A hybrid energy storage system using GA and PSO for an islanded microgrid applications

Abstract

AbstractThe renewable‐based hybrid energy storage systems have gained significant attention in recent times, due to their increased power extraction efficiency, cost‐effectiveness, and eco‐friendly nature. But, the power management, optimal sizing of components, economic cost of energy, and system reliability are considered as the major problems of hybrid energy storage systems. For this purpose, the different types of optimization methodologies are developed in the conventional works for optimizing the size and cost of hybrid energy systems. The main contribution of this work is to design an efficient and reliable hybrid energy storage system based on the combination of solar, wind, biomass, batteries, and generators with optimal sizing of components, and reduced system cost. Hence, two different types of meta‐heuristics optimization techniques such as genetic algorithm (GA), and particle swarm optimization (PSO) are validated and compared with select the most suitable one for the reliable hybrid energy systems. Here, the mathematical modeling of a hybrid energy sources are presented for generating the electricity. It also discussed about the operating principles, working nature, flow of modeling, advantages, and disadvantages of GA and PSO techniques. During simulation, the performance of these algorithms is evaluated and compared by using various measures. Due to the increased convergence rate, reduced overfitting, and local optimum the performance of PSO is highly improved, when compared with the GA. Also, the PSO is discovered to perform better than the GA because it concurrently executes global and local searches, whereas the GA focuses primarily on the global search. The total excess energy for the entire year is estimated to be 5139 kWh/yr based on the findings. The PSO algorithm accurately predicts solar PV, wind turbines, batteries, and biomass gasifier with an ASC of 63 006$/yr and an LCOE of 0.173$/kWh. Hence, the results of PSO are superior than the standard GA mechanism. Moreover, the obtained results indicate that the PSO algorithm provides the better results compared with GA and HOMER.