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Capacity factor prediction and planning in the wind power generation industry

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  • Gurgur, Cigdem Z.
  • Jones, Michael

Abstract

The common practice to calculate wind generation capacity values relies more on heuristic approximations than true system estimations. In this paper we proposed a more accurate method. In the first part of our analysis, a Monte Carlo simulation was created based on Markov chains to provide an independent estimate of the true behavior of wind farm capacity value as a function of system penetration. With this curve as a baseline, a technique for using beta distributions to model the input variables was adopted. A final step to increase accuracy involved the use of numerical convolution within the program to eliminate summation estimates.

Suggested Citation

  • Gurgur, Cigdem Z. & Jones, Michael, 2010. "Capacity factor prediction and planning in the wind power generation industry," Renewable Energy, Elsevier, vol. 35(12), pages 2761-2766.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:12:p:2761-2766
    DOI: 10.1016/j.renene.2010.04.027
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    References listed on IDEAS

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    1. Voorspools, Kris R. & D'haeseleer, William D., 2006. "An analytical formula for the capacity credit of wind power," Renewable Energy, Elsevier, vol. 31(1), pages 45-54.
    2. Voorspools, Kris R. & D'haeseleer, William D., 2007. "Critical evaluation of methods for wind-power appraisal," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(1), pages 78-97, January.
    3. Kahn, Edward P., 2004. "Effective Load Carrying Capability of Wind Generation: Initial Results with Public Data," The Electricity Journal, Elsevier, vol. 17(10), pages 85-95, December.
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    Cited by:

    1. Fan-Yun Pai & Tsu-Ming Yeh & Yung-Hsien Hung, 2015. "Analysis on Accuracy of Bias, Linearity and Stability of Measurement System in Ball screw Processes by Simulation," Sustainability, MDPI, vol. 7(11), pages 1-23, November.
    2. Mabel, M. Carolin & Raj, R. Edwin & Fernandez, E., 2011. "Analysis on reliability aspects of wind power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 1210-1216, February.
    3. Arnold, Uwe & Yildiz, Özgür, 2015. "Economic risk analysis of decentralized renewable energy infrastructures – A Monte Carlo Simulation approach," Renewable Energy, Elsevier, vol. 77(C), pages 227-239.

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