Optimal cascade 2DOF fractional order master-slave controller design for LFC of hybrid microgrid systems with EV charging technology

This paper addresses the increasing complexity and evolving challenges in recent power systems management, particularly under the integration of diverse renewable energy sources (RESs) and advanced control technologies. With the rising unpredictability of power generation due to RESs and the diminishing system inertia, robust load frequency control (LFC) solutions are desperately needed to maintain network stability and manage frequency variations. This work proposes improved FOCs for achieving load frequency control (LFC) and the virtual inertia control (VIC) provision of electric vehicles (EVs) batteries. The proposed method includes a twofold contribution using a new fractional-order LFC method. The proposed technique is innovative by merging the characteristics of cascaded 2-degree of freedom (2DOF) configuration, incorporating both tilt-integral-derivative-filter (FOTIDN) and tilt-derivative-filter (FOTDN) controllers. This method is designed to enhance the robustness of interconnected multi-microgrid systems against disturbances from RESs and load demand fluctuations. Moreover, this paper presents the application of the Marine Predators Algorithm (MPA) for optimizing the proposed controller's parameters across various interconnected systems. Through simulation and time-domain analysis, the efficacy of the proposed technique is assessed in a range of load scenarios and uncertainty levels, exhibiting superior performance in contrast to current LFC methodologies such as TID, FOTID, TID-FOPIDN, and 2DOF TIDN-TDN. The results indicate that the advanced 2DOF FOTIDN-FOTDN controller based on the MPA significantly improves system response and stability, marking a pivotal advancement in power system control strategies. Moreover, the employing of MPA optimizer is compared with the widely-used optimizers in the literature, including the MRFO, ABC, and PSO optimizers at the different objective functions of IAE, ISE, ITAE, and ITSE. For instance, the MPA method has a minimized ITAE after 30 iterations of 0.027 compared to 0.0276 with MRFO, 0.0282 with ABC, and 0.034 with PSO optimizer. Thence, fast and better minimization of objective functions are achieved using the proposed MPA.