Abstract
This paper investigates the accelerated distributed model predictive control (MPC) strategy for the heating, ventilation and air conditioning (HVAC) systems with local and global power input constraints. The problems are firstly formulated in the distributed MPC framework and then the constrained optimization is converted into a quadratic programming problem. In the problem formulation, the thermal couplings between immediate neighboring zones are considered while designing the distributed controller, and the unknown thermal disturbances are incorporated by the robust optimization scheme. Then, using the accelerated dual gradient-projection method, a distributed fast MPC protocol is designed for HVAC systems considering both the electricity cost and occupant comforts. A distributed stopping criterion based on the distributed average consensus algorithm is utilized. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed distributed MPC algorithm, and its computational advantages comparing with an existing distributed method and a centralized algorithm.
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