This research demonstrates how fine-tuned LLMs can effectively tackle the complex task of power distribution network reconfiguration without human expert intervention.

• Transforms electrical grid optimization from a manual process to an automated AI approach
• Achieves faster reconfiguration decisions as electrical networks grow increasingly complex
• Adapts quickly to changing conditions from distributed energy resources and varying load demands
• Opens new possibilities for real-time power grid optimization in smart electrical systems

For engineering teams, this represents a breakthrough in applying AI to critical infrastructure management, potentially reducing power losses and improving reliability while decreasing dependence on scarce human expertise.

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