Online Multiobjective Optimization for Electric Vehicle Charging Station Operation

• Published in: IEEE transactions on transportation electrification Vol. 10; no. 4; pp. 8640 - 8655
• Main Authors: Li, Yuanzheng, Duan, Juntao, Pan, Linqiang, Ishizaki, Takayuki, Gao, Liang, Zeng, Zhigang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Costs; Electric power demand; Electric vehicle charging; Electric vehicle charging stations; Electric vehicles; Electric vehicles charging station (EVCS); Heuristic algorithms; Multiple objective analysis; online multiobjective optimization; Optimization; Quality of service; quality of service (QoS); Transportation; Uncertainty
• ISSN: 2332-7782; 2577-4212; 2332-7782
• DOI: 10.1109/TTE.2024.3362707

With the increasing development of electric vehicles (EVs), their demand for charging has increased. To satisfy their demand with limited public charging posts while minimizing their charging cost online, the charging operation of EV charging stations (EVCSs) should be optimized. In this context, we propose an online multiobjective optimization framework for EVCS charging operation optimization with the quality of service (QoS) and the total charging cost of EVCSs as objectives. In the framework, a novel quantitative definition of QoS for online optimization of EVCSs charging operation is proposed based on the difference between the cumulative charging power demand and supply. We introduce a target-based online dynamic weighted algorithm (TBODWA) into the proposed framework to solve the online multiobjective optimization problem. The advantage of proposed framework is that it can lead the average objectives to converge to a preset target. In the numerical experiment, a real EVCS charging example in California, USA is employed to verify the effectiveness and efficiency of the proposed framework.