Machine Learning Solutions for a Stable Grid Recovery

Technical Report · Tue Nov 01 04:00:00 EDT 2022

DOI:https://doi.org/10.2172/2432233· OSTI ID:2432233

Verzi, Stephen Joseph ^[1]; Guttromson, Ross T. ^[1]; Sorensen, Asael Hal ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are severely violated, existing system security margins will be largely unknown, as would be a secure incremental dispatch path to higher security margins while continuing to serve load. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of machine learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. Several reinforcement learning solution methods were developed using deep learning neural networks, including Deep Q-learning, Mu-Zero, and the continuous algorithms Proximal Reinforcement Learning, and Advantage Actor Critic Learning. The work is demonstrated on a power grid with three control dimensions but can be scaled in size and dimensionality, which is the subject of ongoing research.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE Office of Electricity (OE); USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: NA0003525
• OSTI ID: 2432233
• Report Number(s): SAND--2023-10478
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
97 MATHEMATICS AND COMPUTING