Distributed control for a multi-evaporator air conditioning system

Abstract

An autonomous hierarchical distributed control (AHDC) strategy is proposed for a building multi-evaporator air conditioning (ME A/C) system in this paper. The objectives are to minimize peak demand and energy costs, and to reduce communication resources, computational complexity and conservativeness while maintaining both thermal comfort and indoor air quality (IAQ) in acceptable ranges. The building consists of multiple connected rooms and zones. The proposed control strategy consists of two layers. The upper layer is an open loop optimizer, which only collects local measurement information and solves a distributed steady state resource allocation problem to autonomously and adaptively generate reference points, for low layer controllers. This is achieved by optimizing the demand and energy costs of a multi-zone building ME A/C system under a time-of-use (TOU) rate structure, while meeting the requirements of each zone’s thermal comfort and IAQ within comfortable ranges. The lower layer also uses local information to track the trajectory references, which are calculated by the upper layer, via a distributed model predictive control (DMPC) algorithm. The control strategy is distributed at both layers because they use only local information from the working zone and its neighbors. Simulation results are provided to illustrate the advantages of the designed control schemes.