Artificial Intelligence Architectures for Grid Data Analysis and Safe Control of Electric Power Systems

Anna Scaglione, PhD
Cornell Tech

Abstract: There is a flurry of research activities recently focusing on applications of Artificial Intelligence in electric power systems analysis and operation. It is often motivated by emerging trends that are tied to decarbonizing the grid through the adoption of distributed renewable energy sources and demand response programs that call for methods account for the stochasticity of the net-demand. The vast majority of the research takes AI tools as a black box that internalizes every aspect of the observations, including the physics of the system (namely Ohm’s law) and learns to control the system by being rewarded in choosing good actions. In this talk we discuss how to go a step further. First we introduce Complex Graph Neural Network and show that they are most effective in both inference and reinforcement learning tasks, thanks to their physics inspired feature extraction capabilities. Then we move to introduce the problem of “safe” reinforcement learning, which allows to enforce the physical and safety constraints in the optimal policy.

Biography: Anna Scaglione is a professor of electrical and computer engineering at Cornell Tech, the New York City campus of Cornell University. She has previously held faculty positions at Arizona State University, the University of California at Davis, Cornell University, and the University of New Mexico. She received her M.Sc in 1995 and Ph.D in 1999. She is a Fellow of IEEE since 2011 and has received many awards such as the 2013 IEEE Donald G. Fink Prize Paper Award, the 2000 IEEE Signal Processing Transactions Best Paper Award, and the NSF CAREER grant (2002). She has also received several best student papers awards at conferences and was Distinguished Lecturer of the Signal Processing Society in 2019 and 2020. Dr. Scaglione's research focus is on theoretical and applied problems in statistical signal processing, distributed optimization, and cyber-physical systems, with particular interest on sustainable energy delivery systems. Her talk will include some of her latest research results on novel AI architectures that are tailored to the unique features of electric power systems.

Attendance is mandatory for in-person seminar students. For online and part-time students, seminars will be recorded and made available through Canvas.