主讲人

（单位）

Kai Pan 

(The Hong Kong Polytechnic University)

主持人

（单位）

李四杰、金子亮（东南大学）

讲座时间

2026年4月21日10点30分

讲座地点

经管楼B201

主讲人简介

Kai Pan is currently an Associate Professor in Operations Management at the Faculty of Business of The Hong Kong Polytechnic University (PolyU), the Director of the MSc Program in Operations Management (MScOM), and the Deputy Director of the Doctor of Business Management (DBM) Program. He received his Ph.D. degree from the University of Florida, USA, in 2016 and his Bachelor's degree from Zhejiang University, China, in 2010. Before he joined PolyU in 2016 right after his Ph.D., he worked as a Research Scientist at Amazon (Seattle, Washington) on Supply Chain Optimization and a Power System Engineer at GE Grid Solutions (Redmond, Washington) on Electricity Market Operations. His research interests include Stochastic and Discrete Optimization, Robust and Data-Driven Optimization, Dynamic Programming, and their applications in Energy Market, Smart City, Supply Chain, Shared Mobility, Telecommunication, and Marketing. His research on these topics has been published in Operations Research, Manufacturing and Service Operations Management, INFORMS Journal on Computing, Production and Operations Management, IISE Transactions, European Journal of Operational Research, IEEE Transactions on Power Systems, Transportation Research Part B, etc. He was the first-place winner of the IISE Pritsker Doctoral Dissertation Award in 2017 and the awardee of the PolyU Young Innovative Researcher Award (YIRA) 2025. He serves as an Associate Editor for IISE Transactions, Decision Sciences, and Omega, and has served as a Secretary/Treasurer for the INFORMS Computing Society (ICS).

讲座内容摘要

To address electricity demand uncertainty and dynamics, a power plant with limited ramping capability can collaborate with an electric vehicle (EV) company. With appropriate charging and discharging prices, the EV company voluntarily withdraws electricity from or returns electricity to the grid during suitable phases. We model their interaction as a bargaining game over these prices, followed by the EV company's charging and discharging problem and power plant's electricity generation problem. To solve this bargaining game, we propose a novel “Guess and Verify” approach. We first identify an optimal solution within a restricted price set that minimizes the two parties’ total cost and then verify its global optimality. This approach identifies a bargaining equilibrium set, where EV charging and discharging smooth the power plant's electricity generation across phases and reduce power curtailment. We select a contract from the equilibrium set and quantify the gap between the equilibrium and market-based charging prices. Interestingly, charging and discharging efficiencies influence the equilibrium discharging price in opposite directions. Using data from the New York City power grid and school bus system, we conduct extensive numerical experiments. Our results suggest that as the total EV battery capacity increases, the equilibrium discharging price decreases while cost savings rise. When the electricity demand gap across phases widens or the power plant's ramping capability declines, both the equilibrium discharging price and cost savings increase. The EV company's cost saving can exceed 100%, implying potential profit from the collaboration, while the power plant's cost reduction is 7-13%.