AI for Power & Energy Systems: Computation Acceleration in Modeling and Control
In this course developed in partnership with IEEE Power and Energy Society, we will discuss motivation and techniques of AI applications in power system optimization and control problems via computation acceleration. Many power system problems involve large-scale, difficult to solve optimizations or control problems. Sample motivations and techniques to address these problems will be discussed to illustrate the potentials of AI applications in broad areas in power system optimization and control.
What you will learn:
- To understand the complexity of power system optimization and control problems.
- To learn deep learning, reinforcement learning, safe learning, etc for power system optimization and control
- To understand representative applications of AI in power system computation.
This course is part of the following course program:
Artificial Intelligence for Power & Energy Systems
Courses included in this program:
Who should attend: Practitioners in Power Utilities and Independent System Operators, Project Administrators and Managers in the Power Industry, Researchers in AI for Power, Power Systems Engineers, Smart Grid Analysts, Energy Systems Analysts, Grid Modernization Specialists, Data Scientists in the Energy Sector, AI Engineers in Renewable Energy, Renewable Energy Systems Developers, Sustainability Consultants for Energy, Electrical Engineering Managers, Operations Managers in Utility Companies, Data Center Energy Efficiency Consultants, Machine Learning Engineers in the Power Sector, Energy Market Analysts, Cyber-Physical Systems Engineers, Product Managers for Smart Energy Solutions, Device Vendors for Data Centers, Data Center Operators, Machine Learning Infrastructure Developers
Instructors:
Dr. Fangxing (Fran) Li
Dr. Fangxing (Fran) Li is a distinguished professor and leading researcher in the fields of artificial intelligence for power systems, electricity markets, and grid resilience. He currently serves as the Director of CURENT, a prestigious NSF/DOE Engineering Research Center, where he spearheads innovative research initiatives aimed at transforming the future of energy systems. Dr. Li also chairs the IEEE Working Group on Machine Learning for Power Systems, contributing to the advancement of AI applications in the energy sector.
Since 2020, he has served as Editor-in-Chief of the IEEE Open Access Journal of Power and Energy, guiding scholarly discourse in power and energy research. His work has earned numerous accolades, including the R&D 100 Award (2020), IEEE PES Technical Committee Prize Paper Awards (2019 & 2024), and a total of 13 Best Paper or Poster Awards from international journals and conferences. Dr. Li earned his Ph.D. in Electrical Engineering from Virginia Tech in 2001.
Dr. Buxin She
Dr. Buxin She serves as a Senior Research Engineer at Pacific Northwest National Laboratory, where he focuses on developing AI-driven control and optimization methods for modern power systems. His work centers on improving the stability and resilience of inverter-based resources, advancing dynamics-informed approaches to power system planning and operation, and enhancing the security and control of cyber-physical energy systems. He also leads multiple Laboratory Directed Research and Development (LDRD) projects under the RD2C and E-COMP initiatives and co-leads a DOE Wind Energy Technologies Office (WETO) project focused on grid integration and control.
Publication Year: 2026
ISBN: 978-1-7281-7899-8