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arXiv · 2604.08973

Multi-agent Reinforcement Learning for Low-Carbon P2P Energy Trading among Self-Interested Microgrids

Junhao Ren·Honglin Gao·Lan Zhao·Qiyu Kang·Gaoxi Xiao·Yajuan Sun
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Abstract

Uncertainties in renewable generation and demand dynamics challenge day-ahead scheduling. To enhance renewable penetration and maintain intra-day balance, we develop a multi-agent reinforcement learning framework for self-interested microgrids participating in peer-to-peer (P2P) electricity trading. Each microgrid independently bids both price and quantity while optimizing its own profit via storage arbitrage under time-varying main-grid prices. A market-clearing mechanism coordinating trades and promoting incentive compatibility is proposed. Simulation results show that the learned bidding policy improves renewable utilization and reduces reliance on high-carbon electricity, while increasing community-level economic welfare, delivering a win-win situation in emission reduction and local prosperity.

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Junhao Ren, Honglin Gao, Lan Zhao, Qiyu Kang, Gaoxi Xiao, Yajuan Sun. 2026-04-10. Multi-agent Reinforcement Learning for Low-Carbon P2P Energy Trading among Self-Interested Microgrids. https://arxiv.org/abs/2604.08973

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