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Study on charge and discharge control strategy of improved PSO for EV

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  • Yin, Wanjun
  • Liang, Wenbin
  • Ji, Jianbo

Abstract

In view of the negative influence of electric vehicle (EV) random charging on power grid load stability and charging cost of users, on the premise of guaranteeing users' travel demand, in this paper, an improved PSO model is proposed with the objective function of optimizing the daily load variance of the power grid and minimizing the charge cost of the vehicle owner. In this model, chaotic mapping is used to initialize the position of particles so that the particles are uniformly distributed in space and the diversity of particle solutions is increased, based on the global search ability and local search ability of the improved algorithm, combined with the analysis of a numerical example, the results show that the improved multi-objective PSO algorithm converges quickly and can jump out of the local optimum, better multi-objective optimization to reduce the peak-valley load difference and charging costs.

Suggested Citation

  • Yin, Wanjun & Liang, Wenbin & Ji, Jianbo, 2024. "Study on charge and discharge control strategy of improved PSO for EV," Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:energy:v:304:y:2024:i:c:s0360544224018358
    DOI: 10.1016/j.energy.2024.132061
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    References listed on IDEAS

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    1. Paudel, Diwas & Das, Tapas K., 2023. "A deep reinforcement learning approach for power management of battery-assisted fast-charging EV hubs participating in day-ahead and real-time electricity markets," Energy, Elsevier, vol. 283(C).
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    3. Yin, WanJun & Ming, ZhengFeng & Wen, Tao, 2021. "Scheduling strategy of electric vehicle charging considering different requirements of grid and users," Energy, Elsevier, vol. 232(C).
    4. Wu, Xiaomei & Feng, Qijin & Bai, Chenchen & Lai, Chun Sing & Jia, Youwei & Lai, Loi Lei, 2021. "A novel fast-charging stations locational planning model for electric bus transit system," Energy, Elsevier, vol. 224(C).
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