On June 13, 2024, Dr. Huadong Mo from the School of Engineering and Information Technology at the University of New South Wales was invited by the School of Management to give an academic lecture titled "Frequency Control Ancillary Services and Bidding in National Electricity Markets" in Lecture Hall 214 of the School's building on Chang'an Campus. This lecture was the 14th session of the 2024 Overseas Lecture Series at the School of Management. The lecture was chaired by Associate Professor Cheng Long Li, with participation from faculty and graduate students of the School.

At the beginning of the meeting, Li Chenglong provided a brief introduction to Mo Huadong's personal background and academic achievements, and extended a warm welcome to his visit. During the lecture, Mo Huadong introduced the background of the Australian electricity market, particularly the market rules and bidding models for Frequency Control Ancillary Services (FCAS). He then presented theBilevel Model he constructed, which includes two parts: one is the bidding strategy for adjusting the battery energy storage system to maximize FCAS revenue, and the other is adjusting the purchasing capacity of the national electricity market dispatch engine to minimize the total cost of FCAS. Mo Huadong shared in detail the solutions proposed by his team, which involve using reinforcement learning methods to solve the current Bilevel Model, and also introduced two new methods under study to accelerate computations—quantum reinforcement learning and fixed-time gradient dynamics.

The engaging content of the lecture sparked extensive discussions among the attending faculty and students. The lecture concluded in a lively atmosphere, with both faculty and students expressing their substantial gains from the event.

[Written by Zhang Jinkai, Reviewed by Shao Jing and Zhang Shuang]

Introduction to the Speaker:

Dr. Huadong Mo is currently a Senior Lecturer at the Faculty of Engineering and Information Technology, University of New South Wales, Australia. He obtained his Bachelor's degree in Automation from the University of Science and Technology of China in 2012 and his Ph.D. in Industrial Engineering and Engineering Management from the City University of Hong Kong in 2016. He is committed to long-term research on enhancing the resilience, performance, and security of complex systems using learning-based algorithms, with a focus on electric and energy systems, physical information systems, and manufacturing systems. To date, he has published over 60 SCI and conference papers, as well as one monograph. Dr. Mo serves on the editorial boards of nearly 10 relevant SCI journals and international conferences, and he is a Senior Member of the IEEE and the Chair of the IEEE SMC ACT Chapter.