safe transport demand in unit kinetic energy

scripts/build_transport_demand.py

@@ -19,13 +19,16 @@ logger = logging.getLogger(__name__)
 
 
 def build_nodal_transport_data(fn, pop_layout):
+    # get numbers of car and fuel efficieny per country
     transport_data = pd.read_csv(fn, index_col=0)
-
+    
+    # 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",
@@ -35,10 +38,14 @@ def build_nodal_transport_data(fn, pop_layout):
 
 
 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 kinetic energy.
-    traffic = pd.read_csv(traffic_fn, skiprows=2, usecols=["count"]).squeeze("columns")
+    """
-
+    # 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,
@@ -46,15 +53,6 @@ def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
     )
     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()
 
@@ -67,16 +65,8 @@ 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 = (
@@ -87,8 +77,7 @@ def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
 
     return (
         (transport_shape.multiply(energy_totals_transport) * 1e6 * nyears)
-        .divide(efficiency_gain * ice_correction)
+        .divide(nodal_transport_data["average fuel efficiency"] * ice_correction)
-        .multiply(1 + dd_EV)
     )
 
 
@@ -125,11 +114,14 @@ 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()
     )
@@ -149,7 +141,9 @@ 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"
     ]
@@ -160,7 +154,7 @@ def bev_dsm_profile(snapshots, nodes, options):
         weekly_profile=dsm_week,
     )
 
-
+#%%
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
@@ -168,7 +162,7 @@ if __name__ == "__main__":
         snakemake = mock_snakemake(
             "build_transport_demand",
             simpl="",
-            clusters=48,
+            clusters=37,
         )
     configure_logging(snakemake)
     set_scenario_config(snakemake)