Report Title: An Exponential Cone Programming Approach for Managing Electric Vehicle Charging
Time: May 26, 2023 10:00-11:30 AM
Location: 217, Main Building, Zhongguancun Campus
Reported by: Associate Professor He Long, George Washington University
Reported by:
Long He is an associate professor of decision sciences at the School of Business, George Washington University. Prior to joining GW, Long was an associate professor in the Department of Analytics & Operations at NUS Business School, National University of Singapore. He received his Ph.D. in Operations Research from the University of California, Berkeley, and his B.Eng. in Logistics Management and Engineering from HKUST. His current research involves using data-driven approaches to address problems in smart city operations (e.g., vehicle sharing, last-mile delivery) and supply chain management. This line of research has been recognized with the M&SOM Journal Best Paper Award, Transportation Science & Logistics (TSL) Best Paper Award, and ENRE Best Publication Award in Energy from INFORMS.
Introduction to report content:
We study the problem of an electric vehicle charging service provider, which faces (1) stochastic arrival of customers with distinctive arrival and departure times, and energy requirements as well as (2) a total electricity cost including demand charges, costs related to the highest per-period electricity used in a finite horizon. We formulate its problem of scheduling vehicle charging to minimize the expected total cost as a stochastic program (SP which can be solved by exponential cone program (ECP) approximations. We show that our ECP approach outperforms the sample average approximation (SAA) and a DRO approach using a semi-definite program (SDP) on numerical instances calibrated to real data. We then show that our ECP continues to perform well considering practical implementation issues, including a data-driven setting and an adaptive charging environment. Finally, based on the ECP solutions, we also discuss managerial insights for both charging service providers and policymakers.
(Undertaken by: Department of Management Engineering, Research and Academic Center)
