It has been well acknowledged that buildings account for a large proportion of the world's energy consumption. However, the energy use of buildings, especially the heating, ventilation and air-conditioning (HVAC), is far from being efficient. There still exists a dramatic potential to save energy through improving building energy efficiency. Therefore, this paper studies the control of HVAC system for multi-zone buildings with the objective to reduce energy consumption cost while satisfying thermal comfort. In particular, the thermal couplings due to the heat transfer between the adjacent zones are incorporated in the optimization. Considering that a centralized method is generally computationally prohibitive for large buildings, an efficient decentralized approach is developed, based on the Accelerated Distributed Augmented Lagrangian (ADAL) method [1]. To evaluate the performance of the proposed method, we first compare it with a centralized method, in which the optimal solution of a small-scale problem can be obtained. We find that this decentralized approach can almost approach the optimal solution of the problem. Further, this decentralized approach is compared with the Distributed Token-Based Scheduling Strategy (DTBSS) [2]. The numeric results reveal that when the number of zones is relatively small (less than 20), the two decentralized methods can achieve a comparable performance regarding the cost of the HVAC system. However, with an increase of the number of zones in the building, the proposed decentralized approach demonstrates better performance with a considerable reduction of the total cost. Moreover, the decentralized approach proposed in this paper demonstrate better scalability with less average computation required.
@cite_7: This paper proposes a distributed MPC strategy for bilinear HVAC systems with coupled dynamics and constraints. These couplings arise from the heat transfer between adjacent zones and the limited total supply energy and airflow rate. To handle the coupled dynamics and constraints, a decoupled method is needed for the distributed computation of the online MPC problem. Besides the coupling issue, the bilinear model is also a fundamental obstacle to efficient online computation. To solve this problem, a convex relaxation method is developed in this paper. The performance of the proposed MPC strategy is demonstrated by a numerical example.
Generally speaking, in most of the related works, the thermal couplings among the neighbouring or adjacent zones are ignored or not explicitly considered, which may lead to the degradation of performance in practice. On one hand, the actual energy cost of HVAC may rise due to the heat transfer between the neighbouring zones ignored in the optimization. On the other hand, the realistic thermal condition of each individual zone may deviate from the comfortable range. An attempt was reported in @cite_7 , which studied the distributed MPC strategy for HVAC system in a multi-zone building, where the thermal couplings between the neighbouring zones are explicitly discussed. To cope with the difficulties due to the nonlinearity and nonconvexity, a distributed ADMM method was applied based on some convexity approximation.