Exploring a Physics-Informed Decision Transformer for Distribution System Restoration: Methodology and Performance Analysis
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 33 references that are in the map. First posted on arXiv 30 Jun 2024.
Builds on
5 of 33 references.
References · 5
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
- 2023Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Datacited by 48
- 2020Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfigurationcited by 7
- 2019Resilient Load Restoration in Microgrids Considering Mobile Energy Storage Fleets: A Deep Reinforcement Learning Approachcited by 2
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Dual-Head Physics-Informed Graph Decision Transformer for Distribution System Restoration
- 2026Knowledge Distillation for Efficient Transformer-Based Reinforcement Learning in Hardware-Constrained Energy Management Systems
- 2025From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins
- 2024Safe Imitation Learning-based Optimal Energy Storage Systems Dispatch in Distribution Networks
- 2018Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning
- 2020Rethink AI-based Power Grid Control: Diving Into Algorithm Design
- 2022Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning
- 2021Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer