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• A Fuzzy Logic Sliding Mode Controller (FL-SMC) is used to achieve robust stability and performance in the MicroGrid which includes a photovoltaic system, wind turbine generator, diessel engine generator, fuel cell, micro-turbine, battery energy storage system, and consumer loads. • A new hybrid Grey Wolf Optimizer and Cuckoo Search algorithm (hGWO-CS) is used to tune the gains of the proposed controller. • Renewable energy sources Disturbances and random load disturbances are evaluated on the studied microgrid system to confirm the robust performance of the proposed controller. • The robustness of the FL-SMC controller under load variation, RES changes and parametric changes has been evaluated. Load Frequency Control (LFC) in power systems remains a critical and challenging task for power system engineers, particularly in systems incorporating multiple power generation sources, including renewable energy. These sources add significant complexity due to their inherent variability and nonlinearity. To address this, a novel fuzzy logic sliding mode controller (FL-SMC) has been proposed for the LFC problem in autonomous microgrid systems. This controller uniquely combines the strengths of intelligent controllers, such as fuzzy logic, with the robustness and precision of nonlinear sliding mode controllers, offering a promising solution to the LFC challenge. The design of the FL-SMC controller is further enhanced by employing a hybrid optimization algorithm that combines the Gray Wolf Optimizer (GWO) and Cuckoo Search (CS). This hybrid algorithm (hGWO-CS) is leveraged to optimize the controller's parameters by minimizing the integral time absolute error (ITAE), ensuring optimal system performance. To validate the effectiveness of the proposed controller, it has been rigorously compared with existing control methods through extensive MATLAB-Simulink simulations. The simulations encompass various scenarios, including arbitrary load changes, fluctuations in wind and solar power generation, and parametric variations in the system. These scenarios are designed to test the controller's robustness and adaptability under real-world conditions. The results unequivocally demonstrate that the FL-SMC controller outperforms conventional methods, achieving faster response times and superior robust performance metrics, such as minimal deviations in frequency. Moreover, the robustness study highlights the controller's ability to maintain system stability despite significant uncertainties and variations in operating conditions.
Bouaddi et al. (Sat,) studied this question.