Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms

TL;DR

This paper presents a reinforcement learning-based platform for peer-to-peer energy trading among dairy farms, significantly reducing costs and peak demand while increasing energy sales through renewable energy integration.

Contribution

It introduces MAPDES, a novel multi-agent simulator enabling experimentation with reinforcement learning for P2P energy trading in dairy farm communities.

Findings

43% reduction in electricity costs

42% decrease in peak demand

1.91% increase in energy sales

Abstract

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.