Dynamic and Distributed Online Convex Optimization for Demand Response of Commercial Buildings

作     者：Lesage-Landry, Antoine Callaway, Duncan S. 

作者机构：Univ Calif Berkeley Energy & Resources Grp Berkeley CA 94720 USA 

出 版 物：《IEEE CONTROL SYSTEMS LETTERS》 (IEEE Control Syst. Lett.)

年 卷 期：2020年第4卷第3期

页      面：632-637页

核心收录：

基　　金：Natural Sciences and Engineering Research Council of Canada National Science Foundation Advanced Research Projects Agency-Energy [DE-AR0001061] 

主　　题：Heuristic algorithms Buildings Power system dynamics Fans Real-time systems Convex functions Load management Optimization algorithms machine learning power systems 

摘      要：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.