Multi-Nanogrid Power Management and Control Using Nature-Inspired Optimization Algorithms

This paper deals with the design of an optimal power management and control scheme to improve dynamic behaviors of renewable power generation sources in a multi-nanogrid system. The investigated nanogrids include a solar PV generator and a wind turbine. Storage was also employed to support active power management. Three novel nature-inspired optimization algorithms, namely Marine Predator, African Vultures, and Gorilla Troops, have been used to enhance nanogrid power management. The main tasks were to design a robust controller applied to the storage system, coordinated with an intelligent power management tool that allows the integration of green power in small distributed systems such as nanogrids to increase. In this context, several scenarios have been performed to show the effectiveness of the proposed method. The storage system was first used to support the power supply, and then the application was extended to participate in frequency control. A comparative study between the employed optimization algorithms was carried out in view of peak minimization and settling times. Further, robustness analysis was conducted using different rates of renewable energy penetration. It can be observed from the presented results that using optimization algorithms can create a powerful and smart tool that can manage the generated power from the green units and ensure power equilibrium during load variations.