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A High-Efficiency Charging Service System for Plug-in Electric Vehicles Considering the Capacity Constraint of the Distribution Network

Author

Listed:
  • Rui Ye

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Xueliang Huang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Ziqi Zhang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Zhong Chen

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Ran Duan

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

It takes electric vehicles (EVs) a long time to charge, which is bound to influence the charging experience of vehicle owners. At the same time, large-scale charging behavior also brings about large load pressure on, and elevates the overload risk of, the power distribution network. To solve these problems, we proposed a high-efficiency charging service system based on charging reservation and charging pile binding services. The system can shorten the average charging time of EVs and improve the average immediate utilization rate of new energy sources at charging stations (CSs). In addition, the system also guarantees that the EVs are charged within the allowable range of the capacity of the distribution network and avoids overloading of the distribution network caused by the charging of EVs. The key support for the utility of the system is rooted in the three-level CS selection model and the CS energy control algorithm (CSECA) proposed in the research. Finally, the proposed model and algorithm were verified to be valid through numerous simulation experiments.

Suggested Citation

  • Rui Ye & Xueliang Huang & Ziqi Zhang & Zhong Chen & Ran Duan, 2018. "A High-Efficiency Charging Service System for Plug-in Electric Vehicles Considering the Capacity Constraint of the Distribution Network," Energies, MDPI, vol. 11(4), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:911-:d:140810
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    References listed on IDEAS

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    1. Chao Luo & Yih-Fang Huang & Vijay Gupta, 2018. "Stochastic Dynamic Pricing for EV Charging Stations with Renewables Integration and Energy Storage," Papers 1801.02128, arXiv.org.
    2. Yagcitekin, Bunyamin & Uzunoglu, Mehmet, 2016. "A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account," Applied Energy, Elsevier, vol. 167(C), pages 407-419.
    3. Mu, Yunfei & Wu, Jianzhong & Jenkins, Nick & Jia, Hongjie & Wang, Chengshan, 2014. "A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles," Applied Energy, Elsevier, vol. 114(C), pages 456-465.
    4. Hu, Zechun & Zhan, Kaiqiao & Zhang, Hongcai & Song, Yonghua, 2016. "Pricing mechanisms design for guiding electric vehicle charging to fill load valley," Applied Energy, Elsevier, vol. 178(C), pages 155-163.
    5. Morris Brenna & Michela Longo & Wahiba Yaïci, 2017. "Modelling and Simulation of Electric Vehicle Fast Charging Stations Driven by High Speed Railway Systems," Energies, MDPI, vol. 10(9), pages 1-23, August.
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    Cited by:

    1. Hui Sun & Peng Yuan & Zhuoning Sun & Shubo Hu & Feixiang Peng & Wei Zhou, 2018. "Distribution Network Congestion Dispatch Considering Time-Spatial Diversion of Electric Vehicles Charging," Energies, MDPI, vol. 11(10), pages 1-17, October.

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