A Distributed Model Predictive Control Approach for Optimal Coordination of Multiple Thermal Zones in a Large Open Space

Model Predictive Control (MPC) based approaches have recently seen a significant increase in applications to the supervisory control of building heating, ventilation and air-conditioning (HVAC) systems, thanks to their ability to incorporate weather, occupancy, and utility price information in the optimization of heating/cooling strategy while satisfying the physical constraints of HVAC equipment. Many of the proposedMPC solution approaches are centralized ones that often suffer from high computational cost caused by the large number of decision variables and the overhead in information gathering and distribution. This paper investigates a distributed MPC approach based on a variant of the Alternating DirectionMethod ofMultipliers (ADMM). The proposedmethod is highly scalable, and facilitates a device level plug-and-play implementation. A case study is carried out on one of the Purdue Living Labs, which is an open office space of multiple thermal zones with individual thermostat controls. In view of significant thermal couplings due to direct air exchange and noticeable load gradient between zones, a multiple thermal zones coordination problem is formulated with the objective of optimally scheduling the different thermostat setpoints for energy minimization and comfort delivery while satisfying actuation constraints. Simulation results demonstrate the effectiveness of the proposed method.