Dr Arunachalam Sundaram A.Sundaram@salford.ac.uk
Lecturer
Assessment of off-shore wind turbines for application in Saudi Arabia
Sundaram, Arunachalam; Abubakar Mas’ud, Abdullahi; Al Garni, Hassan Z.; Adewusi, Surajudeen
Authors
Abdullahi Abubakar Mas’ud
Hassan Z. Al Garni
Surajudeen Adewusi
Abstract
This paper presents models and economic analysis of ten different wind turbines for the region of Yanbu, Saudi Arabia using the hybrid optimization models for energy resources (HOMER) software. This study serves as a guide for decision makers to choose the most suitable wind turbine for Yanbu to meet the target of 58.7GW of renewable energy as part of Saudi Vision 2030. The analysis was carried out based on the turbines initial capital cost, operating cost, net present cost (NPC) and the levelized cost of energy (LCOE). Additionally, the wind turbines were compared based on their electricity production, excess energy and the size of the storage devices required. The results show that Enercon E-126 EP4 wind turbine has the least LCOE (0.0885 $/kWh) and NPC ($23.8), while WES 30 has the highest LCOE (0.142 $/kWh) and NPC ($38.3) for a typical load profile of a village in Yanbu.
Citation
Sundaram, A., Abubakar Mas’ud, A., Al Garni, H. Z., & Adewusi, S. (2020). Assessment of off-shore wind turbines for application in Saudi Arabia. International Journal of Electrical and Computer Engineering, 10(5), 4507-4513. https://doi.org/10.11591/ijece.v10i5.pp4507-4513
Journal Article Type | Article |
---|---|
Acceptance Date | Mar 24, 2020 |
Publication Date | 2020 |
Deposit Date | Jul 23, 2024 |
Publicly Available Date | Jul 26, 2024 |
Journal | International Journal of Electrical and Computer Engineering |
Publisher | Institute of Advanced Engineering and Science |
Peer Reviewed | Peer Reviewed |
Volume | 10 |
Issue | 5 |
Pages | 4507-4513 |
DOI | https://doi.org/10.11591/ijece.v10i5.pp4507-4513 |
Keywords | Renewable Energy |
Files
Published Version
(396 Kb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-sa/4.0/
You might also like
TRANSFER LEARNING APPROACH FOR CLASSIFICATION OF WIDELY USED SPICES
(2022)
Journal Article
Downloadable Citations
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search