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Enhancing resilient restoration of distribution systems utilizing electric vehicles and supporting incentive mechanism

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  • Wu, Chuantao
  • Chen, Cen
  • Ma, Yuncong
  • Li, Feiyu
  • Sui, Quan
  • Lin, Xiangning
  • Wei, Fanrong
  • Li, Zhengtian

Abstract

Electric vehicles (EVs) are high-quality mobile power sources that can enhance the resilient restoration of distribution systems (DSs). However, incentivizing and dispatching the orderly participation of EVs in DS restoration has not been studied yet. This paper proposes a resilient restoration strategy for DS utilizing EVs and an EV-oriented incentive mechanism to address the problem. Firstly, a dispatching first and paying later framework for utilizing EVs in restoration is proposed. The problem of restoration utilizing EVs is divided into an EV-based restoration problem and an incentive problem. Secondly, EVs' clustering time–space model and power model are constructed from the clustering perspective via EV's energy discretization. Then, the DS's robust restoration model is developed to obtain the dispatch strategy. Thirdly, an incentive mechanism based on the asymmetric Nash bargaining theory is designed to compensate EVs financially, using the amount of restored load by EVs as the contribution indicator. Finally, numerical simulations are performed with modified IEEE 33-node and 118-node DS. The results show that the proposed EV-based strategy can restore more critical loads and save more than 90% of economic losses in the presence of sufficient renewable energy. The solution efficiency of the proposed strategy is also significantly improved.

Suggested Citation

  • Wu, Chuantao & Chen, Cen & Ma, Yuncong & Li, Feiyu & Sui, Quan & Lin, Xiangning & Wei, Fanrong & Li, Zhengtian, 2022. "Enhancing resilient restoration of distribution systems utilizing electric vehicles and supporting incentive mechanism," Applied Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:appene:v:322:y:2022:i:c:s0306261922007802
    DOI: 10.1016/j.apenergy.2022.119452
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Kaizhe & Xu, Yinliang & Sun, Hongbin, 2024. "Joint chance-constrained program based electric vehicles optimal dispatching strategy considering drivers' response uncertainty," Applied Energy, Elsevier, vol. 356(C).
    2. Dong, Lei & Sun, Shiting & Zhang, Shiming & Zhang, Tao & Pu, Tianjiao, 2024. "Distributed restoration for integrated electricity-gas-heating energy systems with an iterative loop scheme," Energy, Elsevier, vol. 304(C).
    3. Zhang, Xi & Dong, Zihang & Huangfu, Fenyu & Ye, Yujian & Strbac, Goran & Kang, Chongqing, 2024. "Strategic dispatch of electric buses for resilience enhancement of urban energy systems," Applied Energy, Elsevier, vol. 361(C).
    4. Pegah Alaee & Julius Bems & Amjad Anvari-Moghaddam, 2023. "A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management," Energies, MDPI, vol. 16(9), pages 1-28, April.
    5. Firouzi, Mehdi & Setayesh Nazar, Mehrdad & Shafie-khah, Miadreza & Catalão, João P.S., 2023. "Integrated framework for modeling the interactions of plug-in hybrid electric vehicles aggregators, parking lots and distributed generation facilities in electricity markets," Applied Energy, Elsevier, vol. 334(C).
    6. Zhang, Kaizhe & Xu, Yinliang & Sun, Hongbin, 2024. "Bilevel optimal coordination of active distribution network and charging stations considering EV drivers' willingness," Applied Energy, Elsevier, vol. 360(C).

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