Can Large Language Model Agents Balance Energy Systems?

Abstract

This paper presents a hybrid approach that integrates Large Language Models (LLMs) with a multi-scenario Stochastic Unit Commitment (SUC) framework to enhance both efficiency and reliability under high wind generation uncertainties. In a 10-trial study on the test energy system, the traditional SUC approach incurs an average total cost of 187.68 million dollars, whereas the LLM-assisted SUC (LLM-SUC) achieves a mean cost of 185.58 million dollars (range: 182.61 to 188.65 million dollars), corresponding to a cost reduction of 1.1 to 2.7 percent. Furthermore, LLM-SUC reduces load curtailment by 26.3 percent (2.24 plus/minus 0.31 GWh versus 3.04 GWh for SUC), while both methods maintain zero wind curtailment. Detailed temporal analysis shows that LLM-SUC achieves lower costs in the majority of time intervals and consistently outperforms SUC in 90 percent of cases, with solutions clustering in a favorable cost-reliability region (Coefficient of Variation = 0.93 percent for total cost and 13.8 percent for load curtailment). By leveraging an LLM agent to guide generator commitment decisions and dynamically adjust to stochastic conditions, the proposed framework improves demand fulfillment and operational resilience.