IPEE - Track 7

Co-Optimization of Power and Computing Systems in Hybrid AC/DC Grids

The increasing deployment of artificial intelligence, edge computing, and large-scale data centers is creating unprecedented interactions between computing infrastructures and electric power systems. Data centers and computing facilities are no longer conventional static loads; instead, they exhibit spatial-temporal flexibility through workload scheduling, task migration, thermal management, and demand response. Meanwhile, modern power systems are evolving toward hybrid AC/DC grid architectures with high penetration of renewable energy resources, power electronic interfaces, and flexible demand-side resources. These trends call for advanced modeling, optimization, planning, and control methods to enable coordinated operation of power and computing systems.
This special session aims to bring together researchers, engineers, and practitioners working on the interdisciplinary integration of electric power networks and computing infrastructures. Topics of interest include, but are not limited to: workload and power consumption modeling of data centers; flexibility characterization of computing loads; coordinated scheduling of computing tasks and electricity consumption; power-computing co-optimization in hybrid AC/DC grids; demand response and load shifting of data centers; planning and operation of AC/DC grids with large-scale computing loads; market mechanisms for integrated energy and computing resources; resilience, reliability, and cybersecurity of cyber-physical power-computing systems.

Submission Methods: https://easychair.org/conferences/?conf=ipee2026, choose "Track 7" in EasyChair.


Track Chair


Dr. Kai Gong
Zhejiang University, China


Dr. Kai Gong is a postdoctoral researcher at the College of Control Science and Engineering, Zhejiang University, co-head of the Advanced AI and Optimization Control Laboratory (AAICO Lab), and a member of the State Key Laboratory of Industrial Control Technology and the National Engineering Research Center of Industrial Automation. He has published over 15 papers in journals such as IEEE Transactions on Sustainable Energy and held over 30 invention patents, with applications spanning power grids, nuclear energy, and smart manufacturing. His current and future research interests extend to the interdisciplinary application of convex optimization, artificial intelligence, and reinforcement learning in power systems, cancer chemotherapy, drug discovery, and chemical characterization.


Assoc. Prof. Xu Wang
Shanghai Jiao Tong University, China


Dr. Xu Wang received the B.S. degree in electrical engineering from Southeast University, China, in 2010 and the Ph.D. degree in electrical engineering from Shanghai Jiao Tong University, China, in 2016. He was a Postdoctoral Associate at in the Robert W. Galvin Center for Electricity Innovation at Illinois Institute of Technology (IIT), Chicago, USA, from 2016-2018. Currently, he is an Associate Professor with the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China. His research interests include resilient distribution system, power system economics and optimization.