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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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    13. Wang, Xiaobin & Li, Qi & Zhai, Junyi & Jiang, Yuning & Wang, Sheng & Wang, Jianxiao, 2026. "Flexible operation of virtual power plant enabled integrated electricity-heating system under multiple uncertainties via distributionally robust model predictive control," Applied Energy, Elsevier, vol. 404(C).
    14. Chen, Bin & He, Guo & Hu, Lin & Li, Heng & Wang, Miaoben & Zhang, Rui & Gao, Kai, 2025. "Energy management of electric vehicles based on improved long short term memory network and data-enabled predictive control," Applied Energy, Elsevier, vol. 384(C).
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