71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
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"""
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Reads biomass transport costs for different countries of the JRC report
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"The JRC-EU-TIMES model.
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Bioenergy potentials
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for EU and neighbouring countries."
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(2015)
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converts them from units 'EUR per km/ton' -> 'EUR/ (km MWh)'
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assuming as an approximation energy content of wood pellets
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@author: bw0928
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"""
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import pandas as pd
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import tabula as tbl
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ENERGY_CONTENT = 4.8 # unit MWh/tonne (assuming wood pellets)
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def build_biomass_transport_costs():
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df_list = tbl.read_pdf(
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snakemake.input[0],
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pages="145-147",
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multiple_tables=True,
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)
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countries = df_list[0][0].iloc[6:].rename(index=lambda x: x + 1)
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# supply chain 1
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df = df_list[1].copy().rename(index=countries.to_dict())
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df.rename(
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columns=df.iloc[:6].apply(lambda col: col.str.cat(sep=" "), axis=0).to_dict(),
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inplace=True,
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)
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df = df.iloc[6:]
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df.loc[6] = df.loc[6].str.replace("€", "EUR")
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# supply chain 2
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df2 = df_list[2].copy().rename(index=countries.to_dict())
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df2.rename(
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columns=df2.iloc[:6].apply(lambda col: col.str.cat(sep=" "), axis=0).to_dict(),
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inplace=True,
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)
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df2 = df2.iloc[6:]
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df2.loc[6] = df2.loc[6].str.replace("€", "EUR")
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df.to_csv(snakemake.output.supply_chain1)
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df2.to_csv(snakemake.output.supply_chain1)
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transport_costs = pd.concat([df["per km/ton"], df2["per km/ton"]], axis=1).drop(6)
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transport_costs = transport_costs.astype(float, errors="ignore").mean(axis=1)
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# convert unit to EUR/MWh
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transport_costs /= ENERGY_CONTENT
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transport_costs = pd.DataFrame(transport_costs, columns=["cost [EUR/(km MWh)]"])
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# rename
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transport_costs.rename({"UK": "GB", "XK": "KO", "EL": "GR"}, inplace=True)
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# add missing Norway
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transport_costs.loc["NO"] = transport_costs.loc["SE"]
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transport_costs.to_csv(snakemake.output.transport_costs)
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if __name__ == "__main__":
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prepare_biomass_transport_costs()
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