Optimal control development for chilled water plants using a quadratic representation

Abstract

Optimal supervisory control strategy for the set points of controlled variables in the cooling plants has been studied by computer simulation. A quadratic linear regression equation for predicting the total cooling system power in terms of the controlled and uncontrolled variables was developed using simulated data collected under different values of controlled and uncontrolled variables. The optimal set temperatures such as supply air temperature, chilled water temperature, and condenser water temperature, are determined such that energy consumption is minimized as uncontrolled variables, load, ambient wet bulb temperature, and sensible heat ratio are changed. The chilled water loop pump and cooling tower fan speeds are controlled by the PID controller such that the supply air and condenser water set temperatures reach the set points designated by the optimal supervisory controller. The influences of the controlled variables on the total system and component power consumption were determined. The predicted power obtained from the quadratic regression equation was found to be a good fit to the simulated one. Because the Hermitian matrix of the system quadratic cost function was positive, the optimal control variables for the minimum power consumption were able to be obtained. There are relatively high effects of the load and sensible heat ratio on the optimal supply air and chilled water set temperatures, while the effect of ambient wet bulb temperature is less. In contrast to that result, the ambient wet bulb temperature has a much larger effect on the optimal condenser water set temperature, while the load has less, and the sensible heat ratio has no influence on it. The trade-off among the components of power consumption results in that the total system power use in both simulated and predicted systems are minimized at lower supply, higher chilled water, and lower condenser water set temperature conditions.