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Selection of Best-Suited Wind Turbines for New Wind Farm Sites Using Techno-Economic and GIS Analysis in South Korea

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  • Sajid Ali

    (Smart City Construction Engineering, University of Science & Technology (UST), 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
    Department of Land, Water and Environment Research, 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
    Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology (KICT), Daehwa-dong 283, Goyangdae-ro, Ilsanseo-Gu, Goyang-si, Gyeonggi-do 10223, Korea)

Abstract

South Korea greatly depends upon foreign countries to fulfill its energy requirements, and therefore imports billions of barrels of oil every year. For instance, 94.8% of the total primary energy supply (TPES) was imported from other countries in 2015, at the cost of 91.51 billion euros. There is a realistic challenge in front of the government to reduce these oil imports, and to find alternate (local) sources of energy. Renewable energy (RE) technologies can play a vital role in this regard. The South Korean government has shown a great interest in RE, and intends to achieve a target of 11% of the TPES being generated by RE by the end of 2035, as decided in the Korean Renewable Portfolio Standard (RPS); which showed that only 4.9% of TPES was produced by RE at the end of 2015. The present study proposes ten potential onshore wind farm sites. These locations have been identified by using the GIS–MCDM (geographic information system–multi-criteria decision-making) methodology and a detailed techno-economic assessment has also been presented. Furthermore, the appropriate type of wind turbines has been recommended for each site using detailed analysis of wind conditions, 50-year extreme wind speed (EWS) and turbulence intensity (TI). The analysis showed that all the sites have excellent wind conditions, and they are also economically feasible. Parameters such as AEP (annual energy production), CF (capacity factor), LCOE (levelized cost of electricity), and NPV (net present value) have been estimated for each site, using five different wind turbines manufactured in South Korea. The present study can be very useful for the wind energy sector in South Korea.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3140-:d:257956
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    References listed on IDEAS

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    1. Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2017. "Determination of the Most Optimal On-Shore Wind Farm Site Location Using a GIS-MCDM Methodology: Evaluating the Case of South Korea," Energies, MDPI, vol. 10(12), pages 1-22, December.
    2. Lee, Bong-Hee & Ahn, Dong-Joon & Kim, Hyun-Goo & Ha, Young-Cheol, 2012. "An estimation of the extreme wind speed using the Korea wind map," Renewable Energy, Elsevier, vol. 42(C), pages 4-10.
    3. Kim, Ji-Young & Oh, Ki-Yong & Kang, Keum-Seok & Lee, Jun-Shin, 2013. "Site selection of offshore wind farms around the Korean Peninsula through economic evaluation," Renewable Energy, Elsevier, vol. 54(C), pages 189-195.
    4. Lee, Myung Eun & Kim, Gunwoo & Jeong, Shin-Taek & Ko, Dong Hui & Kang, Keum Seok, 2013. "Assessment of offshore wind energy at Younggwang in Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 131-141.
    5. Kim, Hyeonwu & Kim, Bumsuk, 2016. "Wind resource assessment and comparative economic analysis using AMOS data on a 30 MW wind farm at Yulchon district in Korea," Renewable Energy, Elsevier, vol. 85(C), pages 96-103.
    6. Ko, Kyungnam & Kim, Kyoungbo & Huh, Jongchul, 2010. "Variations of wind speed in time on Jeju Island, Korea," Energy, Elsevier, vol. 35(8), pages 3381-3387.
    7. Ali, Sajid & Lee, Sang-Moon & Jang, Choon-Man, 2018. "Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island – Incheon, South Korea," Renewable Energy, Elsevier, vol. 123(C), pages 652-663.
    8. Seoin Baek & Heetae Kim & Hyun Joon Chang, 2015. "Optimal Hybrid Renewable Power System for an Emerging Island of South Korea: The Case of Yeongjong Island," Sustainability, MDPI, vol. 7(10), pages 1-17, October.
    9. Park, Eunil, 2017. "Potentiality of renewable resources: Economic feasibility perspectives in South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 61-70.
    10. Oh, Ki-Yong & Kim, Ji-Young & Lee, Jun-Shin & Ryu, Ki-Wahn, 2012. "Wind resource assessment around Korean Peninsula for feasibility study on 100 MW class offshore wind farm," Renewable Energy, Elsevier, vol. 42(C), pages 217-226.
    11. 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.
    12. 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.
    13. Oh, Ki-Yong & Kim, Ji-Young & Lee, Jae-Kyung & Ryu, Moo-Sung & Lee, Jun-Shin, 2012. "An assessment of wind energy potential at the demonstration offshore wind farm in Korea," Energy, Elsevier, vol. 46(1), pages 555-563.
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    Cited by:

    1. Mengran Li & Ye Xu & Junhong Guo & Ye Li & Wei Li, 2020. "Application of a GIS-Based Fuzzy Multi-Criteria Evaluation Approach for Wind Farm Site Selection in China," Energies, MDPI, vol. 13(10), pages 1-19, May.
    2. Alphan, H., 2021. "Modelling potential visibility of wind turbines: A geospatial approach for planning and impact mitigation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    3. Chia-Nan Wang & Jui-Chung Kao & Yen-Hui Wang & Van Thanh Nguyen & Viet Tinh Nguyen & Syed Tam Husain, 2021. "A Multicriteria Decision-Making Model for the Selection of Suitable Renewable Energy Sources," Mathematics, MDPI, vol. 9(12), pages 1-17, June.
    4. Francisco Haces-Fernandez, 2020. "Wind Energy Implementation to Mitigate Wildfire Risk and Preemptive Blackouts," Energies, MDPI, vol. 13(10), pages 1-19, May.
    5. Alicja Lenarczyk & Marcin Jaskólski & Paweł Bućko, 2022. "The Application of a Multi-Criteria Decision-Making for Indication of Directions of the Development of Renewable Energy Sources in the Context of Energy Policy," Energies, MDPI, vol. 15(24), pages 1-21, December.
    6. Nansheng Pang & Mengfan Nan & Qichen Meng & Siyang Zhao, 2021. "Selection of Wind Turbine Based on Fuzzy Analytic Network Process: A Case Study in China," Sustainability, MDPI, vol. 13(4), pages 1-17, February.

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