IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v289y2021ics0306261921002269.html
   My bibliography  Save this article

Power-traffic network equilibrium incorporating behavioral theory: A potential game perspective

Author

Listed:
  • Zhou, Zhe
  • Moura, Scott J.
  • Zhang, Hongcai
  • Zhang, Xuan
  • Guo, Qinglai
  • Sun, Hongbin

Abstract

This paper examines the interconnections between the power and transportation networks from a game theoretic perspective. Electric vehicle travelers choose the lowest-cost routes in response to the price of electricity and traffic conditions, which in turn affects the operation of the power and transportation networks. In particular, discrete choice models are utilized to describe the behavioral process of electric vehicle drivers. A game theoretic approach is employed to describe the competing behavior between the drivers and power generation units. The power-traffic network equilibrium is proved to possess a potential type structure, which establishes the properties of the network equilibrium. Moreover, the network equilibrium state is shown to be a welfare-maximizing operating point of the electric distribution network considering the spatial demand response of electric vehicle loads. A decentralized algorithm based on the optimality condition decomposition technique is developed to attain the equilibrium flow solutions. Numerical experiments demonstrate how the proposed framework can be used to alleviate both power and traffic congestion.

Suggested Citation

  • Zhou, Zhe & Moura, Scott J. & Zhang, Hongcai & Zhang, Xuan & Guo, Qinglai & Sun, Hongbin, 2021. "Power-traffic network equilibrium incorporating behavioral theory: A potential game perspective," Applied Energy, Elsevier, vol. 289(C).
    • Handle: RePEc:eee:appene:v:289:y:2021:i:c:s0306261921002269
    DOI: 10.1016/j.apenergy.2021.116703
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921002269
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.116703?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Guanyu & Dong, Qianyu & Zhao, Yuming & Wang, Han & Jian, Linni & Jia, Youwei, 2023. "Bilevel optimization approach to fast charging station planning in electrified transportation networks," Applied Energy, Elsevier, vol. 350(C).
    2. Gao, Hongjun & Zhao, Yinbo & He, Shuaijia & Liu, Junyong, 2023. "Demand response management of community integrated energy system: A multi-energy retail package perspective," Applied Energy, Elsevier, vol. 330(PA).
    3. Li, Zepeng & Wu, Qiuwei & Li, Hui & Nie, Chengkai & Tan, Jin, 2024. "Distributed low-carbon economic dispatch of integrated power and transportation system," Applied Energy, Elsevier, vol. 353(PA).
    4. Sheng, Yujie & Guo, Qinglai & Chen, Feng & Xu, Luo & Zhang, Yang, 2021. "Coordinated pricing of coupled urban Power-Traffic Networks: The value of information sharing," Applied Energy, Elsevier, vol. 301(C).
    5. Sheng, Yujie & Zeng, Hongtai & Guo, Qinglai & Yu, Yang & Li, Qiang, 2023. "Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations," Applied Energy, Elsevier, vol. 348(C).
    6. Xiang, Liu, 2022. "A large-scale equilibrium model of energy emergency production: Embedding social choice rules into Nash Q-learning automatically achieving consensus of urgent recovery behaviors," Energy, Elsevier, vol. 259(C).
    7. Chen, Yuanyi & Hu, Simon & Zheng, Yanchong & Xie, Shiwei & Hu, Qinru & Yang, Qiang, 2024. "Coordinated expansion planning of coupled power and transportation networks considering dynamic network equilibrium," Applied Energy, Elsevier, vol. 360(C).
    8. Yin, Linfei & Sun, Zhixiang, 2021. "Multi-layer distributed multi-objective consensus algorithm for multi-objective economic dispatch of large-scale multi-area interconnected power systems," Applied Energy, Elsevier, vol. 300(C).
    9. Zhou, Ze & Liu, Zhitao & Su, Hongye & Zhang, Liyan, 2022. "Integrated pricing strategy for coordinating load levels in coupled power and transportation networks," Applied Energy, Elsevier, vol. 307(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:289:y:2021:i:c:s0306261921002269. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.