new energy totals from eurostat 2023 report scaling JRC IDEES data from 2015
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@ -314,9 +314,9 @@ pypsa_eur:
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# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#energy
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energy:
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energy_totals_year: 2013
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energy_totals_year: 2019
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base_emissions_year: 1990
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eurostat_report_year: 2016
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eurostat_report_year: 2023
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emissions: CO2
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# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#biomass
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@ -1,25 +1,25 @@
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country,item,2010,2011,2012,2013,2014,2015
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CH,total residential,268.2,223.4,243.4,261.3,214.2,229.1
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CH,total residential space,192.2,149.0,168.1,185.5,139.7,154.4
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CH,total residential water,32.2,31.6,31.9,32.2,31.7,31.9
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CH,total residential cooking,9.3,9.3,9.3,9.4,9.5,9.6
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CH,electricity residential,67.9,63.7,65.7,67.6,63.0,64.4
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CH,electricity residential space,15.9,12.8,14.3,15.8,12.3,13.5
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CH,electricity residential water,8.8,8.5,8.5,8.6,8.5,8.6
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CH,electricity residential cooking,4.9,4.9,4.9,4.9,5.0,5.0
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CH,total services,145.9,127.4,136.7,144.0,124.5,132.5
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CH,total services space,80.0,62.2,70.8,77.4,58.3,64.3
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CH,total services water,10.1,10.0,10.1,10.1,10.0,10.0
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CH,total services cooking,2.5,2.4,2.3,2.3,2.4,2.3
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CH,electricity services,60.5,59.2,60.3,61.4,60.3,62.6
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CH,electricity services space,4.0,3.2,3.8,4.2,3.3,3.6
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CH,electricity services water,0.7,0.7,0.7,0.7,0.7,0.7
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CH,electricity services cooking,2.5,2.4,2.3,2.3,2.4,2.3
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CH,total rail,11.5,11.1,11.2,11.4,11.1,11.4
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CH,total road,199.4,200.4,200.4,201.2,202.0,203.1
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CH,electricity road,0.,0.,0.,0.,0.,0.
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CH,electricity rail,11.5,11.1,11.2,11.4,11.1,11.4
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CH,total domestic aviation,3.3,3.2,3.4,3.4,3.5,3.5
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CH,total international aviation,58.0,62.0,63.5,64.2,64.5,66.8
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CH,total domestic navigation,1.6,1.6,1.6,1.6,1.6,1.6
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CH,total international navigation,0.,0.,0.,0.,0.,0.
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country,item,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022
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CH,total residential,268.2,223.4,243.4,261.3,214.2,229.1,241.2,236.5,223.7,226.5,219.1,241.2,211.3
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CH,total residential space,192.2,149,168.1,185.5,139.7,154.4,167.3,161.5,147.2,150.4,140.2,166.2,131.9
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CH,total residential water,32.2,31.6,31.9,32.2,31.7,31.9,31.8,31.8,31.8,31.7,33.3,32.5,32.5
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CH,total residential cooking,9.3,9.3,9.3,9.4,9.5,9.6,9.9,10,10.1,10.2,10.5,10.3,10.3
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CH,electricity residential,67.9,63.7,65.7,67.6,63,64.4,69.7,69.2,67.7,68.1,68.7,70.8,66.8
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CH,electricity residential space,15.9,12.8,14.3,15.8,12.3,13.5,15.8,15.6,14.7,15.3,14.8,17.8,14.8
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CH,electricity residential water,8.8,8.5,8.5,8.6,8.5,8.6,8.9,9,9.2,9.3,9.7,9.5,9.5
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CH,electricity residential cooking,4.9,4.9,4.9,4.9,5,5,5,5.1,5.1,5.1,5.4,5.2,5.3
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CH,total services,145.9,127.4,136.7,144,124.5,132.5,150.5,147.7,141.5,143.1,129.7,144.2,122.5
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CH,total services space,80,62.2,70.8,77.4,58.3,64.3,77,74.4,68.2,69.8,64.3,75.7,58.7
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CH,total services water,10.1,10,10.1,10.1,10,10,11.4,11.3,11.2,11.1,9.7,10.4,12
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CH,total services cooking,2.5,2.4,2.3,2.3,2.4,2.3,3.1,3.1,3.2,3.3,2.1,2.6,3.2
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CH,electricity services,60.5,59.2,60.3,61.4,60.3,62.6,65.9,65.7,65.5,65.6,58.8,61.6,61.6
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CH,electricity services space,4,3.2,3.8,4.2,3.3,3.6,2.7,2.5,2.3,2.3,2.2,2.5,2.5
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CH,electricity services water,0.7,0.7,0.7,0.7,0.7,0.7,1.2,1.1,1.1,1.1,0.9,1,1
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CH,electricity services cooking,2.5,2.4,2.3,2.3,2.4,2.3,3.1,3.1,3.1,3.2,3.3,2.1,3.2
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CH,total rail,11.5,11.1,11.2,11.4,11.1,11.4,11.6,11.4,11.2,11,10.2,10.6,10.8
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CH,total road,199.4,200.4,200.4,201.2,202,203.1,203.9,203.7,202.6,200.5,182.6,188.3,193.3
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CH,electricity road,0,0,0,0,0,0,0.1,0.2,0.3,0.4,0.5,0.8,1.3
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CH,electricity rail,11.5,11.1,11.2,11.4,11.1,11.4,11.5,11.3,11.1,11,10.1,10.6,10.7
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CH,total domestic aviation,3.3,3.2,3.4,3.4,3.5,3.5,3.6,3.1,3.1,2.9,2.5,2.8,3
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CH,total international aviation,58,62,63.5,64.2,64.5,66.8,70.6,72.8,77.2,78.2,28.2,31.2,56.8
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CH,total domestic navigation,1.6,1.6,1.6,1.6,1.6,1.6,1.4,1.4,1.4,1.4,1.4,1.4,1.4
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CH,total international navigation,0,0,0,0,0,0,0,0,0,0,0,0,0
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@ -131,10 +131,11 @@ def has_internet_access(url="www.zenodo.org") -> bool:
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def input_eurostat(w):
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# 2016 includes BA, 2017 does not
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report_year = config_provider("energy", "eurostat_report_year")(w)
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if config["energy"]["eurostat_report_year"] != 2023:
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report_year = config["energy"]["eurostat_report_year"]
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return f"data/bundle-sector/eurostat-energy_balances-june_{report_year}_edition"
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else:
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return "data/bundle-sector/eurostat-energy_balances-april_2023_edition"
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def solved_previous_horizon(w):
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planning_horizons = config_provider("scenario", "planning_horizons")(w)
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@ -16,6 +16,7 @@ import numpy as np
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import pandas as pd
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from _helpers import configure_logging, mute_print, set_scenario_config
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from tqdm import tqdm
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import os
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cc = coco.CountryConverter()
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logger = logging.getLogger(__name__)
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@ -120,6 +121,7 @@ def build_eurostat(input_eurostat, countries, report_year, year):
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"""
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Return multi-index for all countries' energy data in TWh/a.
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"""
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if report_year != 2023:
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filenames = {
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2016: f"/{year}-Energy-Balances-June2016edition.xlsx",
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2017: f"/{year}-ENERGY-BALANCES-June2017edition.xlsx",
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@ -141,7 +143,6 @@ def build_eurostat(input_eurostat, countries, report_year, year):
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if lookup[df.columns[0]] in countries
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}
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df = pd.concat(labelled_dfs, sort=True).sort_index()
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# drop non-numeric and country columns
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non_numeric_cols = df.columns[df.dtypes != float]
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country_cols = df.columns.intersection(lookup.keys())
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@ -151,6 +152,63 @@ def build_eurostat(input_eurostat, countries, report_year, year):
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# convert ktoe/a to TWh/a
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df *= 11.63 / 1e3
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else:
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# read in every country file in countries
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eurostat = pd.DataFrame()
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countries = [country if country != 'GB' else 'UK' for country in countries]
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countries = [country if country != 'GR' else 'EL' for country in countries]
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for country in countries:
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filename = f"/{country}-Energy-balance-sheets-April-2023-edition.xlsb"
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if os.path.exists(input_eurostat + filename):
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df = pd.read_excel(
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input_eurostat + filename,
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engine='pyxlsb',
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sheet_name=str(year),
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skiprows=4,
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index_col=list(range(4)))
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# replace entry 'Z' with 0
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df.replace('Z', 0, inplace=True)
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# write 'International aviation' to the 2nd level of the multiindex
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index_number = (df.index.get_level_values(1) == 'International aviation').argmax()
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new_index = ('-', 'International aviation', 'International aviation', 'ktoe')
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modified_index = list(df.index)
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modified_index[index_number] = new_index
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df.index = pd.MultiIndex.from_tuples(modified_index, names=df.index.names)
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# drop the annoying subhead line
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df.drop(df[df[year] == year].index, inplace=True)
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# replace 'Z' with 0
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df = df.replace('Z', 0)
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# add country to the multiindex
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new_tuple = [(country, *idx) for idx in df.index]
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new_mindex = pd.MultiIndex.from_tuples(new_tuple, names=['country', None, 'name', None, 'unit'])
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df.index = new_mindex
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# make numeric values where possible
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df = df.apply(pd.to_numeric, errors='coerce')
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# drop non-numeric columns
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non_numeric_cols = df.columns[df.dtypes != float]
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df.drop(non_numeric_cols, axis=1, inplace=True)
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# concatenate the dataframes
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eurostat = pd.concat([eurostat, df], axis=0)
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eurostat.drop(["Unnamed: 4", year, "Unnamed: 6"], axis=1, inplace=True)
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# Renaming some indices
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rename = {
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'Households': 'Residential',
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'Commercial & public services': 'Services',
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'Domestic navigation': 'Domestic Navigation'
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}
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for name, rename in rename.items():
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eurostat.index = eurostat.index.set_levels(
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eurostat.index.levels[3].where(eurostat.index.levels[3] != name, rename),
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level=3)
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new_index = eurostat.index.set_levels(eurostat.index.levels[2].where(eurostat.index.levels[2] != 'International maritime bunkers', 'Bunkers'), level=2)
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eurostat.index = new_index
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eurostat.rename(columns={'Total': 'Total all products'}, inplace=True)
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eurostat.index = eurostat.index.set_levels(eurostat.index.levels[0].where(eurostat.index.levels[0] != 'UK', 'GB'), level=0)
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df = eurostat * 11.63 / 1e3
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return df
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@ -669,10 +727,11 @@ def build_eurostat_co2(input_eurostat, countries, report_year, year=1990):
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return eurostat.multiply(specific_emissions).sum(axis=1)
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def build_co2_totals(countries, eea_co2, eurostat_co2):
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def build_co2_totals(countries, eea_co2, eurostat_co2, report_year):
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co2 = eea_co2.reindex(countries)
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for ct in pd.Index(countries).intersection(["BA", "RS", "AL", "ME", "MK"]):
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if report_year != 2023:
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mappings = {
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"electricity": (
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ct,
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@ -694,6 +753,23 @@ def build_co2_totals(countries, eea_co2, eurostat_co2):
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"agriculture": (eurostat_co2.index.get_level_values(0) == ct)
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& eurostat_co2.index.isin(["Agriculture / Forestry", "Fishing"], level=3),
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}
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else:
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mappings = {
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"electricity": (ct, "+", "Electricity & heat generation", np.nan),
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"residential non-elec": (ct, "+", "+", "Residential"),
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"services non-elec": (ct, "+", "+", "Services"),
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"road non-elec": (ct, "+", "+", "Road"),
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"rail non-elec": (ct, "+", "+", "Rail"),
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"domestic navigation": (ct, "+", "+", "Domestic Navigation"),
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"international navigation": (ct, "-", "Bunkers"),
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"domestic aviation": (ct, "+", "+", "Domestic aviation"),
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"international aviation": (ct, "-", "International aviation"),
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# does not include industrial process emissions or fuel processing/refining
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"industrial non-elec": (ct, "+", "Industry sector"),
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# does not include non-energy emissions
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"agriculture": (eurostat_co2.index.get_level_values(0) == ct)
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& eurostat_co2.index.isin(["Agriculture & forestry", "Fishing"], level=3),
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}
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for i, mi in mappings.items():
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co2.at[ct, i] = eurostat_co2.loc[mi].sum()
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@ -736,6 +812,133 @@ def build_transport_data(countries, population, idees):
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return transport_data
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def rescale(idees_countries, energy, eurostat):
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'''
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Takes JRC IDEES data from 2015 and rescales it by the ratio of the
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eurostat data and the 2015 eurostat data.
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missing data: ['passenger car efficiency', 'passenger cars']
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'''
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# read in the eurostat data for 2015
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eurostat_2015 = build_eurostat(input_eurostat, countries, 2023, 2015)[["Total all products", "Electricity"]]
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# eurostat_2015 = eurostat_2015.rename(index={'GB': 'UK'}, level=0)
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eurostat_year = eurostat[["Total all products", "Electricity"]]
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# calculate the ratio of the two data sets
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ratio = eurostat_year / eurostat_2015
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ratio = ratio.droplevel([1,4])
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ratio.rename(columns={"Total all products": "total", "Electricity": "ele"}, inplace=True)
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ratio = ratio.rename(index={"GB": "UK"}, level=0)
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residential_total = [
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"total residential space",
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"total residential water",
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"total residential cooking",
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"total residential",
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"derived heat residential",
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"thermal uses residential",
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]
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residential_ele = [
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"electricity residential space",
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"electricity residential water",
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"electricity residential cooking",
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"electricity residential",
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]
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service_total = [
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"total services space",
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"total services water",
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"total services cooking",
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"total services",
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"derived heat services",
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"thermal uses services",
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]
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service_ele = [
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"electricity services space",
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"electricity services water",
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"electricity services cooking",
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"electricity services",
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]
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agri_total = [
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"total agriculture heat",
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"total agriculture machinery",
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"total agriculture",
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]
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agri_ele = [
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"total agriculture electricity",
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]
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road_total = [
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"total road",
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"total passenger cars",
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"total other road passenger",
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"total light duty road freight",
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]
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road_ele = [
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"electricity road",
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"electricity passenger cars",
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"electricity other road passenger",
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"electricity light duty road freight",
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]
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rail_total = [
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"total rail",
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"total rail passenger",
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"total rail freight",
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]
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rail_ele = [
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"electricity rail",
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"electricity rail passenger",
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"electricity rail freight",
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]
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avia_inter = [
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'total aviation passenger',
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'total aviation freight',
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'total international aviation passenger',
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'total international aviation freight',
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'total international aviation'
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]
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avia_domestic = [
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'total domestic aviation passenger',
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'total domestic aviation freight',
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'total domestic aviation',
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]
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navigation = [
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"total domestic navigation",
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]
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idees_countries = idees_countries.repalce({'GB': 'UK', 'GR': 'EL'})
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for country in idees_countries:
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res = ratio.loc[(country, slice(None), 'Residential')]
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energy.loc[country, residential_total] *= res[['total']].iloc[0,0]
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energy.loc[country, residential_ele] *= res[['ele']].iloc[0,0]
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ser = ratio.loc[(country, slice(None), 'Services')]
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energy.loc[country, service_total] *= ser[['total']].iloc[0,0]
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energy.loc[country, service_ele] *= ser[['ele']].iloc[0,0]
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agri = ratio.loc[(country, slice(None), 'Agriculture & forestry')]
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energy.loc[country, agri_total] *= agri[['total']].iloc[0,0]
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energy.loc[country, agri_ele] *= agri[['ele']].iloc[0,0]
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road = ratio.loc[(country, slice(None), 'Road')]
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energy.loc[country, road_total] *= road[['total']].iloc[0,0]
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energy.loc[country, road_ele] *= road[['ele']].iloc[0,0]
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rail = ratio.loc[(country, slice(None), 'Rail')]
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energy.loc[country, rail_total] *= rail[['total']].iloc[0,0]
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energy.loc[country, rail_ele] *= rail[['ele']].iloc[0,0]
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avi_d = ratio.loc[(country, slice(None), 'Domestic aviation')]
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avi_i = ratio.loc[(country, 'International aviation', slice(None))]
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energy.loc[country, avia_inter] *= avi_i[['total']].iloc[0,0]
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energy.loc[country, avia_domestic] *= avi_d[['total']].iloc[0,0]
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nav = ratio.loc[(country, slice(None), 'Domestic Navigation')]
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energy.loc[country, navigation] *= nav[['total']].iloc[0,0]
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return energy
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if __name__ == "__main__":
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if "snakemake" not in globals():
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@ -759,12 +962,22 @@ if __name__ == "__main__":
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input_eurostat = snakemake.input.eurostat
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eurostat = build_eurostat(input_eurostat, countries, report_year, data_year)
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||||
swiss = build_swiss(data_year)
|
||||
# data from idees only exists for 2015
|
||||
if data_year > 2015:
|
||||
# read in latest data and rescale later
|
||||
idees = build_idees(idees_countries, 2015)
|
||||
else:
|
||||
idees = build_idees(idees_countries, data_year)
|
||||
|
||||
energy = build_energy_totals(countries, eurostat, swiss, idees)
|
||||
|
||||
if data_year > 2015:
|
||||
energy = rescale(idees_countries, energy, eurostat)
|
||||
|
||||
energy.to_csv(snakemake.output.energy_name)
|
||||
|
||||
district_heat_share = build_district_heat_share(countries, idees)
|
||||
# use rescaled idees data to calculate district heat share
|
||||
district_heat_share = build_district_heat_share(countries, energy.loc[idees_countries])
|
||||
district_heat_share.to_csv(snakemake.output.district_heat_share)
|
||||
|
||||
base_year_emissions = params["base_emissions_year"]
|
||||
@ -774,7 +987,7 @@ if __name__ == "__main__":
|
||||
input_eurostat, countries, report_year, base_year_emissions
|
||||
)
|
||||
|
||||
co2 = build_co2_totals(countries, eea_co2, eurostat_co2)
|
||||
co2 = build_co2_totals(countries, eea_co2, eurostat_co2, report_year)
|
||||
co2.to_csv(snakemake.output.co2_name)
|
||||
|
||||
transport = build_transport_data(countries, population, idees)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user