Machine Learning for Power Systems

November 2022

Machine learning (ML) is one of the emerging technologies for implementing the next generation smart grid. In recent years, the PES community has witnessed significant efforts to explore the potential of machine learning for solving complex power system problems. Applications cover almost every area within the interest of PES, including generation, transmission, distribution, microgrid and customers. Also, researchers have been exploring physics-informed, performance-guaranteed, or explainable ML techniques for power systems. 

Active Committees/Task Forces of Interest
Technical Reports & Applicable Papers or Presentations

IEEE PES Electrification Magazine Articles:

IEEE Power & Energy Magazine:

IEEE PES Transactions Articles:

IEEE Transactions on Power Systems

IEEE Transactions on Smart Grid

IEEE Transactions on Sustainable Energy

Other Available Material

Physics-aware and Risk-aware Machine Learning for Power System Operations

IEEE-PES Webinar: Feb 2022
Hao Zhu

Recent years have witnessed rapid transformations of contemporary advances in machine learning (ML) and data science to aid the transition of energy systems into a truly sustainable, resilient, and distributed infrastructure. A blind application of the latest-and-greatest ML algorithms to solve stylized grid operation problems, however, may fail to recognize the underlying physics models or safety constraint requirements. This talk will introduce three examples of bridging physics- and risk-aware ML advances into efficient and reliable grid operations. First, we develop a topology-aware approach using graph neural networks (GNNs) to predict the price and line congestion as the outputs of real-time optimal power flow problem. Building upon the underlying relation between prices and topology, this proposed solution significantly reduces the model complexity of existing end-to-end ML methods while efficiently adapting to varying grid topology. Second, we put forth a risk-aware ML method to ensure the safety guarantees of data-driven, scalable reactive power dispatch policies in distribution grids. The resultant policies can directly account for the statistical risks on prediction error to attain guaranteed voltage violation performance. Last, we consider a reinforcement learning framework for managing a large number of dynamical, flexible energy resources such as electrical vehicles, and demonstrate the need to simplify the system representation through physics-aware state/action aggregation.




Deep Learning for Power System Operation and Planning

IEEE-PES Webinar: July 2021
Fangxing Li, University of Tennessee Knoxville, Di Shi, AINERGY LLC

Deep Learning (DL) and Artificial Intelligence (AI) is the emerging technology for realizing the next generation smart grid. In recent years, significant efforts have been devoted to exploring the potentials of DL and AI for solving the complex power system problems, from generations all the way down to the demand side. In this panel, the focus will be given to the application of DL in broad areas of power system operation and planning. Experts from academia and industry will share their original ideas and insights to this challenging and inspiring topic.