Quantifying the benefits of electric vehicles on the future electricity grid in the midwestern United States

Cong Zhang, Jeffery B. Greenblatt, Pamela MacDougall, Samveg Saxena and Aditya Jayam Prabhakar

Applied Energy, 2020, vol. 270, issue C, No S0306261920306863

Abstract: Rising numbers of electric vehicles (EVs) along with increasing renewable electricity will create challenges for electricity grids, potentially leading to significant increases in peak load, overgeneration of renewables during low load periods, and increased ramping requirements. However, controlled EV charging could largely mitigate these issues, transforming EVs from a grid liability into a valuable asset. In this study, we model the evolution of the Midcontinent Independent System Operator (MISO) grid, located in the midwestern United States (U.S.) with large numbers of forecasted EVs and renewable generation by the late 2030s, over the period 2019–2039. Uncontrolled charging leads to peak load increases of up to 10% or 8 GW, and exacerbated ramping requirements. In contrast, controlled charging significantly ameliorates these challenges, with unidirectional (“V1G”) charging largely avoiding peak load increases, filling valley loads and reducing ramp rates. Bidirectional (“V2G”) charging provides much larger load flexibility to dramatically reduce peak loads and ramp rates. This study investigates EV load growth in the midwestern U.S. and the impact that different types of controlled charging can have on mitigating the grid challenges presented both by large numbers of EVs and increasing levels of renewable generation. Moreover, above ~3 million EVs, V2G charging can exhibit multi-day optimization behavior and very high load flexibility, suggesting the possibility of new load planning tools extending over multiple days. Such capabilities would provide significant aid in optimally scheduling resources with a larger percentage of intermittent renewables.

Keywords: Vehicle-to-grid; Electric vehicle; Peak shaving; Controlled charging; Multi-day optimization (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (19)

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DOI: 10.1016/j.apenergy.2020.115174

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