# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Build industrial production per country.
"""
import logging
from functools import partial
logger = logging.getLogger(__name__)
import multiprocessing as mp
import country_converter as coco
import numpy as np
import pandas as pd
from _helpers import mute_print
from tqdm import tqdm
cc = coco.CountryConverter()
tj_to_ktoe = 0.0238845
ktoe_to_twh = 0.01163
sub_sheet_name_dict = {
"Iron and steel": "ISI",
"Chemicals Industry": "CHI",
"Non-metallic mineral products": "NMM",
"Pulp, paper and printing": "PPA",
"Food, beverages and tobacco": "FBT",
"Non Ferrous Metals": "NFM",
"Transport Equipment": "TRE",
"Machinery Equipment": "MAE",
"Textiles and leather": "TEL",
"Wood and wood products": "WWP",
"Other Industrial Sectors": "OIS",
}
eu28 = cc.EU28as("ISO2").ISO2.values
jrc_names = {"GR": "EL", "GB": "UK"}
sect2sub = {
"Iron and steel": ["Electric arc", "Integrated steelworks"],
"Chemicals Industry": [
"Basic chemicals",
"Other chemicals",
"Pharmaceutical products etc.",
],
"Non-metallic mineral products": [
"Cement",
"Ceramics & other NMM",
"Glass production",
],
"Pulp, paper and printing": [
"Pulp production",
"Paper production",
"Printing and media reproduction",
],
"Food, beverages and tobacco": ["Food, beverages and tobacco"],
"Non Ferrous Metals": [
"Alumina production",
"Aluminium - primary production",
"Aluminium - secondary production",
"Other non-ferrous metals",
],
"Transport Equipment": ["Transport Equipment"],
"Machinery Equipment": ["Machinery Equipment"],
"Textiles and leather": ["Textiles and leather"],
"Wood and wood products": ["Wood and wood products"],
"Other Industrial Sectors": ["Other Industrial Sectors"],
}
sub2sect = {v: k for k, vv in sect2sub.items() for v in vv}
fields = {
"Electric arc": "Electric arc",
"Integrated steelworks": "Integrated steelworks",
"Basic chemicals": "Basic chemicals (kt ethylene eq.)",
"Other chemicals": "Other chemicals (kt ethylene eq.)",
"Pharmaceutical products etc.": "Pharmaceutical products etc. (kt ethylene eq.)",
"Cement": "Cement (kt)",
"Ceramics & other NMM": "Ceramics & other NMM (kt bricks eq.)",
"Glass production": "Glass production (kt)",
"Pulp production": "Pulp production (kt)",
"Paper production": "Paper production (kt)",
"Printing and media reproduction": "Printing and media reproduction (kt paper eq.)",
"Food, beverages and tobacco": "Physical output (index)",
"Alumina production": "Alumina production (kt)",
"Aluminium - primary production": "Aluminium - primary production",
"Aluminium - secondary production": "Aluminium - secondary production",
"Other non-ferrous metals": "Other non-ferrous metals (kt lead eq.)",
"Transport Equipment": "Physical output (index)",
"Machinery Equipment": "Physical output (index)",
"Textiles and leather": "Physical output (index)",
"Wood and wood products": "Physical output (index)",
"Other Industrial Sectors": "Physical output (index)",
}
eb_names = {
"NO": "Norway",
"AL": "Albania",
"BA": "Bosnia and Herzegovina",
"MK": "FYR of Macedonia",
"GE": "Georgia",
"IS": "Iceland",
"KO": "Kosovo",
"MD": "Moldova",
"ME": "Montenegro",
"RS": "Serbia",
"UA": "Ukraine",
"TR": "Turkey",
}
eb_sectors = {
"Iron & steel industry": "Iron and steel",
"Chemical and Petrochemical industry": "Chemicals Industry",
"Non-ferrous metal industry": "Non-metallic mineral products",
"Paper, Pulp and Print": "Pulp, paper and printing",
"Food and Tabacco": "Food, beverages and tobacco",
"Non-metallic Minerals (Glass, pottery & building mat. Industry)": "Non Ferrous Metals",
"Transport Equipment": "Transport Equipment",
"Machinery": "Machinery Equipment",
"Textile and Leather": "Textiles and leather",
"Wood and Wood Products": "Wood and wood products",
"Non-specified (Industry)": "Other Industrial Sectors",
}
# TODO: this should go in a csv in `data`
# Annual energy consumption in Switzerland by sector in 2015 (in TJ)
# From: Energieverbrauch in der Industrie und im Dienstleistungssektor, Der Bundesrat
# http://www.bfe.admin.ch/themen/00526/00541/00543/index.html?lang=de&dossier_id=00775
e_switzerland = pd.Series(
{
"Iron and steel": 7889.0,
"Chemicals Industry": 26871.0,
"Non-metallic mineral products": 15513.0 + 3820.0,
"Pulp, paper and printing": 12004.0,
"Food, beverages and tobacco": 17728.0,
"Non Ferrous Metals": 3037.0,
"Transport Equipment": 14993.0,
"Machinery Equipment": 4724.0,
"Textiles and leather": 1742.0,
"Wood and wood products": 0.0,
"Other Industrial Sectors": 10825.0,
"current electricity": 53760.0,
}
)
def find_physical_output(df):
start = np.where(df.index.str.contains("Physical output", na=""))[0][0]
empty_row = np.where(df.index.isnull())[0]
end = empty_row[np.argmax(empty_row > start)]
return slice(start, end)
def get_energy_ratio(country, eurostat_dir, jrc_dir, year):
if country == "CH":
e_country = e_switzerland * tj_to_ktoe
else:
# estimate physical output, energy consumption in the sector and country
fn = f"{eurostat_dir}/{eb_names[country]}.XLSX"
with mute_print():
df = pd.read_excel(
fn, sheet_name="2016", index_col=2, header=0, skiprows=1
).squeeze("columns")
e_country = df.loc[eb_sectors.keys(), "Total all products"].rename(eb_sectors)
fn = f"{jrc_dir}/JRC-IDEES-2015_Industry_EU28.xlsx"
with mute_print():
df = pd.read_excel(fn, sheet_name="Ind_Summary", index_col=0, header=0).squeeze(
"columns"
)
assert df.index[48] == "by sector"
year_i = df.columns.get_loc(year)
e_eu28 = df.iloc[49:76, year_i]
e_eu28.index = e_eu28.index.str.lstrip()
e_ratio = e_country / e_eu28
return pd.Series({k: e_ratio[v] for k, v in sub2sect.items()})
def industry_production_per_country(country, year, eurostat_dir, jrc_dir):
def get_sector_data(sector, country):
jrc_country = jrc_names.get(country, country)
fn = f"{jrc_dir}/JRC-IDEES-2015_Industry_{jrc_country}.xlsx"
sheet = sub_sheet_name_dict[sector]
with mute_print():
df = pd.read_excel(fn, sheet_name=sheet, index_col=0, header=0).squeeze(
"columns"
)
year_i = df.columns.get_loc(year)
df = df.iloc[find_physical_output(df), year_i]
df = df.loc[map(fields.get, sect2sub[sector])]
df.index = sect2sub[sector]
return df
ct = "EU28" if country not in eu28 else country
demand = pd.concat([get_sector_data(s, ct) for s in sect2sub])
if country not in eu28:
demand *= get_energy_ratio(country, eurostat_dir, jrc_dir, year)
demand.name = country
return demand
def industry_production(countries, year, eurostat_dir, jrc_dir):
nprocesses = snakemake.threads
disable_progress = snakemake.config["run"].get("disable_progressbar", False)
func = partial(
industry_production_per_country,
year=year,
eurostat_dir=eurostat_dir,
jrc_dir=jrc_dir,
)
tqdm_kwargs = dict(
ascii=False,
unit=" country",
total=len(countries),
desc="Build industry production",
disable=disable_progress,
)
with mp.Pool(processes=nprocesses) as pool:
demand_l = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
demand = pd.concat(demand_l, axis=1).T
demand.index.name = "kton/a"
return demand
def separate_basic_chemicals(demand, year):
"""
Separate basic chemicals into ammonia, chlorine, methanol and HVC.
"""
ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
there = ammonia.index.intersection(demand.index)
missing = demand.index.symmetric_difference(there)
logger.info(f"Following countries have no ammonia demand: {missing.tolist()}")
demand["Ammonia"] = 0.0
demand.loc[there, "Ammonia"] = ammonia.loc[there, str(year)]
demand["Basic chemicals"] -= demand["Ammonia"]
# EE, HR and LT got negative demand through subtraction - poor data
demand["Basic chemicals"].clip(lower=0.0, inplace=True)
# assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
distribution_key = demand["Basic chemicals"] / demand["Basic chemicals"].sum()
demand["HVC"] = config["HVC_production_today"] * 1e3 * distribution_key
demand["Chlorine"] = config["chlorine_production_today"] * 1e3 * distribution_key
demand["Methanol"] = config["methanol_production_today"] * 1e3 * distribution_key
demand.drop(columns=["Basic chemicals"], inplace=True)
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("build_industrial_production_per_country")
logging.basicConfig(level=snakemake.config["logging"]["level"])
countries = snakemake.config["countries"]
year = snakemake.config["industry"]["reference_year"]
config = snakemake.config["industry"]
jrc_dir = snakemake.input.jrc
eurostat_dir = snakemake.input.eurostat
demand = industry_production(countries, year, eurostat_dir, jrc_dir)
separate_basic_chemicals(demand, year)
fn = snakemake.output.industrial_production_per_country
demand.to_csv(fn, float_format="%.2f")