A Reliable Load Frequency Control Strategy for Oceanic Mobile Electrical Storage System Isolated Microgrid Using Greylag Goose Optimization for Energy Supply to Pelagic Oceanic Islands

Providing a reliable and rapid energy supply to Pelagic Oceanic Islands (POIs) is a critical challenge due to their remote geographical locations. This study addresses this challenge by introducing the innovative concept of Oceanic Mobile Electrical Storage (OMES), which aims to ensure energy reliability while minimizing environmental impact. OMES systems are electrically powered, floating Microgrids (MGs) equipped with Battery Electrical Storage Packs (BESPs). These MGs integrate Oceanic Biodiesel Generators (OBMDGs) and Oceanic Distributed Energy Resources (ODERs), such as Flywheel Energy Storage Systems (FESS), Proton Exchange Membrane Fuel Cells (PEMFCs), and OMES-Mounted Photovoltaics (OMES-MPVs). The study focuses on enhancing the frequency stability of the OMES Isolated Microgrid (OMESIM) by proposing a novel control topology: the IPD-2DOF-TID (Proportional-Derivative with Two Degrees of Freedom-Tilt Integral Derivative) cascode hybrid controller. This controller is optimized using the Greylag Goose Optimization (GGO) algorithm, a swarm intelligence technique inspired by the natural behaviors of Greylag Geese. The main contribution of this research is the development of the IPD-2DOF-TID/GGO control scheme, which provides a robust framework for the design and performance evaluation of Load Frequency Control (LFC) controllers. The proposed control scheme is benchmarked against established meta-heuristic algorithms, including the Mountaineering Team-Based Optimization (MTBO) and Corona Virus Search Optimizer (CVSO) algorithms, using the Integral of Squared Time multiplied by Absolute Error (ISTSE) as the primary objective function. Additional evaluation metrics include the Integral of Squared Error (ISE) and the Integral of Time multiplied by Squared Error (ITSE). The results demonstrate that the IPD-2DOF-TID/GGO control strategy offers significant performance improvements over alternative schemes, with superior response speed, substantial reductions in maximum overshoot and undershoot, and enhanced robustness to uncertainties. Specifically, the proposed controller achieves an average improvement of 30-60% in overshoot and undershoot reduction across various scenarios, highlighting its effectiveness and reliability.