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Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism

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  • Yin, WanJun
  • Wen, Tao
  • Zhang, Chao

Abstract

Large-scale disordered charging of electric vehicles will lead to problems such as voltage drop, increased network loss, and reduced life of transformers. In order to fully tap the dispatch potential of electric vehicles and promote more extensive participation of electric vehicle users in the orderly interaction of the power grid, this paper combines power grid optimization. The most economic lever is used in dispatching, and an optimized dispatching strategy based on the dynamic electricity price mechanism is designed. In order to fully absorb wind power, ensure the charging and discharging demand of electric vehicles and the safe operation of the distribution network, realize the power generation side and the electric vehicle user side, maximize bilateral interests. This paper verifies that the collaborative optimization scheduling strategy is an effective scheduling scheme that takes into account the interests of all aspects and realizes multi-objective optimization through an example. The optimal scheduling scheme proposed in this paper provides an example for the multi-objective optimization problem.

Suggested Citation

  • Yin, WanJun & Wen, Tao & Zhang, Chao, 2023. "Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222025130
    DOI: 10.1016/j.energy.2022.125627
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    References listed on IDEAS

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    Cited by:

    1. Yang, Zaoli & Li, Qin & Charles, Vincent & Xu, Bing & Gupta, Shivam, 2023. "Supporting personalized new energy vehicle purchase decision-making: Customer reviews and product recommendation platform," International Journal of Production Economics, Elsevier, vol. 265(C).
    2. Chen, Qi & Kuang, Zhonghong & Liu, Xiaohua & Zhang, Tao, 2024. "Application-oriented assessment of grid-connected PV-battery system with deep reinforcement learning in buildings considering electricity price dynamics," Applied Energy, Elsevier, vol. 364(C).

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