In the evolving landscape of energy systems, the integration of multi-microgrids presents significant challenges, particularly in maintaining frequency stability amidst variable renewable energy sources. The central issue lies in the inadequacy of traditional control methods to adapt to the dynamic nature of these systems, leading to potential instability and inefficiencies. This research introduces an innovative approach that combines adaptive fuzzy-recurrent neural networks with fractional-order distributed control, aiming to enhance the robustness of frequency regulation across interconnected microgrids. By addressing the limitations of conventional techniques, this study highlights the necessity for advanced control strategies that can effectively manage the complexities of modern energy networks.
The proposed solution demonstrates a significant advancement in control methodologies, leveraging the adaptability of fuzzy-recurrent neural networks to fine-tune fractional-order control mechanisms. Key insights reveal that this hybrid approach not only improves frequency regulation but also enhances system resilience against disturbances. The implications are profound: as energy systems increasingly rely on decentralized generation, adopting such sophisticated control frameworks will be crucial for ensuring stability and reliability. This research underscores the importance of integrating advanced computational techniques into energy management, paving the way for more sustainable and efficient microgrid operations.
