Model predictive control for thermal energy storage assisted large central cooling systems

Variable speed drivers (VSDs) are commonly used for enhancing energy efficiency in building central cooling systems. However, VSDs often consume about 4–8% of the converted energy. Moreover, the initial and maintenance costs of VSDs for extremely large and high voltage chillers could be extremely high. This study proposes to use thermal energy storage (TES) to enhance energy efficiency of extremely large constant speed chillers. A new model predictive control method is proposed to control the charging/discharging of TES and on/off of chillers to achieve high efficiency. The proposed method partially decouples the demand side and the supply side, so that the large chillers are either operated in high efficiency or turned off. The method can also solve the problem of frequent chiller tripping due to too low load in winter conditions. The proposed optimal control strategy has been validated on a dynamic platform built based on the existing chiller plant in a high-rise commercial building. Validation tests were conducted in both summer and winter conditions based on real operation data. Results show that the proposed method could improve the efficiency of chillers by 3.10% and 22.94% in summer and winter conditions, respectively.