Optimal Setpoints for HVAC Systems via Iterative Cooperative Neighbor Communication

Heating, ventilation, and air conditioning systems in large buildings frequently feature a network topology wherein the outputs of each dynamic subsystem act as disturbances to other subsystems. The distributed optimization technique presented in this paper leverages this topology without requiring a centralized controller or widespread knowledge of the interaction dynamics between subsystems. Each subsystem's controller calculates an optimal steady state condition. The output corresponding to this condition is then communicated to downstream neighbors only. Similarly, each subsystem communicates to its upstream neighbors the predicted costs imposed by the neighbors' own calculated outputs. By judicious construction of the cost functions, all of the cost information is propagated through the network, allowing a Pareto optimal solution to be reached. The novelty of this approach is that communication between all plants is not necessary to achieve a global optimum. Since each optimizer does not require knowledge of its neighbors' dynamics, changes in one controller do not require changes to all controllers in the network. Proofs of convergence to Pareto optimality under certain conditions are presented, and convergence under the approach is demonstrated with a simulation example. The approach is also applied to a laboratory-based water chiller system; several experiments demonstrate the features of the approach and potential for energy savings.