  Driven by advancements in sensing and computing, deep reinforcement learning
(DRL)-based methods have demonstrated significant potential in effectively
tackling distribution system restoration (DSR) challenges under uncertain
operational scenarios. However, the data-intensive nature of DRL poses
obstacles in achieving satisfactory DSR solutions for large-scale, complex
distribution systems. Inspired by the transformative impact of emerging
foundation models, including large language models (LLMs), across various
domains, this paper explores an innovative approach harnessing LLMs' powerful
computing capabilities to address scalability challenges inherent in
conventional DRL methods for solving DSR. To our knowledge, this study
represents the first exploration of foundation models, including LLMs, in
revolutionizing conventional DRL applications in power system operations. Our
contributions are twofold: 1) introducing a novel LLM-powered Physics-Informed
Decision Transformer (PIDT) framework that leverages LLMs to transform
conventional DRL methods for DSR operations, and 2) conducting comparative
studies to assess the performance of the proposed LLM-powered PIDT framework at
its initial development stage for solving DSR problems. While our primary focus
in this paper is on DSR operations, the proposed PIDT framework can be
generalized to optimize sequential decision-making across various power system
operations.
