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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.08973 |
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| _version_ | 1866915929774882816 |
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| author | Ren, Junhao Gao, Honglin Zhao, Lan Kang, Qiyu Xiao, Gaoxi Sun, Yajuan |
| author_facet | Ren, Junhao Gao, Honglin Zhao, Lan Kang, Qiyu Xiao, Gaoxi Sun, Yajuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08973 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-agent Reinforcement Learning for Low-Carbon P2P Energy Trading among Self-Interested Microgrids Ren, Junhao Gao, Honglin Zhao, Lan Kang, Qiyu Xiao, Gaoxi Sun, Yajuan Multiagent Systems 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. |
| title | Multi-agent Reinforcement Learning for Low-Carbon P2P Energy Trading among Self-Interested Microgrids |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2604.08973 |