United States Department of Transportation
i
Electrical Vehicle Charging Infrastructure Design and Operations [Research Brief]
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2023-07-01
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[PDF-571.49 KB]
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Corporate Contributors:State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
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Edition:Research Brief
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Abstract:This research integrates EV charging scheduling and infrastructure design as a two-stage optimization problem. This framework takes various input parameters into account, such as charging demands and allowable waiting time from the California State University, Long Beach (CSULB) campus, time-of-use electricity price, net surplus compensate rate, battery price, charging service fee, local weather short-term forecast, and solar panel cost, to formulate the optimization problem and simulation testbed. Two approaches for EV charging power dispatch are developed, including the robust model predictive 2240control (MPC) and empirical rule. Although MPC has the potential to offer better service quality and profitability, its practical performance is subject to model accuracy and external uncertainty. In addition, MPC’s computational time is substantially longer than that of the empirical rule. Therefore, this research adopts the empirical rule approach to solve the year-long optimal scheduling problem repeatedly under different infrastructure designs. Instead of enumerating all possible design combinations, this research follows the response surface methodology to build a quadratic function of the charging station’s operational revenue over ten years. Consequently, the charging station’s infrastructure, such as the number of chargers, the capacity of PV panels, and the size of the battery are smartly sampled, optimized on the surface function, and finally converged to the most profitable design.
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