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A two-stage ambiguous stochastic program for electric vehicle charging station location problem with valet charging service

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  • Li, Na
  • Jiang, Yue
  • Zhang, Zhi-Hai

Abstract

This paper introduces a new charging service mode that is emerging in the market for electric vehicles called the valet charging service. Through this service mode, a company provides free valet charging services to users that are far away from charging stations, and users can request the fee-based service when they are unwilling to charge electric vehicles themselves. The valet charging service is a new service mode that promises to alleviate users’ worries regarding the lack of nearby charging stations. However, whether such a mode truly benefits a specified system remains open to careful study. We build the charging station locating and sizing model with the valet charging service under demand uncertainty. We first formulate this problem as a two-stage stochastic mixed-integer model (TSMIP). Then, considering that the demand probability distribution is difficult to estimate, we reformulate TSMIP into a risk-averse two-stage stochastic mixed-integer model (RTSMIP) with the ambiguity set. Next, we propose an SAA-based hybrid-cut L-shaped algorithm to solve this model. By comparing the total cost of the system with and without the valet charging service under different scenarios through hypothetical numerical experiments and a realistic case study in Shanghai, meaningful insights are provided that include identifying the conditions in which the valet charging service works well and how it influences the location and capacity of charging stations. These insights provide some understanding of this new service mode that can help companies make rational decisions.

Suggested Citation

  • Li, Na & Jiang, Yue & Zhang, Zhi-Hai, 2021. "A two-stage ambiguous stochastic program for electric vehicle charging station location problem with valet charging service," Transportation Research Part B: Methodological, Elsevier, vol. 153(C), pages 149-171.
    • Handle: RePEc:eee:transb:v:153:y:2021:i:c:p:149-171
    DOI: 10.1016/j.trb.2021.09.006
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    2. Rosebell Paul & Mercy Paul Selvan, 2024. "Predicting and Forecasting of Vehicle Charging Station Using ECNN with DHFO Algorithm," Energies, MDPI, vol. 17(17), pages 1-25, August.
    3. 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).

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