• DocumentCode
    42338
  • Title

    Scalable and Robust Demand Response With Mixed-Integer Constraints

  • Author

    Seung-Jun Kim ; Giannakis, Georgios

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Minnesota, Minneapolis, MN, USA
  • Volume
    4
  • Issue
    4
  • fYear
    2013
  • fDate
    Dec. 2013
  • Firstpage
    2089
  • Lastpage
    2099
  • Abstract
    A demand response (DR) problem is considered entailing a set of devices/subscribers, whose operating conditions are modeled using mixed-integer constraints. Device operational periods and power consumption levels are optimized in response to dynamic pricing information to balance user satisfaction and energy cost. Renewable energy resources and energy storage systems are also incorporated. Since DR becomes more effective as the number of participants grows, scalability is ensured through a parallel distributed algorithm, in which a DR coordinator and DR subscribers solve individual subproblems, guided by certain coordination signals. As the problem scales, the recovered solution becomes near-optimal. Robustness to random variations in electricity price and renewable generation is effected through robust optimization techniques. Real-time extension is also discussed. Numerical tests validate the proposed approach.
  • Keywords
    demand side management; distributed algorithms; energy storage; integer programming; parallel algorithms; power engineering computing; power markets; pricing; renewable energy sources; DR coordinator; DR problem; DR subscribers; coordination signals; device operational periods; dynamic pricing information; electricity price; energy cost; energy storage systems; mixed-integer constraints; numerical tests; parallel distributed algorithm; power consumption levels; random variations; real-time extension; renewable energy resources; renewable generation; robust demand response; robust optimization techniques; user satisfaction; Electricity; Energy storage; Optimization; Power demand; Real-time systems; Robustness; Uncertainty; Lagrange relaxation; mixed-integer programs; parallel and distributed algorithms; real-time demand response; robust optimization;
  • fLanguage
    English
  • Journal_Title
    Smart Grid, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1949-3053
  • Type

    jour

  • DOI
    10.1109/TSG.2013.2257893
  • Filename
    6510544