# Peer-to-Peer Energy Trading in Dairy Farms using Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2511.23148 · HTML: https://mlsanity.com/paper/2511.23148/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 28 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [Truly Proximal Policy Optimization](https://mlsanity.com/paper/1903.07940/) (2019)
- [Research on Autonomous Robots Navigation based on Reinforcement Learning](https://mlsanity.com/paper/2407.02539/) (2024)
- [Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility](https://mlsanity.com/paper/2203.03417/) (2022)
- [Peer-to-Peer Trading in Electricity Networks: An Overview](https://mlsanity.com/paper/2001.06882/) (2020)
- [Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach](https://mlsanity.com/paper/2103.04154/) (2021)
- [A Study of Energy Trading in a Low-Voltage Network: Centralised and Distributed Approaches](https://mlsanity.com/paper/1809.06974/) (2018)

## Abstract (arXiv)

The integration of renewable energy resources in rural areas, such as dairy farming communities, enables decentralized energy management through Peer-to-Peer (P2P) energy trading. This research highlights the role of P2P trading in efficient energy distribution and its synergy with advanced optimization techniques. While traditional rule-based methods perform well under stable conditions, they struggle in dynamic environments. To address this, Multi-Agent Reinforcement Learning (MARL), specifically Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), is combined with community/distributed P2P trading mechanisms. By incorporating auction-based market clearing, a price advisor agent, and load and battery management, the approach achieves significant improvements. Results show that, compared to baseline models, DQN reduces electricity costs by 14.2% in Ireland and 5.16% in Finland, while increasing electricity revenue by 7.24% and 12.73%, respectively. PPO achieves the lowest peak hour demand, reducing it by 55.5% in Ireland, while DQN reduces peak hour demand by 50.0% in Ireland and 27.02% in Finland. These improvements are attributed to both MARL algorithms and P2P energy trading, which together results in electricity cost and peak hour demand reduction, and increase electricity selling revenue. This study highlights the complementary strengths of DQN, PPO, and P2P trading in achieving efficient, adaptable, and sustainable energy management in rural communities.