TY - GEN
T1 - An Incentive Dispatch Algorithm for Utilization-Perfect EV Charging Management
AU - Ting, Lo Pang Yun
AU - Wu, Po Hui
AU - Chung, Hsiu Ying
AU - Chuang, Kun Ta
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-031-05981-0_11
DO - 10.1007/978-3-031-05981-0_11
M3 - Conference contribution
AN - SCOPUS:85130284688
SN - 9783031059803
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 132
EP - 146
BT - Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Proceedings
A2 - Gama, João
A2 - Li, Tianrui
A2 - Yu, Yang
A2 - Chen, Enhong
A2 - Zheng, Yu
A2 - Teng, Fei
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2022
Y2 - 16 May 2022 through 19 May 2022
ER -