@ -42,9 +41,43 @@ def build_gem_lng_data(lng_fn):
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return gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
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def build_gas_input_locations(lng_fn, entry_fn, prod_fn, countries):
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def build_gem_prod_data(fn):
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df = pd.read_excel(fn[0], sheet_name="Gas extraction - main")
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df = df.set_index("GEM Unit ID")
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remove_country = ["Cyprus", "Türkiye"]
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remove_fuel_type = ["oil"]
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df = df.query(
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"Status != 'shut in' \
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& 'Fuel type' != 'oil' \
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& Country != @remove_country \
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& ~Latitude.isna() \
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& ~Longitude.isna()"
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).copy()
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p = pd.read_excel(fn[0], sheet_name="Gas extraction - production")
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p = p.set_index("GEM Unit ID")
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p = p[p["Fuel description"] == 'gas' ]
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capacities = pd.DataFrame(index=df.index)
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for key in ["production", "production design capacity", "reserves"]:
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cap = p.loc[p["Production/reserves"] == key, "Quantity (converted)"].groupby("GEM Unit ID").sum().reindex(df.index)
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# assume capacity such that 3% of reserves can be extracted per year (25% quantile)
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annualization_factor = 0.03 if key == "reserves" else 1.
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capacities[key] = cap * annualization_factor
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df["mcm_per_year"] = capacities["production"] \
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.combine_first(capacities["production design capacity"]) \
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.combine_first(capacities["reserves"])
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geometry = gpd.points_from_xy(df["Longitude"], df["Latitude"])
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return gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
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def build_gas_input_locations(gem_fn, entry_fn, countries):
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# LNG terminals
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lng = build_gem_lng_data(lng_fn)
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lng = build_gem_lng_data(gem_fn)
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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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@ -56,16 +89,14 @@ def build_gas_input_locations(lng_fn, entry_fn, prod_fn, countries):
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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) & (prod.geometry.x < 30) & (prod.country_code != "DE")
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]
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prod = build_gem_prod_data(gem_fn)
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mcm_per_day_to_mw = 437.5 # MCM/day to MWh/h
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mcm_per_year_to_mw = 1.199 # MCM/year to MWh/h
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mtpa_to_mw = 1649.224 # mtpa to MWh/h
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lng["p_nom"] = lng["CapacityInMtpa"] * mtpa_to_mw
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entry["p_nom"] = entry["max_cap_from_to_M_m3_per_d"] * mcm_per_day_to_mw
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prod["p_nom"] = prod["max_supply_M_m3_per_d"] * mcm_per_day_to_mw
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prod["p_nom"] = prod["mcm_per_year"] * mcm_per_year_to_mw
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lng["type"] = "lng"
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entry["type"] = "pipeline"
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@ -83,7 +114,7 @@ if __name__ == "__main__":
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snakemake = mock_snakemake(
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"build_gas_input_locations",
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simpl="",
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clusters="37",
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clusters="128",
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)
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logging.basicConfig(level=snakemake.config["logging"]["level"])
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@ -104,9 +135,8 @@ if __name__ == "__main__":
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countries = 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.gem,
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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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