Guest Speaker: Dr WANG Zhe, Walter
Assistant Professor, Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology
Dr Wang’s current research is about human-building interaction and building energy system control. Prior to joining HKUST, he was a Project Scientist in Lawrence Berkeley National Laboratory working on smart building control. Zhe has published 73 articles in SCI journals, 23 as first author, 8 as corresponding author, 6 as the co-first author. He has first authored 1 ESI highly cited paper and 2 Applied Energy highly cited papers. Recently, Zhe has won the first prize in the 2022 Global AI Challenge for Building E&M Facilities hosted by Hong Kong EMSD.
With higher renewable penetration, demand side management becomes increasingly important to help maintain the real balance between supply and demand, and to help the grid absorb more renewable generations. Buildings are a major electricity end-user, which has large potential for demand response. However, the current rule-based control cannot unlock the demand response potential of buildings. In this talk, we will introduce how model predictive control can facilitate more efficient dynamic energy management, and help building respond to the grid signal in a more effective way. We will prove that battery management can be formulated as a convex optimization problem. We will also demonstrate that model predictive control is robust to model and prediction uncertainty. To achieve our carbon neutrality goal, a new building control paradigm, i.e., model predictive control rather than rule-based control, is needed.