In recent years, energy challenges such as grid congestion and imbalances have emerged from conventional electric grids. Furthermore, the unpredictable nature of these systems poses many challenges in meeting various users’ demands. The Battery Energy Storage System is a potential key for grid instability with improved power quality. The present study investigates the global trend towards integrating battery technology as an energy storage system with renewable energy production and utility grid systems. An extensive review of battery systems such as Lithium-Ion, Lead–Acid, Zinc–Bromide, Nickel–Cadmium, Sodium–Sulphur, and the Vanadium redox flow battery is conducted. Furthermore, a comparative analysis of their working principles, control strategies, optimizations, and technical characteristics is presented. The review findings show that Lead–Acid, Lithium-Ion, Sodium-based, and flow redox batteries have seen increased breakthroughs in the energy storage market. Furthermore, the use of the BESS as an ancillary service and control technique enhances the performance of microgrids and utility grid systems. These control techniques provide potential solutions such as peak load shaving, the smoothing of photovoltaic ramp rates, voltage fluctuation reduction, a large grid, power supply backup, microgrids, renewable energy sources time shift, spinning reserve for industrial consumers, and frequency regulation. Conclusively, a cost summary of the various battery technologies is presented.

The development of offshore wind energy has increased the demand for reliable submarine transmission systems. In South Africa, research remains constrained due to the lack of operational offshore wind farms, despite favorable geographical conditions and persistent energy challenges such as load-shedding. Submarine cable faults, primarily caused by manufacturing deficiencies, environmental factors, and human activities, contribute significantly to system downtime while accounting for only a small portion of overall installation costs. This study reviews submarine cable fault identification, classification, pre-determination, and localization techniques. Conventional methods, including time-domain reflectometry, the Murray loop, the Varley loop, and impulse-based techniques, are reviewed alongside artificial neural network models, such as convolutional and deep learning architectures. Findings imply that traditional techniques offer low error margins but lack the accuracy needed for pinpointing exact faults, as faults may extend over several kilometers. In contrast, neural network-based methods, particularly when integrated with signal processing methods, significantly improve fault classification and localization accuracy. The study concludes that hybrid approaches combining conventional diagnostic techniques with neural networks offer a robust framework for submarine cable fault analysis, providing real-world solutions to enhance reliability and efficiency in future offshore wind transmission systems.