Generative AI for Power Grid Operations Article Swipe
Seong‐Ho Choi
,
Rishabh Jain
,
C. Q. Feng
,
Patrick Emami
,
Hongming Zhang
,
Junho Hong
,
Taesic Kim
,
SangWoo Park
,
Fei Ding
,
Murali Baggu
,
Benjamin Kroposki
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2172/2477920
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2172/2477920
Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.
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- Language
- en
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- https://doi.org/10.2172/2477920
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- Cited By
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All OpenAlex metadata
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https://openalex.org/W4404659634Canonical identifier for this work in OpenAlex
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https://doi.org/10.2172/2477920Digital Object Identifier
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Generative AI for Power Grid OperationsWork title
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reportOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
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2024-11-13Full publication date if available
- Authors
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Seong‐Ho Choi, Rishabh Jain, C. Q. Feng, Patrick Emami, Hongming Zhang, Junho Hong, Taesic Kim, SangWoo Park, Fei Ding, Murali Baggu, Benjamin KroposkiList of authors in order
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.osti.gov/biblio/2477920Direct OA link when available
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Computer science, Generative grammar, Artificial intelligence, Data science, Grid, Systems engineering, Process management, Engineering, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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| publication_date | 2024-11-13 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2794202219, https://openalex.org/W3211983881, https://openalex.org/W2579555219, https://openalex.org/W4403125877, https://openalex.org/W3101498480, https://openalex.org/W1545257872, https://openalex.org/W4398229509, https://openalex.org/W4391557237, https://openalex.org/W3146366485, https://openalex.org/W4387006022, https://openalex.org/W4310584694, https://openalex.org/W4399798207, https://openalex.org/W4385245566, https://openalex.org/W4400668519 |
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