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Impact of near-future turbine technology on the wind power potential of low wind regions

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  • Martin, Sean
  • Jung, Sungmoon
  • Vanli, Arda

Abstract

Near-future wind turbines – with increased hub heights, larger rotors, and improved energy capture methods – have the potential to bring wind power to previously undeveloped low wind regions. To date, however, little published research has been completed for these less profitable locations. This research studies the wind resource characteristics of low wind sites to determine the impact of near-future turbine technologies on wind energy potential. Histograms of hourly wind speed data at nine Florida sites, for the period 2005–2014, were compared with data fit to a Modified-Weibull probability distribution. Estimates of annual energy production and turbine capacity factors were then determined for a near-future wind turbine. Histograms for the low wind sites were found to have three characteristic shapes – Type I, II and III - that are related to the annual 10 m mean wind speed and the probability that observed wind speeds are less than 4 m/s. While sites with a Type I characteristic shape benefitted the most from near-future turbine technology, sites having Type II and Type III characteristic shapes appear to be the most viable for future wind power development. Energy estimates obtained using the Modified-Weibull distribution overestimated annual energy production by 5.6%, on average, as compared to estimates obtained using histograms. Overestimation was the greatest for sites having histograms with a Type I characteristic shape, 8.0% on average, and the least for sites having a Type III characteristic shape, 2.8% on average. Characteristic shapes can be useful to analyze the wind power potential at low wind sites.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:272:y:2020:i:c:s0306261920307637
    DOI: 10.1016/j.apenergy.2020.115251
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    3. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
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    6. Hércules Araújo Oliveira & José Gomes de Matos & Luiz Antonio de Souza Ribeiro & Osvaldo Ronald Saavedra & Jerson Rogério Pinheiro Vaz, 2023. "Assessment of Correction Methods Applied to BEMT for Predicting Performance of Horizontal-Axis Wind Turbines," Sustainability, MDPI, vol. 15(8), pages 1-26, April.
    7. Liu, Fa & Sun, Fubao & Wang, Xunming, 2023. "Impact of turbine technology on wind energy potential and CO2 emission reduction under different wind resource conditions in China," Applied Energy, Elsevier, vol. 348(C).
    8. Mahsa Dehghan Manshadi & Majid Ghassemi & Seyed Milad Mousavi & Amir H. Mosavi & Levente Kovacs, 2021. "Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory," Energies, MDPI, vol. 14(16), pages 1-17, August.
    9. Jung, Christopher & Schindler, Dirk, 2022. "On the influence of wind speed model resolution on the global technical wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

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