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Low wind speed turbines and wind power potential in Minnesota, USA

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  • Wichser, Corinne
  • Klink, Katherine

Abstract

Wind power development in Minnesota largely has been focused in the “windy” southwestern part of the state. This research evaluates the additional power that potentially could be generated via low wind speed turbines, particularly for areas of the state where there has been comparatively little wind energy investment. Data consist of 3 years (2002–2004) of wind speed measurements at 70–75m above ground level, at four sites representing the range of wind speed regimes (Classes 2–5) found in Minnesota. Power estimates use three configurations of the General Electric 1.5-MW series turbine that vary in rotor diameter and in cut-in, cut-out, and rated speeds. Results show that lower cut-in, cut-out, and rated speeds, and especially the larger rotor diameters, yield increases of 15–30% in wind power potential at these sites. Gains are largest at low wind speed (Class 2) sites and during the summer months at all four sites. Total annual wind power at each site shows some year-to-year variability, with peaks at some sites partially compensating for lulls at others. Such compensation does not occur equally in all years: when large-scale atmospheric circulation patterns are strong (e.g., 2002), the four sites show similar patterns of above- and below-average wind power, somewhat reducing the ability of geographic dispersion to mitigate the effects of wind speed variability.

Suggested Citation

  • Wichser, Corinne & Klink, Katherine, 2008. "Low wind speed turbines and wind power potential in Minnesota, USA," Renewable Energy, Elsevier, vol. 33(8), pages 1749-1758.
  • Handle: RePEc:eee:renene:v:33:y:2008:i:8:p:1749-1758
    DOI: 10.1016/j.renene.2007.11.006
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    Cited by:

    1. Katsigiannis, Yiannis A. & Stavrakakis, George S., 2014. "Estimation of wind energy production in various sites in Australia for different wind turbine classes: A comparative technical and economic assessment," Renewable Energy, Elsevier, vol. 67(C), pages 230-236.
    2. Martin, Sean & Jung, Sungmoon & Vanli, Arda, 2020. "Impact of near-future turbine technology on the wind power potential of low wind regions," Applied Energy, Elsevier, vol. 272(C).
    3. Yang, Han & Yuan, Weimin & Zhu, Weijun & Sun, Zhenye & Zhang, Yanru & Zhou, Yingjie, 2024. "Wind turbine airfoil noise prediction using dedicated airfoil database and deep learning technology," Applied Energy, Elsevier, vol. 364(C).
    4. Liu, Hui & Chen, Chao & Tian, Hong-qi & Li, Yan-fei, 2012. "A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks," Renewable Energy, Elsevier, vol. 48(C), pages 545-556.
    5. Ting, Chen-Ching & Lai, Chen-Wei & Huang, Chien-Bang, 2011. "Developing the dual system of wind chiller integrated with wind generator," Applied Energy, Elsevier, vol. 88(3), pages 741-747, March.
    6. Hernandez-Escobedo, Quetzalcoatl & Manzano-Agugliaro, Francisco & Gazquez-Parra, Jose Antonio & Zapata-Sierra, Antonio, 2011. "Is the wind a periodical phenomenon? The case of Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 721-728, January.
    7. Knoll, Aaron & Klink, Katherine, 2009. "Residential- and commercial-scale distributed wind energy in North Dakota, USA," Renewable Energy, Elsevier, vol. 34(11), pages 2493-2500.
    8. Jiale Li & Xiong (Bill) Yu, 2017. "Analyses of the Extensible Blade in Improving Wind Energy Production at Sites with Low-Class Wind Resource," Energies, MDPI, vol. 10(9), pages 1-24, August.
    9. El Alimi, Souheil & Maatallah, Taher & Dahmouni, Anouar Wajdi & Ben Nasrallah, Sassi, 2012. "Modeling and investigation of the wind resource in the gulf of Tunis, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5466-5478.
    10. Hernández, Ó. Soto & Volkov, K. & Martín Mederos, A.C. & Medina Padrón, J.F. & Feijóo Lorenzo, A.E., 2015. "Power output of a wind turbine installed in an already existing viaduct," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 287-299.
    11. Li, Yi & Wu, Xiao-Peng & Li, Qiu-Sheng & Tee, Kong Fah, 2018. "Assessment of onshore wind energy potential under different geographical climate conditions in China," Energy, Elsevier, vol. 152(C), pages 498-511.
    12. Christian Pape, 2017. "The impact of intraday markets on the market value of flexibility–Decomposing effects on profile and the imbalance costs," EWL Working Papers 1711, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Dec 2017.
    13. Dahmouni, A.W. & Salah, M. Ben & Askri, F. & Kerkeni, C. & Nasrallah, S. Ben, 2010. "Wind energy in the Gulf of Tunis, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(4), pages 1303-1311, May.
    14. de la Rosa, Juan José González & Pérez, Agustín Agüera & Palomares Salas, José Carlos & Ramiro Leo, José Gabriel & Muñoz, Antonio Moreno, 2011. "A novel inference method for local wind conditions using genetic fuzzy systems," Renewable Energy, Elsevier, vol. 36(6), pages 1747-1753.
    15. Dahmouni, A.W. & Ben Salah, M. & Askri, F. & Kerkeni, C. & Ben Nasrallah, S., 2011. "Assessment of wind energy potential and optimal electricity generation in Borj-Cedria, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 815-820, January.

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