import os
import pandas as pd
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,
)
[docs]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,
state.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
