• DocumentCode
    184557
  • Title

    Aggregate modeling and control of plug-in electric vehicles for renewable power tracking

  • Author

    Ebrahimi, B. ; Mohammadpour, Javad

  • Author_Institution
    Coll. of Eng., Univ. of Georgia, Athens, GA, USA
  • fYear
    2014
  • fDate
    4-6 June 2014
  • Firstpage
    2035
  • Lastpage
    2040
  • Abstract
    A robust strategy is proposed in this paper to control the aggregate charging power of plug-in electric vehicles (PEVs). The charging flexibility of PEVs provides the intermittent renewable power sources with control authority to cope with load fluctuations caused by the variation of grid-connected PEVs population and their instantaneous power demand. In this paper, we consider an aggregate model of PEVs power in the form of a partial differential equation (PDE). A sliding mode control is then developed for the derived PDE load model with no discretization in the spatial domain. The developed sliding mode controller operates on the real-time measurable imbalance between source and demand power. To evaluate the closed-loop response and demonstrate the controller´s robustness against PEVs population variations, a Monte Carlo simulation is performed for real driving conditions and using renewable power data.
  • Keywords
    Monte Carlo methods; closed loop systems; electric vehicles; partial differential equations; power grids; power system control; variable structure systems; Monte Carlo simulation; PDE; aggregate charging power; aggregate control; aggregate modeling; closed-loop response; grid-connected PEV; instantaneous power demand; intermittent renewable power sources; load fluctuations; partial differential equation; plug-in electric vehicles; renewable power tracking; robust strategy; sliding mode control; spatial domain; Aggregates; Load modeling; Mathematical model; Sociology; Statistics; Switches; Wind power generation; Automotive; Control applications; Power systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference (ACC), 2014
  • Conference_Location
    Portland, OR
  • ISSN
    0743-1619
  • Print_ISBN
    978-1-4799-3272-6
  • Type

    conf

  • DOI
    10.1109/ACC.2014.6859169
  • Filename
    6859169