Comparison of traditional and swarm intelligence based techniques for optimization of hybrid renewable energy system

Abstract

This paper compares the performance of a traditional solver and dynamic particle swarm optimization algorithm for computing the levelized cost of energy for a standalone hybrid renewable energy system. The idea is to find the optimal sizing of the hybrid renewable energy system taking into consideration the levelized cost of photo voltaic power sources, wind turbines, diesel generators and battery banks. The problem is complex due to equality, inequality and binding constraints of a real-world practical system. The seasonal effect of load has been taken into consideration using load data for fall, spring, summer and winter seasons. The results are compared and it is found that the dynamic particle swarm optimization gives the globally optimal solution while the traditional solver produces a locally minimum solution.