Modeling and simulation of a small-scale hybrid energy system using genetic algorithm for Yolan-Bayara Village, Bauchi State

Abstract

To mitigate the downsides of conventional energy generation systems, such as contaminants in the air and the release of greenhouse gases that contribute to environmental degradation, hybrid energy systems (HES) are absolutely essential for revitalizing areas that are rural or remote to obtain favorable technical and economic benefits. It is very difficult to establish a hybrid energy system's ideal size while still trying to meet the reliability requirements and consumer demands for electricity for powering their homes, schools, hospital, and businesses. Using a Genetic Algorithm (GA) optimization approach, this abstract describes a study on the modeling and simulation of a small-scale hybrid system that integrates renewable energy sources with energy storage for Yolan-Bayara village Bauchi state. Photovoltaic (PV) panels, wind turbines, and a battery storage system make up the system. The battery storage system enables energy storage and retrieval when necessary, while the PV panels and wind turbines generate sporadic electricity. A mathematical model is created that includes several system parameters, such as environmental factors, and energy demand profiles in order to arrive at the best sizing approach. However, conventional optimization approaches may have trouble locating the overall optimal solution due to the complexity of the system and the uncertainty surrounding renewable energy sources. A Genetic Algorithm is used to solve this problem. To ensure effective energy use and low operating costs, the GA successfully adjusts the control settings to changing environmental circumstances and energy demand profiles. The outcomes show how GA-based optimization methods can improve the functionality and financial viability of small-scale hybrid systems. Such technologies could be extremely important for promoting green energy options and reducing climate change.