A distributed online pricing strategy for demand response programs

Pan Li, Hao Wang, Baosen Zhang

Research output: Contribution to journalArticleResearchpeer-review

64 Citations (Scopus)


We study a demand response (DR) problem from utility (also referred to as operator)'s perspective with realistic settings, in which the utility faces uncertainty and limited communication. Specifically, the utility does not know the cost function of consumers and cannot have multiple rounds of information exchange with consumers. We formulate an optimization problem for the utility to minimize its operational cost considering time-varying DR targets and responses of consumers. We develop a joint online learning and pricing algorithm. In each time slot, the utility sends out a price signal to all consumers and estimates the cost functions of consumers based on their noisy responses. We measure the performance of our algorithm using regret analysis and show that our online algorithm achieves logarithmic regret with respect to the operating horizon. In addition, our algorithm employs linear regression to estimate the aggregate response of consumers, making it easy to implement in practice. Simulation experiments validate the theoretic results and show that the performance gap between our algorithm and the offline optimality decays quickly.

Original languageEnglish
Pages (from-to)350-360
Number of pages11
JournalIEEE Transactions on Smart Grid
Issue number1
Publication statusPublished - Jan 2019
Externally publishedYes


  • Demand response
  • distributed algorithm
  • linear regression
  • online strategy

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