Merge pull request #358 from FraSanvit/fix-road-transport-profiles

Fix uncontrolled charging RAMP profile

CHANGELOG.md

@@ -10,7 +10,7 @@
 
 * **ADD** fully-electrified heat demand (#284).
 
-* **ADD** fully-electrified road transportation (#270), (#271). A parameter allows to define the share of uncontrolled (timeseries) vs controlled charging (optimised) by the solver (PR #338).
+* **ADD** fully-electrified road transportation (#270, #271, #358). A parameter allows to define the share of uncontrolled (timeseries) vs controlled charging (optimised) by the solver (#338).
 
 * **ADD** nuclear power plant technology with capacity limits. Capacity limits can be equal to today or be bound by a minimum and maximum capacity to represent an available range in future. In either case, capacities are allocated at a subnational resolution based on linear scaling from current capacity geolocations, using the JRC power plant database (#78).
 

config/default.yaml

@@ -19,7 +19,8 @@ data-sources:
     swiss-end-use: https://www.bfe.admin.ch/bfe/en/home/versorgung/statistik-und-geodaten/energiestatistiken/energieverbrauch-nach-verwendungszweck.exturl.html/aHR0cHM6Ly9wdWJkYi5iZmUuYWRtaW4uY2gvZGUvcHVibGljYX/Rpb24vZG93bmxvYWQvOTg1NA==.html
     swiss-energy-balance: https://www.bfe.admin.ch/bfe/en/home/versorgung/statistik-und-geodaten/energiestatistiken/gesamtenergiestatistik.exturl.html/aHR0cHM6Ly9wdWJkYi5iZmUuYWRtaW4uY2gvZGUvcHVibGljYX/Rpb24vZG93bmxvYWQvNzUxOQ==.html
     swiss-industry-energy-balance: https://www.bfe.admin.ch/bfe/en/home/versorgung/statistik-und-geodaten/energiestatistiken/teilstatistiken.exturl.html/aHR0cHM6Ly9wdWJkYi5iZmUuYWRtaW4uY2gvZGUvcHVibGljYX/Rpb24vZG93bmxvYWQvODc4OA==.html
-    ev-data: https://zenodo.org/record/6579421/files/ramp-ev-consumption-profiles.csv.gz?download=1
+    controlled-ev-profiles: https://zenodo.org/record/6579421/files/ramp-ev-consumption-profiles.csv.gz?download=1
+    uncontrolled-ev-profiles: https://sandbox.zenodo.org/records/45530/files/uncontrolled-charging-profiles.csv.gz?download=1 # TODO: convert into Zenodo repository
     gridded-temperature-data: https://zenodo.org/records/6557643/files/temperature.nc?download=1
     gridded-10m-windspeed-data: https://zenodo.org/records/6557643/files/wind10m.nc?download=1
     when2heat-params: https://zenodo.org/records/10965295/files/{dataset}?download=1

config/schema.yaml

@@ -103,10 +103,14 @@ properties:
                 type: string
                 pattern: ^(https?|http?):\/\/.+
                 description: Web address of Swiss industry energy balance data.
-            ev-data:
+            controlled-ev-profiles:
                 type: string
                 pattern: ^(https?|http?):\/\/.+
                 description: Web address of electric vehicle data.
+            uncontrolled-ev-profiles:
+                type: string
+                pattern: ^(https?|http?):\/\/.+
+                description: Web address of electric light-duty vehicle uncontrolled charging.
             gridded-temperature-data:
                 type: string
                 pattern: ^(https?|http?):\/\/.+

rules/transport.smk

@@ -2,15 +2,24 @@
 
 
 rule download_transport_timeseries:
-    # TODO have correct timeseries data once RAMP has generated the new charging profile and it's been put on Zenodo
     message: "Get EV data from RAMP"
     params:
-        url = config["data-sources"]["ev-data"]
+        url = config["data-sources"]["controlled-ev-profiles"]
     conda: "../envs/shell.yaml"
     output: protected("data/automatic/ramp-ev-consumption-profiles.csv.gz")
     localrule: True
     shell: "curl -sSLo {output} {params.url}"
 
+rule download_uncontrolled_timeseries:
+    # TODO: move into rule download_transport_timeseries once PR 356 is merged
+    message: "Get EV uncontrolled charging data from RAMP"
+    params:
+        url = config["data-sources"]["uncontrolled-ev-profiles"]
+    conda: "../envs/shell.yaml"
+    output: protected("data/automatic/ramp-ev-uncontrolled-charging-profiles.csv.gz")
+    localrule: True
+    shell: "curl -sSLo {output} {params.url}"
+
 
 rule annual_transport_demand:
     message: "Calculate future transport energy demand based on JRC IDEES"
@@ -52,7 +61,7 @@ rule create_uncontrolled_road_transport_timeseries:
     message: "Create timeseries for road transport demand  (uncontrolled charging)"
     input:
         annual_data = "build/data/transport/annual-road-transport-distance-demand-uncontrolled.csv",
-        timeseries = "data/automatic/ramp-ev-consumption-profiles.csv.gz"
+        timeseries = "data/automatic/ramp-ev-uncontrolled-charging-profiles.csv.gz"
     params:
         first_year = config["scope"]["temporal"]["first-year"],
         final_year = config["scope"]["temporal"]["final-year"],
@@ -73,7 +82,7 @@ use rule create_uncontrolled_road_transport_timeseries as create_uncontrolled_ro
     message: "Create timeseries for historic electrified road transport demand (uncontrolled charging)"
     input:
         annual_data = "build/data/transport/annual-road-transport-distance-demand-historic-electrification.csv",
-        timeseries = "data/automatic/ramp-ev-consumption-profiles.csv.gz",
+        timeseries = "data/automatic/ramp-ev-uncontrolled-charging-profiles.csv.gz",
     params:
         first_year = config["scope"]["temporal"]["first-year"],
         final_year = config["scope"]["temporal"]["final-year"],

scripts/transport/road_transport_timeseries.py

@@ -16,6 +16,7 @@ def create_road_transport_demand_timeseries(
     path_to_output: str,
 ) -> None:
     # Read annual road transport distance into panda dataframe
+
     df_annual = (
         pd.read_csv(path_to_annual_data, index_col=[0, 1, 2], parse_dates=[2])
         .squeeze()
@@ -30,7 +31,6 @@ def create_road_transport_demand_timeseries(
 
     if vehicle_type in ["passenger-cars", "motorcycles", "light-duty-vehicles"]:
         # Use RAMP time series profiles for small and light vehicles.
-        # TODO check if path_to_timeseries data is charging demand or transport demand
         df_timeseries = (
             pd.read_csv(path_to_timeseries, index_col=[0, 1, 2], parse_dates=[0])
             .xs(slice(first_year, final_year), level="year")
@@ -39,9 +39,9 @@ def create_road_transport_demand_timeseries(
             .groupby(by=lambda idx: idx.year)
             .transform(lambda x: x / x.sum())
             .pipe(fill_empty_country, country_neighbour_dict)
+            .tz_localize("UTC")
             .mul(df_annual)
         )
-
     elif vehicle_type in ["heavy-duty-vehicles", "coaches-and-buses"]:
         # ASSUME flat profiles for heavy transport.
         df_timeseries = df_annual.groupby(by=lambda idx: idx.year).transform(
@@ -69,7 +69,10 @@ def create_road_transport_demand_timeseries(
 
 def fill_empty_country(df, country_neighbour_dict):
     for country, neighbours in country_neighbour_dict.items():
-        assert country not in df.columns
+        if country in df.columns:
+            print(f"Country {country} is already in dataframe")
+            continue
+        print(f"Country {country} is not in dataframe, filling with neighbours")
         df[country] = df[neighbours].mean(axis=1)
     return df