pypsa-eur/scripts/build_biomass_potentials.py

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import pandas as pd
idx = pd.IndexSlice
def build_biomass_potentials():
#delete empty column C from this sheet first before reading it in
df = pd.read_excel(snakemake.input.jrc_potentials,
"Potentials (PJ)",
index_col=[0,1])
df.rename(columns={"Unnamed: 18":"Municipal waste"},inplace=True)
df.drop(columns="Total",inplace=True)
df.replace("-",0.,inplace=True)
df_dict = {}
for i in range(36):
df_dict[df.iloc[i*16,1]] = df.iloc[1+i*16:(i+1)*16].astype(float)
#convert from PJ to MWh
df_new = pd.concat(df_dict).rename({"UK" : "GB", "BH" : "BA"})/3.6*1e6
df_new.index.name = "MWh/a"
df_new.to_csv(snakemake.output.biomass_potentials_all)
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# solid biomass includes: Primary agricultural residues (MINBIOAGRW1),
# Forestry energy residue (MINBIOFRSF1),
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# Secondary forestry residues (MINBIOWOOW1),
# Secondary Forestry residues sawdust (MINBIOWOO1a)',
# Forestry residues from landscape care biomass (MINBIOFRSF1a),
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# Municipal waste (MINBIOMUN1)',
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# biogas includes : Manure biomass potential (MINBIOGAS1),
# Sludge biomass (MINBIOSLU1)
us_type = pd.Series("", df_new.columns)
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for k,v in snakemake.config['biomass']['classes'].items():
us_type.loc[v] = k
biomass_potentials = df_new.swaplevel(0,2).loc[snakemake.config['biomass']['scenario'],snakemake.config['biomass']['year']].groupby(us_type,axis=1).sum()
biomass_potentials.index.name = "MWh/a"
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biomass_potentials.to_csv(snakemake.output.biomass_potentials)
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' not in globals():
from vresutils import Dict
import yaml
snakemake = Dict()
snakemake.input = Dict()
snakemake.input['jrc_potentials'] = "data/biomass/JRC Biomass Potentials.xlsx"
snakemake.output = Dict()
snakemake.output['biomass_potentials'] = 'data/biomass_potentials.csv'
with open('config.yaml', encoding='utf8') as f:
snakemake.config = yaml.safe_load(f)
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build_biomass_potentials()