Abstract
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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Notes
- 1.
For ease of explanation, we assume the equal charging speed in our discussion. It is believed that the implementation issue of variant charging speeds can be seamlessly extended in POSIT.
- 2.
Please refer to https://www.solarreviews.com/blog/tesla-supercharger-guide.
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Acknowledgement
This work was supported in part by Ministry of Science and Technology, R.O.C., under Contract 109-2221-E-006-187-MY3, 110-2221-E-006-001 and 111AT16B.
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Ting, L.PY., Wu, PH., Chung, HY., Chuang, KT. (2022). An Incentive Dispatch Algorithm for Utilization-Perfect EV Charging Management. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13282. Springer, Cham. https://doi.org/10.1007/978-3-031-05981-0_11
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