Scalable and robust demand response with mixed-integer constraints

Seung Jun Kim, Georgios B Giannakis

Research output: Contribution to journalArticlepeer-review

83 Scopus citations

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.

Original languageEnglish (US)
Article number6510544
Pages (from-to)2089-2099
Number of pages11
JournalIEEE Transactions on Smart Grid
Volume4
Issue number4
DOIs
StatePublished - Dec 1 2013

Keywords

  • Lagrange relaxation
  • Mixed-integer programs
  • Parallel and distributed algorithms
  • Real-time demand response
  • Robust optimization

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