Large-scale scenarios of electric vehicle charging with a data-driven model of control

Journal Article · 27 February 2022 · Energy

DOI: https://doi.org/10.1016/j.energy.2022.123592 · OSTI ID:1873375

Powell, Siobhan  ^[1]; Vianna Cezar, Gustavo ^[2]; Apostolaki-Iosifidou, Elpiniki ^[3]; Rajagopal, Ram ^[1]

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1. Stanford Univ., CA (United States)
2. SLAC National Accelerator Lab., Menlo Park, CA (United States); Stanford Univ., CA (United States)
3. SLAC National Accelerator Lab., Menlo Park, CA (United States); ENGIE Impact (United States)

Transportation electrification is forecast to bring millions of new electric vehicles to roads worldwide this decade. Planning to support those vehicles depends on detailed scenarios of their electricity demand in both uncontrolled and controlled or smart charging scenarios. In this work, we present a novel modeling approach to enable rapid generation of demand estimates that represent the impact of controlled charging for large-scale scenarios with millions of individual drivers. To model the effect of load modulation control on aggregate charging profiles, we propose a novel machine learning approach that replaces traditional optimization approaches. We demonstrate its performance modeling workplace charging control under a range of electricity rate schedules, achieving small errors (2.5%–4.5%) while accelerating computations by more than 4000 times. To generate the uncontrolled charging demand for scenarios with residential, workplace, and public charging we use statistical representations of a large data set of real charging sessions. We demonstrate the methodology by generating diverse sets of scenarios for California's charging demand in 2030 which consider multiple charging segments and controls, each run locally in under 50 s. We further demonstrate support for rate design by modeling the large-scale impact of a new, custom rate schedule for workplace charging.

• Research Organization: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
• Sponsoring Organization: California Energy Commission; National Science Foundation (NSF); USDOE Office of Science (SC); USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
• Grant/Contract Number: AC02-76SF00515
• OSTI ID: 1873375
• Journal Information: Energy, Journal Name: Energy Vol. 248; ISSN 0360-5442
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English