• DocumentCode
    3535054
  • Title

    Charging of electric vehicles utilizing random wind: A stochastic optimization approach

  • Author

    Goonewardena, Mathew ; Long Bao Le

  • Author_Institution
    INRS-EMT, Univ. of Quebec, Montreal, QC, Canada
  • fYear
    2012
  • fDate
    3-7 Dec. 2012
  • Firstpage
    1520
  • Lastpage
    1525
  • Abstract
    In this paper, we present a general stochastic framework for the optimization of charging of electric vehicles (EVs) utilizing wind energy. The framework considers different key components of the Smart Grid including stochastic wind energy, bulk and real-time purchase of energy from the grid operator, penalties or sell back of unutilized committed energy and flexible demand of the EV users. We formulate the joint vehicle charging and power purchase problem as a stochastic optimization program. Then we describe how to obtain its solution numerically. We also present a widely used alternative problem formulation using expected values of the random wind and real-time prices. Numerical results are presented to demonstrate the efficacy of the proposed framework and the significant performance gain compared to an expected-value approach.
  • Keywords
    battery powered vehicles; expectation-maximisation algorithm; secondary cells; stochastic programming; wind power; EV users; electric vehicle charging; expected-value approach; random wind; real-time prices; stochastic optimization approach; stochastic wind energy; wind energy; Joints; Numerical models; Optimization; Real-time systems; Vectors; Wind energy; Wind power generation; electric vehicle; power scheduling; smart grid; stochastic optimization; wind power;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Globecom Workshops (GC Wkshps), 2012 IEEE
  • Conference_Location
    Anaheim, CA
  • Print_ISBN
    978-1-4673-4942-0
  • Electronic_ISBN
    978-1-4673-4940-6
  • Type

    conf

  • DOI
    10.1109/GLOCOMW.2012.6477811
  • Filename
    6477811