IEEE POWER & ENERGY SOCIETY
WEBINARTopic: Integrated Transmission–Distribution Co-Simulation: Bridging Data Exchange Gaps in Asynchronous SimulationsPresented by: Dr. Ning Lu, North Carolina State University

This webinar:
The increasing penetration of inverter-based resources (IBRs), particularly distributed photovoltaics (DPVs), energy storage systems, and large data centers, requires accurate transmission–distribution (T&D) co-simulation to capture interactions between bulk power systems, distribution networks, and high-power-density loads. A key challenge arises from the mismatch in simulation timesteps between phasor-domain transmission simulations (typically 10 ms) and electromagnetic transient (EMT) distribution simulations (e.g., 100 µs). Assuming constant transmission voltage magnitude and phase angle within transmission intervals can lead to inaccurate phase-locked loop (PLL)-based frequency estimation in distribution systems, affecting the modeling of IBR-based frequency response services. This talk presents methods to mitigate errors caused by asynchronous simulation steps by predicting voltage magnitude and phase angle variations within transmission intervals. Validation studies conducted on an OPAL-RT-based co-simulation platform demonstrate the resulting accuracy improvements. The talk concludes by discussing the advantages and limitations of these approaches for large-scale power system studies.

Presenter bio:
Dr. Ning Lu is an IEEE Fellow and a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. With over 30 years of experience in electric power engineering, she holds a Ph.D. from Rensselaer Polytechnic Institute, obtained in 2002. Dr. Lu served as a senior research engineer at Pacific Northwest National Laboratory from 2003 to 2012. Her research focuses on power system load modeling, microgrid modeling and control, and the development of real-time and faster-than-real-time large-scale co-simulation systems. Additionally, she explores the application of machine learning in power system data analysis, modeling, and control.