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Merge branch 'energy_totals' of https://github.com/PyPSA/pypsa-eur into energy_totals

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toniseibold 2024-02-27 13:33:08 +01:00
commit 7616ae31fe
2 changed files with 122 additions and 76 deletions
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1
rules/common.smk
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@ -129,6 +129,7 @@ def has_internet_access(url="www.zenodo.org") -> bool:
finally: finally:
conn.close() conn.close()
def solved_previous_horizon(w): def solved_previous_horizon(w):
planning_horizons = config_provider("scenario", "planning_horizons")(w) planning_horizons = config_provider("scenario", "planning_horizons")(w)
i = planning_horizons.index(int(w.planning_horizons)) i = planning_horizons.index(int(w.planning_horizons))

191
scripts/build_energy_totals.py
View File

@ -123,35 +123,45 @@ def build_eurostat(input_eurostat, countries, year):
""" """
# read in every country file in countries # read in every country file in countries
eurostat = pd.DataFrame() eurostat = pd.DataFrame()
countries = [country if country != 'GB' else 'UK' for country in countries] countries = [country if country != "GB" else "UK" for country in countries]
countries = [country if country != 'GR' else 'EL' for country in countries] countries = [country if country != "GR" else "EL" for country in countries]
for country in countries: for country in countries:
filename = f"/{country}-Energy-balance-sheets-April-2023-edition.xlsb" filename = f"/{country}-Energy-balance-sheets-April-2023-edition.xlsb"
if os.path.exists(input_eurostat + filename): if os.path.exists(input_eurostat + filename):
df = pd.read_excel( df = pd.read_excel(
input_eurostat + filename, input_eurostat + filename,
engine='pyxlsb', engine="pyxlsb",
sheet_name=str(year), sheet_name=str(year),
skiprows=4, skiprows=4,
index_col=list(range(4))) index_col=list(range(4)),
)
# replace entry 'Z' with 0 # replace entry 'Z' with 0
df.replace('Z', 0, inplace=True) df.replace("Z", 0, inplace=True)
# write 'International aviation' to the 2nd level of the multiindex # write 'International aviation' to the 2nd level of the multiindex
index_number = (df.index.get_level_values(1) == 'International aviation').argmax() index_number = (
new_index = ('-', 'International aviation', 'International aviation', 'ktoe') df.index.get_level_values(1) == "International aviation"
).argmax()
new_index = (
"-",
"International aviation",
"International aviation",
"ktoe",
)
modified_index = list(df.index) modified_index = list(df.index)
modified_index[index_number] = new_index modified_index[index_number] = new_index
df.index = pd.MultiIndex.from_tuples(modified_index, names=df.index.names) df.index = pd.MultiIndex.from_tuples(modified_index, names=df.index.names)
# drop the annoying subhead line # drop the annoying subhead line
df.drop(df[df[year] == year].index, inplace=True) df.drop(df[df[year] == year].index, inplace=True)
# replace 'Z' with 0 # replace 'Z' with 0
df = df.replace('Z', 0) df = df.replace("Z", 0)
# add country to the multiindex # add country to the multiindex
new_tuple = [(country, *idx) for idx in df.index] new_tuple = [(country, *idx) for idx in df.index]
new_mindex = pd.MultiIndex.from_tuples(new_tuple, names=['country', None, 'name', None, 'unit']) new_mindex = pd.MultiIndex.from_tuples(
new_tuple, names=["country", None, "name", None, "unit"]
)
df.index = new_mindex df.index = new_mindex
# make numeric values where possible # make numeric values where possible
df = df.apply(pd.to_numeric, errors='coerce') df = df.apply(pd.to_numeric, errors="coerce")
# drop non-numeric columns # drop non-numeric columns
non_numeric_cols = df.columns[df.dtypes != float] non_numeric_cols = df.columns[df.dtypes != float]
df.drop(non_numeric_cols, axis=1, inplace=True) df.drop(non_numeric_cols, axis=1, inplace=True)
@ -161,19 +171,27 @@ def build_eurostat(input_eurostat, countries, year):
eurostat.drop(["Unnamed: 4", year, "Unnamed: 6"], axis=1, inplace=True) eurostat.drop(["Unnamed: 4", year, "Unnamed: 6"], axis=1, inplace=True)
# Renaming some indices # Renaming some indices
rename = { rename = {
'Households': 'Residential', "Households": "Residential",
'Commercial & public services': 'Services', "Commercial & public services": "Services",
'Domestic navigation': 'Domestic Navigation' "Domestic navigation": "Domestic Navigation",
} }
for name, rename in rename.items(): for name, rename in rename.items():
eurostat.index = eurostat.index.set_levels( eurostat.index = eurostat.index.set_levels(
eurostat.index.levels[3].where(eurostat.index.levels[3] != name, rename), eurostat.index.levels[3].where(eurostat.index.levels[3] != name, rename),
level=3) level=3,
new_index = eurostat.index.set_levels(eurostat.index.levels[2].where(eurostat.index.levels[2] != 'International maritime bunkers', 'Bunkers'), level=2) )
new_index = eurostat.index.set_levels(
eurostat.index.levels[2].where(
eurostat.index.levels[2] != "International maritime bunkers", "Bunkers"
),
level=2,
)
eurostat.index = new_index eurostat.index = new_index
eurostat.rename(columns={'Total': 'Total all products'}, inplace=True) eurostat.rename(columns={"Total": "Total all products"}, inplace=True)
eurostat.index = eurostat.index.set_levels(eurostat.index.levels[0].where(eurostat.index.levels[0] != 'UK', 'GB'), level=0) eurostat.index = eurostat.index.set_levels(
eurostat.index.levels[0].where(eurostat.index.levels[0] != "UK", "GB"), level=0
)
df = eurostat * 11.63 / 1e3 df = eurostat * 11.63 / 1e3
@ -766,66 +784,95 @@ def rescale(idees_countries, energy, eurostat):
missing data: ['passenger car efficiency', 'passenger cars'] missing data: ['passenger car efficiency', 'passenger cars']
""" """
# read in the eurostat data for 2015 # read in the eurostat data for 2015
eurostat_2015 = build_eurostat(input_eurostat, countries, 2023, 2015)[["Total all products", "Electricity"]] eurostat_2015 = build_eurostat(input_eurostat, countries, 2023, 2015)[
["Total all products", "Electricity"]
]
eurostat_year = eurostat[["Total all products", "Electricity"]] eurostat_year = eurostat[["Total all products", "Electricity"]]
# calculate the ratio of the two data sets # calculate the ratio of the two data sets
ratio = eurostat_year / eurostat_2015 ratio = eurostat_year / eurostat_2015
ratio = ratio.droplevel([1,4]) ratio = ratio.droplevel([1, 4])
ratio.rename(columns={"Total all products": "total", "Electricity": "ele"}, inplace=True) ratio.rename(
columns={"Total all products": "total", "Electricity": "ele"}, inplace=True
)
ratio = ratio.rename(index={"EL": "GR"}, level=0) ratio = ratio.rename(index={"EL": "GR"}, level=0)
mappings = { mappings = {
"Residential": { "Residential": {
"total": ["total residential space", "total": [
"total residential water", "total residential space",
"total residential cooking", "total residential water",
"total residential", "total residential cooking",
"derived heat residential", "total residential",
"thermal uses residential",], "derived heat residential",
"elec": ["electricity residential space", "thermal uses residential",
"electricity residential water", ],
"electricity residential cooking", "elec": [
"electricity residential",]}, "electricity residential space",
"electricity residential water",
"electricity residential cooking",
"electricity residential",
],
},
"Services": { "Services": {
"total": ["total services space", "total": [
"total services water", "total services space",
"total services cooking", "total services water",
"total services", "total services cooking",
"derived heat services", "total services",
"thermal uses services",], "derived heat services",
"elec": ["electricity services space", "thermal uses services",
"electricity services water", ],
"electricity services cooking", "elec": [
"electricity services",]}, "electricity services space",
"electricity services water",
"electricity services cooking",
"electricity services",
],
},
"Agriculture & forestry": { "Agriculture & forestry": {
"total": ["total agriculture heat", "total": [
"total agriculture machinery", "total agriculture heat",
"total agriculture",], "total agriculture machinery",
"elec": ["total agriculture electricity",]}, "total agriculture",
],
"elec": [
"total agriculture electricity",
],
},
"Road": { "Road": {
"total": ["total road", "total": [
"total passenger cars", "total road",
"total other road passenger", "total passenger cars",
"total light duty road freight",], "total other road passenger",
"elec": ["electricity road", "total light duty road freight",
"electricity passenger cars", ],
"electricity other road passenger", "elec": [
"electricity light duty road freight",]}, "electricity road",
"electricity passenger cars",
"electricity other road passenger",
"electricity light duty road freight",
],
},
"Rail": { "Rail": {
"total": ["total rail", "total": [
"total rail passenger", "total rail",
"total rail freight",], "total rail passenger",
"elec": ["electricity rail", "total rail freight",
"electricity rail passenger", ],
"electricity rail freight",]}, "elec": [
"electricity rail",
"electricity rail passenger",
"electricity rail freight",
],
},
} }
avia_inter = [ avia_inter = [
'total aviation passenger', "total aviation passenger",
'total aviation freight', "total aviation freight",
'total international aviation passenger', "total international aviation passenger",
'total international aviation freight', "total international aviation freight",
'total international aviation' "total international aviation",
] ]
avia_domestic = [ avia_domestic = [
"total domestic aviation passenger", "total domestic aviation passenger",
@ -840,13 +887,13 @@ def rescale(idees_countries, energy, eurostat):
for sector, mapping in mappings.items(): for sector, mapping in mappings.items():
sector_ratio = ratio.loc[(country, slice(None), sector)] sector_ratio = ratio.loc[(country, slice(None), sector)]
energy.loc[country, mapping["total"]] *= sector_ratio[['total']].iloc[0,0] energy.loc[country, mapping["total"]] *= sector_ratio[["total"]].iloc[0, 0]
energy.loc[country, mapping["elec"]] *= sector_ratio[['ele']].iloc[0,0] energy.loc[country, mapping["elec"]] *= sector_ratio[["ele"]].iloc[0, 0]
avi_d = ratio.loc[(country, slice(None), 'Domestic aviation')] avi_d = ratio.loc[(country, slice(None), "Domestic aviation")]
avi_i = ratio.loc[(country, 'International aviation', slice(None))] avi_i = ratio.loc[(country, "International aviation", slice(None))]
energy.loc[country, avia_inter] *= avi_i[['total']].iloc[0,0] energy.loc[country, avia_inter] *= avi_i[["total"]].iloc[0, 0]
energy.loc[country, avia_domestic] *= avi_d[['total']].iloc[0,0] energy.loc[country, avia_domestic] *= avi_d[["total"]].iloc[0, 0]
nav = ratio.loc[(country, slice(None), "Domestic Navigation")] nav = ratio.loc[(country, slice(None), "Domestic Navigation")]
energy.loc[country, navigation] *= nav[["total"]].iloc[0, 0] energy.loc[country, navigation] *= nav[["total"]].iloc[0, 0]
@ -898,9 +945,7 @@ if __name__ == "__main__":
base_year_emissions = params["base_emissions_year"] base_year_emissions = params["base_emissions_year"]
emissions_scope = snakemake.params.energy["emissions"] emissions_scope = snakemake.params.energy["emissions"]
eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope) eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)
eurostat_co2 = build_eurostat_co2( eurostat_co2 = build_eurostat_co2(input_eurostat, countries, base_year_emissions)
input_eurostat, countries, base_year_emissions
)
co2 = build_co2_totals(countries, eea_co2, eurostat_co2) co2 = build_co2_totals(countries, eea_co2, eurostat_co2)
co2.to_csv(snakemake.output.co2_name) co2.to_csv(snakemake.output.co2_name)