Artificial Bee Colony Algorithm Based Very Fast Renewable Energy System Optimization Tool Design

Abstract

In this study, an optimization tool was designed to be used in the optimization of hybrid renewable energy systems, working with the artificial bee colony algorithm with a unique dispatch strategy. The designed tool has been compared with the HOMER optimization program. The tool, which can achieve approximately the same results as HOMER, is much faster than the HOMER program. In addition, for the first time, very detailed results were obtained by using a swarm-based optimization algorithm. As a reliability measure, the capacity shortage parameter which is not frequently used in the literature is used. When using the swarm-based algorithm to optimize green energy sources, the capacity shortage parameter was used for the first time. The cost function is the Cost of Energy (COE). The outcomes show promise for thorough optimization research in this field. In conclusion, the precision, complexity, and difficult search space generation processes of the HOMER program have been replaced by a novel optimization tool that can generate results much more quickly. With the help of this tool, it will be simpler to generate a large amount of data and to rapidly obtain the optimization outputs required for training surrogate models, machine learning, or deep learning based optimization systems.