IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i10p1474-d112999.html
   My bibliography  Save this article

Global Gust Climate Evaluation and Its Influence on Wind Turbines

Author

Listed:
  • Christopher Jung

    (Environmental Meteorology, University of Freiburg, 79085 Freiburg, Germany)

  • Dirk Schindler

    (Environmental Meteorology, University of Freiburg, 79085 Freiburg, Germany)

  • Alexander Buchholz

    (Environmental Meteorology, University of Freiburg, 79085 Freiburg, Germany)

  • Jessica Laible

    (Environmental Meteorology, University of Freiburg, 79085 Freiburg, Germany)

Abstract

Strong gusts negatively affect wind turbines in many ways. They (1) harm their structural safety; (2) reduce their wind energy output; and (3) lead to a shorter wind turbine rotor blade fatigue life. Therefore, the goal of this study was to provide a global assessment of the gust climate, considering its influence on wind turbines. The gust characteristics analyzed were: (1) the gust speed return values for 30, 50 and 100 years; (2) the share of gust speed exceedances of cut-out speed; and (3) the gust factor. In order to consider the seasonal variation of gust speed, gust characteristics were evaluated on a monthly basis. The global monthly wind power density was simulated and geographical restrictions were applied to highlight gust characteristics in areas that are generally suitable for wind turbine installation. Gust characteristics were computed based on ERA-interim data on a 1° × 1° spatial resolution grid. After comprehensive goodness-of-fit evaluation of 12 theoretical distributions, Wakeby distribution was used to compute gust speed return values. Finally, the gust characteristics were integrated into the newly developed wind turbine gust index. It was found that the Northeastern United States and Southeast Canada, Newfoundland, the southern tip of South America, and Northwestern Europe are most negatively affected by the impacts of gusts. In regions where trade winds dominate, such as eastern Brazil, the Sahara, southern parts of Somalia, and southeastern parts of the Arabian Peninsula, the gust climate is well suitable for wind turbine installation.

Suggested Citation

  • Christopher Jung & Dirk Schindler & Alexander Buchholz & Jessica Laible, 2017. "Global Gust Climate Evaluation and Its Influence on Wind Turbines," Energies, MDPI, vol. 10(10), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1474-:d:112999
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/10/1474/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/10/1474/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Mentis, Dimitrios & Hermann, Sebastian & Howells, Mark & Welsch, Manuel & Siyal, Shahid Hussain, 2015. "Assessing the technical wind energy potential in Africa a GIS-based approach," Renewable Energy, Elsevier, vol. 83(C), pages 110-125.
    3. Pes, Marcelo P. & Pereira, Enio B. & Marengo, Jose A. & Martins, Fernando R. & Heinemann, Detlev & Schmidt, Michael, 2017. "Climate trends on the extreme winds in Brazil," Renewable Energy, Elsevier, vol. 109(C), pages 110-120.
    4. Cadini, Francesco & Agliardi, Gian Luca & Zio, Enrico, 2017. "A modeling and simulation framework for the reliability/availability assessment of a power transmission grid subject to cascading failures under extreme weather conditions," Applied Energy, Elsevier, vol. 185(P1), pages 267-279.
    5. Payne, James E., 2010. "A survey of the electricity consumption-growth literature," Applied Energy, Elsevier, vol. 87(3), pages 723-731, March.
    6. Jin-Young Kim & Hyun-Goo Kim & Yong-Heack Kang, 2017. "Offshore Wind Speed Forecasting: The Correlation between Satellite-Observed Monthly Sea Surface Temperature and Wind Speed over the Seas around the Korean Peninsula," Energies, MDPI, vol. 10(7), pages 1-15, July.
    7. Lydia, M. & Kumar, S. Suresh & Selvakumar, A. Immanuel & Prem Kumar, G. Edwin, 2014. "A comprehensive review on wind turbine power curve modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 452-460.
    8. Leonie Grau & Christopher Jung & Dirk Schindler, 2017. "On the Annual Cycle of Meteorological and Geographical Potential of Wind Energy: A Case Study from Southwest Germany," Sustainability, MDPI, vol. 9(7), pages 1-11, July.
    9. Pei-Chi Chang & Ray-Yeng Yang & Chi-Ming Lai, 2015. "Potential of Offshore Wind Energy and Extreme Wind Speed Forecasting on the West Coast of Taiwan," Energies, MDPI, vol. 8(3), pages 1-16, February.
    10. Kang, Dongbum & Ko, Kyungnam & Huh, Jongchul, 2015. "Determination of extreme wind values using the Gumbel distribution," Energy, Elsevier, vol. 86(C), pages 51-58.
    11. Wang, Jianzhou & Qin, Shanshan & Jin, Shiqiang & Wu, Jie, 2015. "Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 26-42.
    12. Christopher Jung, 2016. "High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series," Energies, MDPI, vol. 9(5), pages 1-20, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jung, Christopher & Schindler, Dirk, 2018. "On the inter-annual variability of wind energy generation – A case study from Germany," Applied Energy, Elsevier, vol. 230(C), pages 845-854.
    2. Michał Frant & Stanisław Kachel & Wojciech Maślanka, 2023. "Gust Modeling with State-of-the-Art Computational Fluid Dynamics (CFD) Software and Its Influence on the Aerodynamic Characteristics of an Unmanned Aerial Vehicle," Energies, MDPI, vol. 16(19), pages 1-19, September.
    3. Cathal W. O’Donnell & Mahdi Ebrahimi Salari & Daniel J. Toal, 2021. "A Study on Directly Interconnected Offshore Wind Systems during Wind Gust Conditions," Energies, MDPI, vol. 15(1), pages 1-16, December.

    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. Saravanan Bhaskaran & Amrit Shankar Verma & Andrew J. Goupee & Subhamoy Bhattacharya & Amir R. Nejad & Wei Shi, 2023. "Comparison of Extreme Wind and Waves Using Different Statistical Methods in 40 Offshore Wind Energy Lease Areas Worldwide," Energies, MDPI, vol. 16(19), pages 1-26, October.
    2. Jung, Christopher & Schindler, Dirk, 2018. "On the inter-annual variability of wind energy generation – A case study from Germany," Applied Energy, Elsevier, vol. 230(C), pages 845-854.
    3. Kresning, Boma & Hashemi, M. Reza & Shirvani, Amin & Hashemi, Javad, 2024. "Uncertainty of extreme wind and wave loads for marine renewable energy farms in hurricane-prone regions," Renewable Energy, Elsevier, vol. 220(C).
    4. Mehr Gul & Nengling Tai & Wentao Huang & Muhammad Haroon Nadeem & Moduo Yu, 2020. "Evaluation of Wind Energy Potential Using an Optimum Approach based on Maximum Distance Metric," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    5. 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.
    6. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    7. Vega-Bayo, M. & Pérez-Aracil, J. & Prieto-Godino, L. & Salcedo-Sanz, S., 2024. "Improving the prediction of extreme wind speed events with generative data augmentation techniques," Renewable Energy, Elsevier, vol. 221(C).
    8. Leonie Grau & Christopher Jung & Dirk Schindler, 2017. "On the Annual Cycle of Meteorological and Geographical Potential of Wind Energy: A Case Study from Southwest Germany," Sustainability, MDPI, vol. 9(7), pages 1-11, July.
    9. Villanthenkodath, Muhammed Ashiq & Mahalik, Mantu Kumar, 2021. "Does economic growth respond to electricity consumption asymmetrically in Bangladesh? The implication for environmental sustainability," Energy, Elsevier, vol. 233(C).
    10. Schreiner, Lena & Madlener, Reinhard, 2022. "Investing in power grid infrastructure as a flexibility option: A DSGE assessment for Germany," Energy Economics, Elsevier, vol. 107(C).
    11. 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.
    12. Omri, Anis, 2014. "An international literature survey on energy-economic growth nexus: Evidence from country-specific studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 951-959.
    13. Apergis, Nicholas & Payne, James E., 2010. "Energy consumption and growth in South America: Evidence from a panel error correction model," Energy Economics, Elsevier, vol. 32(6), pages 1421-1426, November.
    14. Apergis, Nicholas & Payne, James E., 2010. "Coal consumption and economic growth: Evidence from a panel of OECD countries," Energy Policy, Elsevier, vol. 38(3), pages 1353-1359, March.
    15. Yuyang Gao & Chao Qu & Kequan Zhang, 2016. "A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(10), pages 1-28, September.
    16. Liddle, Brantley, 2013. "Population, Affluence, and Environmental Impact Across Development: Evidence from Panel Cointegration Modeling," MPRA Paper 52088, University Library of Munich, Germany.
    17. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
    18. Apergis, Nicholas & Payne, James E., 2011. "The renewable energy consumption-growth nexus in Central America," Applied Energy, Elsevier, vol. 88(1), pages 343-347, January.
    19. Zhu, Jianhua & Peng, Yan & Gong, Zhuping & Sun, Yanming & Lai, Chaoan & Wang, Qing & Zhu, Xiaojun & Gan, Zhongxue, 2019. "Dynamic analysis of SNG and PNG supply: The stability and robustness view #," Energy, Elsevier, vol. 185(C), pages 717-729.
    20. Bashiri Behmiri, Niaz & Pires Manso, José R., 2012. "Does Portuguese economy support crude oil conservation hypothesis?," Energy Policy, Elsevier, vol. 45(C), pages 628-634.

    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:10:y:2017:i:10:p:1474-:d:112999. 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.