Investigation of the Demand Response Potentials of Residential Air Conditioners Using Grey-box Room Thermal Model

Abstract

Abstract This paper investigates demand response (DR) potentials of residential air conditioners (AC) under different control strategies using a grey-box room thermal model. The proposed resistance-capacitance (RC) thermal model combines the essential prior knowledge of room thermal characteristics with data-driven techniques. With the aim of saving the optimization time and improving the reasonableness of the search results, undetermined parameters were physically estimated prior to the identification with nonlinear optimization method. A typical residential bedroom in Hong Kong was chosen to test the room thermal model. The root mean square errors (RMSE) between the sampled and predicted data sets for training and validation sessions were 0.25 °C and 0.28 °C respectively. After coupling the room RC thermal model and an empirical AC energy consumption model, we can get AC power reductions under different control strategies during the DR period. The simulation results show that the temperature set-point reset control strategies enable the power consumption to decrease during the DR event, and the peak reduction increases when the set-point is set higher. Besides, the precooling control strategy can help to further reduce the electric power.