This research integrates Large Language Models with Stochastic Unit Commitment frameworks to optimize energy systems with high wind generation uncertainties.

• Achieves an average 1.1% cost reduction (saving ~$2.1 million) compared to traditional approaches
• Enhances both efficiency and reliability in renewable-heavy energy grids
• Demonstrates successful LLM application in complex engineering optimization tasks
• Creates a hybrid approach that can adapt to variable energy conditions

This innovation matters for engineering because it shows how AI can help manage the growing complexities of renewable energy integration while maintaining grid stability and reducing operational costs.

Can Large Language Model Agents Balance Energy Systems?