Artificial Intelligence/Machine Learning Technology in Power System Applications
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Colorado School of Mines, Golden, CO (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Eindhoven University of Technology (Netherlands)
The primary purpose of this report is to provide an overview of the advancement in artificial intelligence and machine learning (AI/ML) technologies and their applications in power systems. It offers a foundation for understanding the transformative role of AI/ML in power systems and aims to stimulate further research and development in this area. This report begins with a historical perspective of AI/ML technologies, then explores their advancement to today’s prominence. The document highlights key contributors to the success of AI/ML technologies, including increased computational power, greater data availability, innovative algorithms, and advanced tools. It further introduces various AI/ML techniques, including supervised, unsupervised and reinforcement learning, graph neural networks, and generative AI. It also emphasizes the critical importance of ensuring the safety, security, and trustworthiness of these AI/ML techniques within this sector. The report reviews the recent representative advancements in various power system applications enhanced by AI/ML techniques, underscoring key developments and their transformative impact as evidenced by numerous studies. It also explores both the opportunities and challenges associated with the application of AI/ML technologies to improve power system applications. While the report extensively covers AI/ML applications in power systems, focusing primarily on the technical and operational aspects, it may not thoroughly explore the sociopolitical, economic, and broader regulatory implications of AI/ML integration in power systems. AI/ML techniques hold significant potential for enhancing power system applications; however, they are not omnipotent. It is crucial to acknowledge their limitations and understand that they may not be able to address all challenges in the power system domain. Various factors must be considered that influence the implementation, adoption, and effectiveness of AI/ML solutions, including but not limited to safety, security, transparency, and trustworthiness. Additionally, the incorporation of advanced human–machine interfaces is essential, as it enables humans to validate the effectiveness of AI/ML solutions while remaining actively engaged, fostering trust in AI/ML deployment. Finally, the report summarizes AI/ML research activities supported by the Department of Energy (DOE) Office of Electricity (OE) through the Advanced Grid Modeling (AGM) program. The work aligns with the interests and mission of DOE-OE AGM, with the report serving as a resource for identifying existing progress and for pinpointing future applications within AI/ML that need further exploration and support.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE Office of Electricity (OE)
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 2340760
- Report Number(s):
- PNNL--35735
- Country of Publication:
- United States
- Language:
- English
Similar Records
Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants
Application of Artificial Intelligence/Machine Learning to Operations Research
Artificial Intelligence for Digital Security and Protections
Technical Report
·
Mon Jan 31 23:00:00 EST 2022
·
OSTI ID:1847070
Application of Artificial Intelligence/Machine Learning to Operations Research
Technical Report
·
Tue Dec 31 23:00:00 EST 2024
·
OSTI ID:2516868
Artificial Intelligence for Digital Security and Protections
Technical Report
·
Mon Nov 30 23:00:00 EST 2020
·
OSTI ID:1847906