Presented by: Xingpeng Li, University of Houston

This webinar will describe a general procedure for enforcing grid dynamic performance in look-ahead optimal energy scheduling models. Particularly, the talk will demonstrate this procedure by explaining how to create a machine learning-based frequency stability prediction model that is then integrated into optimal energy scheduling models, creating novel inertia-constrained grid operation models.

Presenter bio:
Xingpeng Li is currently an Associate Professor with the Department of Electrical and Computer Engineering at the University of Houston, where he was an Assistant Professor from 2018 to 2024. He previously worked for ISO New England, PJM Interconnection, and ABB’s Power Grid division (now is Hitachi Energy). He received the NSF CAREER award in 2024. His research interests include power system control and operations, grid reliability and stability, and microgrids optimal sizing and energy management.

After the Webinar:
The recording, slides, and education credit will be available in the PES Resource Center, https://resourcecenter.ieee-pes.org/education/webinars within 3-5 business days.