AI Thrust Seminar| Enabling Safe Decision Making in Clean Energy Systems
Supporting the below United Nations Sustainable Development Goals:支持以下聯合國可持續發展目標：支持以下联合国可持续发展目标：
Machine learning can help achieve the carbon neutrality objective through designing more efficient power grid planning and operation algorithms, such as allocating stochastic renewable generation, controlling the pricing signals, and coordinating EVs or energy storage. However, as the grid operation is safety critical, it becomes a necessity to analyze and promote safety characteristics of data-driven schemes. In this talk, I will start from the modeling of dynamics and physical constraints, and go through the design space we have to ensure safety and reliability in learning-based decision makers. The resulting algorithm can be guaranteed to find the optimal solutions as well as satisfying all engineering constraints. Such design principles can be generalized to a set of practical engineering applications with complex dynamics and constraints.
Yize Chen will be joining the AI Thrust, Information Hub at HKUST GZ this summer. He is currently a postdoc at Berkeley Lab. He got his PhD degree in Electrical and Computer Engineering from University of Washington in 2021, and his bachelor degree from Chu Kochen College at Zhejiang University in 2016. His research interests lie at the intersection of machine learning, optimization, and control theory, with applications in energy systems and cyber-physical systems. He has received several prize paper awards, and held research positions in multiple institutions including Microsoft Research, Los Alamos National Laboratory and Harvard Medical School.