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[pre-commit.ci] auto fixes from pre-commit.com hooks

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pre-commit-ci[bot] 2024-07-31 14:10:09 +00:00
parent 52c8037a3e
commit e37824336e
4 changed files with 57 additions and 62 deletions
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6
scripts/build_energy_totals.py
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@ -913,9 +913,9 @@ def build_district_heat_share(countries: List[str], idees: pd.DataFrame) -> pd.S
"""
# district heating share
district_heat = idees[["distributed heat residential", "distributed heat services"]].sum(
axis=1
)
district_heat = idees[
["distributed heat residential", "distributed heat services"]
].sum(axis=1)
total_heat = (
idees[["thermal uses residential", "thermal uses services"]]
.sum(axis=1)

5
scripts/build_industrial_production_per_country.py
View File

@ -167,7 +167,6 @@ eb_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
@ -201,9 +200,9 @@ def get_energy_ratio(country, eurostat_dir, jrc_dir, year):
if country == "CH":
e_country = e_switzerland * tj_to_ktoe
else:
ct_eurostat = country.replace("GB","UK")
ct_eurostat = country.replace("GB", "UK")
if ct_eurostat == "UK":
year=2019
year = 2019
logger.info("Assume Eurostat data for GB from 2019.")
# estimate physical output, energy consumption in the sector and country
fn = f"{eurostat_dir}/{ct_eurostat}-Energy-balance-sheets-April-2023-edition.xlsb"

88
scripts/build_industry_sector_ratios.py
View File

@ -48,10 +48,9 @@ If the `config["industry"]["ammonia"] <https://pypsa-eur.readthedocs.io/en/lates
The unit of the specific energy consumption is MWh/t material and tCO2/t material for process emissions.
"""
import country_converter as coco
import pandas as pd
from _helpers import mute_print, set_scenario_config
import country_converter as coco
cc = coco.CountryConverter()
@ -61,7 +60,6 @@ toe_to_MWh = 11.630
eu27 = cc.EU27as("ISO2").ISO2.tolist()
sheet_names = {
"Iron and steel": "ISI",
"Chemicals Industry": "CHI",
@ -122,7 +120,8 @@ def iron_and_steel():
approaches to producing iron and steel. The two primary approaches are
integrated steelworks and electric arc furnaces (EAF). The function assumes
that integrated steelworks will be replaced entirely by electric arc
furnaces due to their higher efficiency and greater reliance on electricity.
furnaces due to their higher efficiency and greater reliance on
electricity.
Returns:
pd.DataFrame: A DataFrame containing the energy consumption (in MWh/t material)
@ -237,7 +236,7 @@ def iron_and_steel():
df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
subsector = 'Steel: Sinter/Pellet-making'
subsector = "Steel: Sinter/Pellet-making"
s_fec = idees["fec"][14:20]
s_ued = idees["ued"][14:20]
@ -251,7 +250,7 @@ def iron_and_steel():
df.loc["coal", sector] += s_fec["Solids"]
subsector = 'Steel: Blast /Basic oxygen furnace'
subsector = "Steel: Blast /Basic oxygen furnace"
s_fec = idees["fec"][20:26]
s_ued = idees["ued"][20:26]
@ -272,7 +271,7 @@ def iron_and_steel():
assert s_fec.index[0] == subsector
assert s_ued.index[0] == subsector
key = 'Steel: Furnaces, refining and rolling - Electric'
key = "Steel: Furnaces, refining and rolling - Electric"
eff = s_ued[key] / s_fec[key]
# assume fully electrified, other processes scaled by used energy
@ -285,7 +284,7 @@ def iron_and_steel():
assert s_fec.index[0] == subsector
assert s_ued.index[0] == subsector
key = 'Steel: Product finishing - Electric'
key = "Steel: Product finishing - Electric"
eff = s_ued[key] / s_fec[key]
# assume fully electrified
@ -311,10 +310,11 @@ def iron_and_steel():
def chemicals_industry():
"""
This function calculates the energy consumption and emissions for the chemicals industry,
focusing on various subsectors such as basic chemicals, steam processing, furnaces,
and process cooling. The function also accounts for specific processes in ammonia,
chlorine, methanol production, and other chemicals.
This function calculates the energy consumption and emissions for the
chemicals industry, focusing on various subsectors such as basic chemicals,
steam processing, furnaces, and process cooling. The function also accounts
for specific processes in ammonia, chlorine, methanol production, and other
chemicals.
Returns:
pd.DataFrame: A DataFrame containing the energy consumption (in MWh/t material)
@ -513,14 +513,14 @@ def chemicals_industry():
df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
subsector = 'Chemicals: High-enthalpy heat processing'
subsector = "Chemicals: High-enthalpy heat processing"
s_fec = idees["fec"][70:83]
s_ued = idees["ued"][70:83]
assert s_fec.index[0] == subsector
assert s_ued.index[0] == subsector
key = 'High-enthalpy heat processing - Electric (microwave)'
key = "High-enthalpy heat processing - Electric (microwave)"
eff_elec = s_ued[key] / s_fec[key]
# assume fully electrified
@ -587,14 +587,14 @@ def chemicals_industry():
df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
subsector = 'Chemicals: High-enthalpy heat processing'
subsector = "Chemicals: High-enthalpy heat processing"
s_fec = idees["fec"][119:132]
s_ued = idees["ued"][119:132]
assert s_fec.index[0] == subsector
assert s_ued.index[0] == subsector
key = 'High-enthalpy heat processing - Electric (microwave)'
key = "High-enthalpy heat processing - Electric (microwave)"
eff_elec = s_ued[key] / s_fec[key]
# assume fully electrified
@ -647,9 +647,10 @@ def chemicals_industry():
def nonmetalic_mineral_products():
"""
This function calculates the energy consumption and emissions for the non-metallic mineral
products industry, focusing on three main sectors: cement, ceramics, and glass production.
It takes into account the specific processes and their associated energy types and emissions.
This function calculates the energy consumption and emissions for the non-
metallic mineral products industry, focusing on three main sectors: cement,
ceramics, and glass production. It takes into account the specific
processes and their associated energy types and emissions.
Returns:
pd.DataFrame: A DataFrame containing the energy consumption (in MWh/t material)
@ -691,9 +692,9 @@ def nonmetalic_mineral_products():
# pre-processing: keep existing elec and biomass, rest to methane
df.loc["elec", sector] += s_fec["Cement: Grinding, milling of raw material"]
df.loc["biomass", sector] += s_fec['Biomass and waste']
df.loc["biomass", sector] += s_fec["Biomass and waste"]
df.loc["methane", sector] += (
s_fec["Cement: Pre-heating and pre-calcination"] - s_fec['Biomass and waste']
s_fec["Cement: Pre-heating and pre-calcination"] - s_fec["Biomass and waste"]
)
subsector = "Cement: Clinker production (kilns)"
@ -703,11 +704,11 @@ def nonmetalic_mineral_products():
assert s_fec.index[0] == subsector
assert s_ued.index[0] == subsector
df.loc["biomass", sector] += s_fec['Biomass and waste']
df.loc["biomass", sector] += s_fec["Biomass and waste"]
df.loc["methane", sector] += (
s_fec["Cement: Clinker production (kilns)"] - s_fec['Biomass and waste']
s_fec["Cement: Clinker production (kilns)"] - s_fec["Biomass and waste"]
)
df.loc["elec", sector] += s_fec['Cement: Grinding, packaging and precasting']
df.loc["elec", sector] += s_fec["Cement: Grinding, packaging and precasting"]
# Process emissions
@ -757,7 +758,7 @@ def nonmetalic_mineral_products():
key = "Ceramics: Microwave drying and sintering"
# the values are zero in new JRC-data -> assume here value from JRC-2015
# eff_elec = s_ued[key] / s_fec[key]
eff_elec = 11.6/26
eff_elec = 11.6 / 26
sel = [
"Ceramics: Mixing of raw material",
@ -844,13 +845,12 @@ def nonmetalic_mineral_products():
def pulp_paper_printing():
"""
Models the energy consumption for the pulp, paper, and printing sector,
assuming complete electrification of all processes. This sector does not have
any process emissions associated with it.
assuming complete electrification of all processes. This sector does not
have any process emissions associated with it.
Returns:
pd.DataFrame: A DataFrame containing the energy consumption (in MWh/t material)
for the pulp, paper, and printing sector.
"""
sector = "Pulp, paper and printing"
@ -892,7 +892,7 @@ def pulp_paper_printing():
df.loc["elec", sector] += s_fec[sel].sum()
# Efficiency changes due to biomass
eff_bio = s_ued['Biomass and waste'] / s_fec['Biomass and waste']
eff_bio = s_ued["Biomass and waste"] / s_fec["Biomass and waste"]
df.loc["biomass", sector] += s_ued["Pulp: Pulping thermal"] / eff_bio
s_out = idees["out"][8:9]
@ -947,7 +947,7 @@ def pulp_paper_printing():
assert s_ued.index[0] == "Paper: Paper machine - Steam use"
# Efficiency changes due to biomass
eff_bio = s_ued['Biomass and waste'] / s_fec['Biomass and waste']
eff_bio = s_ued["Biomass and waste"] / s_fec["Biomass and waste"]
df.loc["biomass", sector] += s_ued["Paper: Paper machine - Steam use"] / eff_bio
s_fec = idees["fec"][68:79]
@ -956,7 +956,7 @@ def pulp_paper_printing():
assert s_ued.index[0] == "Paper: Product finishing - Steam use"
# Efficiency changes due to biomass
eff_bio = s_ued['Biomass and waste'] / s_fec['Biomass and waste']
eff_bio = s_ued["Biomass and waste"] / s_fec["Biomass and waste"]
df.loc["biomass", sector] += s_ued["Paper: Product finishing - Steam use"] / eff_bio
s_out = idees["out"][9:10]
@ -1003,10 +1003,9 @@ def pulp_paper_printing():
def food_beverages_tobacco():
"""
Calculates the energy consumption for the food, beverages, and tobacco sector,
assuming complete electrification of all processes. This sector does not have
any process emissions associated with it.
Calculates the energy consumption for the food, beverages, and tobacco
sector, assuming complete electrification of all processes. This sector
does not have any process emissions associated with it.
Returns:
pd.DataFrame: A DataFrame containing the energy consumption (in MWh/t material)
@ -1099,12 +1098,12 @@ def non_ferrous_metals():
s_fec = idees["fec"][14:25]
s_ued = idees["ued"][14:25]
assert s_fec.index[0] == 'Alumina production: High-enthalpy heat'
assert s_ued.index[0] == 'Alumina production: High-enthalpy heat'
assert s_fec.index[0] == "Alumina production: High-enthalpy heat"
assert s_ued.index[0] == "Alumina production: High-enthalpy heat"
eff_met = s_ued["Natural gas and biogas"] / s_fec["Natural gas and biogas"]
df.loc["methane", sector] += (
s_fec['Alumina production: High-enthalpy heat'] / eff_met
s_fec["Alumina production: High-enthalpy heat"] / eff_met
)
# Efficiency changes due to electrification
@ -1205,7 +1204,7 @@ def non_ferrous_metals():
key = "Aluminium processing (metallurgy e.g. cast house, reheating)"
df.loc["elec", sector] += s_ued[key] / eff_elec
key = 'Aluminium finishing - Electric'
key = "Aluminium finishing - Electric"
eff_elec = s_ued[key] / s_fec[key]
df.loc["elec", sector] += s_ued["Aluminium finishing"] / eff_elec
@ -1305,7 +1304,7 @@ def transport_equipment():
df.loc["elec", sector] += s_fec["Trans. Eq.: Product finishing"]
# Steam processing is supplied with biomass
eff_biomass = s_ued['Biomass and waste'] / s_fec['Biomass and waste']
eff_biomass = s_ued["Biomass and waste"] / s_fec["Biomass and waste"]
df.loc["biomass", sector] += s_ued["Trans. Eq.: Steam processing"] / eff_biomass
s_out = idees["out"][3:4]
@ -1357,7 +1356,7 @@ def machinery_equipment():
df.loc["elec", sector] += s_fec["Mach. Eq.: Product finishing"]
# Steam processing is supplied with biomass
eff_biomass = s_ued['Biomass and waste'] / s_fec['Biomass and waste']
eff_biomass = s_ued["Biomass and waste"] / s_fec["Biomass and waste"]
df.loc["biomass", sector] += s_ued["Mach. Eq.: Steam processing"] / eff_biomass
s_out = idees["out"][3:4]
@ -1402,7 +1401,7 @@ def textiles_and_leather():
df.loc["elec", sector] += s_fec["Textiles: Finishing Electric"]
# Steam processing is supplied with biomass
eff_biomass = s_ued[15:26]['Biomass and waste'] / s_fec[15:26]['Biomass and waste']
eff_biomass = s_ued[15:26]["Biomass and waste"] / s_fec[15:26]["Biomass and waste"]
df.loc["biomass", sector] += (
s_ued["Textiles: Pretreatment with steam"] / eff_biomass
)
@ -1450,7 +1449,7 @@ def wood_and_wood_products():
df.loc["elec", sector] += s_fec["Wood: Finishing Electric"]
# Steam processing is supplied with biomass
eff_biomass = s_ued[15:25]['Biomass and waste'] / s_fec[15:25]['Biomass and waste']
eff_biomass = s_ued[15:25]["Biomass and waste"] / s_fec[15:25]["Biomass and waste"]
df.loc["biomass", sector] += (
s_ued["Wood: Specific processes with steam"] / eff_biomass
)
@ -1510,7 +1509,7 @@ def other_industrial_sectors():
df.loc["elec", sector] += s_fec[key]
# Steam processing is supplied with biomass
eff_biomass = s_ued[15:25]['Biomass and waste'] / s_fec[15:25]['Biomass and waste']
eff_biomass = s_ued[15:25]["Biomass and waste"] / s_fec[15:25]["Biomass and waste"]
df.loc["biomass", sector] += (
s_ued["Other Industrial sectors: Steam processing"] / eff_biomass
)
@ -1534,7 +1533,6 @@ if __name__ == "__main__":
snakemake = mock_snakemake("build_industry_sector_ratios")
set_scenario_config(snakemake)
params = snakemake.params.industry
year = params["reference_year"]

2
scripts/retrieve_ammonia_demand.py
View File

@ -33,10 +33,8 @@ if __name__ == "__main__":
to_fn = snakemake.output[0]
# download .zip file
logger.info(f"Downloading Ammonia demand from {url}.")
progress_retrieve(url, to_fn, disable=disable_progress)
logger.info(f"Ammonia demand data available in '{to_fn}'.")