Decentralized Model Predictive Control of a Multiple Evaporator HVAC System
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Vapor compression cooling systems are the primary method used for refrigeration and air conditioning, and as such are a major component of household and commercial building energy consumption. Application of advanced control techniques to these systems is still a relatively unexplored area, and has the potential to significantly improve the energy efficiency of these systems, thereby decreasing their operating costs. This thesis explores a new method of decentralizing the capacity control of a multiple evaporator system in order to meet the separate temperature requirements of two cooling zones. The experimental system used for controller evaluation is a custom built small-scale water chiller with two evaporators; each evaporator services a separate body of water, referred to as a cooling zone. The two evaporators are connected to a single condenser and variable speed compressor, and feature variable water flow and electronic expansion valves. The control problem lies in development of a control architecture that will chill the water in the two tanks (referred to as cooling zones) to a desired temperature setpoint while minimizing the energy consumption of the system. A novel control architecture is developed that relies upon time scale separation of the various dynamics of the system; each evaporator is controlled independently with a model predictive control (MPC) based controller package, while the compressor reacts to system conditions to supply the total cooling required by the system as a whole. MPC’s inherent constraint-handling capability allows the local controllers to directly track an evaporator cooling setpoint while keeping superheat within a tight band, rather than the industrially standard approach of regulating superheat directly. The compressor responds to system conditions to track a pressure setpoint; in this configuration, pressure serves as the signal that informs the compressor of cooling demand changes. Finally, a global controller is developed that has knowledge of the energy consumption characteristics of the system. This global controller calculates the setpoints for the local controllers in pursuit of a global objective; namely, regulating the temperature of a cooling zone to a desired setpoint while minimizing energy usage.
Elliott, Matthew Stuart (2008). Decentralized Model Predictive Control of a Multiple Evaporator HVAC System. Master's thesis, Texas A&M University. Available electronically from