91 lines
1.9 KiB
Python
91 lines
1.9 KiB
Python
"""
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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/t (wood pellets)
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def get_countries():
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pandas_options = dict(
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skiprows=range(6),
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header=None,
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index_col=0
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)
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return tbl.read_pdf(
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str(snakemake.input.transport_cost_data),
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pages="145",
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multiple_tables=False,
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pandas_options=pandas_options
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)[0].index
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def get_cost_per_tkm(page, countries):
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pandas_options = dict(
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skiprows=range(6),
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header=0,
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sep=' |,',
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engine='python',
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index_col=False,
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)
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sc = tbl.read_pdf(
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str(snakemake.input.transport_cost_data),
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pages=page,
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multiple_tables=False,
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pandas_options=pandas_options
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)[0]
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sc.index = countries
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sc.columns = sc.columns.str.replace("€", "EUR")
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return sc
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def build_biomass_transport_costs():
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countries = get_countries()
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sc1 = get_cost_per_tkm(146, countries)
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sc2 = get_cost_per_tkm(147, countries)
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# take mean of both supply chains
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to_concat = [sc1["EUR/km/ton"], sc2["EUR/km/ton"]]
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transport_costs = pd.concat(to_concat, axis=1).mean(axis=1)
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# convert tonnes to MWh
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transport_costs /= ENERGY_CONTENT
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transport_costs.name = "EUR/km/MWh"
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# rename country names
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to_rename = {
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"UK": "GB",
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"XK": "KO",
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"EL": "GR"
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}
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transport_costs.rename(to_rename, inplace=True)
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# add missing Norway with data from Sweden
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transport_costs["NO"] = transport_costs["SE"]
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transport_costs.to_csv(snakemake.output[0])
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if __name__ == "__main__":
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build_biomass_transport_costs()
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