Can Large Language Model Agents Balance Energy Systems?

TL;DR

This paper introduces a hybrid LLM-assisted stochastic unit commitment framework that reduces costs and load curtailment in energy systems with high wind uncertainty, outperforming traditional methods in efficiency and reliability.

Contribution

It presents a novel integration of Large Language Models with stochastic energy scheduling to enhance decision-making under uncertainty.

Findings

Cost reduction of 1.1 to 2.7 percent compared to traditional SUC.

26.3 percent decrease in load curtailment with LLM assistance.

LLM-SUC outperforms SUC in 90 percent of tested scenarios.

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.