Cooling seasonal performance of inverter air conditioner using model prediction control for demand response

Abstract

The supply-demand imbalance of electricity increases the operating burden on smart grids, decreases the average efficiency of power generation equipment, and threatens the safe operation of power grids. Residential air conditioning is a flexible load and a major consumer of electricity. Therefore, demand response control can be applied to air conditioners (ACs) to shift their peak energy consumption and save energy. Model predictive control (MPC) is an effective demand response control method. In this study, we analyze the cooling seasonal performance of an inverter AC with MPC. A time-varying MPC was designed and evaluated using a simulation testbed that was constructed using MATLAB. Subsequently, the energy, cost, and temperature control performances of the MPC were analyzed in detail from electricity pricing model, weather conditions and fluctuation of real-time price. The results show that compared to the proportional–integral–derivative (PID) control method, MPC can shift the peak-hour energy consumption by 6.34%–21.60% and reduce the total electricity costs by 13.44%–27.43%, while maintaining indoor thermal comfort during the whole cooling season, and Demand response with MPC control is very suited to hot weather conditions with highly fluctuating RTP. By applying MPC hybrid demand response under real-time price, there are better performances on peak shifting and cost saving.