Optimal and near-optimal indoor temperature and humidity controls for direct load control and proactive building demand response towards smart grids

Abstract

Shutting down part of operating chillers directly in central air-conditioning systems of buildings to meet the urgent demand reduction needs of power grids has received increasing attention recently. However, due to limited cooling supply during above demand response (DR) events, the indoor air temperature and particularly relative humidity would often increase to unacceptable levels, resulting in the failures of DR controls. Considering the restriction on power use during DR events, rational use of limited cooling supply turns out to be the inevitable choice. The feedback control strategies commonly-used today cannot properly deal with the environment and system control issues under limited cooling supply during DR events. However, no study on this problem can be found in the research literature. As the first effort, two control strategies (i.e., optimal and near-optimal) are developed to address the environment control issues (concerning both indoor temperature and humidity controls) under a pre-determined power limiting threshold during DR events. The optimal control strategy optimizes the air flow set-points of individual AHUs (air handling units) using model-based prediction and genetic algorithm to achieve the best possible indoor environment control. The near-optimal control strategy approaches such best environment control using a simple empirical method. Case studies are conducted and the results show that the air flow settings have significant impacts on the indoor environment controlled under limited cooling supply. Both control strategies can achieve significant improvements in the indoor temperature and humidity controls as well as significant fan power saving.