merge master

README.md

@@ -21,9 +21,9 @@ it.
 PyPSA-Eur-Sec builds on the electricity generation and transmission
 model [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur) to add demand
 and supply for the following sectors: transport, space and water
-heating, biomass, industry and industrial feedstocks. This completes
+heating, biomass, industry and industrial feedstocks, agriculture,
-the energy system and includes all greenhouse gas emitters except
+forestry and fishing. This completes the energy system and includes
-waste management, agriculture, forestry and land use.
+all greenhouse gas emitters except waste management and land use.
 
 Please see the [documentation](https://pypsa-eur-sec.readthedocs.io/)
 for installation instructions and other useful information about the snakemake workflow.

Snakefile

@@ -9,7 +9,6 @@ wildcard_constraints:
     lv="[a-z0-9\.]+",
     simpl="[a-zA-Z0-9]*",
     clusters="[0-9]+m?",
-    sectors="[+a-zA-Z0-9]+",
     opts="[-+a-zA-Z0-9]*",
     sector_opts="[-+a-zA-Z0-9\.\s]*"
 

config.default.yaml

@@ -21,13 +21,14 @@ scenario:
   opts: # only relevant for PyPSA-Eur
     - ''
   sector_opts: # this is where the main scenario settings are
-    - Co2L0-3H-T-H-B-I-solar+p3-dist1
+    - Co2L0-3H-T-H-B-I-A-solar+p3-dist1
   # to really understand the options here, look in scripts/prepare_sector_network.py
   # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
   # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
   # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
   # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation
+  # B for biomass supply, I for industry, shipping and aviation,
+  # A for agriculture, forestry and fishing
   # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
   # solar+p3 multiplies the available installable potential by factor 3
   # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
@@ -181,6 +182,9 @@ sector:
     2050: 0.85
   transport_fuel_cell_efficiency: 0.5
   transport_internal_combustion_efficiency: 0.3
+  agriculture_machinery_electric_share: 0
+  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
+  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
   shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
   shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
   shipping_hydrogen_share: # 1 means all hydrogen FC
@@ -477,6 +481,10 @@ plotting:
     CC: k
     co2: '#123456'
     co2 vent: '#654321'
+    agriculture heat: '#D07A7A'
+    agriculture machinery oil: '#1e1e1e'
+    agriculture machinery oil emissions: '#111111'
+    agriculture electricity: '#222222'
     solid biomass for industry co2 from atmosphere: '#654321'
     solid biomass for industry co2 to stored: '#654321'
     gas for industry co2 to atmosphere: '#654321'

doc/release_notes.rst

@@ -169,7 +169,7 @@ PyPSA-Eur-Sec 0.6.0 (4 October 2021)
 * Implemented changes to ``n.snapshot_weightings`` in PyPSA v0.18.0.
 
 * Compatibility with ``xarray`` version 0.19.
-* 
+
 * New dependencies: ``tqdm``, ``atlite>=0.2.4``, ``pytz`` and ``geopy`` (optional).
   These are included in the environment specifications of PyPSA-Eur v0.3.0.
 

doc/supply_demand.rst

@@ -43,7 +43,7 @@ Heat demand is split into:
 
 * ``urban central``: large-scale district heating networks in urban areas with dense heat demand
 * ``residential/services urban decentral``: heating for individual buildings in urban areas
-* ``residential/services rural``: heating for individual buildings in rural areas
+* ``residential/services rural``: heating for individual buildings in rural areas, agriculture heat uses
 
 
 Heat supply
@@ -189,7 +189,7 @@ Only wastes and residues from the JRC ENSPRESO biomass dataset.
 Oil product demand
 =====================
 
-Transport fuels and naphtha as a feedstock for the chemicals industry.
+Transport fuels, agriculture machinery and naphtha as a feedstock for the chemicals industry.
 
 Oil product supply
 ======================

scripts/build_energy_totals.py

@@ -117,6 +117,7 @@ to_ipcc = {
     "total energy": "1 - Energy",
     "industrial processes": "2 - Industrial Processes and Product Use",
     "agriculture": "3 - Agriculture",
+    "agriculture, forestry and fishing": '1.A.4.c - Agriculture/Forestry/Fishing',
     "LULUCF": "4 - Land Use, Land-Use Change and Forestry",
     "waste management": "5 - Waste management",
     "other": "6 - Other Sector",
@@ -182,7 +183,7 @@ def idees_per_country(ct, year):
 
     ct_idees = idees_rename.get(ct, ct)
     fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx"
-    fn_services = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
+    fn_tertiary = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
     fn_transport = f"{base_dir}/JRC-IDEES-2015_Transport_{ct_idees}.xlsx"
 
     # residential
@@ -220,7 +221,7 @@ def idees_per_country(ct, year):
 
     # services
 
-    df = pd.read_excel(fn_services, "SER_hh_fec", index_col=0)[year]
+    df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)[year]
 
     ct_totals["total services space"] = df["Space heating"]
 
@@ -237,7 +238,7 @@ def idees_per_country(ct, year):
     assert df.index[31] == "Electricity"
     ct_totals["electricity services cooking"] = df[31]
 
-    df = pd.read_excel(fn_services, "SER_summary", index_col=0)[year]
+    df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)[year]
 
     row = "Energy consumption by fuel - Eurostat structure (ktoe)"
     ct_totals["total services"] = df[row]
@@ -251,6 +252,35 @@ def idees_per_country(ct, year):
     assert df.index[53] == 'Thermal uses'
     ct_totals["thermal uses services"] = df[53]
 
+
+    # agriculture, forestry and fishing
+
+    start = "Detailed split of energy consumption (ktoe)"
+    end = "Market shares of energy uses (%)"
+
+    df = pd.read_excel(fn_tertiary, "AGR_fec", index_col=0).loc[start:end, year]
+
+    rows = [
+        "Lighting",
+        "Ventilation",
+        "Specific electricity uses",
+        "Pumping devices (electric)"
+    ]
+    ct_totals["total agriculture electricity"] = df[rows].sum()
+
+    rows = ["Specific heat uses", "Low enthalpy heat"]
+    ct_totals["total agriculture heat"] = df[rows].sum()
+
+    rows = [
+        "Motor drives",
+        "Farming machine drives (diesel oil incl. biofuels)",
+        "Pumping devices (diesel oil incl. biofuels)",
+    ]
+    ct_totals["total agriculture machinery"] = df[rows].sum()
+
+    row = "Agriculture, forestry and fishing"
+    ct_totals["total agriculture"] = df[row]
+
     # transport
 
     df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)[year]
@@ -568,10 +598,13 @@ def build_eea_co2(year=1990):
         "international aviation",
         "domestic navigation",
         "international navigation",
+        "agriculture, forestry and fishing"
     ]
     emissions["industrial non-elec"] = emissions["total energy"] - emissions[to_subtract].sum(axis=1)
 
-    to_drop = ["total energy", "total wL", "total woL"]
+    emissions["agriculture"] += emissions["agriculture, forestry and fishing"]
+
+    to_drop = ["total energy", "total wL", "total woL", "agriculture, forestry and fishing"]
     emissions.drop(columns=to_drop, inplace=True)
 
     # convert from Gg to Mt
@@ -616,7 +649,7 @@ def build_co2_totals(countries, eea_co2, eurostat_co2):
             # does not include industrial process emissions or fuel processing/refining
             "industrial non-elec": (ct, "+", "Industry"),
             # does not include non-energy emissions
-            "agriculture": (ct, "+", "+", "Agriculture / Forestry"),
+            "agriculture": (eurostat_co2.index.get_level_values(0) == ct) & eurostat_co2.index.isin(["Agriculture / Forestry", "Fishing"], level=3),
         }
 
         for i, mi in mappings.items():

scripts/prepare_sector_network.py

@@ -92,6 +92,10 @@ def emission_sectors_from_opts(opts):
             "domestic navigation",
             "international navigation"
         ]
+    if "A" in opts:
+        sectors += [
+            "agriculture"
+        ]
 
     return sectors
 
@@ -882,8 +886,9 @@ def insert_electricity_distribution_grid(n, costs):
         capital_cost=costs.at['electricity distribution grid', 'fixed'] * cost_factor
     )
 
-    # this catches regular electricity load and "industry electricity"
+    # this catches regular electricity load and "industry electricity" and
-    loads = n.loads.index[n.loads.carrier.str.contains("electricity")]
+    # "agriculture machinery electric" and "agriculture electricity"
+    loads = n.loads.index[n.loads.carrier.str.contains("electric")]
     n.loads.loc[loads, "bus"] += " low voltage"
 
     bevs = n.links.index[n.links.carrier == "BEV charger"]
@@ -1321,8 +1326,8 @@ def add_land_transport(n, costs):
 
         co2 = ice_share / ice_efficiency * transport[nodes].sum().sum() / 8760 * costs.at["oil", 'CO2 intensity']
 
-        n.madd("Load",
+        n.add("Load",
-            ["land transport oil emissions"],
+            "land transport oil emissions",
             bus="co2 atmosphere",
             carrier="land transport oil emissions",
             p_set=-co2
@@ -2161,6 +2166,71 @@ def add_waste_heat(n):
             n.links.loc[urban_central + " H2 Fuel Cell", "efficiency2"] = 0.95 - n.links.loc[urban_central + " H2 Fuel Cell", "efficiency"]
 
 
+def add_agriculture(n, costs):
+
+    logger.info('Add agriculture, forestry and fishing sector.')
+
+    nodes = pop_layout.index
+
+    # electricity
+
+    n.madd("Load",
+        nodes,
+        suffix=" agriculture electricity",
+        bus=nodes,
+        carrier='agriculture electricity',
+        p_set=nodal_energy_totals.loc[nodes, "total agriculture electricity"] * 1e6 / 8760
+    )
+
+    # heat
+
+    n.madd("Load",
+        nodes,
+        suffix=" agriculture heat",
+        bus=nodes + " services rural heat",
+        carrier="agriculture heat",
+        p_set=nodal_energy_totals.loc[nodes, "total agriculture heat"] * 1e6 / 8760
+    )
+
+    # machinery
+
+    electric_share = get(options["agriculture_machinery_electric_share"], investment_year)
+    assert electric_share <= 1.
+    ice_share = 1 - electric_share
+
+    machinery_nodal_energy = nodal_energy_totals.loc[nodes, "total agriculture machinery"]
+
+    if electric_share > 0:
+
+        efficiency_gain = options["agriculture_machinery_fuel_efficiency"] / options["agriculture_machinery_electric_efficiency"]
+
+        n.madd("Load",
+            nodes,
+            suffix=" agriculture machinery electric",
+            bus=nodes,
+            carrier="agriculture machinery electric",
+            p_set=electric_share / efficiency_gain * machinery_nodal_energy * 1e6 / 8760,
+        )
+
+    if ice_share > 0:
+
+        n.add("Load",
+            "agriculture machinery oil",
+            bus="EU oil",
+            carrier="agriculture machinery oil",
+            p_set=ice_share * machinery_nodal_energy.sum() * 1e6 / 8760
+        )
+
+        co2 = ice_share * machinery_nodal_energy.sum() * 1e6 / 8760 * costs.at["oil", 'CO2 intensity']
+
+        n.add("Load",
+            "agriculture machinery oil emissions",
+            bus="co2 atmosphere",
+            carrier="agriculture machinery oil emissions",
+            p_set=-co2
+        )
+
+
 def decentral(n):
     """Removes the electricity transmission system."""
     n.lines.drop(n.lines.index, inplace=True)
@@ -2297,6 +2367,9 @@ if __name__ == "__main__":
     if "I" in opts and "H" in opts:
         add_waste_heat(n)
 
+    if "A" in opts:  # requires H and I
+        add_agriculture(n, costs)
+
     if options['dac']:
         add_dac(n, costs)