Gravitational search algorithm‐based optimization of hybrid wind and solar renewable energy system. (12th May 2020)

• Record Type: Journal Article
• Authors: Geleta, Diriba Kajela; Manshahia, Mukhdeep Singh
• Is Part Of: Computational intelligence. Volume 38:Number 3(2022)
• Page Start: 1106
• Page End: 1132
• Publication Date: 2020-05-12
• Subjects: gravitational search algorithm -- hybrid renewable energy -- nature inspired algorithm -- optimization; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12336
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Due to the issue of environmental protection coupled with high energy demand, there was an initiation for exploration of different renewable energy sources. This article aims to optimize the total annual cost of hybrids of wind and solar renewable energy system to satisfy the predesigned load. Minimization of the total annual cost of the system by determining appropriate numbers of the components, so that the desired load can be economically and reliably satisfied under the given constraints. Gravitational Search Algorithm (GSA) was employed for the optimization process. GSA is a recently proposed metaheuristic algorithm which is based on Newton's universal gravitational law of gravity and mass interactions. It uses stochastic rules to escape local optima and find the global optimal solutions. MATLAB codes were designed for the developed fitness function and employed algorithm. The proposed methodology was run for the fitness function through the code and the results were discussed. The result was compared with the results of Particle Swarm Optimization (PSO) and also shown that: GSA has some advantage over PSO algorithm. Even though, the algorithm has several parameters to be adjusted, it is strong in both local and global optimal searches.