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Spatial and Temporal Optimization Strategy for Plug-In Electric Vehicle Charging to Mitigate Impacts on Distribution Network

Author

Listed:
  • Lili Gong

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

  • Wu Cao

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

  • Kangli Liu

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

  • Jianfeng Zhao

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

  • Xiang Li

    (LiuZhou Power Supply Bureau, GuangXi Power Grid Co., Ltd., Liuzhou 545006, China)

Abstract

The large deployment of plug-in electric vehicles (PEVs) challenges the operation of the distribution network. Uncoordinated charging of PEVs will cause a heavy load burden at rush hour and lead to increased power loss and voltage fluctuation. To overcome these problems, a novel coordinated charging strategy which considers the moving characteristics of PEVs is proposed in this paper. Firstly, the concept of trip chain is introduced to analyze the spatial and temporal distribution of PEVs. Then, a stochastic optimization model for PEV charging is established to minimize the distribution network power loss (DNPL) and maximal voltage deviation (MVD). After that, the particle swarm optimization (PSO) algorithm with an embedded power flow program is adopted to solve the model, due to its simplicity and practicality. Last, the feasibility and efficiency of the proposed strategy is tested on the IEEE 33 distribution system. Simulation results show that the proposed charging strategy not only reduces power loss and the peak valley difference, but also improves voltage profile greatly.

Suggested Citation

  • Lili Gong & Wu Cao & Kangli Liu & Jianfeng Zhao & Xiang Li, 2018. "Spatial and Temporal Optimization Strategy for Plug-In Electric Vehicle Charging to Mitigate Impacts on Distribution Network," Energies, MDPI, vol. 11(6), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1373-:d:149432
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Qin Chen & Komla Agbenyo Folly, 2022. "Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review," Energies, MDPI, vol. 16(1), pages 1-26, December.
    2. Lidan Chen & Yao Zhang & Antonio Figueiredo, 2019. "Spatio-Temporal Model for Evaluating Demand Response Potential of Electric Vehicles in Power-Traffic Network," Energies, MDPI, vol. 12(10), pages 1-20, May.
    3. Junpeng Cai & Dewang Chen & Shixiong Jiang & Weijing Pan, 2020. "Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    4. Saleh Aghajan-Eshkevari & Sasan Azad & Morteza Nazari-Heris & Mohammad Taghi Ameli & Somayeh Asadi, 2022. "Charging and Discharging of Electric Vehicles in Power Systems: An Updated and Detailed Review of Methods, Control Structures, Objectives, and Optimization Methodologies," Sustainability, MDPI, vol. 14(4), pages 1-31, February.
    5. Aghajan-Eshkevari, Saleh & Ameli, Mohammad Taghi & Azad, Sasan, 2023. "Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems," Applied Energy, Elsevier, vol. 341(C).

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