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

Autor:	Shah, Mian Ibad Ali, Barrett, Enda, Mason, Karl
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Multiagent Systems
Druh dokumentu:	Working Paper
Popis:	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. Comment: Proc. of the Main Track of 22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, 26th-28th June, 2024, https://www.paams.net/. Includes 6 figures, 1 table and 32 references
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.12716 Zobrazit plný text záznamu View this record from Arxiv