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

Anna Scaglione, Ph.D.
Professor, IEEE Fellow
Department of Electrical and Computer Engineering
Cornell Tech, New York, NY

Date:
02/09/2024

Time:
11:00 am – 12:00 pm CST

Zoom: https://utsa.zoom.us/j/94807623288

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.