HVAC energy cost optimization for a multizone building via a decentralized approach - Yang, Yu (2022)
It has been well acknowledged that buildings account for a large proportion of the world’s energy consumption. In particular, about 40%-50% of the building’s energy is consumed by the heating, ventilation and air-conditioning (HVAC). However, the energy use of buildings, especially the HVAC system, is far from being efficient. There still exists a dramatic potential to save energy through improve building energy efficiency. Therefore, this paper studies the control of HVAC system for multi-zone buildings with the objective to reduce the energy consumption cost of the HVAC system while satisfying the zone thermal comfort requirements. In particular, the thermal coupling due to the heat transfer between the adjacent zones are incorporated in the optimization. Considering that a centralized method is generally computationally prohibitive for buildings with an increasing number of zones, an efficient decentralized approach is developed, based on the Accelerated Distributed Augmented Lagrangian (ADAL) method [1]. To evaluate the performance of this decentralized approach, 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, to evaluate the performance and scalability, this decentralized approach is compared with the Distributed Token-Based Scheduling Strategy (DTBSS) [2], which has been demonstrated with a satisfactory performance in reducing the energy consumption of the HVAC system and in improving scalability. The numeric results reveal that when the number of zones in the buildings 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 average computation time of the two decentralized methods are compared in the case studies. The numeric results shows that the two decentralized methods are both computationally efficient. However, the decentralized approach proposed in this paper demonstrate better scalability with less average computation required.