Multi Objective Power Optimization in DC mi Crogrid Using Hybrid GA-PSO Techniques

Renewable energy resources are viable and trustworthy solutions for ensuring timely and universal access to power. The solar photovoltaic (PV) system stands out among them, due to sun radiation, cell performance, and ambient temperatures, its output power varies. Also, the modules used typically convert less efficiently. In light of this, the deployment of maximum power point monitoring becomes an absolute need in order to maximize the amount of energy that can be harvested from the sun and to improve the overall efficiency of solar systems. This work gives a comparative analysis of maximum power point tracking utilizing Particle Swarm Optimization (PSO) and hybrid Genetic and Particle Swarm Optimization algorithms (GA-PSO) approaches. In addition, a microgrid that incorporates a solar PV cell and a battery system has been constructed in MATLAB. During the course of this investigation, a comparison of the output power with various changing input factors, such as irradiation and temperature, was carried out.