Presenter: Xuebo Liu, Jin Tan 

Authors: Xuebo Liu, Xin Fang, Ningchao Gao, Haoyu Yuan, Andy Hoke, Hongyu Wu, Jin Tan 

Webinar Abstract 

This webinar presents a data-driven unit commitment approach that integrates frequency nadir constraints to enhance frequency stability in islanded and interconnected power systems with high inverter-based generation. By using linear regression models trained on year-long simulations, the method embeds frequency response requirements directly into operational planning. Evaluated on a real island system over a full year, the approach shows improved reliability and cost-effectiveness compared to traditional inertia-based methods, particularly under low-inertia conditions. The session will offer insights for engineers and system planners seeking advanced tools for reliable frequency management in modern power systems.

Bios: 

Xuebo Liu (Member, IEEE) is currently a Transmission System Planning Engineer at Eversource, where he focuses on distributed energy resource (DER) integration and large-scale load integration. He received the B.S. degree in computer engineering from Kansas State University, Manhattan, KS, USA, in 2020, and the Ph.D. degree in electrical and computer engineering from Kansas State University in August 2025.  He worked as an intern at the National Renewable Energy Laboratory (Aug. 2021 to Feb. 2022). His work supports grid stability and reliability, as well as short- and long-term transmission planning. His research interests include grid-connected building energy management, power system operation and planning, machine learning, and modeling for renewable energy integration. 

Dr. Tan has over 15 years of experience in large-scale renewable integration study. Her research interests include power system stability and control, multi-timescale modeling and simulation for renewable integration, and energy storage for grid applications. She has received several awards, including the NREL Chairperson Award (2024), NREL President Award (2018, 2020), the NREL Outstanding Mentor Award (2020, 2021, 2022, 2023,2025), and Best Paper Award (2016, 2018, 2019, 2020, 2026).