• DocumentCode
    3665382
  • Title

    Electric vehicle capacity forecasting model with application to load levelling

  • Author

    Bowen Zhou;Tim Littler;Aoife Foley

  • Author_Institution
    School of Electronics, Electrical Engineering and Computer Science, Queen´s University Belfast, UK
  • fYear
    2015
  • fDate
    7/1/2015 12:00:00 AM
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    There are many uncertainties associated with forecasting electric vehicle charging and discharging capacity due to the stochastic nature of human behavior surrounding usage and intermittent travel patterns. This uncertainty if unmanaged has the potential to radically change traditional load profiles. Therefore optimal capacity forecasting methods are important for large-scale electric vehicle integration in future power systems. This paper develops a capacity forecasting model considering eight particular uncertainties under three categories to overcome this issue. The model is then applied to a UK summer scenario in 2020. The results of this analysis demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale electric vehicle integration.
  • Keywords
    "Uncertainty","Forecasting","System-on-chip","Load modeling","Predictive models","Batteries","Electric vehicles"
  • Publisher
    ieee
  • Conference_Titel
    Power & Energy Society General Meeting, 2015 IEEE
  • ISSN
    1932-5517
  • Type

    conf

  • DOI
    10.1109/PESGM.2015.7285829
  • Filename
    7285829