diff --git a/config.default.yaml b/config.default.yaml index ab807d2f..f8d8082b 100644 --- a/config.default.yaml +++ b/config.default.yaml @@ -181,7 +181,7 @@ sector: transport_fuel_cell_efficiency: 0.5 transport_internal_combustion_efficiency: 0.3 shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011 - shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands + shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands shipping_hydrogen_share: # 1 means all hydrogen FC 2020: 0 2025: 0 @@ -278,9 +278,23 @@ industry: MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB) NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28 petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28 - HVC_primary_fraction: 1.0 #fraction of current non-ammonia basic chemicals produced via primary route + HVC_primary_fraction: 1. # fraction of today's HVC produced via primary route + HVC_mechanical_recycling_fraction: 0. # fraction of today's HVC produced via mechanical recycling + HVC_chemical_recycling_fraction: 0. # fraction of today's HVC produced via chemical recycling + HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX + MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756. + MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking + chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43 + MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43 + MWh_H2_per_tCl: -0.9372 # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process + methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62 + MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65 + MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65 hotmaps_locate_missing: false reference_year: 2015 + # references: + # DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf + # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050 costs: lifetime: 25 #default lifetime diff --git a/doc/release_notes.rst b/doc/release_notes.rst index 6361563b..4d0b37bf 100644 --- a/doc/release_notes.rst +++ b/doc/release_notes.rst @@ -83,6 +83,8 @@ Future release in the new optional rule ``build_biomass_transport_costs``. Biomass transport can be activated with the setting ``sector: biomass_transport: true``. * Compatibility with ``xarray`` version 0.19. +* Separate basic chemicals into HVC, chlorine, methanol and ammonia [`#166 `_]. +* Add option to specify reuse, primary production, and mechanical and chemical recycling fraction of platics [`#166 `_]. PyPSA-Eur-Sec 0.5.0 (21st May 2021) =================================== diff --git a/scripts/build_industrial_energy_demand_per_country_today.py b/scripts/build_industrial_energy_demand_per_country_today.py index 1d906b24..0adf84e7 100644 --- a/scripts/build_industrial_energy_demand_per_country_today.py +++ b/scripts/build_industrial_energy_demand_per_country_today.py @@ -103,6 +103,7 @@ def add_ammonia_energy_demand(demand): demand['Basic chemicals (without ammonia)'] = demand["Basic chemicals"] - demand["Ammonia"] demand['Basic chemicals (without ammonia)'].clip(lower=0, inplace=True) + demand.drop(columns='Basic chemicals', inplace=True) return demand @@ -114,6 +115,11 @@ def add_non_eu28_industrial_energy_demand(demand): fn = snakemake.input.industrial_production_per_country production = pd.read_csv(fn, index_col=0) / 1e3 + #recombine HVC, Chlorine and Methanol to Basic chemicals (without ammonia) + chemicals = ["HVC", "Chlorine", "Methanol"] + production["Basic chemicals (without ammonia)"] = production[chemicals].sum(axis=1) + production.drop(columns=chemicals, inplace=True) + eu28_production = production.loc[eu28].sum() eu28_energy = demand.groupby(level=1).sum() eu28_averages = eu28_energy / eu28_production diff --git a/scripts/build_industrial_production_per_country.py b/scripts/build_industrial_production_per_country.py index 1754752a..eadfb224 100644 --- a/scripts/build_industrial_production_per_country.py +++ b/scripts/build_industrial_production_per_country.py @@ -179,8 +179,8 @@ def industry_production(countries): return demand -def add_ammonia_demand_separately(demand): - """Include ammonia demand separately and remove ammonia from basic chemicals.""" +def separate_basic_chemicals(demand): + """Separate basic chemicals into ammonia, chlorine, methanol and HVC.""" ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0) @@ -189,7 +189,7 @@ def add_ammonia_demand_separately(demand): print("Following countries have no ammonia demand:", missing) - demand.insert(2, "Ammonia", 0.) + demand["Ammonia"] = 0. demand.loc[there, "Ammonia"] = ammonia.loc[there, str(year)] @@ -198,9 +198,13 @@ def add_ammonia_demand_separately(demand): # EE, HR and LT got negative demand through subtraction - poor data demand['Basic chemicals'].clip(lower=0., inplace=True) - to_rename = {"Basic chemicals": "Basic chemicals (without ammonia)"} - demand.rename(columns=to_rename, 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(): @@ -211,12 +215,14 @@ if __name__ == '__main__': year = snakemake.config['industry']['reference_year'] + config = snakemake.config["industry"] + jrc_dir = snakemake.input.jrc eurostat_dir = snakemake.input.eurostat demand = industry_production(countries) - add_ammonia_demand_separately(demand) + separate_basic_chemicals(demand) fn = snakemake.output.industrial_production_per_country demand.to_csv(fn, float_format='%.2f') diff --git a/scripts/build_industrial_production_per_country_tomorrow.py b/scripts/build_industrial_production_per_country_tomorrow.py index ba69e0a6..ccf31839 100644 --- a/scripts/build_industrial_production_per_country_tomorrow.py +++ b/scripts/build_industrial_production_per_country_tomorrow.py @@ -39,11 +39,14 @@ if __name__ == '__main__': al_primary_fraction = get(config["Al_primary_fraction"], investment_year) fraction_persistent_primary = al_primary_fraction * total_aluminium.sum() / production[key_pri].sum() - + production[key_pri] = fraction_persistent_primary * production[key_pri] production[key_sec] = total_aluminium - production[key_pri] - production["Basic chemicals (without ammonia)"] *= config['HVC_primary_fraction'] + production["HVC (mechanical recycling)"] = get(config["HVC_mechanical_recycling_fraction"], investment_year) * production["HVC"] + production["HVC (chemical recycling)"] = get(config["HVC_chemical_recycling_fraction"], investment_year) * production["HVC"] + + production["HVC"] *= get(config['HVC_primary_fraction'], investment_year) fn = snakemake.output.industrial_production_per_country_tomorrow production.to_csv(fn, float_format='%.2f') diff --git a/scripts/build_industrial_production_per_node.py b/scripts/build_industrial_production_per_node.py index b5361e6b..4ceffee9 100644 --- a/scripts/build_industrial_production_per_node.py +++ b/scripts/build_industrial_production_per_node.py @@ -9,7 +9,11 @@ sector_mapping = { 'Integrated steelworks': 'Iron and steel', 'DRI + Electric arc': 'Iron and steel', 'Ammonia': 'Chemical industry', - 'Basic chemicals (without ammonia)': 'Chemical industry', + 'HVC': 'Chemical industry', + 'HVC (mechanical recycling)': 'Chemical industry', + 'HVC (chemical recycling)': 'Chemical industry', + 'Methanol': 'Chemical industry', + 'Chlorine': 'Chemical industry', 'Other chemicals': 'Chemical industry', 'Pharmaceutical products etc.': 'Chemical industry', 'Cement': 'Cement', @@ -40,12 +44,12 @@ def build_nodal_industrial_production(): countries = keys.country.unique() sectors = industrial_production.columns - + for country, sector in product(countries, sectors): buses = keys.index[keys.country == country] mapping = sector_mapping.get(sector, "population") - + key = keys.loc[buses, mapping] nodal_production.loc[buses, sector] = industrial_production.at[country, sector] * key diff --git a/scripts/build_industry_sector_ratios.py b/scripts/build_industry_sector_ratios.py index adfb1d3c..49c82138 100644 --- a/scripts/build_industry_sector_ratios.py +++ b/scripts/build_industry_sector_ratios.py @@ -279,7 +279,7 @@ def chemicals_industry(): df = pd.DataFrame(index=index) - # Basid chemicals + # Basic chemicals sector = "Basic chemicals" @@ -374,52 +374,82 @@ def chemicals_industry(): # putting in ammonia demand for H2 and electricity separately s_emi = idees["emi"][3:57] - s_out = idees["out"][8:9] assert s_emi.index[0] == sector - assert sector in str(s_out.index) - ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0) - - # ktNH3/a - ammonia_total = ammonia.loc[ammonia.index.intersection(eu28), str(year)].sum() - - s_out -= ammonia_total + # convert from MtHVC/a to ktHVC/a + s_out = config["HVC_production_today"] * 1e3 # tCO2/t material df.loc["process emission", sector] += ( s_emi["Process emissions"] - config["petrochemical_process_emissions"] * 1e3 - config["NH3_process_emissions"] * 1e3 - ) / s_out.values + ) / s_out # emissions originating from feedstock, could be non-fossil origin # tCO2/t material df.loc["process emission from feedstock", sector] += ( config["petrochemical_process_emissions"] * 1e3 - ) / s_out.values + ) / s_out # convert from ktoe/a to GWh/a sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"] df.loc[sources, sector] *= toe_to_MWh + # subtract ammonia energy demand (in ktNH3/a) + ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0) + ammonia_total = ammonia.loc[ammonia.index.intersection(eu28), str(year)].sum() df.loc["methane", sector] -= ammonia_total * config["MWh_CH4_per_tNH3_SMR"] df.loc["elec", sector] -= ammonia_total * config["MWh_elec_per_tNH3_SMR"] - # MWh/t material - df.loc[sources, sector] = df.loc[sources, sector] / s_out.values + # subtract chlorine demand + chlorine_total = config["chlorine_production_today"] + df.loc["hydrogen", sector] -= chlorine_total * config["MWh_H2_per_tCl"] + df.loc["elec", sector] -= chlorine_total * config["MWh_elec_per_tCl"] - to_rename = {sector: f"{sector} (without ammonia)"} - df.rename(columns=to_rename, inplace=True) + # subtract methanol demand + methanol_total = config["methanol_production_today"] + df.loc["methane", sector] -= methanol_total * config["MWh_CH4_per_tMeOH"] + df.loc["elec", sector] -= methanol_total * config["MWh_elec_per_tMeOH"] + + # MWh/t material + df.loc[sources, sector] = df.loc[sources, sector] / s_out + + df.rename(columns={sector: "HVC"}, inplace=True) + + # HVC mechanical recycling + + sector = "HVC (mechanical recycling)" + df[sector] = 0.0 + df.loc["elec", sector] = config["MWh_elec_per_tHVC_mechanical_recycling"] + + # HVC chemical recycling + + sector = "HVC (chemical recycling)" + df[sector] = 0.0 + df.loc["elec", sector] = config["MWh_elec_per_tHVC_chemical_recycling"] # Ammonia sector = "Ammonia" - df[sector] = 0.0 - df.loc["hydrogen", sector] = config["MWh_H2_per_tNH3_electrolysis"] df.loc["elec", sector] = config["MWh_elec_per_tNH3_electrolysis"] + # Chlorine + + sector = "Chlorine" + df[sector] = 0.0 + df.loc["hydrogen", sector] = config["MWh_H2_per_tCl"] + df.loc["elec", sector] = config["MWh_elec_per_tCl"] + + # Methanol + + sector = "Methanol" + df[sector] = 0.0 + df.loc["methane", sector] = config["MWh_CH4_per_tMeOH"] + df.loc["elec", sector] = config["MWh_elec_per_tMeOH"] + # Other chemicals sector = "Other chemicals"