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Neighborhood Effects in Wind Farm Performance: An Econometric Approach


  • Matthias Ritter
  • Simone Pieralli
  • HMartin Odening


The optimization of turbine density in wind farms entails a trade-off between the usage of scarce, expensive land and power losses through turbine wake effects. A quantification and prediction of the wake effect, however, is challenging because of the complex aerodynamic nature of the interdependencies of turbines. In this paper, we propose a parsimonious data driven econometric wake model that can be used to predict production losses of existing and potential wind parks. Motivated by simple engineering wake models, the predicting variables are wind speed, turbine alignment angle, and distance. By utilizing data from two wind parks in Germany, a significantly better prediction of wake effect losses is attained compared to the standard Jensen model. A scenario analysis reveals that a distance between turbines can be reduced up to three times the rotor size without entailing substantial production losses. In contrast, a suboptimal configuration of turbines with respect to the main wind direction can result in production losses that are five times higher.

Suggested Citation

  • Matthias Ritter & Simone Pieralli & HMartin Odening, 2016. "Neighborhood Effects in Wind Farm Performance: An Econometric Approach," SFB 649 Discussion Papers SFB649DP2016-012, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2016-012

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    References listed on IDEAS

    1. Marmidis, Grigorios & Lazarou, Stavros & Pyrgioti, Eleftheria, 2008. "Optimal placement of wind turbines in a wind park using Monte Carlo simulation," Renewable Energy, Elsevier, vol. 33(7), pages 1455-1460.
    2. Pieralli, Simone & Ritter, Matthias & Odening, Martin, 2015. "Efficiency of wind power production and its determinants," Energy, Elsevier, vol. 90(P1), pages 429-438.
    3. Kiranoudis, C.T. & Maroulis, Z.B., 1997. "Effective short-cut modelling of wind park efficiency," Renewable Energy, Elsevier, vol. 11(4), pages 439-457.
    4. Wan, Chunqiu & Wang, Jun & Yang, Geng & Gu, Huajie & Zhang, Xing, 2012. "Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy," Renewable Energy, Elsevier, vol. 48(C), pages 276-286.
    5. Croonenbroeck, Carsten & Ambach, Daniel, 2015. "Censored spatial wind power prediction with random effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 613-622.
    6. Turner, S.D.O. & Romero, D.A. & Zhang, P.Y. & Amon, C.H. & Chan, T.C.Y., 2014. "A new mathematical programming approach to optimize wind farm layouts," Renewable Energy, Elsevier, vol. 63(C), pages 674-680.
    7. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    8. Barthelmie, R.J. & Pryor, S.C., 2013. "An overview of data for wake model evaluation in the Virtual Wakes Laboratory," Applied Energy, Elsevier, vol. 104(C), pages 834-844.
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    More about this item


    Wind energy; wake modeling; wind farm designmultiplesystem approach; dual-self model; drift–diffusion model; response times;

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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