Real-time peer-to-peer (P2P) electricity markets dynamically adapt to fluctuations in renewable energy and variations in demand, maximizing economic benefits through instantaneous price responses while enhancing grid flexibility. However, scaling expert guidance for massive personalized prosumers poses critical challenges, including diverse decision-making demands and a lack of customized modeling frameworks. This paper proposes an integrated large language model-multi-agent reinforcement learning (LLM-MARL) framework for real-time P2P energy trading to address challenges such as the limited technical capability of prosumers, the lack of expert experience, and security issues of distribution networks. LLMs are introduced as experts to generate personalized strategies, guiding MARL under the centralized training with decentralized execution (CTDE) paradigm through imitation. To handle the scalability issues inherent in large-scale P2P networks, a differential attention-based critic network is introduced to efficiently extract key interaction features and enhance convergence. Experimental results demonstrate that LLM-generated strategies effectively substitute human experts. The proposed imitative expert MARL algorithms achieve significantly lower economic costs and voltage violation rates on test sets compared to baseline algorithms, while maintaining robust stability. This paper provides an effective solution for the real-time decision-making of the P2P electricity market by bridging expert knowledge with agent learning.

翻译：实时点对点（P2P）电力市场动态适应可再生能源波动与需求变化，通过即时价格响应最大化经济效益，同时增强电网灵活性。然而，为海量个性化产消者扩展专家指导面临关键挑战，包括多样化的决策需求以及缺乏定制化建模框架。本文提出一种集成大语言模型-多智能体强化学习（LLM-MARL）框架，用于实时P2P能源交易，以应对产消者技术能力有限、专家经验缺乏以及配电网安全等问题。该框架引入LLM作为专家生成个性化策略，通过模仿学习在集中训练分散执行（CTDE）范式下指导MARL。为处理大规模P2P网络固有的可扩展性问题，引入基于差分注意力的评论家网络，以高效提取关键交互特征并提升收敛性。实验结果表明，LLM生成的策略能有效替代人类专家。与基线算法相比，所提出的模仿专家MARL算法在测试集上实现了显著更低的经济成本与电压越限率，同时保持了鲁棒的稳定性。本文通过桥接专家知识与智能体学习，为P2P电力市场的实时决策提供了有效解决方案。