Low Carbon Scheduling of Integrated Energy System Based on Large Language Model-Embedded Multi-Agent Reinforcement Learning基于大语言模型嵌入多智能体强化学习的综合能源系统低碳调度
英文 Abstract
The complex multi-energy coupling characteristics inherent to integrated energy system (IES) present unprecedented challenges for the implementation of low-carbon scheduling. Existing optimization methods often exhibit limitations in system scalability, algorithm adaptivity, and carbon reduction efficacy for complex IES. This paper proposes a Large Language Model (LLM)-Embedded Multi-Agent Reinforcement Learning (LEMARL) to address the aforementioned issues. The proposed method integrates the global perception capability of LLMs with the dynamic optimization capability of MARL. Specifically, the LLM-Embedded module generates high-quality reward functions and policy frameworks from a global perspective, while the MARL module leverages these LLM-generated strategies for distributed interactive iterations—greatly enhancing computation efficiency and scalability. Simulation results demonstrate that LEMARL reduces carbon emissions by 7.76% and simultaneously decreases operating costs by 4.49% in a small-scale IES. Furthermore, LEMARL also exhibits superior applicability and scalability in large-scale IES of the IEEE 141-bus power grid integrated with 51-node thermal system.
中文翻译
综合能源系统复杂的多能耦合特性给低碳调度带来新的挑战，现有优化方法在复杂系统中的可扩展性、适应性和减排效果仍存在局限。本文提出大语言模型嵌入式多智能体强化学习方法 LEMARL，将大语言模型的全局感知能力与多智能体强化学习的动态优化能力结合：LLM 模块从全局角度生成高质量奖励函数和策略框架，MARL 模块据此开展分布式交互迭代。仿真结果显示，该方法在小规模综合能源系统中将碳排放降低 7.76%，同时将运行成本降低 4.49%；在 IEEE 141 节点电网与 51 节点热力系统耦合的大规模系统中也表现出良好的适用性和可扩展性。
中文概述
该研究把大语言模型用于生成奖励函数和策略框架，再由多智能体强化学习执行综合能源系统分布式低碳调度。结果表明，LLM 与 MARL 的结合能够同时改善碳排放、运行成本和大规模系统可扩展性，是“生成式 AI + 能源优化决策”的直接应用。