Dynamic and Distributed Online Convex Optimization for Demand Response of Commercial Buildings
This work provides an incremental improvement for demand response in HVAC systems of commercial buildings, enabling more efficient energy management.
The paper tackles the problem of distributed online convex optimization for demand response in commercial buildings by extending an existing algorithm to dynamic settings, achieving a dynamic regret bound linear in cumulative differences between optima and independent of time horizon, and demonstrating in simulations that decisions closely match centralized optimal ones.
We extend the regret analysis of the online distributed weighted dual averaging (DWDA) algorithm [1] 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.