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Optimal Planning of Electric Vehicle Charging Stations Considering User Satisfaction and Charging Convenience

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  • Di Xu

    (School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China)

  • Wenhui Pei

    (School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China)

  • Qi Zhang

    (School of Control Science and Engineering, Shandong University, Jinan 250061, China
    State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

Abstract

To solve the problem of layout design of charging stations in the early stage of the electric vehicle industry, the user’s satisfaction and the charging convenience are considered. An electric vehicle charging station site-selection model is established based on the kernel density analysis of the urban population. The goal of this model is maximum electric vehicle user satisfaction and the highest charging convenience. Then, according to model characteristics, the immune algorithm is designed and optimized to solve the model. The optimization of the immune algorithm includes two aspects. On the one aspect, judging that the stop condition is added in the mutation link. On the other aspect, two mutation operators are designed in the optimized immune algorithm. Finally, the simulation example is determined by a three-step method in Jinan City. The results show that the electric vehicle charging station site-selection model in this paper can better meet user needs compared with traditional models. Compared with the traditional immune algorithm, the convergence speed of the optimized immune algorithm is improved, and the proposed algorithm is superior to the traditional immune algorithm in terms of stability and accuracy.

Suggested Citation

  • Di Xu & Wenhui Pei & Qi Zhang, 2022. "Optimal Planning of Electric Vehicle Charging Stations Considering User Satisfaction and Charging Convenience," Energies, MDPI, vol. 15(14), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5027-:d:859247
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    References listed on IDEAS

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    Cited by:

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    2. Ana Pavlićević & Saša Mujović, 2022. "Impact of Reactive Power from Public Electric Vehicle Stations on Transformer Aging and Active Energy Losses," Energies, MDPI, vol. 15(19), pages 1-24, September.
    3. Oluwasola O. Ademulegun & Paul MacArtain & Bukola Oni & Neil J. Hewitt, 2022. "Multi-Stage Multi-Criteria Decision Analysis for Siting Electric Vehicle Charging Stations within and across Border Regions," Energies, MDPI, vol. 15(24), pages 1-28, December.
    4. Du, Zhili & Zheng, Lirong & Lin, Boqiang, 2024. "Influence of charging stations accessibility on charging stations utilization," Energy, Elsevier, vol. 298(C).
    5. Yongjing Li & Wenhui Pei & Qi Zhang, 2022. "Improved Whale Optimization Algorithm Based on Hybrid Strategy and Its Application in Location Selection for Electric Vehicle Charging Stations," Energies, MDPI, vol. 15(19), pages 1-25, September.
    6. Loni, Abdolah & Asadi, Somayeh, 2023. "Data-driven equitable placement for electric vehicle charging stations: Case study San Francisco," Energy, Elsevier, vol. 282(C).
    7. Ajit Kumar Mohanty & Perli Suresh Babu & Surender Reddy Salkuti, 2022. "Optimal Allocation of Fast Charging Station for Integrated Electric-Transportation System Using Multi-Objective Approach," Sustainability, MDPI, vol. 14(22), pages 1-22, November.

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