用多智能体强化学习优化奶牛场电池管理，提升收益并促进可再生能源利用。
Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms
- 分层控制：上层动态定价，下层多智能体强化学习管理电池
- 能源套利收益提升18%，分布式发电利用率提高且成本可控
- 符合爱尔兰电网电压规范，适合农村能源系统研究者参考
爱尔兰乳品行业在整合可再生能源和减少碳排放方面潜力巨大，但现有分布式发电控制研究多集中于住宅与商业场景。为推动可再生能源在乳品领域的有效集成，本文提出一种基于差分进化与多智能体深度强化学习的多目标优化控制系统。该控制框架采用双层结构：上层利用动态电价机制，下层通过多智能体强化学习实现电池管理。论文还对所提控制策略在农村配电网中的电气响应进行了仿真验证。结果表明，相较于规则基模型，该框架可使能源套利收益提升最高达18%，显著提高分布式发电利用率，同时成本增加不明显，并满足爱尔兰电网代码对电压波动的要求。
原文摘要 · Abstract (English)
The dairy industry in Ireland has a large potential for the integration of renewable energy and the reduction of carbon emissions. However, researchers of distributed generation control are mainly focused on residential and commercial applications. To contribute to the effective integration of renewable energy in the dairy sector, this paper presents a multi-objective optimisation control system based on differential evolution and multi agent Deep Reinforcement Learning. The proposed control is organised in two layers: the upper layer uses dynamic pricing, and the lower layer is based on multi-agent reinforcement learning for battery management. This paper also simulates the electrical response of the proposed control system in a rural distribution circuit. The simulation results show that the proposed control framework can improve profits from energy arbitrage up to 18% compared to using Rule-based models, increase the use of distributed generation without significantly increasing cost, and comply with the Irish grid code in terms of voltage variation.
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