• DocumentCode
    3101639
  • Title

    Data mining techniques contributions to support electrical vehicle demand response

  • Author

    Soares, João ; Ramos, Sérgio ; Vale, Zita ; Morais, Hugo ; Faria, Pedro

  • Author_Institution
    Knowledge Eng. & Decision-Support Res. Group, Polytech. Inst. of Porto, Porto, Portugal
  • fYear
    2012
  • fDate
    7-10 May 2012
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.
  • Keywords
    battery powered vehicles; data mining; distribution networks; integer programming; nonlinear programming; power engineering computing; smart power grids; bus distribution network; data mining techniques; distributed resource diversity; electrical vehicle demand response; minimum battery level requirements; mixed integer nonlinear programming; power system operators; renewable generation; smart grids; vehicle-to-grid capacity; Batteries; Clustering algorithms; Data mining; Discharges (electric); Load management; Partial discharges; Vehicles; Classification; Clustering; Data Mining; Demand Response; Electric Vehicle; Mixed Integer Non-Linear Programming (MINLP);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Transmission and Distribution Conference and Exposition (T&D), 2012 IEEE PES
  • Conference_Location
    Orlando, FL
  • ISSN
    2160-8555
  • Print_ISBN
    978-1-4673-1934-8
  • Electronic_ISBN
    2160-8555
  • Type

    conf

  • DOI
    10.1109/TDC.2012.6281444
  • Filename
    6281444