Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Deep Reinforcement Learning
Multi-Objective Optimization
Battery Management
Renewable Energy Integration
Dairy Farms
Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms
🤖 AI Summary
This study addresses the lack of multi-objective battery management optimization methods for Irish dairy farms integrating distributed renewable energy by proposing a bi-level control framework. The upper level performs economic dispatch based on dynamic electricity pricing, while the lower level synergistically optimizes battery operation through a combination of multi-agent deep reinforcement learning and differential evolution algorithms. This approach represents the first integration of these techniques in agricultural microgrids, simultaneously enhancing energy arbitrage profits—achieving an 18% improvement over rule-based baselines—and increasing renewable energy utilization, all while strictly adhering to Irish grid voltage fluctuation standards. The framework thus effectively balances economic performance, sustainability, and grid compliance.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-Agent Deep Reinforcement Learning
Multi-Objective Optimization
Battery Management
Differential Evolution
Renewable Energy Integration
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