• DocumentCode
    116235
  • Title

    Distributional analysis for model predictive deferrable load control

  • Author

    Niangjun Chen ; Lingwen Gan ; Low, Steven H. ; Wierman, Adam

  • Author_Institution
    California Inst. of Tech., Pasadena, CA, USA
  • fYear
    2014
  • fDate
    15-17 Dec. 2014
  • Firstpage
    6433
  • Lastpage
    6438
  • Abstract
    Deferrable load control is essential for handling the uncertainties associated with the increasing penetration of renewable generation. Model predictive control has emerged as an effective approach for deferrable load control, and has received considerable attention. Though the average-case performance of model predictive deferrable load control has been analyzed in prior works, the distribution of the performance has been elusive. In this paper, we prove strong concentration results on the load variation obtained by model predictive deferrable load control. These results highlight that the typical performance of model predictive deferrable load control is tightly concentrated around the average-case performance.
  • Keywords
    load regulation; optimal control; predictive control; uncertain systems; average-case performance; distributional analysis; load variation; model predictive deferrable load control; performance distribution; renewable generation penetration; uncertainty handling; Algorithm design and analysis; Analytical models; Load flow control; Load management; Load modeling; Prediction algorithms; Predictive models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-1-4799-7746-8
  • Type

    conf

  • DOI
    10.1109/CDC.2014.7040398
  • Filename
    7040398