Special Session 2
AI-Enabled Cyber-Physical Integration for Next-Generation Integrated Energy System
The increasing complexity of modern energy systems, characterized by the integration of multiple energy sources such as electricity, thermal, hydrogen, and renewable resources, necessitates the development of Integrated Energy Systems (IES). These systems are designed to couple and optimize the interactions between different energy carriers, enhancing overall efficiency, sustainability, and resilience. A critical enabler for achieving this seamless integration is the application of Artificial Intelligence (AI) within Cyber-Physical Systems (CPS), which bridges the physical energy infrastructure with digital intelligence for dynamic management.This special session, "AI-Enabled Cyber-Physical Integration for Next-Generation Integrated Energy Systems", focuses on the transformative role of AI in optimizing the operation, control, and management of IES by ensuring smooth information and energy flows between the physical energy network and the digital control systems. The integration of AI with CPS facilitates real-time decision-making, adaptive control, and automated responses, driving the efficient coupling of different energy carriers—electricity, heating, cooling, hydrogen, and renewable energy systems. This synergy allows for the seamless exchange of energy and data between the physical and cyber layers, leading to more flexible, resilient, and intelligent energy systems.
AI techniques, including machine learning, deep learning, and advanced optimization methods, enhance the ability of CPS to intelligently manage energy flows across integrated networks. AI algorithms enable the prediction of energy demands, forecasting of renewable generation, and dynamic balancing of resources between various energy sectors. The continuous flow of data between the cyber and physical domains ensures that energy management is not only optimized but also adaptive to real-time conditions, such as fluctuations in energy production and consumption.
This session will explore how AI can empower CPS to facilitate smarter energy flow management and improve coordination between integrated energy systems. Topics of interest include, but are not limited to:
1. AI-based optimization and control strategies for the coupling of electricity, heating, cooling, and hydrogen systems.
2. Machine learning for real-time energy forecasting, load prediction, and resource allocation in integrated energy systems.
3. AI-enhanced demand-side management and flexible energy demand strategies across multiple energy carriers.
4. Resilience and fault detection mechanisms in cyber-physical integrated energy systems using AI.
5. AI-Driven Threat Detection and Response in Smart Grids.
6. Cyber-attack Security and Privacy in Data-Intensive Integrated Energy Systems
Chairs:
Research Assist. Prof. Yuechuan Tao, City University of Hong Kong, China
Yuechuan Tao (Member, IEEE) received the B.Sc. degree in Electrical Engineering and Automation from Shanghai Normal University, Shanghai, China, in 2017, and an M.Sc. degree in Electrical Engineering from the University of Sydney, Australia in 2019, and a Ph.D. degree in the University of Sydney, Australia. He was the Wallenberg-NTU Presidential Postdoctoral Fellow in Nanyang Technological University. Currently, he is a global research assistant professor in City University of Hong Kong. His main fields of interest include power system operation and planning, electric vehicles, smart grid, etc.
Assoc. Prof. Jiaqi Ruan, Sichuan University, China
Jiaqi Ruan (Member, IEEE) received the B.E. degree in automation from Hubei Engineering University, Xiaogan, China, in 2016, the M.E. degree in control science and engineering from Shenzhen University, Shenzhen, China, in 2019, and the Ph.D. degree in computer and information engineering from The Chinese University of Hong Kong, Shenzhen, in 2023. He was a Postdoctoral Research Fellow with the Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong. Currently, he is an associate professor in Sichuan University. His research interests include smart grid, cyber-physical security, demand response, and artificial intelligence.
Dr. Xianzhuo Sun, The Hong Kong Polytechnic University, China
Xianzhuo Sun (Member, IEEE) received the B.Eng. degree in electrical engineering from Taiyuan University of Technology, Taiyuan, China, in 2016, the M.E. degree in electrical engineering from Shandong University, Jinan, China, in 2019, and the Ph.D. degree in electrical engineering from the University of Sydney, Sydney, NSW, in 2023. He is currently working as a Postdoctoral Fellow with the Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China. His research interests include power system operation and voltage control, machine learning, electrical vehicles, and all-electric ships.