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

arXiv: https://arxiv.org/abs/2405.12716 · HTML: https://mlsanity.com/paper/2405.12716/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 21 May 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 5 of 5)

- [On Cognitive Preferences and the Plausibility of Rule-based Models](https://mlsanity.com/paper/1803.01316/) (2018)
- [Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility](https://mlsanity.com/paper/2203.03417/) (2022)
- [Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach](https://mlsanity.com/paper/2103.04154/) (2021)
- [Modelling Electricity Consumption in Irish Dairy Farms Using Agent-Based Modelling](https://mlsanity.com/paper/2308.09488/) (2023)
- [A Multi-Agent Systems Approach for Peer-to-Peer Energy Trading in Dairy Farming](https://mlsanity.com/paper/2310.05932/) (2023)

## Built on by (most cited first, 3 of 3)

- [Scalable Fairness Shaping with LLM-Guided Multi-Agent Reinforcement Learning for Peer-to-Peer Electricity Markets](https://mlsanity.com/paper/2508.18610/) (2025)
- [FairMarket-RL: LLM-Guided Fairness Shaping for Multi-Agent Reinforcement Learning in Peer-to-Peer Markets](https://mlsanity.com/paper/2506.22708/) (2025)
- [Uncertainty-Aware Knowledge Transformers for Peer-to-Peer Energy Trading with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2507.16796/) (2025)

## Abstract (arXiv)

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.