Merge pull request #957 from PyPSA/land-transport-fix

Land transport fix

.gitignore

@@ -2,6 +2,8 @@
 #
 # SPDX-License-Identifier: CC0-1.0
 
+master
+
 .snakemake*
 .ipynb_checkpoints
 __pycache__

config/config.default.yaml

@@ -431,7 +431,6 @@ sector:
   bev_availability: 0.5
   bev_energy: 0.05
   bev_charge_efficiency: 0.9
-  bev_plug_to_wheel_efficiency: 0.2
   bev_charge_rate: 0.011
   bev_avail_max: 0.95
   bev_avail_mean: 0.8
@@ -460,8 +459,9 @@ sector:
     2040: 0.3
     2045: 0.15
     2050: 0
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
+  transport_electric_efficiency: 53.19 # 1 MWh_el = 53.19*100 km
+  transport_fuel_cell_efficiency: 30.003 # 1 MWh_H2 = 30.003*100 km
+  transport_ice_efficiency: 16.0712 # 1 MWh_oil = 16.0712 * 100 km
   agriculture_machinery_electric_share: 0
   agriculture_machinery_oil_share: 1
   agriculture_machinery_fuel_efficiency: 0.7
@@ -1041,6 +1041,7 @@ plotting:
     BEV charger: '#baf238'
     V2G: '#e5ffa8'
     land transport EV: '#baf238'
+    land transport demand: '#38baf2'
     Li ion: '#baf238'
     # hot water storage
     water tanks: '#e69487'

doc/configtables/sector.csv

@@ -24,16 +24,16 @@ bev_dsm,--,"{true, false}",Add the option for battery electric vehicles (BEV) to
 bev_availability,--,float,The share for battery electric vehicles (BEV) that are able to do demand side management (DSM)
 bev_energy,--,float,The average size of battery electric vehicles (BEV) in MWh
 bev_charge_efficiency,--,float,Battery electric vehicles (BEV) charge and discharge efficiency
-bev_plug_to_wheel _efficiency,km/kWh,float,The distance battery electric vehicles (BEV) can travel in km per kWh of energy charge in battery.  Base value comes from `Tesla Model S <https://www.fueleconomy.gov/feg/>`_
 bev_charge_rate,MWh,float,The power consumption for one electric vehicle (EV) in MWh. Value derived from 3-phase charger with 11 kW.
 bev_avail_max,--,float,The maximum share plugged-in availability for passenger electric vehicles.
 bev_avail_mean,--,float,The average share plugged-in availability for passenger electric vehicles.
 v2g,--,"{true, false}",Allows feed-in to grid from EV battery
 land_transport_fuel_cell _share,--,Dictionary with planning horizons as keys.,The share of vehicles that uses fuel cells in a given year
 land_transport_electric _share,--,Dictionary with planning horizons as keys.,The share of vehicles that uses electric vehicles (EV) in a given year
 land_transport_ice _share,--,Dictionary with planning horizons as keys.,The share of vehicles that uses internal combustion engines (ICE) in a given year. What is not EV or FCEV is oil-fuelled ICE.
-transport_fuel_cell _efficiency,--,float,The H2 conversion efficiencies of fuel cells in transport
-transport_internal _combustion_efficiency,--,float,The oil conversion efficiencies of internal combustion engine (ICE) in transport
+transport_electric_efficiency,MWh/100km,float,The conversion efficiencies of electric vehicles in transport
+transport_fuel_cell_efficiency,MWh/100km,float,The H2 conversion efficiencies of fuel cells in transport
+transport_ice_efficiency,MWh/100km,float,The oil conversion efficiencies of internal combustion engine (ICE) in transport
 agriculture_machinery _electric_share,--,float,The share for agricultural machinery that uses electricity
 agriculture_machinery _oil_share,--,float,The share for agricultural machinery that uses oil
 agriculture_machinery _fuel_efficiency,--,float,The efficiency of electric-powered machinery in the conversion of electricity to meet agricultural needs.

doc/release_notes.rst

@@ -81,6 +81,9 @@ Upcoming Release
 
 * bugfix: convert Strings to pathlib.Path objects as input to ConfigSettings
 
+* bugfix: fix distinction of temperature-dependent correction factors for the
+  energy demand of electric vehicles, ICES fuel cell cars.
+
 * Allow the use of more solvers in clustering (Xpress, COPT, Gurobi, CPLEX, SCIP, MOSEK).
 
 * Enhanced support for choosing different weather years

scripts/build_energy_totals.py

@@ -396,13 +396,12 @@ def build_idees(countries):
         names=["country", "year"],
     )
 
+    # efficiency kgoe/100km -> ktoe/100km
+    totals.loc[:, "passenger car efficiency"] *= 1e3
     # convert ktoe to TWh
     exclude = totals.columns.str.fullmatch("passenger cars")
     totals.loc[:, ~exclude] *= 11.63 / 1e3
 
-    # convert TWh/100km to kWh/km
-    totals.loc[:, "passenger car efficiency"] *= 10
-
     return totals
 
 

scripts/build_transport_demand.py

@@ -24,14 +24,17 @@
 
 
 def build_nodal_transport_data(fn, pop_layout, year):
+    # get numbers of car and fuel efficiency per country
     transport_data = pd.read_csv(fn, index_col=[0, 1])
     transport_data = transport_data.xs(min(2015, year), level="year")
 
+    # break number of cars down to nodal level based on population density
     nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(0.0)
     nodal_transport_data.index = pop_layout.index
     nodal_transport_data["number cars"] = (
         pop_layout["fraction"] * nodal_transport_data["number cars"]
     )
+    # fill missing fuel efficiency with average data
     nodal_transport_data.loc[
         nodal_transport_data["average fuel efficiency"] == 0.0,
         "average fuel efficiency",
@@ -41,26 +44,20 @@ def build_nodal_transport_data(fn, pop_layout, year):
 
 
 def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
-    ## Get overall demand curve for all vehicles
-
+    """
+    Returns transport demand per bus in unit km driven [100 km].
+    """
+    # averaged weekly counts from the year 2010-2015
     traffic = pd.read_csv(traffic_fn, skiprows=2, usecols=["count"]).squeeze("columns")
 
+    # create annual profile take account time zone + summer time
     transport_shape = generate_periodic_profiles(
         dt_index=snapshots,
         nodes=nodes,
         weekly_profile=traffic.values,
     )
     transport_shape = transport_shape / transport_shape.sum()
 
-    # electric motors are more efficient, so alter transport demand
-
-    plug_to_wheels_eta = options["bev_plug_to_wheel_efficiency"]
-    battery_to_wheels_eta = plug_to_wheels_eta * options["bev_charge_efficiency"]
-
-    efficiency_gain = (
-        nodal_transport_data["average fuel efficiency"] / battery_to_wheels_eta
-    )
-
     # get heating demand for correction to demand time series
     temperature = xr.open_dataarray(airtemp_fn).to_pandas()
 
@@ -73,16 +70,7 @@ def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
         options["ICE_upper_degree_factor"],
     )
 
-    dd_EV = transport_degree_factor(
-        temperature,
-        options["transport_heating_deadband_lower"],
-        options["transport_heating_deadband_upper"],
-        options["EV_lower_degree_factor"],
-        options["EV_upper_degree_factor"],
-    )
-
     # divide out the heating/cooling demand from ICE totals
-    # and multiply back in the heating/cooling demand for EVs
     ice_correction = (transport_shape * (1 + dd_ICE)).sum() / transport_shape.sum()
 
     energy_totals_transport = (
@@ -91,10 +79,11 @@ def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
         - pop_weighted_energy_totals["electricity rail"]
     )
 
-    return (
-        (transport_shape.multiply(energy_totals_transport) * 1e6 * nyears)
-        .divide(efficiency_gain * ice_correction)
-        .multiply(1 + dd_EV)
+    # convert average fuel efficiency from kW/100 km -> MW/100km
+    eff = nodal_transport_data["average fuel efficiency"] / 1e3
+
+    return (transport_shape.multiply(energy_totals_transport) * 1e6 * nyears).divide(
+        eff * ice_correction
     )
 
 
@@ -131,11 +120,14 @@ def bev_availability_profile(fn, snapshots, nodes, options):
     """
     Derive plugged-in availability for passenger electric vehicles.
     """
+    # car count in typical week
     traffic = pd.read_csv(fn, skiprows=2, usecols=["count"]).squeeze("columns")
-
+    # maximum share plugged-in availability for passenger electric vehicles
     avail_max = options["bev_avail_max"]
+    # average share plugged-in availability for passenger electric vehicles
     avail_mean = options["bev_avail_mean"]
 
+    # linear scaling, highest when traffic is lowest, decreases if traffic increases
     avail = avail_max - (avail_max - avail_mean) * (traffic - traffic.min()) / (
         traffic.mean() - traffic.min()
     )
@@ -156,6 +148,8 @@ def bev_availability_profile(fn, snapshots, nodes, options):
 def bev_dsm_profile(snapshots, nodes, options):
     dsm_week = np.zeros((24 * 7,))
 
+    # assuming that at a certain time ("bev_dsm_restriction_time") EVs have to
+    # be charged to a minimum value (defined in bev_dsm_restriction_value)
     dsm_week[(np.arange(0, 7, 1) * 24 + options["bev_dsm_restriction_time"])] = options[
         "bev_dsm_restriction_value"
     ]
@@ -167,14 +161,15 @@ def bev_dsm_profile(snapshots, nodes, options):
     )
 
 
+# %%
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
 
         snakemake = mock_snakemake(
             "build_transport_demand",
             simpl="",
-            clusters=60,
+            clusters=37,
         )
     configure_logging(snakemake)
     set_scenario_config(snakemake)