TY - GEN
T1 - A Forward-Looking Dataset of EV Managed Charging Resource and Costs
AU - Borlaug, Brennan
AU - Reinicke, Nick
AU - Yip, Arthur
AU - Ledna, Catherine
AU - Jadun, Paige
AU - Sun, Jiayun
AU - Liu, Bo
AU - Hale, Elaine
AU - Vanatta, Max
AU - Matsuda-Dunn, Reiko
AU - Konar-Steenberg, Gabriel
AU - Lavin, Luke
PY - 2025
Y1 - 2025
N2 - This presentation summarizes a high-resolution, forward-looking dataset of EV adoption, EV charging, and managed charging resource. Vehicle-level data are grounded in current adoption and charging patterns, and ~200,000 real-world vehicle-weeks of travel data covering all on-road segments (i.e., light-duty, transit and school buses, local, regional and long-haul medium- and heavy-duty). The data, which include multiple charging profiles per vehicle to bound flexibility, are then processed and aggregated to describe baseline charging and charge management resource by county, hour, year, scenario, and vehicle type. Coupled with one of four scenarios of how EV managed charging costs might evolve over time, the dataset enables a power sector capacity expansion model to select cost-optimal quantities of EV managed charging and supply-side resources to reliably satisfy demand. Five integration strategies: Baseline, Daytime and Flat (passive), Flex (active), and Stress (anti-strategy), illustrate how baseline charging and flexibility potential changes with EVSE build-out and charging preferences.
AB - This presentation summarizes a high-resolution, forward-looking dataset of EV adoption, EV charging, and managed charging resource. Vehicle-level data are grounded in current adoption and charging patterns, and ~200,000 real-world vehicle-weeks of travel data covering all on-road segments (i.e., light-duty, transit and school buses, local, regional and long-haul medium- and heavy-duty). The data, which include multiple charging profiles per vehicle to bound flexibility, are then processed and aggregated to describe baseline charging and charge management resource by county, hour, year, scenario, and vehicle type. Coupled with one of four scenarios of how EV managed charging costs might evolve over time, the dataset enables a power sector capacity expansion model to select cost-optimal quantities of EV managed charging and supply-side resources to reliably satisfy demand. Five integration strategies: Baseline, Daytime and Flat (passive), Flex (active), and Stress (anti-strategy), illustrate how baseline charging and flexibility potential changes with EVSE build-out and charging preferences.
KW - dataset
KW - electric vehicles
KW - managed charging
KW - power systems
KW - supply curves
M3 - Presentation
T3 - Presented at the PLMA EV Symposium, 26-27 August 2025, Denver, Colorado
ER -