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Coevolution and Evaluation of Electric Vehicles and Power Grids Based on Complex Networks

Author

Listed:
  • Di Zhang

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Yaxiong Kang

    (College of Information Science and Engineering, China University of Petroleum, Beijing 102249, China)

  • Li Ji

    (College of Information Science and Engineering, China University of Petroleum, Beijing 102249, China)

  • Ruifeng Shi

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Limin Jia

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

Abstract

The connection of electric vehicles to the power grid has a significant impact on the structure and stability of the power grid. To evaluate the performance index of a power grid, this paper introduces a collaborative evolution model of a power grid containing electric vehicles based on a complex network. The location of the electric vehicle network node is obtained by combining the evolution mode of the complex network and the restrictive conditions of the electric vehicle access network. Due to the dual attributes of electric vehicles, such as load and battery, electric vehicle access points will be used as special nodes in the network to calculate their effect on the power grid. In this paper, the Monte Carlo method is used to evaluate the effect of electric vehicle nodes on the whole network in the case of each probability failure in the power grid. The proposed method is numerically illustrated in the test case of the rbt-bus-f4 feeder system.

Suggested Citation

  • Di Zhang & Yaxiong Kang & Li Ji & Ruifeng Shi & Limin Jia, 2022. "Coevolution and Evaluation of Electric Vehicles and Power Grids Based on Complex Networks," Sustainability, MDPI, vol. 14(12), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7052-:d:834724
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    References listed on IDEAS

    as
    1. Pagani, Giuliano Andrea & Aiello, Marco, 2014. "Power grid complex network evolutions for the smart grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 248-266.
    2. Sen Guo & Huiru Zhao & Haoran Zhao, 2017. "The Most Economical Mode of Power Supply for Remote and Less Developed Areas in China: Power Grid Extension or Micro-Grid?," Sustainability, MDPI, vol. 9(6), pages 1-18, May.
    3. Mwasilu, Francis & Justo, Jackson John & Kim, Eun-Kyung & Do, Ton Duc & Jung, Jin-Woo, 2014. "Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 501-516.
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    Cited by:

    1. Ruifeng Shi & Yuqin Gao & Jin Ning & Keyi Tang & Limin Jia, 2023. "Research on Highway Self-Consistent Energy System Planning with Uncertain Wind and Photovoltaic Power Output," Sustainability, MDPI, vol. 15(4), pages 1-30, February.
    2. Abdulaziz Almutairi, 2022. "Impact Assessment of Diverse EV Charging Infrastructures on Overall Service Reliability," Sustainability, MDPI, vol. 14(20), pages 1-16, October.

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