1492 lines
44 KiB
Python
1492 lines
44 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2023 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 specific energy consumption by carrier and industries.
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"""
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import pandas as pd
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from _helpers import mute_print
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# GWh/ktoe OR MWh/toe
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toe_to_MWh = 11.630
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eu28 = [
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"FR",
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"DE",
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"GB",
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"IT",
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"ES",
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"PL",
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"SE",
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"NL",
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"BE",
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"FI",
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"DK",
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"PT",
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"RO",
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"AT",
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"BG",
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"EE",
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"GR",
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"LV",
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"CZ",
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"HU",
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"IE",
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"SK",
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"LT",
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"HR",
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"LU",
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"SI",
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"CY",
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"MT",
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]
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sheet_names = {
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"Iron and steel": "ISI",
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"Chemicals Industry": "CHI",
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"Non-metallic mineral products": "NMM",
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"Pulp, paper and printing": "PPA",
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"Food, beverages and tobacco": "FBT",
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"Non Ferrous Metals": "NFM",
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"Transport Equipment": "TRE",
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"Machinery Equipment": "MAE",
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"Textiles and leather": "TEL",
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"Wood and wood products": "WWP",
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"Other Industrial Sectors": "OIS",
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}
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index = [
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"elec",
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"coal",
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"coke",
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"biomass",
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"methane",
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"hydrogen",
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"heat",
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"naphtha",
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"ammonia",
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"process emission",
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"process emission from feedstock",
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]
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def load_idees_data(sector, country="EU28"):
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suffixes = {"out": "", "fec": "_fec", "ued": "_ued", "emi": "_emi"}
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sheets = {k: sheet_names[sector] + v for k, v in suffixes.items()}
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def usecols(x):
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return isinstance(x, str) or x == year
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with mute_print():
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idees = pd.read_excel(
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f"{snakemake.input.idees}/JRC-IDEES-2015_Industry_{country}.xlsx",
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sheet_name=list(sheets.values()),
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index_col=0,
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header=0,
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usecols=usecols,
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)
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for k, v in sheets.items():
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idees[k] = idees.pop(v).squeeze()
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return idees
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def iron_and_steel():
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# There are two different approaches to produce iron and steel:
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# i.e., integrated steelworks and electric arc.
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# Electric arc approach has higher efficiency and relies more on electricity.
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# We assume that integrated steelworks will be replaced by electric arc entirely.
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sector = "Iron and steel"
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idees = load_idees_data(sector)
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df = pd.DataFrame(index=index)
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## Electric arc
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sector = "Electric arc"
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df[sector] = 0.0
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s_fec = idees["fec"][51:57]
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assert s_fec.index[0] == sector
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sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
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df.at["elec", sector] += s_fec[sel].sum()
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df.at["heat", sector] += s_fec["Low enthalpy heat"]
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subsector = "Steel: Smelters"
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s_fec = idees["fec"][61:67]
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s_ued = idees["ued"][61:67]
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assert s_fec.index[0] == subsector
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assert s_ued.index[0] == subsector
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# efficiency changes due to transforming all the smelters into methane
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key = "Natural gas (incl. biogas)"
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eff_met = s_ued[key] / s_fec[key]
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df.at["methane", sector] += s_ued[subsector] / eff_met
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subsector = "Steel: Electric arc"
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s_fec = idees["fec"][67:68]
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assert s_fec.index[0] == subsector
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df.at["elec", sector] += s_fec[subsector]
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subsector = "Steel: Furnaces, Refining and Rolling"
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s_fec = idees["fec"][68:75]
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s_ued = idees["ued"][68:75]
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assert s_fec.index[0] == subsector
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assert s_ued.index[0] == subsector
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key = "Steel: Furnaces, Refining and Rolling - Electric"
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eff = s_ued[key] / s_fec[key]
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# assume fully electrified, other processes scaled by used energy
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df.at["elec", sector] += s_ued[subsector] / eff
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subsector = "Steel: Products finishing"
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s_fec = idees["fec"][75:92]
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s_ued = idees["ued"][75:92]
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assert s_fec.index[0] == subsector
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assert s_ued.index[0] == subsector
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key = "Steel: Products finishing - Electric"
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eff = s_ued[key] / s_fec[key]
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# assume fully electrified
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df.at["elec", sector] += s_ued[subsector] / eff
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# Process emissions (per physical output)
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s_emi = idees["emi"][51:93]
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assert s_emi.index[0] == sector
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s_out = idees["out"][7:8]
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assert s_out.index[0] == sector
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# tCO2/t material
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df.loc["process emission", sector] += s_emi["Process emissions"] / s_out[sector]
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# final energy consumption MWh/t material
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sel = ["elec", "heat", "methane"]
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df.loc[sel, sector] = df.loc[sel, sector] * toe_to_MWh / s_out[sector]
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## DRI + Electric arc
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# For primary route: DRI with H2 + EAF
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sector = "DRI + Electric arc"
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df[sector] = df["Electric arc"]
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# add H2 consumption for DRI at 1.7 MWh H2 /ton steel
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df.at["hydrogen", sector] = params["H2_DRI"]
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# add electricity consumption in DRI shaft (0.322 MWh/tSl)
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df.at["elec", sector] += params["elec_DRI"]
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## Integrated steelworks
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# could be used in combination with CCS)
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# Assume existing fuels are kept, except for furnaces, refining, rolling, finishing
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# Ignore 'derived gases' since these are top gases from furnaces
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sector = "Integrated steelworks"
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df[sector] = 0.0
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s_fec = idees["fec"][3:9]
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assert s_fec.index[0] == sector
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sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
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df.loc["elec", sector] += s_fec[sel].sum()
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df.loc["heat", sector] += s_fec["Low enthalpy heat"]
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subsector = "Steel: Sinter/Pellet making"
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s_fec = idees["fec"][13:19]
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s_ued = idees["ued"][13:19]
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assert s_fec.index[0] == subsector
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assert s_ued.index[0] == subsector
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df.loc["elec", sector] += s_fec["Electricity"]
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sel = ["Natural gas (incl. biogas)", "Residual fuel oil"]
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df.loc["methane", sector] += s_fec[sel].sum()
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df.loc["coal", sector] += s_fec["Solids"]
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subsector = "Steel: Blast /Basic oxygen furnace"
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s_fec = idees["fec"][19:25]
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s_ued = idees["ued"][19:25]
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assert s_fec.index[0] == subsector
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assert s_ued.index[0] == subsector
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sel = ["Natural gas (incl. biogas)", "Residual fuel oil"]
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df.loc["methane", sector] += s_fec[sel].sum()
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df.loc["coal", sector] += s_fec["Solids"]
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df.loc["coke", sector] = s_fec["Coke"]
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subsector = "Steel: Furnaces, Refining and Rolling"
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s_fec = idees["fec"][25:32]
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s_ued = idees["ued"][25:32]
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assert s_fec.index[0] == subsector
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assert s_ued.index[0] == subsector
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key = "Steel: Furnaces, Refining and Rolling - Electric"
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eff = s_ued[key] / s_fec[key]
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# assume fully electrified, other processes scaled by used energy
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df.loc["elec", sector] += s_ued[subsector] / eff
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subsector = "Steel: Products finishing"
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s_fec = idees["fec"][32:49]
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s_ued = idees["ued"][32:49]
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assert s_fec.index[0] == subsector
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assert s_ued.index[0] == subsector
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key = "Steel: Products finishing - Electric"
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eff = s_ued[key] / s_fec[key]
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# assume fully electrified
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df.loc["elec", sector] += s_ued[subsector] / eff
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# Process emissions (per physical output)
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s_emi = idees["emi"][3:50]
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assert s_emi.index[0] == sector
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s_out = idees["out"][6:7]
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assert s_out.index[0] == sector
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# tCO2/t material
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df.loc["process emission", sector] = s_emi["Process emissions"] / s_out[sector]
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# final energy consumption MWh/t material
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sel = ["elec", "heat", "methane", "coke", "coal"]
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df.loc[sel, sector] = df.loc[sel, sector] * toe_to_MWh / s_out[sector]
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return df
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def chemicals_industry():
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sector = "Chemicals Industry"
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idees = load_idees_data(sector)
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df = pd.DataFrame(index=index)
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# Basic chemicals
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sector = "Basic chemicals"
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df[sector] = 0.0
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s_fec = idees["fec"][3:9]
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assert s_fec.index[0] == sector
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sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
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df.loc["elec", sector] += s_fec[sel].sum()
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df.loc["heat", sector] += s_fec["Low enthalpy heat"]
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subsector = "Chemicals: Feedstock (energy used as raw material)"
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# There are Solids, Refinery gas, LPG, Diesel oil, Residual fuel oil,
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# Other liquids, Naphtha, Natural gas for feedstock.
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# Naphta represents 47%, methane 17%. LPG (18%) solids, refinery gas,
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# diesel oil, residual fuel oils and other liquids are asimilated to Naphtha
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s_fec = idees["fec"][13:22]
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assert s_fec.index[0] == subsector
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df.loc["naphtha", sector] += s_fec["Naphtha"]
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df.loc["methane", sector] += s_fec["Natural gas"]
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# LPG and other feedstock materials are assimilated to naphtha
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# since they will be produced through Fischer-Tropsh process
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sel = [
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"Solids",
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"Refinery gas",
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"LPG",
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"Diesel oil",
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"Residual fuel oil",
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"Other liquids",
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]
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df.loc["naphtha", sector] += s_fec[sel].sum()
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subsector = "Chemicals: Steam processing"
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# All the final energy consumption in the steam processing is
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# converted to methane, since we need >1000 C temperatures here.
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# The current efficiency of methane is assumed in the conversion.
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s_fec = idees["fec"][22:33]
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s_ued = idees["ued"][22:33]
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assert s_fec.index[0] == subsector
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assert s_ued.index[0] == subsector
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# efficiency of natural gas
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eff_ch4 = s_ued["Natural gas (incl. biogas)"] / s_fec["Natural gas (incl. biogas)"]
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# replace all fec by methane
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df.loc["methane", sector] += s_ued[subsector] / eff_ch4
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subsector = "Chemicals: Furnaces"
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s_fec = idees["fec"][33:41]
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s_ued = idees["ued"][33:41]
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assert s_fec.index[0] == subsector
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assert s_ued.index[0] == subsector
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# efficiency of electrification
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key = "Chemicals: Furnaces - Electric"
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eff_elec = s_ued[key] / s_fec[key]
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# assume fully electrified
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df.loc["elec", sector] += s_ued[subsector] / eff_elec
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subsector = "Chemicals: Process cooling"
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s_fec = idees["fec"][41:55]
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s_ued = idees["ued"][41:55]
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assert s_fec.index[0] == subsector
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assert s_ued.index[0] == subsector
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key = "Chemicals: Process cooling - Electric"
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eff_elec = s_ued[key] / s_fec[key]
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# assume fully electrified
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df.loc["elec", sector] += s_ued[subsector] / eff_elec
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subsector = "Chemicals: Generic electric process"
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s_fec = idees["fec"][55:56]
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assert s_fec.index[0] == subsector
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df.loc["elec", sector] += s_fec[subsector]
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# Process emissions
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# Correct everything by subtracting 2015's ammonia demand and
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# putting in ammonia demand for H2 and electricity separately
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s_emi = idees["emi"][3:57]
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assert s_emi.index[0] == sector
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# convert from MtHVC/a to ktHVC/a
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s_out = params["HVC_production_today"] * 1e3
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# tCO2/t material
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df.loc["process emission", sector] += (
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s_emi["Process emissions"]
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- params["petrochemical_process_emissions"] * 1e3
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- params["NH3_process_emissions"] * 1e3
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) / s_out
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# emissions originating from feedstock, could be non-fossil origin
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# tCO2/t material
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df.loc["process emission from feedstock", sector] += (
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params["petrochemical_process_emissions"] * 1e3
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) / s_out
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# convert from ktoe/a to GWh/a
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sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
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df.loc[sources, sector] *= toe_to_MWh
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# subtract ammonia energy demand (in ktNH3/a)
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ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
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ammonia_total = ammonia.loc[ammonia.index.intersection(eu28), str(year)].sum()
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df.loc["methane", sector] -= ammonia_total * params["MWh_CH4_per_tNH3_SMR"]
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df.loc["elec", sector] -= ammonia_total * params["MWh_elec_per_tNH3_SMR"]
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# subtract chlorine demand
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chlorine_total = params["chlorine_production_today"]
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df.loc["hydrogen", sector] -= chlorine_total * params["MWh_H2_per_tCl"]
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df.loc["elec", sector] -= chlorine_total * params["MWh_elec_per_tCl"]
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# subtract methanol demand
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methanol_total = params["methanol_production_today"]
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df.loc["methane", sector] -= methanol_total * params["MWh_CH4_per_tMeOH"]
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df.loc["elec", sector] -= methanol_total * params["MWh_elec_per_tMeOH"]
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# MWh/t material
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df.loc[sources, sector] = df.loc[sources, sector] / s_out
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df.rename(columns={sector: "HVC"}, inplace=True)
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# HVC mechanical recycling
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sector = "HVC (mechanical recycling)"
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df[sector] = 0.0
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df.loc["elec", sector] = params["MWh_elec_per_tHVC_mechanical_recycling"]
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# HVC chemical recycling
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sector = "HVC (chemical recycling)"
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df[sector] = 0.0
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df.loc["elec", sector] = params["MWh_elec_per_tHVC_chemical_recycling"]
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# Ammonia
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sector = "Ammonia"
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df[sector] = 0.0
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if snakemake.params["sector_amonia"]:
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df.loc["ammonia", sector] = params["MWh_NH3_per_tNH3"]
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else:
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df.loc["hydrogen", sector] = params["MWh_H2_per_tNH3_electrolysis"]
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df.loc["elec", sector] = params["MWh_elec_per_tNH3_electrolysis"]
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# Chlorine
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sector = "Chlorine"
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df[sector] = 0.0
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df.loc["hydrogen", sector] = params["MWh_H2_per_tCl"]
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df.loc["elec", sector] = params["MWh_elec_per_tCl"]
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# Methanol
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sector = "Methanol"
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df[sector] = 0.0
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df.loc["methane", sector] = params["MWh_CH4_per_tMeOH"]
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df.loc["elec", sector] = params["MWh_elec_per_tMeOH"]
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# Other chemicals
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sector = "Other chemicals"
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df[sector] = 0.0
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s_fec = idees["fec"][58:64]
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assert s_fec.index[0] == sector
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sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
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df.loc["elec", sector] += s_fec[sel].sum()
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df.loc["heat", sector] += s_fec["Low enthalpy heat"]
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subsector = "Chemicals: High enthalpy heat processing"
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s_fec = idees["fec"][68:81]
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s_ued = idees["ued"][68:81]
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assert s_fec.index[0] == subsector
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assert s_ued.index[0] == subsector
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key = "High enthalpy heat processing - Electric (microwave)"
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eff_elec = s_ued[key] / s_fec[key]
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# assume fully electrified
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df.loc["elec", sector] += s_ued[subsector] / eff_elec
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subsector = "Chemicals: Furnaces"
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s_fec = idees["fec"][81:89]
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s_ued = idees["ued"][81:89]
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assert s_fec.index[0] == subsector
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assert s_ued.index[0] == subsector
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key = "Chemicals: Furnaces - Electric"
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eff_elec = s_ued[key] / s_fec[key]
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# assume fully electrified
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df.loc["elec", sector] += s_ued[subsector] / eff_elec
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subsector = "Chemicals: Process cooling"
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|
|
s_fec = idees["fec"][89:103]
|
|
s_ued = idees["ued"][89:103]
|
|
assert s_fec.index[0] == subsector
|
|
assert s_ued.index[0] == subsector
|
|
|
|
key = "Chemicals: Process cooling - Electric"
|
|
eff = s_ued[key] / s_fec[key]
|
|
|
|
# assume fully electrified
|
|
df.loc["elec", sector] += s_ued[subsector] / eff
|
|
|
|
subsector = "Chemicals: Generic electric process"
|
|
|
|
s_fec = idees["fec"][103:104]
|
|
assert s_fec.index[0] == subsector
|
|
|
|
df.loc["elec", sector] += s_fec[subsector]
|
|
|
|
# Process emissions
|
|
|
|
s_emi = idees["emi"][58:105]
|
|
s_out = idees["out"][9:10]
|
|
assert s_emi.index[0] == sector
|
|
assert sector in str(s_out.index)
|
|
|
|
# tCO2/t material
|
|
df.loc["process emission", sector] += s_emi["Process emissions"] / s_out.values
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values
|
|
|
|
# Pharmaceutical products
|
|
|
|
sector = "Pharmaceutical products etc."
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][106:112]
|
|
assert s_fec.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
subsector = "Chemicals: High enthalpy heat processing"
|
|
|
|
s_fec = idees["fec"][116:129]
|
|
s_ued = idees["ued"][116:129]
|
|
assert s_fec.index[0] == subsector
|
|
assert s_ued.index[0] == subsector
|
|
|
|
key = "High enthalpy heat processing - Electric (microwave)"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
|
|
# assume fully electrified
|
|
df.loc["elec", sector] += s_ued[subsector] / eff_elec
|
|
|
|
subsector = "Chemicals: Furnaces"
|
|
|
|
s_fec = idees["fec"][129:137]
|
|
s_ued = idees["ued"][129:137]
|
|
assert s_fec.index[0] == subsector
|
|
assert s_ued.index[0] == subsector
|
|
|
|
key = "Chemicals: Furnaces - Electric"
|
|
eff = s_ued[key] / s_fec[key]
|
|
|
|
# assume fully electrified
|
|
df.loc["elec", sector] += s_ued[subsector] / eff
|
|
|
|
subsector = "Chemicals: Process cooling"
|
|
|
|
s_fec = idees["fec"][137:151]
|
|
s_ued = idees["ued"][137:151]
|
|
assert s_fec.index[0] == subsector
|
|
assert s_ued.index[0] == subsector
|
|
|
|
key = "Chemicals: Process cooling - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
|
|
# assume fully electrified
|
|
df.loc["elec", sector] += s_ued[subsector] / eff_elec
|
|
|
|
subsector = "Chemicals: Generic electric process"
|
|
|
|
s_fec = idees["fec"][151:152]
|
|
s_out = idees["out"][10:11]
|
|
assert s_fec.index[0] == subsector
|
|
assert sector in str(s_out.index)
|
|
|
|
df.loc["elec", sector] += s_fec[subsector]
|
|
|
|
# tCO2/t material
|
|
df.loc["process emission", sector] += 0.0
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values
|
|
|
|
return df
|
|
|
|
|
|
def nonmetalic_mineral_products():
|
|
# This includes cement, ceramic and glass production.
|
|
# This includes process emissions related to the fabrication of clinker.
|
|
|
|
sector = "Non-metallic mineral products"
|
|
idees = load_idees_data(sector)
|
|
|
|
df = pd.DataFrame(index=index)
|
|
|
|
# Cement
|
|
|
|
# This sector has process-emissions.
|
|
# Includes three subcategories:
|
|
# (a) Grinding, milling of raw material,
|
|
# (b) Pre-heating and pre-calcination,
|
|
# (c) clinker production (kilns),
|
|
# (d) Grinding, packaging.
|
|
# (b)+(c) represent 94% of fec. So (a) is joined to (b) and (d) is joined to (c).
|
|
# Temperatures above 1400C are required for processing limestone and sand into clinker.
|
|
# Everything (except current electricity and heat consumption and existing biomass)
|
|
# is transformed into methane for high T.
|
|
|
|
sector = "Cement"
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][3:25]
|
|
s_ued = idees["ued"][3:25]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# 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"]
|
|
df.loc["methane", sector] += (
|
|
s_fec["Cement: Pre-heating and pre-calcination"] - s_fec["Biomass"]
|
|
)
|
|
|
|
subsector = "Cement: Clinker production (kilns)"
|
|
|
|
s_fec = idees["fec"][34:43]
|
|
s_ued = idees["ued"][34:43]
|
|
assert s_fec.index[0] == subsector
|
|
assert s_ued.index[0] == subsector
|
|
|
|
df.loc["biomass", sector] += s_fec["Biomass"]
|
|
df.loc["methane", sector] += (
|
|
s_fec["Cement: Clinker production (kilns)"] - s_fec["Biomass"]
|
|
)
|
|
df.loc["elec", sector] += s_fec["Cement: Grinding, packaging"]
|
|
|
|
# Process emissions
|
|
|
|
# come from calcination of limestone to chemically reactive calcium oxide (lime).
|
|
# Calcium carbonate -> lime + CO2
|
|
# CaCO3 -> CaO + CO2
|
|
|
|
s_emi = idees["emi"][3:44]
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = idees["out"][7:8]
|
|
assert sector in str(s_out.index)
|
|
|
|
# tCO2/t material
|
|
df.loc["process emission", sector] += s_emi["Process emissions"] / s_out.values
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values
|
|
|
|
# Ceramics & other NMM
|
|
|
|
# This sector has process emissions.
|
|
# Includes four subcategories:
|
|
# (a) Mixing of raw material,
|
|
# (b) Drying and sintering of raw material,
|
|
# (c) Primary production process,
|
|
# (d) Product finishing.
|
|
# (b) represents 65% of fec and (a) 4%. So (a) is joined to (b).
|
|
# Everything is electrified
|
|
|
|
sector = "Ceramics & other NMM"
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][45:94]
|
|
s_ued = idees["ued"][45:94]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Efficiency changes due to electrification
|
|
key = "Ceramics: Microwave drying and sintering"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
|
|
sel = [
|
|
"Ceramics: Mixing of raw material",
|
|
"Ceramics: Drying and sintering of raw material",
|
|
]
|
|
df.loc["elec", sector] += s_ued[sel].sum() / eff_elec
|
|
|
|
key = "Ceramics: Electric kiln"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
|
|
df.loc["elec", sector] += s_ued["Ceramics: Primary production process"] / eff_elec
|
|
|
|
key = "Ceramics: Electric furnace"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
|
|
df.loc["elec", sector] += s_ued["Ceramics: Product finishing"] / eff_elec
|
|
|
|
s_emi = idees["emi"][45:94]
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = idees["out"][8:9]
|
|
assert sector in str(s_out.index)
|
|
|
|
# tCO2/t material
|
|
df.loc["process emission", sector] += s_emi["Process emissions"] / s_out.values
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values
|
|
|
|
# Glass production
|
|
|
|
# This sector has process emissions.
|
|
# Includes four subcategories:
|
|
# (a) Melting tank
|
|
# (b) Forming
|
|
# (c) Annealing
|
|
# (d) Finishing processes.
|
|
# (a) represents 73%. (b), (d) are joined to (c).
|
|
# Everything is electrified.
|
|
|
|
sector = "Glass production"
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][95:123]
|
|
s_ued = idees["ued"][95:123]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Efficiency changes due to electrification
|
|
key = "Glass: Electric melting tank"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
|
|
df.loc["elec", sector] += s_ued["Glass: Melting tank"] / eff_elec
|
|
|
|
key = "Glass: Annealing - electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
|
|
sel = ["Glass: Forming", "Glass: Annealing", "Glass: Finishing processes"]
|
|
df.loc["elec", sector] += s_ued[sel].sum() / eff_elec
|
|
|
|
s_emi = idees["emi"][95:124]
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = idees["out"][9:10]
|
|
assert sector in str(s_out.index)
|
|
|
|
# tCO2/t material
|
|
df.loc["process emission", sector] += s_emi["Process emissions"] / s_out.values
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values
|
|
|
|
return df
|
|
|
|
|
|
def pulp_paper_printing():
|
|
# Pulp, paper and printing can be completely electrified.
|
|
# There are no process emissions associated to this sector.
|
|
|
|
sector = "Pulp, paper and printing"
|
|
idees = load_idees_data(sector)
|
|
|
|
df = pd.DataFrame(index=index)
|
|
|
|
# Pulp production
|
|
|
|
# Includes three subcategories:
|
|
# (a) Wood preparation, grinding;
|
|
# (b) Pulping;
|
|
# (c) Cleaning.
|
|
#
|
|
# (b) Pulping is either biomass or electric; left like this (dominated by biomass).
|
|
# (a) Wood preparation, grinding and (c) Cleaning represent only 10% of their current
|
|
# energy consumption is assumed to be electrified without any change in efficiency
|
|
|
|
sector = "Pulp production"
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][3:28]
|
|
s_ued = idees["ued"][3:28]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Industry-specific
|
|
sel = [
|
|
"Pulp: Wood preparation, grinding",
|
|
"Pulp: Cleaning",
|
|
"Pulp: Pulping electric",
|
|
]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
# Efficiency changes due to biomass
|
|
eff_bio = s_ued["Biomass"] / s_fec["Biomass"]
|
|
df.loc["biomass", sector] += s_ued["Pulp: Pulping thermal"] / eff_bio
|
|
|
|
s_out = idees["out"][8:9]
|
|
assert sector in str(s_out.index)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = (
|
|
df.loc[sources, sector] * toe_to_MWh / s_out["Pulp production (kt)"]
|
|
)
|
|
|
|
# Paper production
|
|
|
|
# Includes three subcategories:
|
|
# (a) Stock preparation;
|
|
# (b) Paper machine;
|
|
# (c) Product finishing.
|
|
#
|
|
# (b) Paper machine and (c) Product finishing are left electric
|
|
# and thermal is moved to biomass. The efficiency is calculated
|
|
# from the pulping process that is already biomass.
|
|
#
|
|
# (a) Stock preparation represents only 7% and its current energy
|
|
# consumption is assumed to be electrified without any change in efficiency.
|
|
|
|
sector = "Paper production"
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][29:78]
|
|
s_ued = idees["ued"][29:78]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Industry-specific
|
|
df.loc["elec", sector] += s_fec["Paper: Stock preparation"]
|
|
|
|
# add electricity from process that is already electrified
|
|
df.loc["elec", sector] += s_fec["Paper: Paper machine - Electricity"]
|
|
|
|
# add electricity from process that is already electrified
|
|
df.loc["elec", sector] += s_fec["Paper: Product finishing - Electricity"]
|
|
|
|
s_fec = idees["fec"][53:64]
|
|
s_ued = idees["ued"][53:64]
|
|
assert s_fec.index[0] == "Paper: Paper machine - Steam use"
|
|
assert s_ued.index[0] == "Paper: Paper machine - Steam use"
|
|
|
|
# Efficiency changes due to biomass
|
|
eff_bio = s_ued["Biomass"] / s_fec["Biomass"]
|
|
df.loc["biomass", sector] += s_ued["Paper: Paper machine - Steam use"] / eff_bio
|
|
|
|
s_fec = idees["fec"][66:77]
|
|
s_ued = idees["ued"][66:77]
|
|
assert s_fec.index[0] == "Paper: Product finishing - Steam use"
|
|
assert s_ued.index[0] == "Paper: Product finishing - Steam use"
|
|
|
|
# Efficiency changes due to biomass
|
|
eff_bio = s_ued["Biomass"] / s_fec["Biomass"]
|
|
df.loc["biomass", sector] += s_ued["Paper: Product finishing - Steam use"] / eff_bio
|
|
|
|
s_out = idees["out"][9:10]
|
|
assert sector in str(s_out.index)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values
|
|
|
|
# Printing and media reproduction
|
|
|
|
# (a) Printing and publishing is assumed to be
|
|
# electrified without any change in efficiency.
|
|
|
|
sector = "Printing and media reproduction"
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][79:90]
|
|
s_ued = idees["ued"][79:90]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
df.loc["elec", sector] += s_ued[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
df.loc["heat", sector] += s_ued["Low enthalpy heat"]
|
|
|
|
# Industry-specific
|
|
df.loc["elec", sector] += s_fec["Printing and publishing"]
|
|
df.loc["elec", sector] += s_ued["Printing and publishing"]
|
|
|
|
s_out = idees["out"][10:11]
|
|
assert sector in str(s_out.index)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values
|
|
|
|
return df
|
|
|
|
|
|
def food_beverages_tobacco():
|
|
# Food, beverages and tobaco can be completely electrified.
|
|
# There are no process emissions associated to this sector.
|
|
|
|
sector = "Food, beverages and tobacco"
|
|
idees = load_idees_data(sector)
|
|
|
|
df = pd.DataFrame(index=index)
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][3:78]
|
|
s_ued = idees["ued"][3:78]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Efficiency changes due to electrification
|
|
|
|
key = "Food: Direct Heat - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Food: Oven (direct heat)"] / eff_elec
|
|
|
|
key = "Food: Process Heat - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Food: Specific process heat"] / eff_elec
|
|
|
|
key = "Food: Electric drying"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Food: Drying"] / eff_elec
|
|
|
|
key = "Food: Electric cooling"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += (
|
|
s_ued["Food: Process cooling and refrigeration"] / eff_elec
|
|
)
|
|
|
|
# Steam processing goes all to biomass without change in efficiency
|
|
df.loc["biomass", sector] += s_fec["Food: Steam processing"]
|
|
|
|
# add electricity from process that is already electrified
|
|
df.loc["elec", sector] += s_fec["Food: Electric machinery"]
|
|
|
|
s_out = idees["out"][3:4]
|
|
assert "Physical output" in str(s_out.index)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = (
|
|
df.loc[sources, sector] * toe_to_MWh / s_out["Physical output (index)"]
|
|
)
|
|
|
|
return df
|
|
|
|
|
|
def non_ferrous_metals():
|
|
sector = "Non Ferrous Metals"
|
|
idees = load_idees_data(sector)
|
|
|
|
df = pd.DataFrame(index=index)
|
|
|
|
# Alumina
|
|
|
|
# High enthalpy heat is converted to methane.
|
|
# Process heat at T>500C is required here.
|
|
# Refining is electrified.
|
|
# There are no process emissions associated to Alumina manufacturing.
|
|
|
|
sector = "Alumina production"
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][3:31]
|
|
s_ued = idees["ued"][3:31]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# High-enthalpy heat is transformed into methane
|
|
|
|
s_fec = idees["fec"][13:24]
|
|
s_ued = idees["ued"][13:24]
|
|
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 (incl. biogas)"] / s_fec["Natural gas (incl. biogas)"]
|
|
df.loc["methane", sector] += (
|
|
s_fec["Alumina production: High enthalpy heat"] / eff_met
|
|
)
|
|
|
|
# Efficiency changes due to electrification
|
|
|
|
s_fec = idees["fec"][24:30]
|
|
s_ued = idees["ued"][24:30]
|
|
assert s_fec.index[0] == "Alumina production: Refining"
|
|
assert s_ued.index[0] == "Alumina production: Refining"
|
|
|
|
eff_elec = s_ued["Electricity"] / s_fec["Electricity"]
|
|
df.loc["elec", sector] += s_ued["Alumina production: Refining"] / eff_elec
|
|
|
|
s_out = idees["out"][9:10]
|
|
assert sector in str(s_out.index)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = (
|
|
df.loc[sources, sector] * toe_to_MWh / s_out["Alumina production (kt)"]
|
|
)
|
|
|
|
# Aluminium primary route
|
|
|
|
# Production through the primary route is divided into 50% remains
|
|
# as today and 50% is transformed into secondary route.
|
|
|
|
sector = "Aluminium - primary production"
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][31:66]
|
|
s_ued = idees["ued"][31:66]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Add aluminium electrolysis (smelting
|
|
df.loc["elec", sector] += s_fec["Aluminium electrolysis (smelting)"]
|
|
|
|
# Efficiency changes due to electrification
|
|
key = "Aluminium processing - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
|
|
key = "Aluminium processing (metallurgy e.g. cast house, reheating)"
|
|
df.loc["elec", sector] += s_ued[key] / eff_elec
|
|
|
|
key = "Aluminium finishing - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Aluminium finishing"] / eff_elec
|
|
|
|
s_emi = idees["emi"][31:67]
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = idees["out"][11:12]
|
|
assert sector in str(s_out.index)
|
|
|
|
# tCO2/t material
|
|
df.loc["process emission", sector] = (
|
|
s_emi["Process emissions"] / s_out["Aluminium - primary production"]
|
|
)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = (
|
|
df.loc[sources, sector] * toe_to_MWh / s_out["Aluminium - primary production"]
|
|
)
|
|
|
|
# Aluminium secondary route
|
|
|
|
# All is converted into secondary route fully electrified.
|
|
|
|
sector = "Aluminium - secondary production"
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][68:109]
|
|
s_ued = idees["ued"][68:109]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Efficiency changes due to electrification
|
|
key = "Secondary aluminium - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
key = "Secondary aluminium (incl. pre-treatment, remelting)"
|
|
df.loc["elec", sector] += s_ued[key] / eff_elec
|
|
|
|
key = "Aluminium processing - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
key = "Aluminium processing (metallurgy e.g. cast house, reheating)"
|
|
df.loc["elec", sector] += s_ued[key] / eff_elec
|
|
|
|
key = "Aluminium finishing - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Aluminium finishing"] / eff_elec
|
|
|
|
s_out = idees["out"][12:13]
|
|
assert sector in str(s_out.index)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = (
|
|
df.loc[sources, sector] * toe_to_MWh / s_out["Aluminium - secondary production"]
|
|
)
|
|
|
|
# Other non-ferrous metals
|
|
|
|
sector = "Other non-ferrous metals"
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][110:152]
|
|
s_ued = idees["ued"][110:152]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Efficiency changes due to electrification
|
|
key = "Metal production - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Other Metals: production"] / eff_elec
|
|
|
|
key = "Metal processing - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
key = "Metal processing (metallurgy e.g. cast house, reheating)"
|
|
df.loc["elec", sector] += s_ued[key] / eff_elec
|
|
|
|
key = "Metal finishing - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Metal finishing"] / eff_elec
|
|
|
|
s_emi = idees["emi"][110:153]
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = idees["out"][13:14]
|
|
assert sector in str(s_out.index)
|
|
|
|
# tCO2/t material
|
|
df.loc["process emission", sector] = (
|
|
s_emi["Process emissions"] / s_out["Other non-ferrous metals (kt lead eq.)"]
|
|
)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = (
|
|
df.loc[sources, sector]
|
|
* toe_to_MWh
|
|
/ s_out["Other non-ferrous metals (kt lead eq.)"]
|
|
)
|
|
|
|
return df
|
|
|
|
|
|
def transport_equipment():
|
|
sector = "Transport Equipment"
|
|
idees = load_idees_data(sector)
|
|
|
|
df = pd.DataFrame(index=index)
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][3:45]
|
|
s_ued = idees["ued"][3:45]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Efficiency changes due to electrification
|
|
key = "Trans. Eq.: Electric Foundries"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Trans. Eq.: Foundries"] / eff_elec
|
|
|
|
key = "Trans. Eq.: Electric connection"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Trans. Eq.: Connection techniques"] / eff_elec
|
|
|
|
key = "Trans. Eq.: Heat treatment - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Trans. Eq.: Heat treatment"] / eff_elec
|
|
|
|
df.loc["elec", sector] += s_fec["Trans. Eq.: General machinery"]
|
|
df.loc["elec", sector] += s_fec["Trans. Eq.: Product finishing"]
|
|
|
|
# Steam processing is supplied with biomass
|
|
eff_biomass = s_ued["Biomass"] / s_fec["Biomass"]
|
|
df.loc["biomass", sector] += s_ued["Trans. Eq.: Steam processing"] / eff_biomass
|
|
|
|
s_out = idees["out"][3:4]
|
|
assert "Physical output" in str(s_out.index)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = (
|
|
df.loc[sources, sector] * toe_to_MWh / s_out["Physical output (index)"]
|
|
)
|
|
|
|
return df
|
|
|
|
|
|
def machinery_equipment():
|
|
sector = "Machinery Equipment"
|
|
|
|
idees = load_idees_data(sector)
|
|
|
|
df = pd.DataFrame(index=index)
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][3:45]
|
|
s_ued = idees["ued"][3:45]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Efficiency changes due to electrification
|
|
key = "Mach. Eq.: Electric Foundries"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Mach. Eq.: Foundries"] / eff_elec
|
|
|
|
key = "Mach. Eq.: Electric connection"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Mach. Eq.: Connection techniques"] / eff_elec
|
|
|
|
key = "Mach. Eq.: Heat treatment - Electric"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
|
|
df.loc["elec", sector] += s_ued["Mach. Eq.: Heat treatment"] / eff_elec
|
|
|
|
df.loc["elec", sector] += s_fec["Mach. Eq.: General machinery"]
|
|
df.loc["elec", sector] += s_fec["Mach. Eq.: Product finishing"]
|
|
|
|
# Steam processing is supplied with biomass
|
|
eff_biomass = s_ued["Biomass"] / s_fec["Biomass"]
|
|
df.loc["biomass", sector] += s_ued["Mach. Eq.: Steam processing"] / eff_biomass
|
|
|
|
s_out = idees["out"][3:4]
|
|
assert "Physical output" in str(s_out.index)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = (
|
|
df.loc[sources, sector] * toe_to_MWh / s_out["Physical output (index)"]
|
|
)
|
|
|
|
return df
|
|
|
|
|
|
def textiles_and_leather():
|
|
sector = "Textiles and leather"
|
|
|
|
idees = load_idees_data(sector)
|
|
|
|
df = pd.DataFrame(index=index)
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][3:57]
|
|
s_ued = idees["ued"][3:57]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Efficiency changes due to electrification
|
|
key = "Textiles: Electric drying"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Textiles: Drying"] / eff_elec
|
|
|
|
df.loc["elec", sector] += s_fec["Textiles: Electric general machinery"]
|
|
df.loc["elec", sector] += s_fec["Textiles: Finishing Electric"]
|
|
|
|
# Steam processing is supplied with biomass
|
|
eff_biomass = s_ued[15:26]["Biomass"] / s_fec[15:26]["Biomass"]
|
|
df.loc["biomass", sector] += (
|
|
s_ued["Textiles: Pretreatment with steam"] / eff_biomass
|
|
)
|
|
df.loc["biomass", sector] += (
|
|
s_ued["Textiles: Wet processing with steam"] / eff_biomass
|
|
)
|
|
|
|
s_out = idees["out"][3:4]
|
|
assert "Physical output" in str(s_out.index)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = (
|
|
df.loc[sources, sector] * toe_to_MWh / s_out["Physical output (index)"]
|
|
)
|
|
|
|
return df
|
|
|
|
|
|
def wood_and_wood_products():
|
|
sector = "Wood and wood products"
|
|
|
|
idees = load_idees_data(sector)
|
|
|
|
df = pd.DataFrame(index=index)
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][3:46]
|
|
s_ued = idees["ued"][3:46]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Efficiency changes due to electrification
|
|
key = "Wood: Electric drying"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Wood: Drying"] / eff_elec
|
|
|
|
df.loc["elec", sector] += s_fec["Wood: Electric mechanical processes"]
|
|
df.loc["elec", sector] += s_fec["Wood: Finishing Electric"]
|
|
|
|
# Steam processing is supplied with biomass
|
|
eff_biomass = s_ued[15:25]["Biomass"] / s_fec[15:25]["Biomass"]
|
|
df.loc["biomass", sector] += (
|
|
s_ued["Wood: Specific processes with steam"] / eff_biomass
|
|
)
|
|
|
|
s_out = idees["out"][3:4]
|
|
assert "Physical output" in str(s_out.index)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = (
|
|
df.loc[sources, sector] * toe_to_MWh / s_out["Physical output (index)"]
|
|
)
|
|
|
|
return df
|
|
|
|
|
|
def other_industrial_sectors():
|
|
sector = "Other Industrial Sectors"
|
|
|
|
idees = load_idees_data(sector)
|
|
|
|
df = pd.DataFrame(index=index)
|
|
|
|
df[sector] = 0.0
|
|
|
|
s_fec = idees["fec"][3:67]
|
|
s_ued = idees["ued"][3:67]
|
|
assert s_fec.index[0] == sector
|
|
assert s_ued.index[0] == sector
|
|
|
|
sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
|
|
df.loc["elec", sector] += s_fec[sel].sum()
|
|
|
|
df.loc["heat", sector] += s_fec["Low enthalpy heat"]
|
|
|
|
# Efficiency changes due to electrification
|
|
key = "Other Industrial sectors: Electric processing"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += (
|
|
s_ued["Other Industrial sectors: Process heating"] / eff_elec
|
|
)
|
|
|
|
key = "Other Industries: Electric drying"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += s_ued["Other Industrial sectors: Drying"] / eff_elec
|
|
|
|
key = "Other Industries: Electric cooling"
|
|
eff_elec = s_ued[key] / s_fec[key]
|
|
df.loc["elec", sector] += (
|
|
s_ued["Other Industrial sectors: Process Cooling"] / eff_elec
|
|
)
|
|
|
|
# Diesel motors are electrified
|
|
key = "Other Industrial sectors: Diesel motors (incl. biofuels)"
|
|
df.loc["elec", sector] += s_fec[key]
|
|
key = "Other Industrial sectors: Electric machinery"
|
|
df.loc["elec", sector] += s_fec[key]
|
|
|
|
# Steam processing is supplied with biomass
|
|
eff_biomass = s_ued[15:25]["Biomass"] / s_fec[15:25]["Biomass"]
|
|
df.loc["biomass", sector] += (
|
|
s_ued["Other Industrial sectors: Steam processing"] / eff_biomass
|
|
)
|
|
|
|
s_out = idees["out"][3:4]
|
|
assert "Physical output" in str(s_out.index)
|
|
|
|
# MWh/t material
|
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
|
df.loc[sources, sector] = (
|
|
df.loc[sources, sector] * toe_to_MWh / s_out["Physical output (index)"]
|
|
)
|
|
|
|
return df
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if "snakemake" not in globals():
|
|
from _helpers import mock_snakemake
|
|
|
|
snakemake = mock_snakemake("build_industry_sector_ratios")
|
|
|
|
# TODO make params option
|
|
year = 2015
|
|
|
|
params = snakemake.params["industry"]
|
|
|
|
df = pd.concat(
|
|
[
|
|
iron_and_steel(),
|
|
chemicals_industry(),
|
|
nonmetalic_mineral_products(),
|
|
pulp_paper_printing(),
|
|
food_beverages_tobacco(),
|
|
non_ferrous_metals(),
|
|
transport_equipment(),
|
|
machinery_equipment(),
|
|
textiles_and_leather(),
|
|
wood_and_wood_products(),
|
|
other_industrial_sectors(),
|
|
],
|
|
axis=1,
|
|
)
|
|
|
|
df.index.name = "MWh/tMaterial"
|
|
df.to_csv(snakemake.output.industry_sector_ratios)
|