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

TL;DR

This paper presents LLM4DistReconfig, a fine-tuned large language model that efficiently predicts optimal power distribution network reconfigurations, reducing inference time and maintaining high accuracy in minimizing system losses.

Contribution

It introduces a novel deep learning approach using a fine-tuned LLM with custom prompts and loss functions for real-time power network reconfiguration, outperforming traditional methods.

Findings

Achieves near real-time reconfiguration predictions.

Minimizes system loss effectively across multiple datasets.

Produces valid network configurations with minimal invalid edges.

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.