面向设备与系统级缺失量测的网络化微电网中大语言模型与强化学习协同兼容方法

The deep reinforcement learning (DRL) approach with its end-to-end and data-driven features enhances the operational strategies for networked microgrids (NMGs). Well-trained DRL agents can make optimal decisions for system operations by observing NMG online measurements. However, missing measurements pose an unpredictable challenge for the secure operation of DRL-based NMGs. This paper proposes a large language model (LLM)-based agent designed for compatibility with multi-agent DRL online decision-making for networked microgrids considering missing measurements. The novel design rules for the LLM-based agent are proposed to unleash the potential of LLM in imputing missing NMG measurements using only few-shot learning. Subsequently, the designed LLM-based agent is embedded into multi-agent DRL in a compatible manner for NMG online operations considering missing online measurements. Experimental results indicate that the proposed method exhibits robustness and trustworthiness in device and system-level measurement imputations while holding the merits of DRL. The proposed method reduces NMG’s total operation costs by up to 23.33%, achieving a balance between security and optimality under missing measurements.