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

Mian Ibad Ali Shah, Enda Barrett, Karl Mason
5/21/2024
cs.AIcs.LGcs.MA

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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.

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Cite this paper

@article{shah2024reinforcement,
  title  = {Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms},
  author = {Mian Ibad Ali Shah and Enda Barrett and Karl Mason},
  year   = {2024},
  eprint = {2405.12716},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.12716v1}
}

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