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Wind resource assessment of the Southern Appalachian Ridges in the Southeastern United States

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  • Raichle, Brian W.
  • Carson, W. Richard

Abstract

The analysis of wind data collected throughout the Southern Appalachian Mountain region of the Southeastern US is presented. Data were collected at 50Â m above ground level on nine ridge top sites between 2002 and 2005. Monthly average wind speeds, power densities, wind sheers, and turbulence intensities, along with monthly maximum gusts, are presented. Measured annual average wind speeds are compared to AWS TrueWind predictions. Diurnal variations in wind speed are also reported. Annual wind roses for each site are presented. Annual wind speeds range from 5.5 to 7.4Â m/s with the highest annual average wind speeds found on ridges near the northern TN-NC border. A 20% winter and nighttime enhancement of the wind speed was observed. The prevailing wind is from the westerly directions. The estimated annual energy outputs from a small wind farm consisting of fifteen 1.5Â MW GE turbines range from 50 to 75Â MkWh, and estimated capacity factors range from 25% to 35%. This analysis suggests that ridges in the region are suitable for utility-scale wind development.

Suggested Citation

  • Raichle, Brian W. & Carson, W. Richard, 2009. "Wind resource assessment of the Southern Appalachian Ridges in the Southeastern United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 1104-1110, June.
    • Handle: RePEc:eee:rensus:v:13:y:2009:i:5:p:1104-1110
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    Cited by:

    1. Mohandes, M. & Rehman, S. & Rahman, S.M., 2011. "Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)," Applied Energy, Elsevier, vol. 88(11), pages 4024-4032.
    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. 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.
    4. Xydis, G. & Koroneos, C. & Loizidou, M., 2009. "Exergy analysis in a wind speed prognostic model as a wind farm sitting selection tool: A case study in Southern Greece," Applied Energy, Elsevier, vol. 86(11), pages 2411-2420, November.
    5. Rehman, Shafiqur, 2014. "Tower distortion and scatter factors of co-located wind speed sensors and turbulence intensity behavior," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 20-29.
    6. Liu, Feng-Jiao & Chen, Pai-Hsun & Kuo, Shyi-Shiun & Su, De-Chuan & Chang, Tian-Pau & Yu, Yu-Hua & Lin, Tsung-Chi, 2011. "Wind characterization analysis incorporating genetic algorithm: A case study in Taiwan Strait," Energy, Elsevier, vol. 36(5), pages 2611-2619.
    7. Baseer, M.A. & Meyer, J.P. & Alam, Md. Mahbub & Rehman, S., 2015. "Wind speed and power characteristics for Jubail industrial city, Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1193-1204.
    8. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
    9. Baseer, M.A. & Meyer, J.P. & Rehman, S. & Md. Mahbub Alam, & Al-Hadhrami, L.M. & Lashin, A., 2016. "Performance evaluation of cup-anemometers and wind speed characteristics analysis," Renewable Energy, Elsevier, vol. 86(C), pages 733-744.
    10. Chang, Tian Pau, 2011. "Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application," Applied Energy, Elsevier, vol. 88(1), pages 272-282, January.
    11. Gualtieri, Giovanni, 2018. "Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height: method's test at a mountain site," Renewable Energy, Elsevier, vol. 120(C), pages 457-467.
    12. 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.
    13. 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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