Hybrid Optimization for Robust Frequency Regulation in Shipboard Microgrids With Renewable Sources, Energy Storage, and Controllable Loads

ABSTRACT The integration of intermittent renewable energy sources (RESs) in shipboard microgrids exacerbates frequency instability owing to low system inertia and variable loads, necessitating advanced load frequency control (LFC) strategies amid global shifts toward sustainable energy. This study introduces a new chaotic chimp‐mountain gazelle optimizer (CCMGO), which hybridizes chaotic maps with the chimp optimization algorithm and mountain gazelle optimizer to enhance exploration and avoid premature convergence in tuning LFC controllers (PID, cascaded PD–PID, fractional‐order PID, and the recently popular TID) controllers. Key contributions include: (a) comprehensive modeling of an isolated shipboard microgrid with autonomous RESs (Archimedes wave energy converter, geothermal energy converter, tidal power generator, wind turbine generator, solar tower power generator) and controlled sources (biogas turbine generator, micro hydro turbine generator, biodiesel engine generator, solid oxide fuel cell), supported by a flywheel energy storage system and capacitive energy storage; (b) CCMGO's application for optimized LFC under multistep and random load disturbances; and (c) comparative dynamic performance analysis demonstrating CCMGO‐TID's superiority. Simulations revealed that CCMGO‐TID achieved an integral time absolute error (ITAE) of 181.3493, an integral absolute error of 9.2943, an integral of time square error of 2.0540, and an integral square error of 0.0876, reducing errors by up to 86% compared to CCMGO‐PD–PID (ITAE: 347.8649) and enhancing settling times under real‐world fluctuations. These results demonstrate the efficacy of CCMGO for multiobjective optimization and advanced frequency control in complex shipboard microgrids.