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Ordered charge control considering the uncertainty of charging load of electric vehicles based on Markov chain

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  • Han, Xiaojuan
  • Wei, Zixuan
  • Hong, Zhenpeng
  • Zhao, Song

Abstract

In order to improve the consumption capacity of renewable energy, an ordered charging control method of electric vehicles based on Markov chain prediction is proposed considering the uncertainty of the electric vehicle charging load. The quantity of electric vehicles in different parking lots and different time periods is predicted by the Markov chain to calculate the electric vehicle charging load of the photovoltaic charging station. Taking the minimum power sum of a photovoltaic charging station, the minimum cost of users and the maximum consumption of the renewable energy as the objective functions, an ordered charging control model with the interest and willingness of electric vehicle users is established. The peak-to-valley time period is divided by the particle swarm optimization algorithm, and the genetic algorithm is used to solve the model. The simulation tests of the electric vehicle driving data provided by the Unite State Travel Survey website verify the correctness and effectiveness of the proposed method, the effects of different peak and valley periods and the responsiveness of electric vehicle users on the ordered charging control of electric vehicles are compared and analyzed. The simulation results show that the prediction accuracy of the charging load obtained by the Markov chain is significantly higher than that of Monte Carlo, and when the responsiveness of electric vehicle users reaches 90%, the total charging cost can be reduced by up to 16.89%. The ordered charging strategy of electric vehicles proposed in this paper can effectively reduce the peak-to-valley difference, increase the consumption of renewable energy, and reduce the charging cost of electric vehicle users.

Suggested Citation

  • Han, Xiaojuan & Wei, Zixuan & Hong, Zhenpeng & Zhao, Song, 2020. "Ordered charge control considering the uncertainty of charging load of electric vehicles based on Markov chain," Renewable Energy, Elsevier, vol. 161(C), pages 419-434.
    • Handle: RePEc:eee:renene:v:161:y:2020:i:c:p:419-434
    DOI: 10.1016/j.renene.2020.07.013
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    References listed on IDEAS

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    1. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
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    2. Ren, Fei & Tian, Chenlu & Zhang, Guiqing & Li, Chengdong & Zhai, Yuan, 2022. "A hybrid method for power demand prediction of electric vehicles based on SARIMA and deep learning with integration of periodic features," Energy, Elsevier, vol. 250(C).
    3. Yao, Zhaosheng & Wang, Zhiyuan & Ran, Lun, 2023. "Smart charging and discharging of electric vehicles based on multi-objective robust optimization in smart cities," Applied Energy, Elsevier, vol. 343(C).
    4. Yang, Chuxiao & Hao, Yu & Irfan, Muhammad, 2021. "Energy consumption structural adjustment and carbon neutrality in the post-COVID-19 era," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 442-453.
    5. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).

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