Modeling of Electric Vehicle Charging Effects on Existing Grid Infrastructure

The U.S. is rapidly electrifying its vehicle fleet. The electric vehicle (EV) market in the United States has grown from just over ten thousand vehicles in 2011 to more than 215,000 vehicles sold in 2020. In 2020, the EV share of new vehicle sales was approximately 1.7%, an increase from about 1.4% in 2019. With a national goal of EV sales accounting for half of all new car sales by 2030, the grid is going to see some significant changes to its power consumption. The forecasted surge in the number of EVs elucidates the issue of rapidly increasing power loads from EV charging in residential and workplace applications. This rapid power variation on the existing grid infrastructure can have some important effects on the grid system and should be better understood. Many parts of the current electric grid date back more than 50 years and rapid changes to the power consumption curve can have negative effects on grid reliability. This project focuses on the modeling of the effects of EV charging stations on the existing grid infrastructure. The modeling incorporates time-series and stochastic modeling approaches to identify key locations where the risk of failure may occur. A series of EV charging scenarios are modeled to evaluate the changes in grid voltage based on the localized grid power consumption. The nodal grid array that is simulated is modeled after data provided from Duquesne Light Company (DLC), a City of Pittsburgh electricity utility provider. The charging scenarios are modeled in the open-source OpenDSS software developed by the Electric Power Research Institute (EPRI) and utilize EV scenario data from the Department of Energy's Electric Vehicle Infrastructure Projection Tool (EVI-Pro) Lite. This research provides a successful example of an industry-academia collaboration of an engineering student research project. The findings from grid modeling will be presented along with the student educational components that were involved throughout the research and collaboration process.