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Charging and relocating optimization for electric vehicle car-sharing: An event-based strategy improvement approach

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  • Lu, Xiaonong
  • Zhang, Qiang
  • Peng, Zhanglin
  • Shao, Zhen
  • Song, Hao
  • Wang, Wanying

Abstract

In this paper, we study the charging and relocating problem for an electrical vehicle car-sharing (EVCS) system, aiming to dynamically match the user request, electrical load and vehicle supply at the lowest total cost of charging and lost sales. The scheduling problem is first formulated as a stochastic sequential decision program. To solve the strategy for an EVCS system with multiple city regions, we deploy the distributional event-based dynamic optimization approach that can coordinate the serving, charging and relocating decisions of shared electrical vehicles (SEV). To maximize the daily income of system, a gradient-based strategy iterative algorithm is applied to solve the scheduling problem. Finally, a computational experiment is performed, and the results show that the proposed optimization framework is applicable to the EVCS system scheduling problem by its efficiency as well as the capability to handle the fluctuating user requests and electrical load.

Suggested Citation

  • Lu, Xiaonong & Zhang, Qiang & Peng, Zhanglin & Shao, Zhen & Song, Hao & Wang, Wanying, 2020. "Charging and relocating optimization for electric vehicle car-sharing: An event-based strategy improvement approach," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s036054422031392x
    DOI: 10.1016/j.energy.2020.118285
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    References listed on IDEAS

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    Cited by:

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    2. Zeng, Shouzhen & Zhang, Na & Zhang, Chonghui & Su, Weihua & Carlos, Llopis-Albert, 2022. "Social network multiple-criteria decision-making approach for evaluating unmanned ground delivery vehicles under the Pythagorean fuzzy environment," Technological Forecasting and Social Change, Elsevier, vol. 175(C).

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