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Equilibrium configuration strategy of vehicle-to-grid-based electric vehicle charging stations in low-carbon resilient distribution networks

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  • Wang, Zhaoqi
  • Zhang, Lu
  • Tang, Wei
  • Ma, Ziyao
  • Huang, Jiajin

Abstract

Distribution networks (DNs) are under severe requirements of security and ecology, such as maintaining continuous power supply for critical loads under extreme disasters and contributing to the carbon neutrality by 2060. However, the investment of infrastructures in DNs, such as electric vehicle charging stations (EVCSs), are hardly balanced as a result of the coupled relationship between the resilience, economics and ecology with different magnitudes and dimensions. This paper proposes to avoid an overinvestment of the equilibrium configuration strategy of EVCSs in low-carbon resilient urban DNs. Firstly, a bi-level optimization model is established to configurate EVCSs. Multiple objectives, such as the security, economics and ecology of DNs, are enhanced simultaneously in the upper level. Indexes of these objectives are formulated by generating typical scenarios in the lower level, such as economic operation, fault recovery and extreme disaster restoration. Then, the generalized Nash equilibrium (GNE) model is utilized to deal with the equilibrium and coupled relationship between multiple objectives in the upper level, which is solved by the incremental penalty function algorithm. Finally, simulation tests of a 3-feeder 62-node 10 kV DN verify the superiority of the proposed GNE-based equilibrium configuration model of EVCSs compared with other approaches, and the resilience and carbon emission reduction can be improved with an equilibrium configuration scheme of EVCSs under a limitation of investment.

Suggested Citation

  • Wang, Zhaoqi & Zhang, Lu & Tang, Wei & Ma, Ziyao & Huang, Jiajin, 2024. "Equilibrium configuration strategy of vehicle-to-grid-based electric vehicle charging stations in low-carbon resilient distribution networks," Applied Energy, Elsevier, vol. 361(C).
    • Handle: RePEc:eee:appene:v:361:y:2024:i:c:s0306261924003143
    DOI: 10.1016/j.apenergy.2024.122931
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    References listed on IDEAS

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    Cited by:

    1. Taorong Jia & Guoqing Yang & Lixiao Yao, 2024. "The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization," Energies, MDPI, vol. 17(19), pages 1-20, October.
    2. Qian, Tao & Liang, Zeyu & Shao, Chengcheng & Guo, Zishan & Hu, Qinran & Wu, Zaijun, 2025. "Unsupervised learning for efficiently distributing EVs charging loads and traffic flows in coupled power and transportation systems," Applied Energy, Elsevier, vol. 377(PB).
    3. Soliman, Ismail A. & Tulsky, Vladimir & Abd el-Ghany, Hossam A. & ElGebaly, Ahmed E., 2025. "Efficient allocation of capacitors and vehicle-to-grid integration with electric vehicle charging stations in radial distribution networks," Applied Energy, Elsevier, vol. 377(PD).

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