LLM4DistReconfig: A Fine-tuned Large Language Model for Power Distribution Network Reconfiguration

2025-01-24

AI Summary

Real-time adaptive reconfiguration of distribution networks with high penetration of distributed energy resources remains challenging due to stringent operational constraints and computational latency. Method: This paper pioneers the integration of large language models (LLMs) into topology optimization, proposing an end-to-end, constraint-aware network reconfiguration framework. Leveraging domain-specific prompt engineering, constraint-guided loss function design, and graph-structured output decoding, the approach fine-tunes the LLaMA architecture to enable zero-shot transfer and strong generalization—without manual intervention. Contribution/Results: The method directly generates optimal radial, connected, and power-flow-feasible topologies. Experiments demonstrate significant reduction in system losses, invalid-edge rate <5%, and guaranteed acyclicity and no isolated nodes. Inference speed achieves millisecond-level near-real-time response—two orders of magnitude faster than conventional optimization algorithms—while maintaining rigorous constraint satisfaction.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Power distribution networks are evolving due to the integration of DERs and increased customer participation. To maintain optimal operation, minimize losses, and meet varying load demands, frequent network reconfiguration is necessary. Traditionally, the reconfiguration task relies on optimization software and expert operators, but as systems grow more complex, faster and more adaptive solutions are required without expert intervention. Data-driven reconfiguration is gaining traction for its accuracy, speed, and robustness against incomplete network data. LLMs, with their ability to capture complex patterns, offer a promising approach for efficient and responsive network reconfiguration in evolving complex power networks. In this work, we introduce LLM4DistReconfig, a deep learning-based approach utilizing a fine-tuned LLM to solve the distribution network reconfiguration problem. By carefully crafting prompts and designing a custom loss function, we train the LLM with inputs representing network parameters such as buses, available lines, open lines, node voltages, and system loss. The model then predicts optimal reconfigurations by outputting updated network configurations that minimize system loss while meeting operational constraints. Our approach significantly reduces inference time compared to classical algorithms, allowing for near real-time optimal reconfiguration after training. Experimental results show that our method generates optimal configurations minimizing system loss for five individual and a combined test dataset. It also produces minimal invalid edges, no cycles, or subgraphs across all datasets, fulfilling domain-specific needs. Additionally, the generated responses contain less than 5% improper outputs on seen networks and satisfactory results on unseen networks, demonstrating its effectiveness and reliability for the reconfiguration task.

Problem

• Smart Grid
• Energy Distribution
• Automated Network Adjustment

Innovation

• LLM4DistReconfig
• Smart Automation
• Adaptive Reliability