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Multi-objective optimisation method of electric vehicle charging station based on non-dominated sorting genetic algorithm

Author

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  • Jia Liu
  • Jin Huang
  • Jinzhi Hu

Abstract

There are some problems in the existing objective optimisation planning methods of electric vehicle charging station, such as low accuracy and long optimisation time. By calculating the input cost, combined closure flow and minimum node voltage of the charging station through the objective function, the optimisation objective was determined. According to the determined optimisation objective, the multi-objective comprehensive planning model of the electric vehicle charging station is constructed. After the initial solution setting, coding, decoding and other iterative operations, the multi-objective comprehensive planning model of the electric vehicle charging station is solved and the optimisation result is obtained. The multi-objective optimisation of electric vehicle charging station is realised. The results show that the highest accuracy is about 95%.

Suggested Citation

  • Jia Liu & Jin Huang & Jinzhi Hu, 2022. "Multi-objective optimisation method of electric vehicle charging station based on non-dominated sorting genetic algorithm," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 44(5/6), pages 413-426.
    • Handle: RePEc:ids:ijgeni:v:44:y:2022:i:5/6:p:413-426
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    Citations

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    Cited by:

    1. Sami M. Alshareef & Ahmed Fathy, 2023. "Efficient Red Kite Optimization Algorithm for Integrating the Renewable Sources and Electric Vehicle Fast Charging Stations in Radial Distribution Networks," Mathematics, MDPI, vol. 11(15), pages 1-30, July.
    2. Hamza El Hafdaoui & Hamza El Alaoui & Salma Mahidat & Zakaria El Harmouzi & Ahmed Khallaayoun, 2023. "Impact of Hot Arid Climate on Optimal Placement of Electric Vehicle Charging Stations," Energies, MDPI, vol. 16(2), pages 1-19, January.
    3. Loni, Abdolah & Asadi, Somayeh, 2023. "Data-driven equitable placement for electric vehicle charging stations: Case study San Francisco," Energy, Elsevier, vol. 282(C).
    4. Abid, Md. Shadman & Apon, Hasan Jamil & Hossain, Salman & Ahmed, Ashik & Ahshan, Razzaqul & Lipu, M.S. Hossain, 2024. "A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning," Applied Energy, Elsevier, vol. 353(PA).
    5. Li, Yanbin & Wang, Jiani & Wang, Weiye & Liu, Chang & Li, Yun, 2023. "Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning," Energy, Elsevier, vol. 281(C).
    6. Deveci, Muhammet & Erdogan, Nuh & Pamucar, Dragan & Kucuksari, Sadik & Cali, Umit, 2023. "A rough Dombi Bonferroni based approach for public charging station type selection," Applied Energy, Elsevier, vol. 345(C).

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