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Quality, Reliability, Security and Robustness in Heterogeneous Systems. 16th EAI International Conference, QShine 2020, Virtual Event, November 29–30, 2020, Proceedings

Research Article

Research on Optimizing the Location and Capacity of Electric Vehicle Charging Stations

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  • @INPROCEEDINGS{10.1007/978-3-030-77569-8_6,
        author={Lingling Yang and Jiali Chen and Wenzao Li and Zhan Wen},
        title={Research on Optimizing the Location and Capacity of Electric Vehicle Charging Stations},
        proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 16th EAI International Conference, QShine 2020, Virtual Event, November 29--30, 2020, Proceedings},
        proceedings_a={QSHINE},
        year={2021},
        month={6},
        keywords={EV Charging stations Location Capacity allocation DEIPSO algorithm},
        doi={10.1007/978-3-030-77569-8_6}
    }
    
  • Lingling Yang
    Jiali Chen
    Wenzao Li
    Zhan Wen
    Year: 2021
    Research on Optimizing the Location and Capacity of Electric Vehicle Charging Stations
    QSHINE
    Springer
    DOI: 10.1007/978-3-030-77569-8_6
Lingling Yang1, Jiali Chen1, Wenzao Li1,*, Zhan Wen1
  • 1: College of Communication Engineering
*Contact email: lwz@cuit.edu.cn

Abstract

Charging stations deployment is an important problem in Electric Vehicle (EV) networks. The distribution of EV is complicated in urban environments. Therefore, reasonable location deployment will avail to reduce construction costs and improve user experience. Aim to this, this paper comprehensively considers the cost of charging stations and the charging costs of EVs. Studied the charging station location, charging station capacity and the optimization algorithms for charging station location, and proposed a method for estimating the optimal location and optimal capacity allocation of EV charging stations. Firstly, this paper uses the Voronoi diagram to divide the service range of the charging stations, then uses the differential evolution algorithm combined with the particle swarm optimization algorithm (DEIPSO) to solve the charging station location model, and finally consider the residence time of EV in the charging station, use queuing theory to solve the charging station capacity allocation model. The experimental results shows that DEIPSO can better jump out of the local optimum and achieve the global optimum; the proposed model can plan the charging station on the basis of fully considering the total charging costs of charging stations and EVs.

Keywords
EV Charging stations Location Capacity allocation DEIPSO algorithm
Published
2021-06-02
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-77569-8_6
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