HVAC systems in large buildings frequently feature a network topology wherein the outputs of each dynamic subsystem act as disturbances to other subsystems in a well-defined local neighborhood. The distributed optimization technique presented in this paper leverages this topology without requiring a centralized optimizer or widespread knowledge of the interaction dynamics between subsystems. Each subsystem’s optimizer communicates to its neighbors its calculated optimum setpoint, as well as the costs imposed by the neighbor’s calculated set-points. 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, and that changes in one controller do not require changes to all controllers in the network. Proofs of Pareto optimality are presented, and convergence under the approach is demonstrated with a numerical and experimental example.

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