Merge pull request #327 from Breakthrough-Energy/dmuldrew/demand_prof…

…ile_interface

feat: state demand profile interface function

prereise/gather/demanddata/transportation_electrification/const.py

@@ -141,3 +141,21 @@
 ER = 1
 
 emfacvmt = 758118400
+
+census_region2state = {
+    1: ["CT", "MA", "ME", "NH", "RI", "VT"],
+    2: ["PA", "NJ", "NY"],
+    3: ["IL", "IN", "MI", "OH", "WI"],
+    4: ["IA", "KS", "MN", "MO", "ND", "NE", "SD"],
+    5: ["DE", "FL", "GA", "MD", "NC", "SC", "VA", "WV"],
+    6: ["AL", "KY", "MS", "TN"],
+    7: ["AR", "LA", "OK", "TX"],
+    8: ["AZ", "CO", "ID", "MT", "NM", "NV", "UT", "WY"],
+    9: ["AK", "CA", "HI", "OR", "WA"],
+}
+
+state2census_region = {
+    state: census_region
+    for census_region, state_list in census_region2state.items()
+    for state in state_list
+}

prereise/gather/demanddata/transportation_electrification/generate_BEV_vehicle_profiles.py

@@ -0,0 +1,167 @@
+import os
+
+import pandas as pd
+
+from prereise.gather.const import abv2state
+from prereise.gather.demanddata.transportation_electrification import (
+    const,
+    immediate,
+    immediate_charging_HDV,
+    smart_charging,
+)
+from prereise.gather.demanddata.transportation_electrification.data_helper import (
+    generate_daily_weighting,
+    get_kwhmi,
+    load_rural_scaling_factor,
+    load_urbanized_scaling_factor,
+)
+
+
+def generate_bev_vehicle_profiles(
+    vehicle_trip_data_filepath,
+    charging_strategy,
+    veh_type,
+    veh_range,
+    projection_year,
+    state,
+    external_signal=None,
+    power=6.6,
+    location_strategy=2,
+    trip_strategy=1,
+):
+    """Generate Battery Electric Vehicle (BEV) profiles
+
+    :param str vehicle_trip_data_filepath: filepath of collected trip data from external sources
+        representing driving patterns
+    :param str charging_strategy: establishes whether charging happens immediately ("immediate")
+         or optimize based on external signals, i.e. smart charging ("smart")
+    :param str veh_type: vehicle category: LDV: light duty vehicle, LDT: light duty truck,
+        MDV: medium duty vehicle, HDV: heavy duty vehicle
+    :param int veh_range: 100, 200, or 300, represents how far vehicle can travel on
+        single charge in miles.
+    :param int projection_year: year that is being modelled/projected to, 2017, 2030, 2040,
+        2050.
+    :param str state: US state abbreviation
+    :param numpy.ndarray (optional) external_signal: initial load demand (MW for each hour)
+    :param int power: (optional) charger power, EVSE kW; default value: 6.6 kW;
+    :param int location_strategy: (optional) where the vehicle can charge-1, 2, 3, 4, or 5;
+        1-home only, 2-home and work related, 3-anywhere if possibile,
+        4-home and school only, 5-home and work and school.
+        default value: 2
+    :param int trip_strategy: (optional) determine to charge after any trip (1) or only after the
+        last trip (2); default value: 1
+    :return: (*pandas.DataFrame*) -- yearly charging profiles for all urban areas and the rural area
+        in each state (MW for each hour)
+    """
+
+    census_region = const.state2census_region[state]
+    kwhmi = get_kwhmi(projection_year, veh_type, veh_range)
+
+    daily_values = generate_daily_weighting(projection_year)
+
+    if power > 19.2:
+        charging_efficiency = 0.95
+    else:
+        charging_efficiency = 0.9
+
+    geographic_area_bev_vmt = {}
+
+    urban_scaling_filepath = os.path.join(
+        const.data_folder_path,
+        "regional_scaling_factors",
+        "Regional_scaling_factors_UA_",
+    )
+    urban_scaling_factors = pd.read_csv(
+        urban_scaling_filepath + str(projection_year) + ".csv", index_col="State"
+    )
+    state_urban_areas = urban_scaling_factors.loc[state.upper(), "UA"]
+
+    # scaling factors for listed urban areas
+    for urban_area in state_urban_areas.to_list():
+        urban_bev_vmt = load_urbanized_scaling_factor(
+            model_year=projection_year,
+            veh_type=veh_type,
+            veh_range=veh_range,
+            urbanized_area=urban_area,
+            state=state,
+            filepath=urban_scaling_filepath,
+        )
+        geographic_area_bev_vmt.update({f"{state}_{urban_area}": urban_bev_vmt})
+
+    # scaling factors for rural areas
+    rural_bev_vmt = load_rural_scaling_factor(
+        projection_year,
+        veh_type,
+        veh_range,
+        abv2state[state.upper()].upper(),
+        filepath=os.path.join(
+            const.data_folder_path,
+            "regional_scaling_factors",
+            "Regional_scaling_factors_RA_",
+        ),
+    )
+    geographic_area_bev_vmt.update({f"{state}_rural": rural_bev_vmt})
+
+    # calculate demand for all geographic areas with scaling factors
+    state_demand_profiles = {}
+    for geographic_area, bev_vmt in geographic_area_bev_vmt.items():
+
+        if charging_strategy == "immediate":
+
+            if veh_type.lower() in {"ldv", "ldt"}:
+                normalized_demand = immediate.immediate_charging(
+                    census_region=census_region,
+                    model_year=projection_year,
+                    veh_range=veh_range,
+                    power=power,
+                    location_strategy=location_strategy,
+                    veh_type=veh_type,
+                    filepath=vehicle_trip_data_filepath,
+                )
+            elif veh_type.lower() in {"mdv", "hdv"}:
+                normalized_demand = immediate_charging_HDV.immediate_charging(
+                    model_year=projection_year,
+                    veh_range=veh_range,
+                    power=power,
+                    location_strategy=location_strategy,
+                    veh_type=veh_type,
+                    filepath=vehicle_trip_data_filepath,
+                )
+
+            final_demand = immediate.adjust_bev(
+                hourly_profile=normalized_demand,
+                adjustment_values=daily_values,
+                model_year=projection_year,
+                veh_type=veh_type,
+                veh_range=veh_range,
+                bev_vmt=bev_vmt,
+                charging_efficiency=charging_efficiency,
+            )
+
+        elif charging_strategy == "smart":
+            final_demand = smart_charging.smart_charging(
+                census_region=census_region,
+                model_year=projection_year,
+                veh_range=veh_range,
+                kwhmi=kwhmi,
+                power=power,
+                location_strategy=location_strategy,
+                veh_type=veh_type,
+                filepath=vehicle_trip_data_filepath,
+                daily_values=daily_values,
+                external_signal=external_signal,
+                bev_vmt=bev_vmt,
+                trip_strategy=trip_strategy,
+            )
+
+        state_demand_profiles.update({geographic_area: final_demand})
+
+        state_demand_profiles_df = pd.DataFrame(
+            state_demand_profiles,
+            index=pd.date_range(
+                start=f"{projection_year}-01-01 00:00:00",
+                end=f"{projection_year}-12-31 23:00:00",
+                freq="H",
+            ),
+        )
+    return state_demand_profiles_df