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Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning

Author

Listed:
  • Li, Yanbin
  • Wang, Jiani
  • Wang, Weiye
  • Liu, Chang
  • Li, Yun

Abstract

To accommodate the increased demand for electric vehicle(EV) charging in China, this paper examines electric vehicle charging station(EVCS) location strategies from the perspective of private investors, taking full account of the competitive environment of existing EVCSs in the region. First of all, a three-level location model considering dynamic pricing is developed, which includes user decisions, EVCS pricing, and EVCS location decisions. Then, the soft actor-critic(SAC) reinforcement learning algorithm is used to train the optimal pricing strategy for EVCS to guarantee the maximum cumulative revenue. The proposed methodology is verified through case studies based on an industrial park in China. The results show that the proposed methodology can make more economical and scientific location decisions than the traditional method. The dynamic pricing method based on reinforcement learning can provide a reference for the location and operation of more EVCSs.

Suggested Citation

  • Li, Yanbin & Wang, Jiani & Wang, Weiye & Liu, Chang & Li, Yun, 2023. "Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning," Energy, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:energy:v:281:y:2023:i:c:s036054422301678x
    DOI: 10.1016/j.energy.2023.128284
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    References listed on IDEAS

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    1. Men, Jinkun & Zhao, Chunmeng, 2024. "A Type-2 fuzzy hybrid preference optimization methodology for electric vehicle charging station location," Energy, Elsevier, vol. 293(C).

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