IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i8p2139-d1639141.html
   My bibliography  Save this article

Wind Resource Assessment for Potential Wind Turbine Operations in the City of Yanbu, Saudi Arabia

Author

Listed:
  • Makbul A. M. Ramli

    (Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Houssem R. E. H. Bouchekara

    (Department of Electrical Engineering, University of Hafr Al Batin, Hafr Al Batin 31991, Saudi Arabia)

Abstract

Energy generated from wind (in the form of wind farms (WFs)) is expected to help alleviate rising energy demand in Saudi Arabia, driven by industrial development and population growth. However, before implementing wind farms, conducting a comprehensive wind resource assessment (WRA) study is of paramount importance. This paper presents the analysis of the wind resource potential of a site in Yanbu city, which is located on the western coastal area of Saudi Arabia, using a comprehensive study. The hourly data on wind speed and direction over a one-year period was used in the presented analysis. The plant capacity factor (CF) and annual energy production (AEP) are evaluated for more than 100 commercial wind turbines (WTs). The highest AEP was achieved by the ‘Enercon E126/7.5 MW’ turbine, generating 14.49 GWh, with a corresponding CF of 21.82%. In contrast, the lowest AEP was observed for the ‘Northern Power d’ turbine, producing only 0.13 GWh, with a CF of 14.89%. The highest CF was recorded for the ‘Leitwind LTW104/2.0 MW’ turbine at 40.67%, corresponding to an AEP of 7.12 GWh. The results obtained are very valuable for designers in selecting the appropriate WT to obtain the predicted AEP and CF with the appropriate turbine class. Furthermore, this study applied the K-means clustering algorithm to classify WTs into three distinct categories. Building on this classification, synthetic datasets representing tailored WT configurations were generated—a novel methodology that enables the simulation of site-specific designs not yet available in existing market offerings. These datasets equip wind farm developers with the ability to define WT specifications for manufacturers, guided by two key criteria: the site’s wind resource profile and the target performance metrics of the WT.

Suggested Citation

  • Makbul A. M. Ramli & Houssem R. E. H. Bouchekara, 2025. "Wind Resource Assessment for Potential Wind Turbine Operations in the City of Yanbu, Saudi Arabia," Energies, MDPI, vol. 18(8), pages 1-29, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2139-:d:1639141
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/8/2139/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/8/2139/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wenchuan Meng & Zaimin Yang & Zhi Rao & Shuang Li & Xin Lin & Jingkang Peng & Yuwei Cao & Yingquan Chen, 2025. "Refined Assessment Method of Offshore Wind Resources Based on Interpolation Method," Energies, MDPI, vol. 18(1), pages 1-17, January.
    2. Sharma, Kaushik & Ahmed, M. Rafiuddin, 2016. "Wind energy resource assessment for the Fiji Islands: Kadavu Island and Suva Peninsula," Renewable Energy, Elsevier, vol. 89(C), pages 168-180.
    3. Ayik, A. & Ijumba, N. & Kabiri, C. & Goffin, P., 2021. "Preliminary wind resource assessment in South Sudan using reanalysis data and statistical methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    4. Neupane, Deependra & Kafle, Sagar & Karki, Kaji Ram & Kim, Dae Hyun & Pradhan, Prajal, 2022. "Solar and wind energy potential assessment at provincial level in Nepal: Geospatial and economic analysis," Renewable Energy, Elsevier, vol. 181(C), pages 278-291.
    5. Dayal, Kunal K. & Cater, John E. & Kingan, Michael J. & Bellon, Gilles D. & Sharma, Rajnish N., 2021. "Wind resource assessment and energy potential of selected locations in Fiji," Renewable Energy, Elsevier, vol. 172(C), pages 219-237.
    6. Elshafei, Basem & Peña, Alfredo & Xu, Dong & Ren, Jie & Badger, Jake & Pimenta, Felipe M. & Giddings, Donald & Mao, Xuerui, 2021. "A hybrid solution for offshore wind resource assessment from limited onshore measurements," Applied Energy, Elsevier, vol. 298(C).
    7. Coville, Aidan & Siddiqui, Afzal & Vogstad, Klaus-Ole, 2011. "The effect of missing data on wind resource estimation," Energy, Elsevier, vol. 36(7), pages 4505-4517.
    8. Olaofe, Z.O., 2018. "Review of energy systems deployment and development of offshore wind energy resource map at the coastal regions of Africa," Energy, Elsevier, vol. 161(C), pages 1096-1114.
    9. Jain, Anjali & Das, Partha & Yamujala, Sumanth & Bhakar, Rohit & Mathur, Jyotirmay, 2020. "Resource potential and variability assessment of solar and wind energy in India," Energy, Elsevier, vol. 211(C).
    10. Liu, Jun & Xiong, Guojiang & Suganthan, Ponnuthurai Nagaratnam, 2025. "Differential evolution-based mixture distribution models for wind energy potential assessment: A comparative study for coastal regions of China," Energy, Elsevier, vol. 321(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. He, J.Y. & Chan, P.W. & Li, Q.S. & Lee, C.W., 2022. "Characterizing coastal wind energy resources based on sodar and microwave radiometer observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    2. Dayal, Kunal K. & Bellon, Gilles & Cater, John E. & Kingan, Michael J. & Sharma, Rajnish N., 2021. "High-resolution mesoscale wind-resource assessment of Fiji using the Weather Research and Forecasting (WRF) model," Energy, Elsevier, vol. 232(C).
    3. Majidi Nezhad, Meysam & Heydari, Azim & Neshat, Mehdi & Keynia, Farshid & Piras, Giuseppe & Garcia, Davide Astiaso, 2022. "A Mediterranean Sea Offshore Wind classification using MERRA-2 and machine learning models," Renewable Energy, Elsevier, vol. 190(C), pages 156-166.
    4. Hugo Gaspar Hernandez-Palma & Dairo J. Novoa & Jorge Enrique Taboada à lvarez, 2024. "New Trends in Green Projects Aimed at Clean Energy: An Analysis of the Scientific Literature," International Journal of Energy Economics and Policy, Econjournals, vol. 14(6), pages 278-286, November.
    5. Dayal, Kunal K. & Cater, John E. & Kingan, Michael J. & Bellon, Gilles D. & Sharma, Rajnish N., 2021. "Wind resource assessment and energy potential of selected locations in Fiji," Renewable Energy, Elsevier, vol. 172(C), pages 219-237.
    6. Tiam Kapen, Pascalin & Jeutho Gouajio, Marinette & Yemélé, David, 2020. "Analysis and efficient comparison of ten numerical methods in estimating Weibull parameters for wind energy potential: Application to the city of Bafoussam, Cameroon," Renewable Energy, Elsevier, vol. 159(C), pages 1188-1198.
    7. Hwangbo, Soonho & Heo, SungKu & Yoo, ChangKyoo, 2022. "Development of deterministic-stochastic model to integrate variable renewable energy-driven electricity and large-scale utility networks: Towards decarbonization petrochemical industry," Energy, Elsevier, vol. 238(PC).
    8. King, Marcus & Jain, Anjali & Bhakar, Rohit & Mathur, Jyotirmay & Wang, Jihong, 2021. "Overview of current compressed air energy storage projects and analysis of the potential underground storage capacity in India and the UK," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    9. Olaofe, Z.O., 2019. "Quantification of the near-surface wind conditions of the African coast: A comparative approach (satellite, NCEP CFSR and WRF-based)," Energy, Elsevier, vol. 189(C).
    10. Elshafei, Basem & Peña, Alfredo & Popov, Atanas & Giddings, Donald & Ren, Jie & Xu, Dong & Mao, Xuerui, 2023. "Offshore wind resource assessment based on scarce spatio-temporal measurements using matrix factorization," Renewable Energy, Elsevier, vol. 202(C), pages 1215-1225.
    11. Wasiu Olalekan Idris & Mohd Zamri Ibrahim & Aliashim Albani, 2020. "The Status of the Development of Wind Energy in Nigeria," Energies, MDPI, vol. 13(23), pages 1-16, November.
    12. Romanic, Djordje & Parvu, Dan & Refan, Maryam & Hangan, Horia, 2018. "Wind and tornado climatologies and wind resource modelling for a modern development situated in “Tornado Alley”," Renewable Energy, Elsevier, vol. 115(C), pages 97-112.
    13. Yang, Hua & Xu, Yong & Zhou, Kan & Li, Jiuyi, 2024. "Mapping development potential and priority zones for utility-scale photovoltaic on the Qinghai-Tibet Plateau," Renewable Energy, Elsevier, vol. 237(PA).
    14. Liu, Fa & Sun, Fubao & Liu, Wenbin & Wang, Tingting & Wang, Hong & Wang, Xunming & Lim, Wee Ho, 2019. "On wind speed pattern and energy potential in China," Applied Energy, Elsevier, vol. 236(C), pages 867-876.
    15. Anan Zhang & Hong Zhang & Meysam Qadrdan & Wei Yang & Xiaolong Jin & Jianzhong Wu, 2019. "Optimal Planning of Integrated Energy Systems for Offshore Oil Extraction and Processing Platforms," Energies, MDPI, vol. 12(4), pages 1-28, February.
    16. Ismail Kamdar & Shahid Ali & Juntakan Taweekun & Hafiz Muhammad Ali, 2021. "Wind Farm Site Selection Using WAsP Tool for Application in the Tropical Region," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    17. Yang, Zihao & Dong, Sheng, 2024. "A novel framework for wind energy assessment at multi-time scale based on non-stationary wind speed models: A case study in China," Renewable Energy, Elsevier, vol. 226(C).
    18. Hosseini Dehshiri, Seyyed Shahabaddin, 2022. "A new application of multi criteria decision making in energy technology in traditional buildings: A case study of Isfahan," Energy, Elsevier, vol. 240(C).
    19. Liu, Ming-Lei & Ji, Qiang & Fan, Ying, 2013. "How does oil market uncertainty interact with other markets? An empirical analysis of implied volatility index," Energy, Elsevier, vol. 55(C), pages 860-868.
    20. Zhang, Jiaan & Liu, Dong & Li, Zhijun & Han, Xu & Liu, Hui & Dong, Cun & Wang, Junyan & Liu, Chenyu & Xia, Yunpeng, 2021. "Power prediction of a wind farm cluster based on spatiotemporal correlations," Applied Energy, Elsevier, vol. 302(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2139-:d:1639141. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.