Merge branch 'master' into enspreso-biomass
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Fabian Neumann
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5fb169c31f
@ -175,7 +175,7 @@ sector:
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transport_fuel_cell_efficiency: 0.5
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transport_internal_combustion_efficiency: 0.3
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shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
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shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
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shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
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shipping_hydrogen_share: # 1 means all hydrogen FC
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2020: 0
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2025: 0
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@ -272,9 +272,23 @@ industry:
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MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
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NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
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petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
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HVC_primary_fraction: 1.0 #fraction of current non-ammonia basic chemicals produced via primary route
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HVC_primary_fraction: 1. # fraction of today's HVC produced via primary route
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HVC_mechanical_recycling_fraction: 0. # fraction of today's HVC produced via mechanical recycling
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HVC_chemical_recycling_fraction: 0. # fraction of today's HVC produced via chemical recycling
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HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
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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.
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MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
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chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
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MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
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MWh_H2_per_tCl: -0.9372 # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
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methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
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MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
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MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
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hotmaps_locate_missing: false
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reference_year: 2015
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# references:
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# DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
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# Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
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costs:
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lifetime: 25 #default lifetime
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@ -76,9 +76,18 @@ Future release
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* The share of shipping transformed into hydrogen fuel cell can be now defined for different years in the ``config.yaml`` file. The carbon emission from the remaining share is treated as a negative load on the atmospheric carbon dioxide bus, just like aviation and land transport emissions.
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* The transformation of the Steel and Aluminium production can be now defined for different years in the ``config.yaml`` file.
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* Include the option to alter the maximum energy capacity of a store via the ``carrier+factor`` in the ``{sector_opts}`` wildcard. This can be useful for sensitivity analyses. Example: ``co2 stored+e2`` multiplies the ``e_nom_max`` by factor 2. In this example, ``e_nom_max`` represents the CO2 sequestration potential in Europe.
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* Add option to regionally disaggregate biomass potential to individual nodes
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(currently given per country, then distributed by population density within)
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and allow the transport of solid biomass.
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The transport costs are determined based on the `JRC-EU-Times Bioenergy report <http://dx.doi.org/10.2790/01017>`_
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in the new optional rule ``build_biomass_transport_costs``.
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Biomass transport can be activated with the setting ``sector: biomass_transport: true``.
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* Use `JRC ENSPRESO database <https://data.jrc.ec.europa.eu/dataset/74ed5a04-7d74-4807-9eab-b94774309d9f>`_ to
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spatially disaggregate biomass potentials to PyPSA-Eur regions based on overlaps with NUTS2 regions from ENSPRESO
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(proportional to area) (`#151 <https://github.com/PyPSA/pypsa-eur-sec/pull/151>`_).
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* Compatibility with ``xarray`` version 0.19.
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* Separate basic chemicals into HVC, chlorine, methanol and ammonia [`#166 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/166>`_].
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* Add option to specify reuse, primary production, and mechanical and chemical recycling fraction of platics [`#166 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/166>`_].
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PyPSA-Eur-Sec 0.5.0 (21st May 2021)
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===================================
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@ -103,6 +103,7 @@ def add_ammonia_energy_demand(demand):
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demand['Basic chemicals (without ammonia)'] = demand["Basic chemicals"] - demand["Ammonia"]
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demand['Basic chemicals (without ammonia)'].clip(lower=0, inplace=True)
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demand.drop(columns='Basic chemicals', inplace=True)
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return demand
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@ -114,6 +115,11 @@ def add_non_eu28_industrial_energy_demand(demand):
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fn = snakemake.input.industrial_production_per_country
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production = pd.read_csv(fn, index_col=0) / 1e3
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#recombine HVC, Chlorine and Methanol to Basic chemicals (without ammonia)
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chemicals = ["HVC", "Chlorine", "Methanol"]
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production["Basic chemicals (without ammonia)"] = production[chemicals].sum(axis=1)
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production.drop(columns=chemicals, inplace=True)
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eu28_production = production.loc[eu28].sum()
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eu28_energy = demand.groupby(level=1).sum()
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eu28_averages = eu28_energy / eu28_production
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@ -179,8 +179,8 @@ def industry_production(countries):
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return demand
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def add_ammonia_demand_separately(demand):
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"""Include ammonia demand separately and remove ammonia from basic chemicals."""
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def separate_basic_chemicals(demand):
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"""Separate basic chemicals into ammonia, chlorine, methanol and HVC."""
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ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
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@ -189,7 +189,7 @@ def add_ammonia_demand_separately(demand):
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print("Following countries have no ammonia demand:", missing)
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demand.insert(2, "Ammonia", 0.)
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demand["Ammonia"] = 0.
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demand.loc[there, "Ammonia"] = ammonia.loc[there, str(year)]
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@ -198,9 +198,13 @@ def add_ammonia_demand_separately(demand):
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# EE, HR and LT got negative demand through subtraction - poor data
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demand['Basic chemicals'].clip(lower=0., inplace=True)
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to_rename = {"Basic chemicals": "Basic chemicals (without ammonia)"}
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demand.rename(columns=to_rename, inplace=True)
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# assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
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distribution_key = demand["Basic chemicals"] / demand["Basic chemicals"].sum()
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demand["HVC"] = config["HVC_production_today"] * 1e3 * distribution_key
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demand["Chlorine"] = config["chlorine_production_today"] * 1e3 * distribution_key
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demand["Methanol"] = config["methanol_production_today"] * 1e3 * distribution_key
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demand.drop(columns=["Basic chemicals"], inplace=True)
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if __name__ == '__main__':
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if 'snakemake' not in globals():
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@ -211,12 +215,14 @@ if __name__ == '__main__':
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year = snakemake.config['industry']['reference_year']
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config = snakemake.config["industry"]
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jrc_dir = snakemake.input.jrc
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eurostat_dir = snakemake.input.eurostat
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demand = industry_production(countries)
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add_ammonia_demand_separately(demand)
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separate_basic_chemicals(demand)
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fn = snakemake.output.industrial_production_per_country
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demand.to_csv(fn, float_format='%.2f')
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@ -39,11 +39,14 @@ if __name__ == '__main__':
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al_primary_fraction = get(config["Al_primary_fraction"], investment_year)
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fraction_persistent_primary = al_primary_fraction * total_aluminium.sum() / production[key_pri].sum()
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production[key_pri] = fraction_persistent_primary * production[key_pri]
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production[key_sec] = total_aluminium - production[key_pri]
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production["Basic chemicals (without ammonia)"] *= config['HVC_primary_fraction']
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production["HVC (mechanical recycling)"] = get(config["HVC_mechanical_recycling_fraction"], investment_year) * production["HVC"]
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production["HVC (chemical recycling)"] = get(config["HVC_chemical_recycling_fraction"], investment_year) * production["HVC"]
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production["HVC"] *= get(config['HVC_primary_fraction'], investment_year)
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fn = snakemake.output.industrial_production_per_country_tomorrow
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production.to_csv(fn, float_format='%.2f')
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@ -9,7 +9,11 @@ sector_mapping = {
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'Integrated steelworks': 'Iron and steel',
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'DRI + Electric arc': 'Iron and steel',
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'Ammonia': 'Chemical industry',
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'Basic chemicals (without ammonia)': 'Chemical industry',
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'HVC': 'Chemical industry',
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'HVC (mechanical recycling)': 'Chemical industry',
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'HVC (chemical recycling)': 'Chemical industry',
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'Methanol': 'Chemical industry',
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'Chlorine': 'Chemical industry',
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'Other chemicals': 'Chemical industry',
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'Pharmaceutical products etc.': 'Chemical industry',
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'Cement': 'Cement',
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@ -40,12 +44,12 @@ def build_nodal_industrial_production():
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countries = keys.country.unique()
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sectors = industrial_production.columns
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for country, sector in product(countries, sectors):
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buses = keys.index[keys.country == country]
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mapping = sector_mapping.get(sector, "population")
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key = keys.loc[buses, mapping]
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nodal_production.loc[buses, sector] = industrial_production.at[country, sector] * key
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@ -279,7 +279,7 @@ def chemicals_industry():
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df = pd.DataFrame(index=index)
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# Basid chemicals
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# Basic chemicals
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sector = "Basic chemicals"
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@ -374,52 +374,82 @@ def chemicals_industry():
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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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s_out = idees["out"][8:9]
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assert s_emi.index[0] == sector
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assert sector in str(s_out.index)
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ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
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# ktNH3/a
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ammonia_total = ammonia.loc[ammonia.index.intersection(eu28), str(year)].sum()
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s_out -= ammonia_total
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# convert from MtHVC/a to ktHVC/a
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s_out = config["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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- config["petrochemical_process_emissions"] * 1e3
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- config["NH3_process_emissions"] * 1e3
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) / s_out.values
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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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config["petrochemical_process_emissions"] * 1e3
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) / s_out.values
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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 * config["MWh_CH4_per_tNH3_SMR"]
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df.loc["elec", sector] -= ammonia_total * config["MWh_elec_per_tNH3_SMR"]
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# MWh/t material
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df.loc[sources, sector] = df.loc[sources, sector] / s_out.values
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# subtract chlorine demand
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chlorine_total = config["chlorine_production_today"]
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df.loc["hydrogen", sector] -= chlorine_total * config["MWh_H2_per_tCl"]
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df.loc["elec", sector] -= chlorine_total * config["MWh_elec_per_tCl"]
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to_rename = {sector: f"{sector} (without ammonia)"}
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df.rename(columns=to_rename, inplace=True)
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# subtract methanol demand
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methanol_total = config["methanol_production_today"]
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df.loc["methane", sector] -= methanol_total * config["MWh_CH4_per_tMeOH"]
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df.loc["elec", sector] -= methanol_total * config["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] = config["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] = config["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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df.loc["hydrogen", sector] = config["MWh_H2_per_tNH3_electrolysis"]
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df.loc["elec", sector] = config["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] = config["MWh_H2_per_tCl"]
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df.loc["elec", sector] = config["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] = config["MWh_CH4_per_tMeOH"]
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df.loc["elec", sector] = config["MWh_elec_per_tMeOH"]
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# Other chemicals
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sector = "Other chemicals"
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