In the context of increasing reliance on renewable energy sources, the stability of multi-microgrid systems has emerged as a critical challenge, particularly concerning frequency regulation. Traditional control methods often struggle to maintain balance amidst the dynamic nature of these systems, leading to potential disruptions. The introduction of an adaptive fuzzy-recurrent neural network (AFRNN) tuned with fractional-order distributed control presents a promising solution. This innovative approach not only enhances the robustness of frequency regulation but also adapts to varying operational conditions, addressing the central problem of maintaining system stability in the face of fluctuating energy inputs and demand.
The key takeaway from this research is the effectiveness of integrating AFRNN with fractional-order control strategies, which collectively improve the responsiveness and reliability of frequency regulation in multi-microgrid environments. This synthesis of advanced control techniques offers significant implications for the design of future energy systems, enabling more resilient and efficient management of distributed energy resources. By leveraging adaptive learning mechanisms, the proposed framework can effectively mitigate the risks associated with frequency deviations, ensuring a more stable and sustainable energy landscape.
