SPS-DSI Webinar: Graph Neural Networks for Learning Nonlinear Power System Operations

Date: 12 April 2023
Time: 3:00 PM CET
Speaker(s): Dr. Hao Zhu

SPS-DSI Webinar Series: Data SciEnce on GrAphS (DEGAS)

Initiated by the Data Science Initiative of IEEE Signal Processing Society, the DEGAS Webinar Series serves to provide the SP community with updates and advances in learning and inference on graphs. Signal processing and machine learning often deal with data living in regular domains such as space and time.

This webinar series will cover the extension of these methods to network data, including topics such as graph filtering, graph sampling, spectral analysis of network data, graph topology identification, geometric deep learning, and so on. Applications can, for instance, be found in image processing, social networks, epidemics, wireless communications, brain science, recommender systems, and sensor networks.

Webinars are hosted on Zoom, with recordings available on the IEEE SPS YouTube channel following the live event. Each speaker presentation is followed by time for Q&A and discussions.


Recent years have witnessed rapid transformations of contemporary advances in machine learning (ML) and data science to aid the transition of energy systems into a truly sustainable, resilient, and distributed infrastructure. A blind application of the latest-and-greatest ML algorithms to solve stylized grid operation problems, however, may fail to recognize the underlying physical grid models. This talk will introduce two examples of bridging physics-aware ML advances into efficient and resilient grid operations. First, we develop a topology-aware approach using graph neural networks (GNNs) to predict the price and line congestion as the outputs of real-time AC optimal power flow (OPF) problem. Building upon the relationship between prices and topology, this proposed solution significantly reduces the model complexity of existing methods while being able to efficiently adapt to varying grid topology. The second example aims to design scalable emergency response policies of optimal load shedding during disastrous contingency of line failures. We have shown changes of localized measurements only can predict the marginal price of load shedding. The effectiveness of our approaches in predicting both normal and emergency operation actions have been validated using realistic power system test cases.


Gerald MatzHao Zhu is an Associate Professor of Electrical and Computer Engineering (ECE) and the TAE Research Foundation Centennial Fellow at The University of Texas at Austin. She received the B.S. degree from Tsinghua University in 2006, and the M.Sc. and Ph.D. degrees from the University of Minnesota in 2009 and 2012, all in electrical engineering. From 2012 to 2017, she was a Postdoctoral Research Associate and then an Assistant Professor of ECE at the University of Illinois at Urbana-Champaign. Her research focus is on developing algorithmic solutions for problems related to learning and optimization in future energy systems. Her current research interest includes machine learning for power system operations and resilience enhancements, and data-driven approaches for grid dynamic modeling and control. She is a recipient of the NSF CAREER Award and the faculty advisor for three Best Student Papers awarded at the North American Power Symposium. She is currently an Editor for the IEEE Transactions on Smart Grid and IEEE Transactions on Signal Processing.