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Wind farm electrical power production model for load flow analysis

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  • Segura-Heras, Isidoro
  • Escrivá-Escrivá, Guillermo
  • Alcázar-Ortega, Manuel

Abstract

The importance of renewable energy increases in activities relating to new forms of managing and operating electrical power: especially wind power. Wind generation is increasing its share in the electricity generation portfolios of many countries. Wind power production in Spain has doubled over the past four years and has reached 20GW. One of the greatest problems facing wind farms is that the electrical power generated depends on the variable characteristics of the wind. To become competitive in a liberalized market, the reliability of wind energy must be guaranteed. Good local wind forecasts are therefore essential for the accurate prediction of generation levels for each moment of the day.

Suggested Citation

  • Segura-Heras, Isidoro & Escrivá-Escrivá, Guillermo & Alcázar-Ortega, Manuel, 2011. "Wind farm electrical power production model for load flow analysis," Renewable Energy, Elsevier, vol. 36(3), pages 1008-1013.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:3:p:1008-1013
    DOI: 10.1016/j.renene.2010.09.007
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    References listed on IDEAS

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    1. Joselin Herbert, G.M. & Iniyan, S. & Sreevalsan, E. & Rajapandian, S., 2007. "A review of wind energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(6), pages 1117-1145, August.
    2. García-Gracia, Miguel & Comech, M. Paz & Sallán, Jesús & Llombart, Andrés, 2008. "Modelling wind farms for grid disturbance studies," Renewable Energy, Elsevier, vol. 33(9), pages 2109-2121.
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    Cited by:

    1. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Economic performance indicators of wind energy based on wind speed stochastic modeling," Applied Energy, Elsevier, vol. 154(C), pages 290-297.
    2. Liang Chen & Heng Nian & Yunyang Xu, 2019. "Impedance Aggregation Method of Multiple Wind Turbines and Accuracy Analysis," Energies, MDPI, vol. 12(11), pages 1-17, May.
    3. Carpinelli, Guido & Caramia, Pierluigi & Varilone, Pietro, 2015. "Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems," Renewable Energy, Elsevier, vol. 76(C), pages 283-295.
    4. Nuño Martinez, Edgar & Cutululis, Nicolaos & Sørensen, Poul, 2018. "High dimensional dependence in power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 197-213.
    5. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    6. Daniel Villanueva & Andrés Feijóo & José L. Pazos, 2013. "Multivariate Weibull Distribution for Wind Speed and Wind Power Behavior Assessment," Resources, MDPI, vol. 2(3), pages 1-15, September.
    7. Shargh, S. & Khorshid ghazani, B. & Mohammadi-ivatloo, B. & Seyedi, H. & Abapour, M., 2016. "Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties," Renewable Energy, Elsevier, vol. 94(C), pages 10-21.
    8. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2014. "Wind speed and energy forecasting at different time scales: A nonparametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 59-66.

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