Can Large Language Model Agents Balance Energy Systems?

TL;DR

The paper tackles the challenge of balancing high-penetration wind generation in power systems by introducing a hybrid LLM-SUC framework. A two-stage, multi-agent LLM approach generates quantile-based wind scenario trees and refines the inputs to a stochastic MILP-based unit commitment model, enabling adaptive decisions under uncertainty. Across 10 trials, the LLM-SUC method reduces mean daily cost from 187.68 M$ to 185.58 M$ (1.1–2.7% savings) and lowers load curtailment by 26.3% while keeping wind curtailment at zero, with 90% of trials outperforming the baseline and a dynamic, location-specific cost profile. The work demonstrates the practical potential of integrating LLM agents to enhance efficiency and reliability in renewable-rich energy systems, and points to future gains from improved prompt design and tighter constraint integration to further suppress simultaneous curtailment phenomena.

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.

Figures (7)

• Figure 1: Architecture diagram of LLM Agent-enabled energy system balancing framework. (A) User-defined balancing policy configuration interface. (B) Energy System Operator Agent decision process.
• Figure 2: Wind error under different quantile settings.
• Figure 3: Energy dispatch and load curtailment by stochastic unit commitment. (a) Generation dispatch. (b) Load and wind curtailment.
• Figure 4: Energy dispatch decision-making for different scenarios.
• Figure 5: Curtailment decision-making for different scenarios.