• Login
    View Item 
    •   DORA Home
    • Faculty of Computing, Engineering and Media
    • School of Computer Science and Informatics
    • View Item
    •   DORA Home
    • Faculty of Computing, Engineering and Media
    • School of Computer Science and Informatics
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Particle swarm optimisation for scheduling electric vehicles with microgrids

    Thumbnail
    View/Open
    Main article (382.4Kb)
    Date
    2020-07-19
    Author
    Zheng, Zedong;
    Yang, Shengxiang
    Metadata
    Show attachments and full item record
    Abstract
    The explosion in the number of electric vehicles (EVs) has had a significant impact on the energy systems and structures of cities. Large-scale EVs inevitably increase the load on the grid, while uncoordinated vehicle to Grid (V2G) technologies pose challenges to the stability and security of the grid. This paper introduces a global intelligent method to find optimal cooperation charging/discharging strategies for EVs to minimize the operation cost. EVs aggregates co-ordinate the energy information and needs of all EVs and use real-time pricing based on micro-grid loads to influence EV charge-discharge behavior. Particle swarm optimization (PSO) is introduced to solve the EV scheduling problem. This study also discusses the negative impact on the energy system of different strategies for charging EVs. Simulation shows that this smart charging strategy and improved PSO can effectively decrease the operation cost of EVs and reduce the load for each micro-grid.
    Description
    The file attached to this record is the author's final peer reviewed version.
    Citation : Zheng, Z. and Yang, S. (2020) Particle swarm optimisation for scheduling electric vehicles with microgrids. Proceedings of the 2020 IEEE Congress on Evolutionary Computation, Glasgow, UK, July 2020. (in press).
    URI
    https://dora.dmu.ac.uk/handle/2086/19582
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
    Collections
    • School of Computer Science and Informatics [2970]

    Submission Guide | Reporting Guide | Reporting Tool | DMU Open Access Libguide | Take Down Policy | Connect with DORA
    DMU LIbrary
     

     

    Browse

    All of DORACommunities & CollectionsAuthorsTitlesSubjects/KeywordsResearch InstituteBy Publication DateBy Submission DateThis CollectionAuthorsTitlesSubjects/KeywordsResearch InstituteBy Publication DateBy Submission Date

    My Account

    Login

    Submission Guide | Reporting Guide | Reporting Tool | DMU Open Access Libguide | Take Down Policy | Connect with DORA
    DMU LIbrary