In the evolving landscape of energy management, the challenge of maintaining frequency stability in multi-microgrid systems is paramount. Traditional control methods often fall short in dynamic environments characterized by variable loads and renewable energy sources. This research introduces an innovative approach that leverages an adaptive fuzzy-recurrent neural network combined with fractional-order distributed control to enhance frequency regulation. By addressing the inherent complexities of multi-microgrid interactions, this method promises to deliver robust performance even under fluctuating operational conditions, thereby ensuring reliability and efficiency in energy distribution.
The findings underscore the potential of integrating advanced neural network architectures with fractional-order control strategies to achieve superior frequency regulation. Key insights reveal that this hybrid approach not only improves response times but also enhances system resilience against disturbances. The implications for energy systems are significant: as microgrids become increasingly prevalent, adopting such sophisticated control mechanisms could lead to more stable and sustainable energy networks, ultimately supporting the transition towards a more decentralized energy future.
