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Techno-Economic Assessment of Wind Energy Potential at Three Locations in South Korea Using Long-Term Measured Wind Data

Author

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  • Sajid Ali

    (Smart City Construction Engineering, University of Science & Technology (UST), 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
    Environmental & Plant Engineering Research Division, Korea Institute of Civil Engineering and Building Technology (KICT), Daehwa-dong 283, Goyangdae-Ro, Ilsanseo-Gu, Goyang-Si, Gyeonggi-do 10223, Korea)

  • Sang-Moon Lee

    (Environmental & Plant Engineering Research Division, Korea Institute of Civil Engineering and Building Technology (KICT), Daehwa-dong 283, Goyangdae-Ro, Ilsanseo-Gu, Goyang-Si, Gyeonggi-do 10223, Korea)

  • Choon-Man Jang

    (Smart City Construction Engineering, University of Science & Technology (UST), 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
    Environmental & Plant Engineering Research Division, Korea Institute of Civil Engineering and Building Technology (KICT), Daehwa-dong 283, Goyangdae-Ro, Ilsanseo-Gu, Goyang-Si, Gyeonggi-do 10223, Korea)

Abstract

The present study deals with wind energy analysis and the selection of an optimum type of wind turbine in terms of the feasibility of installing wind power system at three locations in South Korea: Deokjeok-do, Baengnyeong-do and Seo-San. The wind data measurements were conducted during 2005–2015 at Deokjeok-do, 2001–2016 at Baengnyeong-do and 1997–2016 at Seo-San. In the first part of this paper wind conditions, like mean wind speed, wind rose diagrams and Weibull shape and scale parameters are presented, so that the wind potential of all the locations could be assessed. It was found that the prevailing wind directions at all locations was either southeast or southwest in which the latter one being more dominant. After analyzing the wind conditions, 50-year and 1-year extreme wind speeds (EWS) were estimated using the graphical method of Gumbel distribution. Finally, according to the wind conditions at each site and international electro-technical commission (IEC) guidelines, a set of five different wind turbines best suited for each location were shortlisted. Each wind turbine was evaluated on the basis of technical parameters like monthly energy production, annual energy production (AEP) and capacity factors (CF). Similarly, economical parameters including net present value (NPV), internal rate of return (IRR), payback period (PBP) and levelized cost of electricity (LCOE) were considered. The analysis shows that a Doosan model WinDS134/3000 wind turbine is the most suitable for Deokjeok-do and Baengnyeong-do, whereas a Hanjin model HJWT 87/2000 is the most suitable wind turbine for Seo-San. Economic sensitivity analysis is also included and discussed in detail to analyze the impact on economics of wind power by varying turbine’s hub height.

Suggested Citation

  • Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2017. "Techno-Economic Assessment of Wind Energy Potential at Three Locations in South Korea Using Long-Term Measured Wind Data," Energies, MDPI, vol. 10(9), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1442-:d:112480
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    3. Sajid Ali & Choon-Man Jang, 2019. "Selection of Best-Suited Wind Turbines for New Wind Farm Sites Using Techno-Economic and GIS Analysis in South Korea," Energies, MDPI, vol. 12(16), pages 1-22, August.
    4. Suzer, Ahmet Esat & Atasoy, Vehbi Emrah & Ekici, Selcuk, 2021. "Developing a holistic simulation approach for parametric techno-economic analysis of wind energy," Energy Policy, Elsevier, vol. 149(C).
    5. Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2018. "Forecasting the Long-Term Wind Data via Measure-Correlate-Predict (MCP) Methods," Energies, MDPI, vol. 11(6), pages 1-17, June.
    6. Sajid Ali & Choon-Man Jang, 2020. "Optimum Design of Hybrid Renewable Energy System for Sustainable Energy Supply to a Remote Island," Sustainability, MDPI, vol. 12(3), pages 1-16, February.
    7. Dongbum Kang & Kyungnam Ko & Jongchul Huh, 2018. "Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea," Energies, MDPI, vol. 11(2), pages 1-19, February.
    8. Laura Cornejo-Bueno & Lucas Cuadra & Silvia Jiménez-Fernández & Javier Acevedo-Rodríguez & Luis Prieto & Sancho Salcedo-Sanz, 2017. "Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data," Energies, MDPI, vol. 10(11), pages 1-27, November.
    9. Peláez-Rodríguez, C. & Pérez-Aracil, J. & Fister, D. & Prieto-Godino, L. & Deo, R.C. & Salcedo-Sanz, S., 2022. "A hierarchical classification/regression algorithm for improving extreme wind speed events prediction," Renewable Energy, Elsevier, vol. 201(P2), pages 157-178.
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    11. Mohammed H. Alsharif & Jeong Kim & Jin Hong Kim, 2018. "Opportunities and Challenges of Solar and Wind Energy in South Korea: A Review," Sustainability, MDPI, vol. 10(6), pages 1-23, June.

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