A Dynamic Water-Filling Method for Real-Time HVAC Load Control Based on Model Predictive Control

Heating ventilation and air-conditioning (HVAC) system can be viewed as elastic load to provide demand response. Existing work usually used HVAC to do the load following or load shaping based on given control signals or objectives. However, optimal external control signals may not always be available. Without such control signals, how to make a tradeoff between the fluctuation of non-renewable power generation and the limited demand response potential of the elastic load, while still guaranteeing user comfort level, is still an open problem. To solve this problem, we first model the temperature evolution process of a room and propose an approach to estimate the key parameters of the model. Second, based on the model predictive control, a centralized and a distributed algorithm are proposed to minimize the fluctuation and maximize user comfort level. In addition, we propose a dynamic water level adjustment algorithm to make the demand response always available in two directions. Extensive simulations based on practical data sets show that the proposed algorithms can effectively reduce the load fluctuation.