HVAC Energy Cost Optimization for a Multi-zone Building via a Decentralized Approach
For building energy managers, this work offers a scalable decentralized control method that reduces HVAC energy costs in multi-zone buildings, though it is an incremental improvement over existing distributed optimization techniques.
This paper proposes a decentralized approach based on the Accelerated Distributed Augmented Lagrangian method to optimize HVAC energy cost in multi-zone buildings while maintaining thermal comfort. The method nearly matches the optimal solution of centralized methods for small-scale problems and outperforms a distributed token-based strategy for larger buildings, achieving considerable cost reduction and better scalability.
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.