• DocumentCode
    79207
  • Title

    Quantifying the Long-Term Impact of Electric Vehicles on the Generation Portfolio

  • Author

    Shortt, Aonghus ; O´Malley, Mark

  • Author_Institution
    Sch. of Electr., Electron. & Commun. Eng., Univ. Coll. Dublin, Dublin, Ireland
  • Volume
    5
  • Issue
    1
  • fYear
    2014
  • fDate
    Jan. 2014
  • Firstpage
    71
  • Lastpage
    83
  • Abstract
    Electric Vehicles (EVs) charged in a manner that is optimal to the power system will tend to increase the utilization of the lowest cost power generating units on the system, which in turn encourages investment in these preferable forms of generation. Were these gains to be substantial, they could be reflected in future charging tariffs as a means of encouraging EV ownership. However, where the impact of EVs is being quantified, much of the system benefit can only be observed where generator scheduling is performed by unit-commitment based methods. By making use of a rapid, yet robust unit-commitment algorithm, in the context of a capacity expansion procedure, this paper quantifies the impact of EVs for a variety of demand and wind time-series, relative fuel costs and EV penetrations. Typically, the net-cost of EV charging increases with EV penetration and CO2 cost, and falls with increasing wind. Frequently however these relationships do not apply, where changes in an input often lead to step-changes in the optimal plant mix. The impact of EVs is thus strongly dependent on the dynamics of the underlying generation portfolio.
  • Keywords
    electric vehicles; power generation dispatch; power generation scheduling; tariffs; time series; EV charging; EV penetrations; capacity expansion; charging tariffs; electric vehicles; generation portfolio; generator scheduling; investment; long-term impact; optimal plant mix; power generating units; power system; relative fuel costs; robust unit commitment; wind time-series; Biological system modeling; Computational modeling; Generators; Power systems; Production; Schedules; Vehicles; Electric vehicles; power generation planning; wind power generation;
  • fLanguage
    English
  • Journal_Title
    Smart Grid, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1949-3053
  • Type

    jour

  • DOI
    10.1109/TSG.2013.2286353
  • Filename
    6654292