Improving energy flexibility and PV self-consumption for a tropical net zero energy office building

Building energy flexibility is crucial for improving the local consumption of renewable energy and enhancing building self-sufficiency. The abundant solar energy resource in the tropics presents a great opportunity to reduce carbon emission and achieve net-zero, but the building energy flexibility remains understudied in the region. Hence, this study proposed and implemented a practical control framework based on Model Predictive Control (MPC) that uncovers the energy flexibility potential of a tropical office building with hybrid cooling systems. Considering the impact of data availability on the actual control performance, MPC with alternative data usage configurations were also investigated in actual and virtual end-to-end experiments. It was first demonstrated that the proposed framework effectively regulated the building load. Compared with the baseline control, the PV self-consumption and the building self-sufficiency were respectively improved by 19.5% and 10.6%. Among the three data categories tested (in-ternal disturbance, external disturbance, and system condition), accurate local weather conditions were shown to be the most critical for desirable control results. Moreover, the benefit of higher data granular-ity under different building characteristics was quantified in the simulation. Based on the systematic experiments, the relationships between the data availability and control performance were established. Accordingly, a data-centric framework was proposed to enhance the reproducibility and scalability of optimal control studies. Future research can be guided to facilitate large-scale real-world implementations.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Building energy flexibility is crucial for improving local renewable energy consumption and building self-sufficiency. However, the study of building energy flexibility in the tropical region is limited. This study proposes a practical control framework based on Model Predictive Control (MPC) to uncover the energy flexibility potential of a tropical office building. The effects of data availability on control performance are also investigated. The results show that accurate local weather data is critical for desirable control results, and higher data granularity can benefit control performance under different building characteristics.