Index policies for demand response under uncertainty

Joshua A. Taylor, Johanna L. Mathieu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Uncertainty is an intrinsic aspect of demand response because electrical loads are subject to many random factors and their capabilities are often not directly measurable until they have been deployed. Demand response algorithms must therefore balance utilizing well-characterized, good loads and learning about poorly characterized but potentially good loads; this is a manifestation of the classical tradeoff between exploration and exploitation. We address this tradeoff in a restless bandit framework, a generalization of the well-known multi-armed bandit problem. The formulation yields index policies, in which loads are ranked by a scalar index and those with the highest are deployed. The policy is particularly appropriate for demand response because the indices have explicit analytical expressions that may be evaluated separately for each load, making them both simple and scalable. We numerically evaluate the performance of the index policy, and discuss implications of the policies in demand response.

Original languageEnglish (US)
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781467357173
StatePublished - 2013
Externally publishedYes
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: Dec 10 2013Dec 13 2013

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference52nd IEEE Conference on Decision and Control, CDC 2013

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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