AI for Power and Energy Systems: Applications, Challenges, and Opportunities
This course, developed in partnership with IEEE Power and Energy Society, will offer an in-depth understanding of the role and effectiveness of convolutional neural networks (CNN) in solving sample and fundamental power system problems—power flow. Highlighting the unique challenges and opportunities that AI presents in the field of power and energy systems, this course will also examine applications, case studies, and real-world situations where AI and ML have been used in power systems. In addition, you will learn how to appreciate the concept of transfer gap in AI and how it can be tackled in power systems. This course will also explain the security challenges and potential benefits faced during training and implementing AI in power systems, such as computational simplification, creation of effective model-free systems, and data processing improvements. Lastly, this course will cover how to evaluate different power system testing options for real-world, model-free reinforcement learning environments. At the end of this course, you will be able to describe the basics of artificial intelligence (AI), machine learning (ML), and deep learning (DL) and how generic AI techniques apply to power and energy systems.
What you will learn:
- Describe AI, ML, & DL and their application to power and energy systems
- Understand the role & effectiveness of CNN in solving power flow
- Highlight the challenges & opportunities AI presents in the field
- Examine applications, case studies, & real-world situations
- Appreciate the concept of transfer gap in AI and how it can be tackled in power systems
- Explain security challenges & potential benefits
- Evaluate different power system testing options
This course is part of the following course program:
Artificial Intelligence for Power & Energy Systems
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
Instructor
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.
Publication Year: 2025
ISBN: 978-1-7281-7898-1