Energy Systems using PS and PSO for a Micro-grid Applications

Abstract

The generation of electricity from renewable sources might assist nations in achieving their objectives for sustainable development by providing them with access to energy that is not only clean but also secure, dependable, and affordable. Over last thirty years, there has been rapid acceleration of industrialization as consequence of globalisation, and increased use of energy at home by individuals living in urban areas. Because of this, there has been surge in demand for energy, on scale that has never been seen before. Because of this, there is significant gap between amount of power that is available and amount that is required. Because of this, producing electricity from conventional sources of energy is not good idea any more from either an environmental or an economic point of view. This is due to many factors, including depletion of conventional energy sources, an increase in cost of fuel, and emission of pollutants into atmosphere by combustion of fossil fuels. For this reason, different types of methodologies are established in conservative works for enhancing size and cost of hybrid energy systems. main contribution of this work is to design an effective and consistent hybrid energy system based on combination of solar energy, wind energy, biomass, batterie unit, and generators with optimal sizing of modules, and reduced system cost. Hence, two different types of metaheuristics techniques such as Pattern Search (PS), and Particle Swarm Optimization (PSO) are authenticated and compared to select most suitable one for reliable hybrid energy systems. Here, modeling of hybrid energy sources are presented for generating electricity. It also discussed about working principles, flow of modeling, advantages and disadvantages of PS and PSO techniques. During simulation, performance of these algorithms are assessed and compared by by various measures. moreover, obtained results indicate that PSO algorithm provides better results compared by PS and HOMER.