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Research on the Collaborative Optimization of the Power Distribution Network and Traffic Network Based on Dynamic Traffic Allocation

Author

Listed:
  • Baoqun Zhang

    (Electric Power Research Institute, State Grid Beijing Electric Power Company, Beijing 100075, China)

  • Cheng Gong

    (Electric Power Research Institute, State Grid Beijing Electric Power Company, Beijing 100075, China)

  • Yan Wang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Longfei Ma

    (Electric Power Research Institute, State Grid Beijing Electric Power Company, Beijing 100075, China)

  • Dongying Zhang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Shiwei Xia

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

With the increasing penetration rate of electric vehicles, the spatiotemporal coupling relationship between the power distribution network and traffic network is stronger than ever before. Under the dynamic wireless charging mode, traffic jam charging is introduced and the dynamic loading process of traffic flow is described using a cellular transmission model. The charging load is related to traffic flow and serves as a bond between the power distribution network and traffic network. The traffic flow achieves balanced allocation under dynamic user equilibrium conditions, and cooperatively optimizes the power flow of the power distribution network in conjunction with charging loads. Numerical analysis shows that this model can accurately depict the congestion situation during peak travel periods, and alleviate traffic congestion and distribution network voltage out of range.

Suggested Citation

  • Baoqun Zhang & Cheng Gong & Yan Wang & Longfei Ma & Dongying Zhang & Shiwei Xia, 2023. "Research on the Collaborative Optimization of the Power Distribution Network and Traffic Network Based on Dynamic Traffic Allocation," Energies, MDPI, vol. 16(14), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5259-:d:1190045
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    References listed on IDEAS

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    1. Gu, Haifei & Li, Yang & Yu, Jie & Wu, Chen & Song, Tianli & Xu, Jinzhou, 2020. "Bi-level optimal low-carbon economic dispatch for an industrial park with consideration of multi-energy price incentives," Applied Energy, Elsevier, vol. 262(C).
    2. Niu, Songyan & Yu, Hang & Niu, Shuangxia & Jian, Linni, 2020. "Power loss analysis and thermal assessment on wireless electric vehicle charging technology: The over-temperature risk of ground assembly needs attention," Applied Energy, Elsevier, vol. 275(C).
    3. Zhou, Zhe & Zhang, Xuan & Guo, Qinglai & Sun, Hongbin, 2021. "Analyzing power and dynamic traffic flows in coupled power and transportation networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    4. 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.
    5. Long, Jiancheng & Szeto, W.Y. & Huang, Hai-Jun & Gao, Ziyou, 2015. "An intersection-movement-based stochastic dynamic user optimal route choice model for assessing network performance," Transportation Research Part B: Methodological, Elsevier, vol. 74(C), pages 182-217.
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