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Research on Location and Capacity Optimization Method for Electric Vehicle Charging Stations Considering User’s Comprehensive Satisfaction

Author

Listed:
  • Tao Yi

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Xiao-bin Cheng

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Hao Zheng

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Jin-peng Liu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

Abstract

The development of electric vehicles has significant value for the sustainable utilization of energy resources. However, the unreasonable construction of charging stations causes problems such as low user satisfaction, waste of land resources, unstable power systems, and so on. Reasonable planning of the location and capacity of charging stations is of great significance to users, investors and power grids. This paper synthetically considers three indicators of user satisfaction: charging convenience, charging cost and charging time. Considering the load and charging requirements, the model of electric vehicle charging station location and volume is established, and the model based on artificial immune algorithm is used to optimize the solution. An empirical analysis was conducted based on a typical regional survey. The research results show that increasing the density of charging stations, lowering the charging price and shortening the charging time can effectively improve user satisfaction. The constructed site and capacity selection optimization solving model can scientifically guide charging station resource allocation under the constraints of the optimal user comprehensive satisfaction target, improve the capacity of scientific planning and resource allocation of regional electric vehicle charging stations, and support the large-scale promotion and application of electric vehicles.

Suggested Citation

  • Tao Yi & Xiao-bin Cheng & Hao Zheng & Jin-peng Liu, 2019. "Research on Location and Capacity Optimization Method for Electric Vehicle Charging Stations Considering User’s Comprehensive Satisfaction," Energies, MDPI, vol. 12(10), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1915-:d:232498
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    Citations

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    Cited by:

    1. Men, Jinkun & Zhao, Chunmeng, 2024. "A Type-2 fuzzy hybrid preference optimization methodology for electric vehicle charging station location," Energy, Elsevier, vol. 293(C).
    2. Yi, Tao & Cheng, Xiaobin & Chen, Yaxuan & Liu, Jinpeng, 2020. "Joint optimization of charging station and energy storage economic capacity based on the effect of alternative energy storage of electric vehicle," Energy, Elsevier, vol. 208(C).
    3. Pourvaziri, H. & Sarhadi, H. & Azad, N. & Afshari, H. & Taghavi, M., 2024. "Planning of electric vehicle charging stations: An integrated deep learning and queueing theory approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    4. Yi, Tao & Cheng, Xiaobin & Peng, Peng, 2022. "Two-stage optimal allocation of charging stations based on spatiotemporal complementarity and demand response: A framework based on MCS and DBPSO," Energy, Elsevier, vol. 239(PC).
    5. Lin, Boqiang & Yang, Mengqi, 2024. "Changes in consumer satisfaction with electric vehicle charging infrastructure: Evidence from two cross-sectional surveys in 2019 and 2023," Energy Policy, Elsevier, vol. 185(C).
    6. Bong-Gi Choi & Byeong-Chan Oh & Sungyun Choi & Sung-Yul Kim, 2020. "Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method," Energies, MDPI, vol. 13(7), pages 1-20, April.
    7. Hong Gao & Kai Liu & Xinchao Peng & Cheng Li, 2020. "Optimal Location of Fast Charging Stations for Mixed Traffic of Electric Vehicles and Gasoline Vehicles Subject to Elastic Demands," Energies, MDPI, vol. 13(8), pages 1-16, April.

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