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Orderly charging strategy of electric vehicle based on improved PSO algorithm

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  • Du, Wenyi
  • Ma, Juan
  • Yin, Wanjun

Abstract

With the increasing penetration of electric vehicles (EVs), the harmful impact caused by EV's disorderly charging becomes larger. Aiming for mitigating the impact of disorderly charging on the grid and improving the user's satisfaction, this paper firstly performs the Monte Carlo simulation (MCS) to obtain the distribution information of EVs' disorderly charging. Then an improved particle swarm optimization (PSO) algorithm is presented to model the orderly charging strategy. In order to maintain the diversity of the population better, a rotation matrix is utilized to yaw particle's search direction slightly in the improved PSO. And by adjusting the inertia weight index and learning factor, the problems of poor local optimization ability and premature convergence of the original PSO is alleviated. Finally, the proposed approach is verified by a practical engineering case. The outcome demonstrates that the proposed orderly charging strategy can significantly lower the charging cost and peak-valley difference.

Suggested Citation

  • Du, Wenyi & Ma, Juan & Yin, Wanjun, 2023. "Orderly charging strategy of electric vehicle based on improved PSO algorithm," Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004826
    DOI: 10.1016/j.energy.2023.127088
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Yagang & Pan, Zhiya & Wang, Hui & Wang, Jingchao & Zhao, Zheng & Wang, Fei, 2023. "Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach," Energy, Elsevier, vol. 283(C).
    2. Meng, Weiqi & Song, Dongran & Huang, Liansheng & Chen, Xiaojiao & Yang, Jian & Dong, Mi & Talaat, M., 2024. "A Bi-level optimization strategy for electric vehicle retailers based on robust pricing and hybrid demand response," Energy, Elsevier, vol. 289(C).
    3. Haoyu Pan & Junhui Gong, 2023. "Application of Particle Swarm Optimization (PSO) Algorithm in Determining Thermodynamics of Solid Combustibles," Energies, MDPI, vol. 16(14), pages 1-16, July.
    4. Zhong Guan & Hui Wang & Zhi Li & Xiaohu Luo & Xi Yang & Jugang Fang & Qiang Zhao, 2024. "Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm," Energies, MDPI, vol. 17(7), pages 1-20, April.

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