Review on Application and Challenges of Large Language Models in Power Grids

Power grids are critical infrastructures that require real-time safe and reliable operation. Large Language Models (LLMs), have demonstrated significant potential in power grid applications. This paper presents a comprehensive review of the applications of LLMs in power grids, covering areas such as intelligent assistance for grid operation, generation and load forecasting, power system planning and operation, and power converter design. Additionally, the paper discusses key challenges associated with the integration of LLMs into power grids, including data quality issues, model explainability, cybersecurity risks, and real-time computational efficiency. Potential solutions are provided to enhance the reliability and applicability of LLMs in power grid applications. By leveraging the capabilities of LLMs, power system operators can enhance decision-making processes, improve operational efficiency, and foster a more resilient and adaptive power infrastructure.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3580439
Citations
4
Primary Topic
Power Systems and Technologies
Type
article
Field-Weighted Citation Impact
2.92
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Review on Application and Challenges of Large Language Models in Power Grids

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4 citations
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article

Review on Application and Challenges of Large Language Models in Power Grids

Wenlu Ji, Bingjun Qian, L.F. Zhang, Yang Zhao, Di Huang
article en
4 citations

Abstract

Power grids are critical infrastructures that require real-time safe and reliable operation. Large Language Models (LLMs), have demonstrated significant potential in power grid applications. This paper presents a comprehensive review of the applications of LLMs in power grids, covering areas such as intelligent assistance for grid operation, generation and load forecasting, power system planning and operation, and power converter design. Additionally, the paper discusses key challenges associated with the integration of LLMs into power grids, including data quality issues, model explainability, cybersecurity risks, and real-time computational efficiency. Potential solutions are provided to enhance the reliability and applicability of LLMs in power grid applications. By leveraging the capabilities of LLMs, power system operators can enhance decision-making processes, improve operational efficiency, and foster a more resilient and adaptive power infrastructure.

IEEE AccessVol. 13
Shanghai Electric (China) (CN), State Grid Nanjing Power Supply Company (China)
Openalex Percentile: Top 9%
Power Systems and Technologies
2.92
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