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Wind resource estimation using wind speed and power curve models

Author

Listed:
  • Lydia, M.
  • Suresh Kumar, S.
  • Immanuel Selvakumar, A.
  • Edwin Prem Kumar, G.

Abstract

Estimation of wind resource in a given area helps in identifying potential sites for establishing wind farm and aids in the calculation of annual energy produced. Estimation of annual energy improves the wind power penetration in the electricity grid and in electricity trading. In this paper, wind resource estimation has been carried out using wind speed forecasting models and wind turbine power curve model. The time series model of wind speed for day ahead forecasting is developed based on linear and non-linear autoregressive models with and without exogenous variables. The daily wind speed data of five different locations in New Zealand have been used for this analysis and the annual energy produced has been obtained. The standard deviation between the mean wind speed of the previous day and the mean wind speed during corresponding day five years and ten years ago has been used as exogenous variables. The neuralnet based non-linear model built using exogenous variables (NLARX) performs better in three locations and wavenet based non-linear model performs better in the remaining two locations. Wind resource is estimated using a wind turbine power curve modeled using a five parametric logistic expression, whose parameters were solved using Differential Evolution (DE).

Suggested Citation

  • Lydia, M. & Suresh Kumar, S. & Immanuel Selvakumar, A. & Edwin Prem Kumar, G., 2015. "Wind resource estimation using wind speed and power curve models," Renewable Energy, Elsevier, vol. 83(C), pages 425-434.
    • Handle: RePEc:eee:renene:v:83:y:2015:i:c:p:425-434
    DOI: 10.1016/j.renene.2015.04.045
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    References listed on IDEAS

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    1. Jung, Sungmoon & Arda Vanli, O. & Kwon, Soon-Duck, 2013. "Wind energy potential assessment considering the uncertainties due to limited data," Applied Energy, Elsevier, vol. 102(C), pages 1492-1503.
    2. Poncela, Marta & Poncela, Pilar & Perán, José Ramón, 2013. "Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting," Applied Energy, Elsevier, vol. 108(C), pages 349-362.
    3. Wu, Jie & Wang, Jianzhou & Chi, Dezhong, 2013. "Wind energy potential assessment for the site of Inner Mongolia in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 215-228.
    4. Rusu, Eugen & Onea, Florin, 2013. "Evaluation of the wind and wave energy along the Caspian Sea," Energy, Elsevier, vol. 50(C), pages 1-14.
    5. Celik, Ali N. & Kolhe, Mohan, 2013. "Generalized feed-forward based method for wind energy prediction," Applied Energy, Elsevier, vol. 101(C), pages 582-588.
    6. Lim, Hee-Chang & Jeong, Tae-Yoon, 2010. "Wind energy estimation of the Wol-Ryong coastal region," Energy, Elsevier, vol. 35(12), pages 4700-4709.
    7. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    8. Jung, Sungmoon & Kwon, Soon-Duck, 2013. "Weighted error functions in artificial neural networks for improved wind energy potential estimation," Applied Energy, Elsevier, vol. 111(C), pages 778-790.
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    3. Nedaei, Mojtaba & Assareh, Ehsanolah & Walsh, Philip R., 2018. "A comprehensive evaluation of the wind resource characteristics to investigate the short term penetration of regional wind power based on different probability statistical methods," Renewable Energy, Elsevier, vol. 128(PA), pages 362-374.
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    6. Miguel Á. Rodríguez-López & Emilio Cerdá & Pablo del Rio, 2020. "Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation," Energies, MDPI, vol. 13(18), pages 1-21, September.
    7. Izadyar, Nima & Ong, Hwai Chyuan & Chong, W.T. & Leong, K.Y., 2016. "Resource assessment of the renewable energy potential for a remote area: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 908-923.
    8. Bahamonde, Manuel Ignacio & Litrán, Salvador P., 2019. "Study of the energy production of a wind turbine in the open sea considering the continuous variations of the atmospheric stability and the sea surface roughness," Renewable Energy, Elsevier, vol. 135(C), pages 163-175.
    9. Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
    10. Han, Shuang & Qiao, Yanhui & Yan, Ping & Yan, Jie & Liu, Yongqian & Li, Li, 2020. "Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles," Renewable Energy, Elsevier, vol. 157(C), pages 190-203.

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