eprintid: 16729
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/67/29
datestamp: 2023-12-19 03:23:15
lastmod: 2023-12-19 03:23:15
status_changed: 2023-12-19 03:06:47
type: article
metadata_visibility: show
creators_name: Geleta, D.K.
creators_name: Manshahia, M.S.
creators_name: Vasant, P.
creators_name: Banik, A.
title: Grey wolf optimizer for optimal sizing of hybrid wind and solar renewable energy system
ispublished: pub
keywords: Hybrid systems; Iterative methods; Particle swarm optimization (PSO), Electric generation; Fast convergence rate; New energy sources; Optimal solutions; Population and energy; Renewable energies; Renewable energy systems; Renewable sources, Geothermal energy
note: cited By 17
abstract: By taking facts such as oil depletion, increasing number of population and energy demand into account, alternative electric generation scheme called renewable energy has entered into a new phase. These new energy sources are environmentally clean, exhaustible and friendly with affordable cost, and high reliability. Nowadays, energy generators such as photovoltaic (PV), wind turbine (WT), and geothermal energies are among the commonly used renewable sources. In this article, grey wolf optimization (GWO) methodology is proposed for minimizing the total annual cost of hybrid of wind and solar renewable energy system. Here, determining the optimal number of solar panels, WTs, and batteries which can satisfy the desired load is the main objective of this research. The obtained result shows that the proposed methodology finds optimal solution of sizing of the hybrid system with relatively lower total annual cost and fast convergence rate. To check whether the obtained result was feasible, GWO results are compared with the results of PSO, iteration method and by the work of other scholars in literature. Here the superior capabilities of GWO algorithm have been seen. It is hoped that this research would be beneficial and can be benchmark for researchers of the field. © 2020 Wiley Periodicals LLC.
date: 2022
publisher: John Wiley and Sons Inc
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086096200&doi=10.1111%2fcoin.12349&partnerID=40&md5=55ff28c4aa52db890226834f1c3d5fb9
id_number: 10.1111/coin.12349
full_text_status: none
publication: Computational Intelligence
volume: 38
number: 3
pagerange: 1133-1162
refereed: TRUE
issn: 08247935
citation:   Geleta, D.K. and Manshahia, M.S. and Vasant, P. and Banik, A.  (2022) Grey wolf optimizer for optimal sizing of hybrid wind and solar renewable energy system.  Computational Intelligence, 38 (3).  pp. 1133-1162.  ISSN 08247935