Optimized control strategy for frequency and voltage stabilization in renewable maritime microgrids

• A cascaded PIDD2–PI controller is developed for coordinated voltage and frequency control in maritime microgrids. • Osprey Optimization Algorithm is employed for optimal tuning under multi-objective performance indices. • Renewable maritime microgrid integrates wind, solar, sea-wave, biodiesel, and energy storage systems. • Significant reductions in settling time, overshoot, and undershoot are achieved under real data scenarios. • Robust performance is validated under ±40% parametric uncertainties and renewable intermittency. . Global climate change poses one of the greatest threats to the planet today, and the reduction of greenhouse gas (GHG) emissions concerns all industries contributing to the problem. Marine power grids are reducing GHG emissions by reducing the usage of traditional energy, and renewable energy sources (RESs) are rapidly expanding. These factors prompted the amalgamation of RESs into maritime microgrid system (MMGS) and the investigation of the resulting predominant control mechanism. Frequency and voltage stability are key components to the reliable and efficient operation of a marine microgrid system. Thus, this study explores the first attempt to incorporate frequency and voltage regulation into the MMGS. The maritime microgrid consists of a biodiesel generator, wind turbine, solar, an energy storage system (flywheel and battery energy storage), and sea wave energy. To address the issue of voltage and frequency stabilization in MMGS with renewable energy sources and energy storage, a unique cascade structure combining a proportional integral double derivative (PIDD2) controller with a proportional integral (PI) controller has been developed. The cascade controller coefficients are enhanced employing the strong and recent metaheuristic-based algorithm known as Osprey optimization algorithm (OOA). A maritime microgrid's performance is evaluated using real time data from sea waves, wind power fluctuations, and solar radiation. To authenticate the supremacy of the proposed PIDD2–PI controller over existing PIDD2, PID, and PI controllers, comprehensive results are provided. The proposed approach achieves improvements in settling time by 39.28%, 63.72%, and 31.92%, while reducing peak overshoot by 72.12%, 56.83%, and 66.45%, and minimizing undershoot by 89.23%, 78.45%, and 82.34%respectively. Demonstrating the efficacy of OOA, its performance is compared with recent optimization algorithms, including the Fox Optimizer Algorithm (FOA) and Whale Optimization Algorithm (WOA).