IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i10p2820-d176769.html
   My bibliography  Save this article

Distribution Network Congestion Dispatch Considering Time-Spatial Diversion of Electric Vehicles Charging

Author

Listed:
  • Hui Sun

    (School of Electrical Engineering, Dalian University of Technology; Dalian 116024, China)

  • Peng Yuan

    (School of Electrical Engineering, Dalian University of Technology; Dalian 116024, China)

  • Zhuoning Sun

    (State Grid Liaoning Maintenance Company, Shenyang 110000, China)

  • Shubo Hu

    (School of Electrical Engineering, Dalian University of Technology; Dalian 116024, China)

  • Feixiang Peng

    (School of Electrical Engineering, Dalian University of Technology; Dalian 116024, China)

  • Wei Zhou

    (School of Electrical Engineering, Dalian University of Technology; Dalian 116024, China)

Abstract

With the popularization of electric vehicles, free charging behaviors of electric vehicle owners can lead to uncertainty about charging in both time and space. A time-spatial dispatching strategy for the distribution network guided by electric vehicle charging fees is proposed in this paper, which aims to solve the network congestion problem caused by the unrestrained and free charging behaviors of large numbers of electric vehicles. In this strategy, congestion severity of different lines is analyzed and the relationship between the congested lines and the charging stations is clarified. A price elastic matrix is introduced to reflect the degree of owners’ response to the charging prices. A pricing scheme for optimal real-time charging fees for multiple charging stations is designed according to the congestion severity of the lines and the charging power of the related charging stations. Charging price at different charging station at different time is different, it can influence the charging behaviors of vehicle owners. The simulation results confirmed that the proposed congestion dispatching strategy considers the earnings of the operators, charging cost to the owners and the satisfaction of the owners. Moreover, the strategy can influence owners to make judicious charging plans that help to solve congestion problems in the network and improve the safety and economy of the power grid.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2820-:d:176769
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/10/2820/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/10/2820/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Shubo Hu & Hui Sun & Feixiang Peng & Wei Zhou & Wenping Cao & Anlong Su & Xiaodong Chen & Mingze Sun, 2018. "Optimization Strategy for Economic Power Dispatch Utilizing Retired EV Batteries as Flexible Loads," Energies, MDPI, vol. 11(7), pages 1-21, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xin Sui & Shengyang Lu & Hai He & Yuting Zhao & Shubo Hu & Ziqian Liu & Hong Gu & Hui Sun, 2020. "Wind-Thermal-Nuclear-Storage Combined Time Division Power Dispatch Based on Numerical Characteristics of Net Load," Energies, MDPI, vol. 13(2), pages 1-24, January.
    2. Suyang Zhou & Yuxuan Zhuang & Wei Gu & Zhi Wu, 2018. "Operation and Economic Assessment of Hybrid Refueling Station Considering Traffic Flow Information," Energies, MDPI, vol. 11(8), pages 1-20, July.
    3. Shubo Hu & Feixiang Peng & Zhengnan Gao & Changqiang Ding & Hui Sun & Wei Zhou, 2019. "Sample Entropy Based Net Load Tracing Dispatch of New Energy Power System," Energies, MDPI, vol. 12(1), pages 1-23, January.
    4. Hu, Shu-bo & Gao, Zheng-nan & He, Hai & Cao, Wen-ping & Zhao, Yu-ting & Zhou, Wei & Gu, Hong & Sun, Hui, 2020. "Adaptive time division power dispatch based on numerical characteristics of net loads," Energy, Elsevier, vol. 205(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:gam:jeners:v:11:y:2018:i:10:p:2820-:d:176769. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.