• DocumentCode
    40735
  • Title

    Forecasting Plug-In Electric Vehicle Sales and the Diurnal Recharging Load Curve

  • Author

    Zhaoyang Duan ; Gutierrez, Brittni ; Lizhi Wang

  • Author_Institution
    Iowa State Univ., Ames, IA, USA
  • Volume
    5
  • Issue
    1
  • fYear
    2014
  • fDate
    Jan. 2014
  • Firstpage
    527
  • Lastpage
    535
  • Abstract
    The expected market penetration of plug-in electric vehicles has drawn much attention to the potential impact on power systems, which to a large extent depends on the number of electric vehicles on the road and the fleet´s diurnal recharging load curve. We present two interactive models to jointly forecast plug-in electric vehicle sales and the diurnal recharging load curve in the U.S. between 2012 and 2020. The sales forecasting model is based on information about consumer preferences between hybrid electric vehicles and internal combustion engine vehicles, which can be extracted from historical data. The recharging load forecasting model is based on the assumption that electric vehicle users´ recharging behavior will demonstrate a gradual transition from a convenience driven mode in 2012 to a cost driven mode in 2020. A case study is conducted for the Midwest ISO region. Compared to the sales forecasts from the literature, our results turn out to be less optimistic. Our recharging load forecast results also suggest that, if appropriately managed, the impact of plug-in electric vehicles on electricity load would not be overwhelming in the next decade.
  • Keywords
    electric vehicles; internal combustion engines; load forecasting; power systems; Midwest ISO region; diurnal recharging load curve; electricity load; expected market penetration; internal combustion engine vehicles; load forecasting; plug-in electric vehicle; power systems; sales forecasting; Biological system modeling; Electricity; Fuels; Hybrid electric vehicles; Load modeling; Predictive models; Diurnal recharging load; plug-in electric vehicles;
  • fLanguage
    English
  • Journal_Title
    Smart Grid, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1949-3053
  • Type

    jour

  • DOI
    10.1109/TSG.2013.2294436
  • Filename
    6693758