A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning

TL;DR

This paper presents a Q-learning algorithm to optimize battery management in dairy farms, effectively reducing energy costs and peak demand while integrating renewable wind energy, demonstrating reinforcement learning's potential in sustainable agriculture.

Contribution

It introduces a novel reinforcement learning approach for battery scheduling in dairy farms, incorporating renewable energy data and multiple case studies.

Findings

Reduces grid electricity costs by 13.41%

Decreases peak demand by 2%

Achieves 24.49% cost reduction with wind energy

Abstract

Dairy farming consumes a significant amount of energy, making it an energy-intensive sector within agriculture. Integrating renewable energy generation into dairy farming could help address this challenge. Effective battery management is important for integrating renewable energy generation. Managing battery charging and discharging poses significant challenges because of fluctuations in electrical consumption, the intermittent nature of renewable energy generation, and fluctuations in energy prices. Artificial Intelligence (AI) has the potential to significantly improve the use of renewable energy in dairy farming, however, there is limited research conducted in this particular domain. This research considers Ireland as a case study as it works towards attaining its 2030 energy strategy centered on the utilization of renewable sources. This study proposes a Q-learning-based algorithm for scheduling battery charging and discharging in a dairy farm setting. This research also explores the effect of the proposed algorithm by adding wind generation data and considering additional case studies. The proposed algorithm reduces the cost of imported electricity from the grid by 13.41%, peak demand by 2%, and 24.49% when utilizing wind generation. These results underline how reinforcement learning is highly effective in managing batteries in the dairy farming sector.