用强化学习让奶牛场之间自动买卖电，省钱又降峰。
Peer-to-Peer Energy Trading in Dairy Farms using Multi-Agent Reinforcement Learning
- 多智能体强化学习+拍卖机制，让农场自主交易电力。
- 爱尔兰用电成本降14.2%，芬兰峰值负荷降27.02%。
- 适合研究农村能源自治、智能电网的从业者参考。
将可再生能源引入农村地区（如乳制品农场社区），可通过点对点（P2P）电力交易实现去中心化能源管理。传统基于规则的方法在稳定环境下表现良好，但在动态环境中表现不佳。为此，本文结合多智能体强化学习（MARL），特别是近端策略优化（PPO）和深度Q网络（DQN），与社区/分布式P2P交易机制，引入拍卖式市场出清、价格顾问代理及负荷与电池管理。结果显示，相较于基线模型，DQN在爱尔兰降低电费14.2%、在芬兰降低5.16%，同时分别提升售电收入7.24%和12.73%；PPO在爱尔兰将峰值负荷降低55.5%，DQN则分别降低50.0%（爱尔兰）和27.02%（芬兰）。这些改进得益于MARL算法与P2P交易的协同作用，显著降低了用电成本与峰值需求，并提升了售电收益。本研究凸显了DQN、PPO与P2P交易在实现高效、自适应、可持续农村能源管理中的互补优势。
原文摘要 · Abstract (English)
The integration of renewable energy resources in rural areas, such as dairy farming communities, enables decentralized energy management through Peer-to-Peer (P2P) energy trading. This research highlights the role of P2P trading in efficient energy distribution and its synergy with advanced optimization techniques. While traditional rule-based methods perform well under stable conditions, they struggle in dynamic environments. To address this, Multi-Agent Reinforcement Learning (MARL), specifically Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), is combined with community/distributed P2P trading mechanisms. By incorporating auction-based market clearing, a price advisor agent, and load and battery management, the approach achieves significant improvements. Results show that, compared to baseline models, DQN reduces electricity costs by 14.2% in Ireland and 5.16% in Finland, while increasing electricity revenue by 7.24% and 12.73%, respectively. PPO achieves the lowest peak hour demand, reducing it by 55.5% in Ireland, while DQN reduces peak hour demand by 50.0% in Ireland and 27.02% in Finland. These improvements are attributed to both MARL algorithms and P2P energy trading, which together results in electricity cost and peak hour demand reduction, and increase electricity selling revenue. This study highlights the complementary strengths of DQN, PPO, and P2P trading in achieving efficient, adaptable, and sustainable energy management in rural communities.
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