Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Marcos Eduardo Cruz Victorio, Karl Mason
• Venue: arXiv: cs.AI
• Published: 2026-07-07
• Collected: 2026-07-08
• URL: http://arxiv.org/abs/2607.06489v1
• PDF: https://arxiv.org/pdf/2607.06489v1
• DOI: UNKNOWN

Plain-Language Brief

This research introduces a multi-objective optimization control system for battery management in dairy farms that utilizes differential evolution and multi-agent deep reinforcement learning. The system aims to improve energy arbitrage profits, increase the use of renewable energy sources without significant cost increases, and comply with Irish grid code voltage variation requirements.

What the Document Says

• The dairy industry in Ireland has potential for renewable energy integration and carbon emissions reduction
• Researchers focus on residential and commercial applications for distributed generation control
• Proposed control system is multi-objective optimization based on differential evolution and multi-agent deep reinforcement learning
• System has two layers: upper layer with dynamic pricing, lower layer with multi-agent reinforcement learning for battery management
• Simulation results show improvement in energy arbitrage profits (up to 18%), increased use of distributed generation without significant cost increase, and compliance with Irish grid code voltage variation requirements

Key Concepts Mentioned

• Renewable energy integration
• Carbon emissions reduction
• Distributed generation control
• Multi-objective optimization
• Deep reinforcement learning
• Battery management
• Dynamic pricing
• Simulation results

Claims Made by the Document

• Improvement in energy arbitrage profits (up to 18%)
• Increased use of distributed generation without significant cost increase
• Compliance with Irish grid code voltage variation requirements

Evidence Used

• Simulation results presented in the paper

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

The dairy industry in Ireland has a large potential for the integration of renewable energy and the reduction of carbon emissions. However, researchers of distributed generation control are mainly focused on residential and commercial applications. To contribute to the effective integration of renewable energy in the dairy sector, this paper presents a multi-objective optimisation control system based on differential evolution and multi agent Deep Reinforcement Learning. The proposed control is organised in two layers: the upper layer uses dynamic pricing, and the lower layer is based on multi-agent reinforcement learning for battery management. This paper also simulates the electrical response of the proposed control system in a rural distribution circuit. The simulation results show that the proposed control framework can improve profits from energy arbitrage up to 18% compared to using Rule-based models, increase the use of distributed generation without significantly increasing cost, and comply with the Irish grid code in terms of voltage variation.