Analysis of energy management in a hybrid renewable power system using MOA technique

A hybrid energy system is made up of intermittent, nonlinear, and fluctuating renewable energy sources like wind and solar. The cost of implementing and maintaining hybrid energy system can be a significant drawback, particularly due to the high upfront investment required for renewable energy infrastructure and energy storage technologies. The demand for electricity, the quantity of energy generated, the climate, and power management are some of the variables that affect how well HES performs. A hybrid renewable power system's energy management is proposed in this manuscript. The proposed method is Mother Optimization Algorithm (MOA). The system is made up of several components that use renewable energy sources, including a solar energy conservation system (SECS), battery storage system (BSS) and a wind energy conservation system (WECS). The proposed method's primary aim is to improves the system's efficiency and to lessen the system's operating expenses. Additionally, an energy management (EM) based on the battery state of charge is established in order to maintain an energy balance in the hybrid power system. The proposed MOA approach controls the DC/AC converter. The specific contributions include enhancing system efficiency and reducing operating expenses in hybrid renewable power systems using the MOA technique and also the practical implications involve promoting economic viability and environmental sustainability while advancing the transition to cleaner energy systems. By then, the operational MATLAB platform has adopted the proposed model, and the current processes are used to calculate its execution. Compared to all other ways, the proposed strategy yields better outcomes like Genetic Algorithm (GA), Mixed Integer Linear Problem and Particle Swarm Optimization. The proposed method cost is 7100$ and the existing methods cost are 8100$, 1100$, 9100$. From the result, the proposed method displays the cost is lower compared to existing methods.