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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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    3. 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.
    4. 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.
    5. 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.
    6. Emejeamara, F.C. & Tomlin, A.S., 2020. "A method for estimating the potential power available to building mounted wind turbines within turbulent urban air flows," Renewable Energy, Elsevier, vol. 153(C), pages 787-800.
    7. 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.
    8. 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.
    9. 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.
    10. Li, Chong & Liu, Youying & Li, Gang & Li, Jianyan & Zhu, Dasheng & Jia, Wenhua & Li, Guo & Zhi, Youran & Zhai, Xinyu, 2016. "Evaluation of wind energy resource and wind turbine characteristics at two locations in China," Technology in Society, Elsevier, vol. 47(C), pages 121-128.

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