Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022
Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format.
Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035.
The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip.
These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week.
Citation Formats
TY - DATA
AB - Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format.
Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035.
The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip.
These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week.
AU - Yip, Arthur
A2 - Hoehne, Christopher
A3 - Jadun, Paige
A4 - Ledna, Catherine
A5 - Hale, Elaine
A6 - Muratori, Matteo
A7 - Thom, Daniel
A8 - Mooney, Meghan
A9 - Liu, Lixi
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO - 10.25984/2373091
KW - energy
KW - power
KW - TEMPO
KW - dsgrid
KW - demand-side grid
KW - demand model
KW - transportation
KW - electric vehicle
KW - EV
KW - electrification
KW - bulk power systems
KW - energy modeling
KW - projection
KW - long-term load forecasting
KW - electric vehicle charging
KW - passenger vehicles
KW - model
KW - charging load
KW - United States
KW - computational science
LA - English
DA - 2023/08/29
PY - 2023
PB - National Renewable Energy Laboratory
T1 - Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022
UR - https://doi.org/10.25984/2373091
ER -
Yip, Arthur, et al. Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022. National Renewable Energy Laboratory, 29 August, 2023, Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2373091.
Yip, A., Hoehne, C., Jadun, P., Ledna, C., Hale, E., Muratori, M., Thom, D., Mooney, M., & Liu, L. (2023). Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022. [Data set]. Open Energy Data Initiative (OEDI). National Renewable Energy Laboratory. https://doi.org/10.25984/2373091
Yip, Arthur, Christopher Hoehne, Paige Jadun, Catherine Ledna, Elaine Hale, Matteo Muratori, Daniel Thom, Meghan Mooney, and Lixi Liu. Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022. National Renewable Energy Laboratory, August, 29, 2023. Distributed by Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2373091
@misc{OEDI_Dataset_5958,
title = {Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022},
author = {Yip, Arthur and Hoehne, Christopher and Jadun, Paige and Ledna, Catherine and Hale, Elaine and Muratori, Matteo and Thom, Daniel and Mooney, Meghan and Liu, Lixi},
abstractNote = {Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format.
Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035.
The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip.
These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week. },
url = {https://data.openei.org/submissions/5958},
year = {2023},
howpublished = {Open Energy Data Initiative (OEDI), National Renewable Energy Laboratory, https://doi.org/10.25984/2373091},
note = {Accessed: 2025-04-27},
doi = {10.25984/2373091}
}
https://dx.doi.org/10.25984/2373091
Details
Data from Aug 29, 2023
Last updated Jun 17, 2024
Submitted Jun 12, 2024
Organization
National Renewable Energy Laboratory
Contact
Elaine T. Hale
303.384.7812
Authors
Research Areas
Keywords
energy, power, TEMPO, dsgrid, demand-side grid, demand model, transportation, electric vehicle, EV, electrification, bulk power systems, energy modeling, projection, long-term load forecasting, electric vehicle charging, passenger vehicles, model, charging load, United States, computational scienceDOE Project Details
Project Name Demand-side Grid (dsgrid)
Project Number 29323