An Incentive Dispatch Algorithm for Utilization-Perfect EV Charging Management

Lo Pang Yun Ting, Po Hui Wu, Hsiu Ying Chung, Kun Ta Chuang

研究成果: Conference contribution


Due to the rapid growth of electric vehicles (EVs), the charging scheduling of EVs has become highly important. In order to reduce the total operating cost, how to arrange the charging of each EV becomes the main issue. However, existing scheduling methods usually obtain schedules without considering EVs users’ charging willingness, which will let EVs users be reluctant to follow the arranged charging schedule, thereby incurring low charging utilization and high operational overhead. To solve this problem, we devise an online charging registration mechanism, an incentive-based framework called POSIT, to provide a feasible schedule for different EVs to enhance the quality of user experience. In the proposed mechanism, the charging scheduler will provide a relevant reward (as an incentive) for users to properly enhance users’ willingness to accept the arranged schedule. In addition, the interactive learning is adopted to improve the next recommendation based on the user’s feedback. The POSIT framework is able to satisfy the energy demand of EVs charging and the commercial building. The implemented experiments indicate that the proposed framework can not only increase the charging utilization, but also can significantly reduce the electrical operating costs and increase the revenue at the cost of small incentives.

主出版物標題Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Proceedings
編輯João Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng
發行者Springer Science and Business Media Deutschland GmbH
出版狀態Published - 2022
事件26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2022 - Chengdu, China
持續時間: 2022 5月 162022 5月 19


名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13282 LNAI


Conference26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2022

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 電腦科學(全部)


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