AI Large Models for Power System: A Survey and Outlook
Qi Yao, Fang Fang, Yuanye Chen, Jizhen Liu, Huadong Mo, Yingfang Ao
Abstract
ABSTRACT In recent years, AI large models, also known as large pre‐trained models or foundational models, have achieved remarkable success in various tasks across multiple domains. These models leverage extensive unlabelled datasets from multiple fields and modalities, enabling them to generalize across tasks with minimal labelled data. Their ability has led to advancements in numerous domains. However, the application of large models in power systems remains in their early stages, and the potential of large models has not been fully explored. This paper aims to help researchers and engineers grasp the latest advances and trends in large models to foster the development and applications in the power industry. It traces the development stages of large models, introduces the concept and architecture of large models, and concludes the verified and remarkable capabilities of the large model. Additionally, by integrating existing research, this paper reviews recent advancements and potential applications of large models in power systems, with a focus on perception, planning, and control. Moreover, it summarizes the key enhancement technologies for optimizing the effectiveness of large models of power systems. Finally, the challenges and risks associated with developing large models including computing power requirements, reliability, and safety considerations for power systems are also discussed. Based on the survey, large models for power systems are proven to be a promising paradigm that can improve the efficiency and effectiveness of intelligent power systems, which contributes to the reference for the intelligent development of the power industry.