  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.
