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

Mian Ibad Ali Shah; Enda Barrett; Karl Mason

doi:10.1007/978-3-031-70415-4_3

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

Mian Ibad Ali Shah
, Enda Barrett
, Karl Mason

Computer Science

University of Galway

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins
Subtitle of host publication	The PAAMS Collection - 22nd International Conference, PAAMS 2024, Proceedings
Editors	Philippe Mathieu, Fernando De la Prieta
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	27-38
Number of pages	12
ISBN (Print)	9783031704147
DOIs	https://doi.org/10.1007/978-3-031-70415-4_3
Publication status	Published - 2025
Event	22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024 - Salamanca, Spain Duration: 26 Jun 2024 → 28 Jun 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15157 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024
Country/Territory	Spain
City	Salamanca
Period	26/06/24 → 28/06/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Dairy Farming
Multi-agent Systems
Peer-to-peer Energy Trading
Reinforcement Learning
Sustainability

Access to Document

10.1007/978-3-031-70415-4_3

Cite this

Shah, M. I. A., Barrett, E., & Mason, K. (2025). Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms. In P. Mathieu, & F. De la Prieta (Eds.), Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection - 22nd International Conference, PAAMS 2024, Proceedings (pp. 27-38). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15157 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-70415-4_3

Shah, Mian Ibad Ali ; Barrett, Enda ; Mason, Karl. / Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms. Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection - 22nd International Conference, PAAMS 2024, Proceedings. editor / Philippe Mathieu ; Fernando De la Prieta. Springer Science and Business Media Deutschland GmbH, 2025. pp. 27-38 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms",

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{\textquoteright} 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.",

keywords = "Dairy Farming, Multi-agent Systems, Peer-to-peer Energy Trading, Reinforcement Learning, Sustainability",

author = "Shah, \{Mian Ibad Ali\} and Enda Barrett and Karl Mason",

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Shah, MIA, Barrett, E & Mason, K 2025, Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms. in P Mathieu & F De la Prieta (eds), Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection - 22nd International Conference, PAAMS 2024, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15157 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 27-38, 22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024, Salamanca, Spain, 26/06/24. https://doi.org/10.1007/978-3-031-70415-4_3

Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms. / Shah, Mian Ibad Ali; Barrett, Enda ; Mason, Karl.
Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection - 22nd International Conference, PAAMS 2024, Proceedings. ed. / Philippe Mathieu; Fernando De la Prieta. Springer Science and Business Media Deutschland GmbH, 2025. p. 27-38 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15157 LNAI).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

TY - GEN

T1 - Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms

AU - Shah, Mian Ibad Ali

AU - Barrett, Enda

AU - Mason, Karl

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - Dairy Farming

KW - Multi-agent Systems

KW - Peer-to-peer Energy Trading

KW - Reinforcement Learning

KW - Sustainability

UR - https://www.scopus.com/pages/publications/85210317094

U2 - 10.1007/978-3-031-70415-4_3

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SN - 9783031704147

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 27

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BT - Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins

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A2 - De la Prieta, Fernando

PB - Springer Science and Business Media Deutschland GmbH

T2 - 22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

Y2 - 26 June 2024 through 28 June 2024

ER -

Shah MIA, Barrett E , Mason K. Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms. In Mathieu P, De la Prieta F, editors, Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection - 22nd International Conference, PAAMS 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2025. p. 27-38. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-70415-4_3