Farm businesses are increasingly adopting renewables to enhance energy efficiency and reduce reliance on fossil fuels and the grid. This shift aims to decrease dairy farms' dependence on traditional electricity grids by enabling the sale of surplus renewable energy in Peer-to-Peer markets. However, the dynamic nature of farm communities poses challenges, requiring specialized algorithms for P2P energy trading. To address this, the Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator (MAPDES) has been developed, providing a platform to experiment with Reinforcement Learning techniques. The simulations demonstrate significant cost savings, including a 43% reduction in electricity expenses, a 42% decrease in peak demand, and a 1.91% increase in energy sales compared to baseline scenarios lacking peer-to-peer energy trading or renewable energy sources.

翻译：农业企业日益采用可再生能源以提高能源效率并减少对化石燃料和电网的依赖。这一转变旨在通过允许在点对点市场中出售多余的可再生能源，降低奶牛场对传统电网的依赖。然而，农场社区的动态特性带来了挑战，需要专门针对点对点能源交易的算法。为此，我们开发了多智能体奶牛场点对点能源模拟器（MAPDES），提供了一个实验强化学习技术的平台。仿真结果显示，与缺乏点对点能源交易或可再生能源的基准场景相比，电费支出降低了43%，峰值需求减少了42%，能源销售收入增加了1.91%。