Merge pull request #147 from PyPSA/agriculture-energy-co2
Add agriculture, forestry and fishing
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Fabian Neumann
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cecee2c9c7
@ -21,9 +21,9 @@ it.
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PyPSA-Eur-Sec builds on the electricity generation and transmission
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model [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur) to add demand
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and supply for the following sectors: transport, space and water
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heating, biomass, industry and industrial feedstocks. This completes
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the energy system and includes all greenhouse gas emitters except
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waste management, agriculture, forestry and land use.
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heating, biomass, industry and industrial feedstocks, agriculture,
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forestry and fishing. This completes the energy system and includes
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all greenhouse gas emitters except waste management and land use.
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Please see the [documentation](https://pypsa-eur-sec.readthedocs.io/)
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for installation instructions and other useful information about the snakemake workflow.
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@ -9,7 +9,6 @@ wildcard_constraints:
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lv="[a-z0-9\.]+",
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simpl="[a-zA-Z0-9]*",
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clusters="[0-9]+m?",
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sectors="[+a-zA-Z0-9]+",
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opts="[-+a-zA-Z0-9]*",
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sector_opts="[-+a-zA-Z0-9\.\s]*"
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@ -21,13 +21,14 @@ scenario:
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opts: # only relevant for PyPSA-Eur
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- ''
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sector_opts: # this is where the main scenario settings are
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- Co2L0-3H-T-H-B-I-solar+p3-dist1
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- Co2L0-3H-T-H-B-I-A-solar+p3-dist1
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# to really understand the options here, look in scripts/prepare_sector_network.py
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# Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
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# Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
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# xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
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# single letters are sectors: T for land transport, H for building heating,
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# B for biomass supply, I for industry, shipping and aviation
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# B for biomass supply, I for industry, shipping and aviation,
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# A for agriculture, forestry and fishing
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# solar+c0.5 reduces the capital cost of solar to 50\% of reference value
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# solar+p3 multiplies the available installable potential by factor 3
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# co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
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@ -181,6 +182,9 @@ sector:
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2050: 0.85
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transport_fuel_cell_efficiency: 0.5
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transport_internal_combustion_efficiency: 0.3
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agriculture_machinery_electric_share: 0
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agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
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agriculture_machinery_electric_efficiency: 0.3 # electricity per use
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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: 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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@ -477,6 +481,10 @@ plotting:
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CC: k
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co2: '#123456'
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co2 vent: '#654321'
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agriculture heat: '#D07A7A'
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agriculture machinery oil: '#1e1e1e'
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agriculture machinery oil emissions: '#111111'
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agriculture electricity: '#222222'
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solid biomass for industry co2 from atmosphere: '#654321'
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solid biomass for industry co2 to stored: '#654321'
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gas for industry co2 to atmosphere: '#654321'
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@ -86,6 +86,10 @@ Future release
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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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* Added option to include emissions and energy demands of agriculture, forestry and fishing sector via the letter ``A`` in the ``{sector_opts}`` wildcard.
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Demands are separated into electricity, heat and oil for machinery.
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Fuel-switching for machinery from oil to electricity can be set exogenously in the ``config.yaml``
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`#147 <https://github.com/PyPSA/PyPSA/pull/147>`_.
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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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* Include today's district heating shares in myopic optimisation and add option to specify exogenous path for district heating share increase under ``sector: district_heating:`` [`#149 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/149>`_].
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@ -43,7 +43,7 @@ Heat demand is split into:
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* ``urban central``: large-scale district heating networks in urban areas with dense heat demand
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* ``residential/services urban decentral``: heating for individual buildings in urban areas
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* ``residential/services rural``: heating for individual buildings in rural areas
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* ``residential/services rural``: heating for individual buildings in rural areas, agriculture heat uses
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Heat supply
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@ -189,7 +189,7 @@ Only wastes and residues from the JRC ENSPRESO biomass dataset.
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Oil product demand
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=====================
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Transport fuels and naphtha as a feedstock for the chemicals industry.
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Transport fuels, agriculture machinery and naphtha as a feedstock for the chemicals industry.
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Oil product supply
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======================
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@ -117,6 +117,7 @@ to_ipcc = {
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"total energy": "1 - Energy",
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"industrial processes": "2 - Industrial Processes and Product Use",
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"agriculture": "3 - Agriculture",
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"agriculture, forestry and fishing": '1.A.4.c - Agriculture/Forestry/Fishing',
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"LULUCF": "4 - Land Use, Land-Use Change and Forestry",
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"waste management": "5 - Waste management",
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"other": "6 - Other Sector",
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@ -182,7 +183,7 @@ def idees_per_country(ct, year):
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ct_idees = idees_rename.get(ct, ct)
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fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx"
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fn_services = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
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fn_tertiary = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
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fn_transport = f"{base_dir}/JRC-IDEES-2015_Transport_{ct_idees}.xlsx"
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# residential
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@ -213,14 +214,14 @@ def idees_per_country(ct, year):
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ct_totals["electricity residential"] = df[47]
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assert df.index[46] == "Derived heat"
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ct_totals["Derived heat residential"] = df[46]
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ct_totals["derived heat residential"] = df[46]
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assert df.index[50] == 'Thermal uses'
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ct_totals["thermal uses residential"] = df[50]
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# services
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df = pd.read_excel(fn_services, "SER_hh_fec", index_col=0)[year]
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df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)[year]
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ct_totals["total services space"] = df["Space heating"]
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@ -237,7 +238,7 @@ def idees_per_country(ct, year):
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assert df.index[31] == "Electricity"
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ct_totals["electricity services cooking"] = df[31]
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df = pd.read_excel(fn_services, "SER_summary", index_col=0)[year]
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df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)[year]
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row = "Energy consumption by fuel - Eurostat structure (ktoe)"
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ct_totals["total services"] = df[row]
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@ -251,6 +252,35 @@ def idees_per_country(ct, year):
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assert df.index[53] == 'Thermal uses'
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ct_totals["thermal uses services"] = df[53]
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# agriculture, forestry and fishing
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start = "Detailed split of energy consumption (ktoe)"
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end = "Market shares of energy uses (%)"
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df = pd.read_excel(fn_tertiary, "AGR_fec", index_col=0).loc[start:end, year]
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rows = [
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"Lighting",
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"Ventilation",
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"Specific electricity uses",
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"Pumping devices (electric)"
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]
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ct_totals["total agriculture electricity"] = df[rows].sum()
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rows = ["Specific heat uses", "Low enthalpy heat"]
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ct_totals["total agriculture heat"] = df[rows].sum()
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rows = [
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"Motor drives",
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"Farming machine drives (diesel oil incl. biofuels)",
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"Pumping devices (diesel oil incl. biofuels)",
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]
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ct_totals["total agriculture machinery"] = df[rows].sum()
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row = "Agriculture, forestry and fishing"
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ct_totals["total agriculture"] = df[row]
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# transport
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df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)[year]
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@ -568,10 +598,13 @@ def build_eea_co2(year=1990):
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"international aviation",
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"domestic navigation",
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"international navigation",
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"agriculture, forestry and fishing"
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]
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emissions["industrial non-elec"] = emissions["total energy"] - emissions[to_subtract].sum(axis=1)
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to_drop = ["total energy", "total wL", "total woL"]
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emissions["agriculture"] += emissions["agriculture, forestry and fishing"]
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to_drop = ["total energy", "total wL", "total woL", "agriculture, forestry and fishing"]
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emissions.drop(columns=to_drop, inplace=True)
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# convert from Gg to Mt
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@ -616,7 +649,7 @@ def build_co2_totals(countries, eea_co2, eurostat_co2):
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# does not include industrial process emissions or fuel processing/refining
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"industrial non-elec": (ct, "+", "Industry"),
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# does not include non-energy emissions
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"agriculture": (ct, "+", "+", "Agriculture / Forestry"),
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"agriculture": (eurostat_co2.index.get_level_values(0) == ct) & eurostat_co2.index.isin(["Agriculture / Forestry", "Fishing"], level=3),
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}
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for i, mi in mappings.items():
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@ -92,6 +92,10 @@ def emission_sectors_from_opts(opts):
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"domestic navigation",
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"international navigation"
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]
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if "A" in opts:
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sectors += [
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"agriculture"
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]
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return sectors
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@ -882,8 +886,9 @@ def insert_electricity_distribution_grid(n, costs):
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capital_cost=costs.at['electricity distribution grid', 'fixed'] * cost_factor
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)
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# this catches regular electricity load and "industry electricity"
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loads = n.loads.index[n.loads.carrier.str.contains("electricity")]
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# this catches regular electricity load and "industry electricity" and
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# "agriculture machinery electric" and "agriculture electricity"
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loads = n.loads.index[n.loads.carrier.str.contains("electric")]
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n.loads.loc[loads, "bus"] += " low voltage"
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bevs = n.links.index[n.links.carrier == "BEV charger"]
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@ -1321,8 +1326,8 @@ def add_land_transport(n, costs):
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co2 = ice_share / ice_efficiency * transport[nodes].sum().sum() / 8760 * costs.at["oil", 'CO2 intensity']
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n.madd("Load",
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["land transport oil emissions"],
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n.add("Load",
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"land transport oil emissions",
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bus="co2 atmosphere",
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carrier="land transport oil emissions",
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p_set=-co2
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@ -2161,6 +2166,71 @@ def add_waste_heat(n):
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n.links.loc[urban_central + " H2 Fuel Cell", "efficiency2"] = 0.95 - n.links.loc[urban_central + " H2 Fuel Cell", "efficiency"]
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def add_agriculture(n, costs):
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logger.info('Add agriculture, forestry and fishing sector.')
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nodes = pop_layout.index
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# electricity
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n.madd("Load",
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nodes,
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suffix=" agriculture electricity",
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bus=nodes,
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carrier='agriculture electricity',
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p_set=nodal_energy_totals.loc[nodes, "total agriculture electricity"] * 1e6 / 8760
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)
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# heat
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n.madd("Load",
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nodes,
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suffix=" agriculture heat",
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bus=nodes + " services rural heat",
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carrier="agriculture heat",
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p_set=nodal_energy_totals.loc[nodes, "total agriculture heat"] * 1e6 / 8760
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)
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# machinery
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electric_share = get(options["agriculture_machinery_electric_share"], investment_year)
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assert electric_share <= 1.
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ice_share = 1 - electric_share
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machinery_nodal_energy = nodal_energy_totals.loc[nodes, "total agriculture machinery"]
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if electric_share > 0:
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efficiency_gain = options["agriculture_machinery_fuel_efficiency"] / options["agriculture_machinery_electric_efficiency"]
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n.madd("Load",
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nodes,
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suffix=" agriculture machinery electric",
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bus=nodes,
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carrier="agriculture machinery electric",
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p_set=electric_share / efficiency_gain * machinery_nodal_energy * 1e6 / 8760,
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)
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if ice_share > 0:
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n.add("Load",
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"agriculture machinery oil",
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bus="EU oil",
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carrier="agriculture machinery oil",
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p_set=ice_share * machinery_nodal_energy.sum() * 1e6 / 8760
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)
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co2 = ice_share * machinery_nodal_energy.sum() * 1e6 / 8760 * costs.at["oil", 'CO2 intensity']
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n.add("Load",
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"agriculture machinery oil emissions",
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bus="co2 atmosphere",
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carrier="agriculture machinery oil emissions",
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p_set=-co2
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)
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def decentral(n):
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"""Removes the electricity transmission system."""
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n.lines.drop(n.lines.index, inplace=True)
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@ -2297,6 +2367,9 @@ if __name__ == "__main__":
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if "I" in opts and "H" in opts:
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add_waste_heat(n)
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if "A" in opts: # requires H and I
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add_agriculture(n, costs)
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if options['dac']:
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add_dac(n, costs)
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