Journal
IEEE CONTROL SYSTEMS LETTERS
Volume 4, Issue 3, Pages 632-637Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCSYS.2020.2989110
Keywords
Heuristic algorithms; Buildings; Power system dynamics; Fans; Real-time systems; Convex functions; Load management; Optimization algorithms; machine learning; power systems
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Funding
- Natural Sciences and Engineering Research Council of Canada
- National Science Foundation [1351900]
- Advanced Research Projects Agency-Energy [DE-AR0001061]
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We extend the regret analysis of the online distributed weighted dual averaging (DWDA) algorithm from Hosseini et al. to the dynamic setting and provide the tightest dynamic regret bound known to date with respect to the time horizon for a distributed online convex optimization (OCO) algorithm. Our bound is linear in the cumulative difference between consecutive optima and does not depend explicitly on the time horizon. We use dynamic-online DWDA (D-ODWDA) and formulate a performance-guaranteed distributed online demand response approach for heating, ventilation, and air-conditioning (HVAC) systems of commercial buildings. We show the performance of our approach for fast timescale demand response in numerical simulations and obtain demand response decisions that closely reproduce the centralized optimal ones.
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