Abstract
The dairy industry in Ireland has significant potential for integrating renewable energy and reducing carbon emissions. However, research on distributed generation control has mainly focused on residential and commercial applications. To effectively integrate renewable energy in the dairy sector, this paper presents a multi-objective optimization control system based on differential evolution and multi-agent Deep Reinforcement Learning.
Control System Structure
The proposed control is organized in two layers:
- Upper Layer: Utilizes dynamic pricing strategies.
- Lower Layer: Based on multi-agent reinforcement learning for battery management.
Simulation Experiments
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 by up to 18% compared to rule-based models.
- It increases the use of distributed generation without significantly raising costs.
- It complies with the Irish grid code regarding voltage variation.
Blogger's Review: This paper demonstrates the potential of multi-agent deep reinforcement learning to enhance battery management in dairy farms, highlighting the significant role of renewable energy in agricultural applications. Future work could incorporate more environmental factors to further optimize control strategies.