Completed Short Course “Introduction of Artificial Intelligence in Power Systems” Held in Person at TAMU on March 4-6, 2024

The course is designed to provide state-of-the-art introduction of Artificial Intelligence (AI) and machine learning that is tailored for power engineering applications. The electricity industry is transforming itself from a hierarchical, passive, and sparsely-sensed engineering system into a flat, active, and ubiquitously-sensed cyber-physical system. The emerging multi-scale data from synchrophasors, smart meters, weather, and electricity markets offers tremendous opportunities as well as challenges for the industry to dynamically learn and adaptively control a smart grid.

Who Should Attend?
Power engineers, operational engineers who have an interest in artificial intelligence and data sciences should attend this short course. Basic background in linear algebra and power systems is expected.

Expected Deliverables/Learning Outcome
Participants were given access to all the course materials, including lecture notes, computer simulation codes, and individual discussions with the instructors. This training introduces the foundation of high dimensional spaces and data analytical tools necessary to model and operate a modern power system. We will introduce a suite of tools for statistical time series analysis and dimensionality reduction. We will discuss the differences between first principle models and data-driven models in real-time operations. Discussions and computer-based simulation projects will prepare the participants to understand better how to integrate data-driven and physics-based reasoning in modern power systems. It is ideally suited for those who work in areas associated with the electric grid and need to better understand the latest advance in AI and machine learning and how their work might be affected by this change.

Course Logistics
Monday, March 4, 2024
8:30 – 9:00: Arrival and check in; breakfast provided.
9:00 – 10:30 Grid Operation Basics
10:30–10:45 Break
10:45 – 12:00 Introduction to Data Availability in Power Systems
12:00 – 13:00: Lunch (provided)
13:00 – 14:30: Introduction to AI and Its Recent Advances
14:30 – 14:45: Break
14:45 – 16:15: High Dimensional Spaces
16:15 – 17:30: Hands-on, Programming Demo

There was a course dinner (provided) on Monday at The University Club located on the 11th floor of Rudder Tower (on campus close to Kyle Field) at 6:30 pm.

Tuesday, March 5, 2024
8:00 – 8:30 Breakfast provided.
8:30 – 10:00 Data Anomaly and State Estimation
10:00 – 10:15 Break
10:15– 12:00 Neural Networks and Deep Neural Networks
12:00 – 13:00: Lunch (provided)
13:00 – 14:15 Hands-on Programming Demo on Neural Networks
14:15 – 14:30 Break
14:30 – 16:00 Machine Learning Overview and Support Vector Machine
16:00 – 16:15 Break
16:15 – 17:15 Application of Machine Learning/SVM in Power Systems

Wednesday, March 6, 2024
8:00 – 8:30 Breakfast provided.
8:30 – 10:00 ChatGPT and Large Language Models Introduction
10:00 – 10:15 Break
10:15 – 11:45 Demo and Examples of LLM in Power Systems
11:45 – 13:00: Lunch and Discussion (provided)
13:00 – 14:30: Reinforcement Learning
14:30 – 14:45: Break
14:45 – 15:45: Application of Reinforcement Learning in Power Systems
15:45– 16:00: Wrap-up (All)

Instructors

Dr. Le Xie

Dr. Le Xie is the Segers Family Dean’s Excellence Professor, chancellor EDGES Fellow, and Presidential Impact Fellow in the Department of Electrical and Computer Engineering at Texas A&M University, and the Associate Director-Energy Digitization at Texas A&M Energy Institute. He received B.E. in Electrical Engineering from Tsinghua University in 2004, S.M. in Engineering Sciences from Harvard in 2005, and Ph.D. in Electrical and Computer Engineering from Carnegie Mellon in 2009. His industry experience includes ISO-New England and Edison Mission Energy Marketing and Trading. His research interest includes modeling and control in data-rich large-scale systems, grid integration of clean energy resources, and electricity markets.
Dr. Xie is a Fellow of IEEE and a Power and Energy Society (PES) Distinguished Lecturer. He received the National Science Foundation CAREER Award, and Oak Ridge Ralph E. Powe Junior Faculty Enhancement Award. He was awarded the 2021 IEEE Technical Committee on Cyber-Physical Systems Mid-Career Award, and 2017 IEEE PES Outstanding Young Engineer Award. He was the recipient of Texas A&M Dean of Engineering Excellence Award, ECE Outstanding Professor Award, and TEES Select Young Fellow. He serves or have served on the Editorial Board of IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, and IET Transaction on Smart Grid. He is the founding chair of IEEE PES Subcommittee on Big
Data & Analytics for Grid Operations. His team received the PES AMPS Technical Committee Prize Paper 2023, Best Paper awards at North American Power Symposium 2012, IEEE SmartGridComm 2013, HICSS 2019 and 2021, IEEE Sustainable Power & Energy Conference 2019, and IEEE PES General Meeting 2020.

Dr. Rayan El Helou

Dr. Rayan El Helou is a Trading Analyst at REV Renewables, currently primarily focused on dispatch and commercial optimization of REV’s battery assets in CAISO. His role includes developing data-driven models, automation tools and user interfaces to improve performance and operational efficiency. He received all three of his degrees in Electrical and Computer Engineering, the latest of which was his PhD degree from Texas A&M University, College Station, TX, after having received both his B.S. and M.S. degrees from The Ohio State University, Columbus, OH. Other industry experience includes ISO-New England in Holyoke, MA. His research interests include modeling and optimization in power systems and energy markets.

Dr. Yannan Sun

Dr. Yannan Sun is a data scientist at Oncor Electric Delivery. She received the B.S. degree in mathematics from the University of Science and Technology of China, Hefei, China, in 2004, and the M.S. degree in statistics and the Ph.D. degree in mathematics from Washington State University, Pullman, WA, USA, in 2007 and 2010, respectively. She was with the Pacific Northwest National Laboratory, Richland, WA, USA, from 2010 to 2017. Her research interests include multivariate risk (multivariate extremes and risk measures), statistical modeling, experimental design and simulation, and applying mathematical and statistical methods in power engineering and environmental science. She is the Chair of the IEEE PES Subcommittee on Big Data & Analytics for Grid Operations.

Dr. Dileep Kalathil

Dr. Dileep Kalathil is an Assistant Professor in the Electrical and Computer Engineering Department at Texas A&M. Before joining Texas A&M, he was a postdoctoral scholar in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He received his PhD from the
University of Southern California (USC) in 2014 where he won the best PhD Dissertation Prize in the USC Department of Electrical Engineering. He received an M.Tech from IIT Madras where he won the award for the best academic performance in the EE department. His research interests include control theory, sequential learning, game theory, and sustainable energy systems.

Dr. Chao Tian

Dr. Chao Tian is an Associate Professor in the Electrical and Computer Engineering Department at Texas A&M University. Dr. Tian obtained his B.E degree from Tsinghua University, Beijing China, and his M.S. and Ph.D. degrees from Cornell University, Ithaca NY. He worked at AT&T Labs-Research (previously known as the Shannon Labs) as a researcher on communication and signal processing for seven years, before returning to academia. He was with the University of Tennessee Knoxville for a few years before joining Texas A&M University.