No abstract

At sea, the electrical power system of a ship can be considered as an islanded microgrid. When connected to shore power at berth, the same power system acts as a grid connected microgrid or an extension of the grid. Therefore, ship microgrids show some resemblance to terrestrial microgrids. Nevertheless, due to the presence of large dynamic loads, such as electric propulsion loads, keeping the voltage and frequency within a permissible range and ensuring the continuity of supply are more challenging in ship microgrids. Moreover, with the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in ship microgrids. In this context, the integration of multiple energy sources and storage systems in ship microgrids requires an efficient power management system (PMS). These challenging environments and trends demand advanced control and power management solutions that are customized for ship microgrids. This paper presents a review on recent developments of control technologies and power management strategies proposed for AC ship microgrids.

In the revolution of green energy development, microgrids with renewable energy sources such as solar, wind and fuel cells are becoming a popular and effective way of controlling and managing these sources. On the other hand, owing to the intermittency and wide range of dynamic responses of renewable energy sources, battery energy-storage systems have become an integral feature of microgrids. Intelligent energy management and battery sizing are essential requirements in the microgrids to ensure the optimal use of the renewable sources and reduce conventional fuel utilization in such complex systems. This paper presents a novel approach to meet these requirements by using the grey wolf optimization (GWO) technique. The proposed algorithm is implemented for different scenarios, and the numerical simulation results are compared with other optimization methods including the genetic algorithm (GA), particle swarm optimization (PSO), the Bat algorithm (BA), and the improved bat algorithm (IBA). The proposed method (GWO) shows outstanding results and superior performance compared with other algorithms in terms of solution quality and computational efficiency. The numerical results show that the GWO with a smart utilization of battery energy storage (BES) helped to minimize the operational costs of microgrid by 33.185% in comparison with GA, PSO, BA and IBA.