278 lines
8.1 KiB
Python
278 lines
8.1 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Build industrial energy demand per country.
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Inputs
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-------
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- ``data/jrc-idees-2021``
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- ``industrial_production_per_country.csv``
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Outputs
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-------
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- ``resources/industrial_energy_demand_per_country_today.csv``
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Description
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-------
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This rule uses the industrial_production_per_country.csv file and the JRC-IDEES data to derive an energy demand per country and sector. If the country is not in the EU28, an average energy demand depending on the production volume is derived.
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For each country and each subcategory of
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- Alumina production
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- Aluminium - primary production
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- Aluminium - secondary production
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- Ammonia
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- Cement
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- Ceramics & other NMM
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- Chlorine
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- Electric arc
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- Food, beverages and tobacco
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- Glass production
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- HVC
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- Integrated steelworks
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- Machinery equipment
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- Methanol
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- Other industrial sectors
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- Other chemicals
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- Other non-ferrous metals
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- Paper production
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- Pharmaceutical products etc.
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- Printing and media reproduction
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- Pulp production
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- Textiles and leather
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- Transport equipment
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- Wood and wood products
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the output file contains the energy demand in TWh/a for the following carriers
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- biomass
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- electricity
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- gas
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- heat
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- hydrogen
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- liquid
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- other
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- solid
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- waste
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"""
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import multiprocessing as mp
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from functools import partial
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import country_converter as coco
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import pandas as pd
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from _helpers import set_scenario_config
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from tqdm import tqdm
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cc = coco.CountryConverter()
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ktoe_to_twh = 0.011630
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# name in JRC-IDEES Energy Balances
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sector_sheets = {
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"Integrated steelworks": "FC_IND_IS_BF_E",
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"Electric arc": "FC_IND_IS_EA_E",
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"Alumina production": "FC_IND_NFM_AM_E",
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"Aluminium - primary production": "FC_IND_NFM_PA_E",
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"Aluminium - secondary production": "FC_IND_NFM_SA_E",
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"Other non-ferrous metals": "FC_IND_NFM_OM_E",
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"Basic chemicals": "FC_IND_CPC_BC_E",
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"Other chemicals": "FC_IND_CPC_OC_E",
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"Pharmaceutical products etc.": "FC_IND_CPC_PH_E",
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"Basic chemicals feedstock": "FC_IND_CPC_NE",
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"Cement": "FC_IND_NMM_CM_E",
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"Ceramics & other NMM": "FC_IND_NMM_CR_E",
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"Glass production": "FC_IND_NMM_GL_E",
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"Pulp production": "FC_IND_PPP_PU_E",
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"Paper production": "FC_IND_PPP_PA_E",
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"Printing and media reproduction": "FC_IND_PPP_PR_E",
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"Food, beverages and tobacco": "FC_IND_FBT_E",
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"Transport equipment": "FC_IND_TE_E",
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"Machinery equipment": "FC_IND_MAC_E",
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"Textiles and leather": "FC_IND_TL_E",
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"Wood and wood products": "FC_IND_WP_E",
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"Mining and quarrying": "FC_IND_MQ_E",
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"Construction": "FC_IND_CON_E",
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"Non-specified": "FC_IND_NSP_E",
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}
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fuels = {
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"Total": "all",
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"Solid fossil fuels": "solid",
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"Peat and peat products": "solid",
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"Oil shale and oil sands": "solid",
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"Oil and petroleum products": "liquid",
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"Manufactured gases": "gas",
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"Natural gas": "gas",
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"Nuclear heat": "heat",
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"Heat": "heat",
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"Renewables and biofuels": "biomass",
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"Non-renewable waste": "waste",
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"Electricity": "electricity",
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}
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eu27 = cc.EU27as("ISO2").ISO2.tolist()
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jrc_names = {"GR": "EL", "GB": "UK"}
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def industrial_energy_demand_per_country(country, year, jrc_dir):
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jrc_country = jrc_names.get(country, country)
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fn = f"{jrc_dir}/{jrc_country}/JRC-IDEES-2021_EnergyBalance_{jrc_country}.xlsx"
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sheets = list(sector_sheets.values())
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df_dict = pd.read_excel(fn, sheet_name=sheets, index_col=0)
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def get_subsector_data(sheet):
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df = df_dict[sheet][year].groupby(fuels).sum()
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df["hydrogen"] = 0.0
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df["other"] = df["all"] - df.loc[df.index != "all"].sum()
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return df
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df = pd.concat(
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{sub: get_subsector_data(sheet) for sub, sheet in sector_sheets.items()}, axis=1
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)
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sel = ["Mining and quarrying", "Construction", "Non-specified"]
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df["Other industrial sectors"] = df[sel].sum(axis=1)
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df["Basic chemicals"] += df["Basic chemicals feedstock"]
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df.drop(columns=sel + ["Basic chemicals feedstock"], index="all", inplace=True)
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df *= ktoe_to_twh
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return df
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def separate_basic_chemicals(demand, production):
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ammonia = pd.DataFrame(
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{
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"hydrogen": production["Ammonia"] * params["MWh_H2_per_tNH3_electrolysis"],
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"electricity": production["Ammonia"]
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* params["MWh_elec_per_tNH3_electrolysis"],
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}
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).T
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chlorine = pd.DataFrame(
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{
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"hydrogen": production["Chlorine"] * params["MWh_H2_per_tCl"],
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"electricity": production["Chlorine"] * params["MWh_elec_per_tCl"],
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}
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).T
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methanol = pd.DataFrame(
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{
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"gas": production["Methanol"] * params["MWh_CH4_per_tMeOH"],
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"electricity": production["Methanol"] * params["MWh_elec_per_tMeOH"],
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}
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).T
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demand["Ammonia"] = ammonia.unstack().reindex(index=demand.index, fill_value=0.0)
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demand["Chlorine"] = chlorine.unstack().reindex(index=demand.index, fill_value=0.0)
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demand["Methanol"] = methanol.unstack().reindex(index=demand.index, fill_value=0.0)
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demand["HVC"] = (
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demand["Basic chemicals"]
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- demand["Ammonia"]
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- demand["Methanol"]
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- demand["Chlorine"]
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)
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demand.drop(columns="Basic chemicals", inplace=True)
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demand["HVC"] = demand["HVC"].clip(lower=0)
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return demand
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def add_non_eu27_industrial_energy_demand(countries, demand, production):
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non_eu27 = countries.difference(eu27)
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if non_eu27.empty:
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return demand
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eu27_production = production.loc[countries.intersection(eu27)].sum()
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eu27_energy = demand.groupby(level=1).sum()
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eu27_averages = eu27_energy / eu27_production
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demand_non_eu27 = pd.concat(
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{k: v * eu27_averages for k, v in production.loc[non_eu27].iterrows()}
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)
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return pd.concat([demand, demand_non_eu27])
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def industrial_energy_demand(countries, year):
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nprocesses = snakemake.threads
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disable_progress = snakemake.config["run"].get("disable_progressbar", False)
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func = partial(
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industrial_energy_demand_per_country, year=year, jrc_dir=snakemake.input.jrc
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)
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tqdm_kwargs = dict(
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ascii=False,
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unit=" country",
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total=len(countries),
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desc="Build industrial energy demand",
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disable=disable_progress,
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)
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with mp.Pool(processes=nprocesses) as pool:
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demand_l = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
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return pd.concat(demand_l, keys=countries)
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def add_coke_ovens(demand, fn, year):
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df = pd.read_csv(fn, index_col=[0,1]).xs(year, level=1)
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df = df.rename(columns={'Total all products':'Total'})[fuels.keys()]
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df = df.rename(columns=fuels).T.groupby(level=0).sum().T
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df["other"] = df["all"] - df.loc[:,df.columns != "all"].sum(axis=1)
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df = df.T.reindex_like(demand.xs("Integrated steelworks", axis=1, level=1)).fillna(0)
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sel = demand.columns.get_level_values(1)=="Integrated steelworks"
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demand.loc[:,sel] += 0.75 * df.values
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return demand
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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snakemake = mock_snakemake("build_industrial_energy_demand_per_country_today")
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set_scenario_config(snakemake)
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params = snakemake.params.industry
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year = params.get("reference_year", 2019)
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countries = pd.Index(snakemake.params.countries)
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demand = industrial_energy_demand(countries.intersection(eu27), year)
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# output in MtMaterial/a
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production = (
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pd.read_csv(snakemake.input.industrial_production_per_country, index_col=0)
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/ 1e3
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)
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demand = separate_basic_chemicals(demand, production)
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demand = add_non_eu27_industrial_energy_demand(countries, demand, production)
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# for format compatibility
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demand = demand.stack(future_stack=True).unstack(level=[0, 2])
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# add energy consumption of coke ovens
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demand = add_coke_ovens(demand, snakemake.input.transformation_output_coke,
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year)
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# style and annotation
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demand.index.name = "TWh/a"
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demand.sort_index(axis=1, inplace=True)
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fn = snakemake.output.industrial_energy_demand_per_country_today
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demand.to_csv(fn)
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