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Energy and reliability benefits of wind energy conversion systems

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  • Xie, Kaigui
  • Billinton, Roy

Abstract

The electrical energy production and reliability benefits of a wind energy conversion system (WECS) at a specific site depend on many factors, including the statistical characteristics of the site wind speed and the design characteristics of the wind turbine generator (WTG) itself, particularly the cut-in, rated and cut-out wind speed parameters. In general, the higher the degree of the wind site matching with a WECS is, the more are the energy and reliability benefits. An electrical energy production and reliability benefit index designated as the Equivalent Capacity Ratio (ECR) is introduced in this paper. This index can be used to indicate the electrical energy production, the annual equivalent utilization time and the credit of a WECS, and quantify the degree of wind site matching with a WECS. The equivalent capacity of a WECS is modeled as the expected value of the power output random variable with the probability density function of the site wind speed. The analytical formulation of the ECR is based on a mathematical derivation with high accuracy. Twelve WTG types and two test systems are used to demonstrate the effectiveness of the proposed model. The results show that the ECR provides a useful index for a WTG to evaluate the energy production and the relative reliability performance in a power system, and can be used to assist in the determination of the optimal WTG type for a specific wind site.

Suggested Citation

  • Xie, Kaigui & Billinton, Roy, 2011. "Energy and reliability benefits of wind energy conversion systems," Renewable Energy, Elsevier, vol. 36(7), pages 1983-1988.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:7:p:1983-1988
    DOI: 10.1016/j.renene.2010.12.011
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    Cited by:

    1. Volkanovski, Andrija, 2017. "Wind generation impact on electricity generation adequacy and nuclear safety," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 85-92.
    2. Wu, Zhong-Qiang & Jia, Wen-Jing & Zhao, Li-Ru & Wu, Chang-Han, 2016. "Maximum wind power tracking based on cloud RBF neural network," Renewable Energy, Elsevier, vol. 86(C), pages 466-472.
    3. Chong, W.T. & Pan, K.C. & Poh, S.C. & Fazlizan, A. & Oon, C.S. & Badarudin, A. & Nik-Ghazali, N., 2013. "Performance investigation of a power augmented vertical axis wind turbine for urban high-rise application," Renewable Energy, Elsevier, vol. 51(C), pages 388-397.
    4. Ciulla, G. & D’Amico, A. & Di Dio, V. & Lo Brano, V., 2019. "Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks," Renewable Energy, Elsevier, vol. 140(C), pages 477-492.
    5. Gu, Chenghong & Zhang, Xin & Ma, Kang & Yan, Jie & Song, Yonghua, 2018. "Impact analysis of electricity supply unreliability to interdependent economic sectors by an economic-technical approach," Renewable Energy, Elsevier, vol. 122(C), pages 108-117.
    6. Joselin Herbert, G.M. & Iniyan, S. & Amutha, D., 2014. "A review of technical issues on the development of wind farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 619-641.
    7. Zhou, P. & Jin, R.Y. & Fan, L.W., 2016. "Reliability and economic evaluation of power system with renewables: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 537-547.

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