refactor: LDV uses bev_vmt scaling; simplify model_year_profile updat…

…es (#321)
Breakthrough-Energy · Nov 14, 2022 · 9dc4801 · 9dc4801
1 parent b2dd326
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diff --git a/prereise/gather/demanddata/transportation_electrification/data/Fuel_Efficiencies.csv b/prereise/gather/demanddata/transportation_electrification/data/Fuel_Efficiencies.csv
@@ -1,12 +1,10 @@
-veh_type,2017,2030,2040,2050,,,,,,
-LDV_100,0.242,0.214,0.211,0.209,,,,,,
-LDV_200,0.259,0.22,0.215,0.214,,,,,,
-LDV_300,0.274,0.226,0.22,0.217,,,,,,
-LDT_100,0.324,0.318,0.315,0.312,,,,,,
-LDT_200,0.344,0.324,0.321,0.321,,,,,,
-LDT_300,0.355,0.334,0.331,0.327,,,,,,
-MDV,2.13,1.60,1.48,1.37,,,,,,
-HDV,2.72,2.13,1.99,1.85,,,,,,
-Transit,2.59,2.04,1.91,1.78,,,,,,
-,,,,,,,,,,
-,,,,,,,,,,
+veh_type,2017,2030,2040,2050
+LDV_100,0.242,0.214,0.211,0.209
+LDV_200,0.259,0.22,0.215,0.214
+LDV_300,0.274,0.226,0.22,0.217
+LDT_100,0.324,0.318,0.315,0.312
+LDT_200,0.344,0.324,0.321,0.321
+LDT_300,0.355,0.334,0.331,0.327
+MDV,2.13,1.60,1.48,1.37
+HDV,2.72,2.13,1.99,1.85
+Transit,2.59,2.04,1.91,1.78
diff --git a/prereise/gather/demanddata/transportation_electrification/data_helper.py b/prereise/gather/demanddata/transportation_electrification/data_helper.py
@@ -64,7 +64,7 @@ def get_kwhmi(model_year, veh_type, veh_range):
     data = pd.read_csv(filepath, index_col="veh_type")
 
     if (veh_type.upper() == "LDV") or (veh_type.upper() == "LDT"):
-        kwhmi = data.loc[f"{veh_type.upper()}_{veh_range}", model_year]
+        kwhmi = data.loc[f"{veh_type.upper()}_{veh_range}", str(model_year)]
 
     elif (veh_type.upper() == "MDV") or (veh_type.upper() == "HDV"):
         kwhmi = data.loc[f"{veh_type.upper()}", str(model_year)]

diff --git a/prereise/gather/demanddata/transportation_electrification/immediate.py b/prereise/gather/demanddata/transportation_electrification/immediate.py
@@ -104,18 +104,17 @@ def immediate_charging(
     census_region,
     model_year,
     veh_range,
-    kwhmi,
     power,
     location_strategy,
     veh_type,
     filepath,
     trip_strategy=1,
 ):
     """Immediate charging function
+
     :param int census_region: any of the 9 census regions defined by US census bureau.
     :param int model_year: year that is being modelled/projected to, 2017, 2030, 2040, 2050.
     :param int veh_range: 100, 200, or 300, represents how far vehicle can travel on single charge.
-    :param int kwhmi: fuel efficiency, should vary based on vehicle type and model_year.
     :param int power: charger power, EVSE kW.
     :param int location_strategy: where the vehicle can charge-1, 2, 3, 4, or 5;
         1-home only, 2-home and work related, 3-anywhere if possibile,
@@ -125,16 +124,20 @@ def immediate_charging(
     :param int trip_strategy: determine to charge after any trip (1) or only after the last trip (2)
     :return: (*numpy.ndarray*) -- charging profiles.
     """
-    # Constants
-    battery_capacity = kwhmi * veh_range
-    input_day = data_helper.get_input_day(data_helper.get_model_year_dti(model_year))
 
-    # load NHTS data from function
     if veh_type.lower() == "ldv":
         trips = data_helper.remove_ldt(data_helper.load_data(census_region, filepath))
-
     elif veh_type.lower() == "ldt":
         trips = data_helper.remove_ldv(data_helper.load_data(census_region, filepath))
+    elif veh_type.lower() == "mdv":
+        trips = data_helper.load_hdv_data("mhdv", filepath)
+    elif veh_type.lower() == "hdv":
+        trips = data_helper.load_hdv_data("hhdv", filepath)
+
+    # Constants
+    kwhmi = data_helper.get_kwhmi(model_year, veh_type, veh_range)
+    battery_capacity = kwhmi * veh_range
+    input_day = data_helper.get_input_day(data_helper.get_model_year_dti(model_year))
 
     # updates the weekend and weekday values in the nhts data
     trips = data_helper.update_if_weekend(trips)
@@ -148,6 +151,7 @@ def immediate_charging(
     new_columns = [
         "trip start battery charge",
         "trip end battery charge",
+        "charging power",
         "charging time",
         "charging consumption",
         "BEV could be used",
@@ -172,7 +176,11 @@ def immediate_charging(
     # Add booleans for whether the dell time is long enough to allow charging
     trips["dwell_allowed"] = trips["dwell_time"] > 0.2
     # Add boolean for whether this trip allows charging
-    allowed_cols = ["location_allowed", "trip_allowed", "dwell_allowed"]
+    allowed_cols = [
+        "location_allowed",
+        "trip_allowed",
+        "dwell_allowed",
+    ]
     trips["charging_allowed"] = trips[allowed_cols].apply(all, axis=1)
 
     # Evaluate weekend vs. weekday for each trip

diff --git a/prereise/gather/demanddata/transportation_electrification/immediate_charging_HDV.py b/prereise/gather/demanddata/transportation_electrification/immediate_charging_HDV.py
@@ -237,7 +237,7 @@ def immediate_charging(
     if trip_strategy == 1:
         trips["trip_allowed"] = True
     elif trip_strategy == 2:
-        trips["trip_allowed"] = trips["trip_number"] == trips["total vehicle trips"]
+        trips["trip_allowed"] = trips["trip_number"] == trips["total_trips"]
 
     # Add booleans for whether the dell time is long enough to allow charging -- (1)
     trips["dwell_allowed"] = trips["dwell_time"] > 0.2

diff --git a/prereise/gather/demanddata/transportation_electrification/smart_charging.py b/prereise/gather/demanddata/transportation_electrification/smart_charging.py
@@ -22,6 +22,7 @@ def smart_charging(
     filepath,
     daily_values,
     load_demand,
+    bev_vmt,
     trip_strategy=1,
 ):
     """Smart charging function
@@ -64,11 +65,11 @@ def smart_charging(
         "trip start battery charge",
         "trip end battery charge",
         "BEV could be used",
-        "trip_number",
+        "Battery size",
         "Electricity cost",
         "Battery discharge",
         "Battery charge",
-        "Battery size",
+        "trip_number",
     ]
     newdata = newdata.reindex(list(newdata.columns) + new_columns, axis=1, fill_value=0)
 
@@ -82,7 +83,6 @@ def smart_charging(
     daily_vmt_total = data_helper.get_total_daily_vmt(newdata, input_day, daily_values)
 
     kwh = kwhmi * veh_range
-    emfacvmt = const.emfacvmt
     if power > 19.2:
         charging_efficiency = 0.95
     else:
@@ -222,7 +222,9 @@ def smart_charging(
 
                             segcum = np.cumsum(seg)
                             trip_g2v_load[:, start : end + 1] = (
-                                x[segcum[n] - seg[n] : segcum[n]] / charging_efficiency
+                                #  possibly? x[segcum[n] - seg[n] + 1 : segcum[n]] / charging_efficiency
+                                x[segcum[n] - seg[n] : segcum[n]]
+                                / charging_efficiency
                             )
                             g2v_load[dwell_location, :] += trip_g2v_load[0, :]
                             individual_g2v_load[i + n][:] = trip_g2v_load
@@ -237,9 +239,9 @@ def smart_charging(
                             electricitycost = trip_g2v_cost
                             tripload = trip_v2g_load + trip_g2v_load
 
-                            # update the cost function and vonvert from KW to MW
+                            # update the cost function and convert from KW to MW
                             cost += (
-                                tripload / 1000 / daily_vmt_total[day_iter] * emfacvmt
+                                tripload / 1000 / daily_vmt_total[day_iter] * bev_vmt
                             )[0, :]
 
                             # SOC rise in kwh, from charging
@@ -278,45 +280,19 @@ def smart_charging(
                             :, newdata.columns.get_loc("Battery size")
                         ] = batterysize
 
-                        # copy individual back to newdata if it can be an EV
-                        newdata.iloc[i : i + total_trips] = individual
-
             # update the counter to the next vehicle
             i += total_trips
 
         outputelectricload = sum(g2v_load)
 
-        if day_iter == len(input_day) - 1:
-            # MW
-            model_year_profile[day_iter * 24 :] += (
-                outputelectricload[:24] / (daily_vmt_total[day_iter] * 1000) * emfacvmt
-            )
-            model_year_profile[:24] += (
-                outputelectricload[24:48]
-                / (daily_vmt_total[day_iter] * 1000)
-                * emfacvmt
-            )
-            model_year_profile[24:48] += (
-                outputelectricload[48:72]
-                / (daily_vmt_total[day_iter] * 1000)
-                * emfacvmt
-            )
-
-        elif day_iter == len(input_day) - 2:
-            # MW
-            model_year_profile[day_iter * 24 : day_iter * 24 + 48] += (
-                outputelectricload[:48] / (daily_vmt_total[day_iter] * 1000) * emfacvmt
-            )
-            model_year_profile[:24] += (
-                outputelectricload[48:72]
-                / (daily_vmt_total[day_iter] * 1000)
-                * emfacvmt
-            )
+        # create wrap-around indexing function
+        profile_window_indices = np.arange(day_iter * 24, day_iter * 24 + 72) % len(
+            model_year_profile
+        )
 
-        else:
-            # MW
-            model_year_profile[day_iter * 24 : day_iter * 24 + 72] += (
-                outputelectricload / (daily_vmt_total[day_iter] * 1000) * emfacvmt
-            )
+        # MW
+        model_year_profile[profile_window_indices] += (
+            outputelectricload / (daily_vmt_total[day_iter] * 1000) * bev_vmt
+        )
 
     return model_year_profile
diff --git a/prereise/gather/demanddata/transportation_electrification/smart_charging_HDV.py b/prereise/gather/demanddata/transportation_electrification/smart_charging_HDV.py
@@ -60,9 +60,9 @@ def smart_charging(
         "trip end battery charge",
         "BEV could be used",
         "Battery size",
+        "Electricity cost",
         "Battery discharge",
         "Battery charge",
-        "Electricity cost",
     ]
     newdata = newdata.reindex(list(newdata.columns) + new_columns, axis=1, fill_value=0)
 
@@ -95,33 +95,20 @@ def smart_charging(
         charging_efficiency,
     )
 
-    for day_iter in range(model_year_len):
-
-        # Expand load vector linearly to 7200 points instead of 72
-        if day_iter == model_year_len - 1:  # 365
-            adjusted_load = [
-                load_demand[i] + model_year_profile[i]
-                for i in range(day_iter * 24, (day_iter + 1) * 24)
-            ]
-            adjusted_load += [load_demand[i] + model_year_profile[i] for i in range(48)]
+    day_num = model_year_len
+    for day_iter in range(day_num):
 
-        elif day_iter == model_year_len - 2:  # 364
-            adjusted_load = [
-                load_demand[i] + model_year_profile[i]
-                for i in range(day_iter * 24, ((day_iter + 1) * 24) + 24)
-            ]
-            adjusted_load += [load_demand[i] + model_year_profile[i] for i in range(24)]
-
-        elif day_iter == model_year_len - 3:  # 363
-            adjusted_load = [
-                load_demand[i] + model_year_profile[i]
-                for i in range(day_iter * 24, ((day_iter + 1) * 24) + 48)
-            ]
+        adjusted_load = [
+            load_demand[i] + model_year_profile[i]
+            for i in range(
+                day_iter * 24, (day_iter + 1) * 24 + min(day_num - day_iter - 1, 2) * 24
+            )
+        ]
 
-        else:
-            adjusted_load = [
+        if 3 - day_num + day_iter > 0:
+            adjusted_load += [
                 load_demand[i] + model_year_profile[i]
-                for i in range(day_iter * 24, (day_iter * 24) + 72)
+                for i in range(24 * (3 - day_num + day_iter))
             ]
 
         cost = np.array(adjusted_load)
@@ -269,39 +256,22 @@ def smart_charging(
                     :, newdata.columns.get_loc("Battery size")
                 ] = batterysize
 
-                # copy individual back to newdata if it can be an EV
-                newdata.iloc[i : i + total_trips] = individual
+                # # copy individual back to newdata if it can be an EV
+                # newdata.iloc[i : i + total_trips] = individual
 
             # update the counter to the next vehicle
             i += total_trips
 
         outputelectricload = sum(g2v_load)
 
-        if day_iter == model_year_len - 1:
-            # MW
-            model_year_profile[day_iter * 24 :] += (
-                outputelectricload[:24] / (daily_vmt_total[day_iter] * 1000) * bev_vmt
-            )
-            model_year_profile[:24] += (
-                outputelectricload[24:48] / (daily_vmt_total[day_iter] * 1000) * bev_vmt
-            )
-            model_year_profile[24:48] += (
-                outputelectricload[48:72] / (daily_vmt_total[day_iter] * 1000) * bev_vmt
-            )
+        # create wrap-around indexing function
+        profile_window_indices = np.arange(day_iter * 24, day_iter * 24 + 72) % len(
+            model_year_profile
+        )
 
-        elif day_iter == model_year_len - 2:
-            # MW
-            model_year_profile[day_iter * 24 : day_iter * 24 + 48] += (
-                outputelectricload[:48] / (daily_vmt_total[day_iter] * 1000) * bev_vmt
-            )
-            model_year_profile[:24] += (
-                outputelectricload[48:72] / (daily_vmt_total[day_iter] * 1000) * bev_vmt
-            )
-
-        else:
-            # MW
-            model_year_profile[day_iter * 24 : day_iter * 24 + 72] += (
-                outputelectricload / (daily_vmt_total[day_iter] * 1000) * bev_vmt
-            )
+        # MW
+        model_year_profile[profile_window_indices] += (
+            outputelectricload / (daily_vmt_total[day_iter] * 1000) * bev_vmt
+        )
 
     return model_year_profile
diff --git a/prereise/gather/demanddata/transportation_electrification/tests/test_immediate_charging.py b/prereise/gather/demanddata/transportation_electrification/tests/test_immediate_charging.py
@@ -17,7 +17,6 @@ def test_immediate_charging_region1():
         census_region=1,
         model_year=2017,
         veh_range=100,
-        kwhmi=0.242,
         power=6.6,
         location_strategy=2,
         veh_type="LDV",

diff --git a/prereise/gather/demanddata/transportation_electrification/tests/test_smart_charging.py b/prereise/gather/demanddata/transportation_electrification/tests/test_smart_charging.py
@@ -37,6 +37,7 @@ def test_smart_charging():
         ),
         daily_values=daily_values,
         load_demand=load_demand,
+        bev_vmt=const.emfacvmt,
         trip_strategy=1,
     )