An optimization platform based on coupled indoor environment and HVAC simulation and its application in optimal thermostat placement

Abstract

Model-based optimization can help improve the indoor thermal comfort and energy efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems. The models used in previous optimization studies either omit the dynamic interaction between indoor airflow and HVAC or are too slow for model-based optimization. To address this limitation, we propose an optimization methodology using coupled simulation of the airflow and HVAC that captures the dynamics of both systems. We implement an optimization platform using the coupled models of a coarse grid Fast Fluid Dynamics model for indoor airflow and Modelica models for HVAC which is linked to the GenOpt optimization engine. Then, we demonstrate the new optimization platform by studying the optimal thermostat placement in a typical office room with a VAV terminal box in the design phase. After validating the model, we perform an optimization study, in which the VAV terminal box is dynamically controlled, and find that our optimization platform can determine the optimal location of thermostat to achieve either best thermal comfort or least energy consumption, or the combined. Finally, the time cost for performing such optimization study is about 6.2 hours, which is acceptable in the design phase.

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

“…3 [68]. The thermostat is employed in BEMS with respect to minimizing power fluctuations [69]; reducing cooling electricity cost [70]; controlling space heating [71]; improving thermal comfort [72], and increasing energy efficiency [73]. This method has the simplest control operations; nevertheless, the controlled devices always operate at full or at a default capacity when they are ON, thus, resulting in a large amount of power being consumed in each operation [74].…”

Dynamic modelling and optimal control of virtual power plant driven by commercial HVACs

“…The chiller water outlet temperature of this HVAC system is adjustable in the range of 7-12 • C. The air volume of the fan can be varied by adjusting its valve, thus enabling its power regulation. The thermal resistance and heat capacity of each area are randomly distributed in the ranges [0.05, 0.15] and [15,18], respectively. The dynamic characteristics of the chillers under the chilled water temperature regulation are concluded in Table 1.…”

“…VPP participates in power system dispatching through a two‐layer architecture, with the system dispatch operator at the upper layer to schedule VPP, and the VPP control centre at the lower layer to control internal resources [13]. The internal control architecture of VPP can be divided into three categories: Centralized control [14], decentralized control [15], and integrated control [12]. The integrated control approach combines the advantages of centralized and decentralized control, which reduces the computational and communication burden of the control centre, and effectively ensures the power output stability.…”

Temperature Sensing Optimization for Home Thermostat Retrofit

“…Previous works have partially addressed this reported issue. In particular, in [ 19 ], the authors proposed a framework based on a coupled CFD (computational fluid dynamics)–BES (building energy simulation) simulation model, in order to optimize HVAC systems with non-uniform airflow and temperature distributions in the building design, to achieve good thermal comfort and energy efficiency. The optimization platform was demonstrated to search for the optimal thermostat placement in an office room with displacement ventilation and a VAV terminal box.…”