Effective frequency regulation in multi-microgrid systems is critical for maintaining grid stability and reliability, especially as renewable energy sources become more prevalent. Traditional control methods often struggle to adapt to the dynamic nature of these systems, leading to inefficiencies and potential disruptions. The introduction of an adaptive fuzzy-recurrent neural network (AFRNN) combined with fractional-order distributed control presents a novel solution to this pressing challenge. By leveraging the strengths of both fuzzy logic and recurrent neural networks, this approach enhances the system's ability to respond to fluctuations in demand and generation, ensuring robust performance across diverse operating conditions.
The implementation of AFRNN tuned fractional-order control not only improves frequency regulation but also offers significant insights into the interplay between control strategies and system dynamics. This method demonstrates a marked increase in adaptability and resilience, which is essential for future-proofing microgrid operations. As energy systems evolve, the implications of this research extend beyond technical enhancements; they pave the way for more sustainable and efficient energy management practices, ultimately contributing to the broader goal of energy transition and grid modernization.
