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Energies 2017, 10(3), 365;

Neighborhood Effects in Wind Farm Performance: A Regression Approach

Department of Agricultural Economics, Faculty of Life Sciences, Humboldt-Universität zu Berlin, Philippstr. 13, 10115 Berlin, Germany
The author works now at the European Commission, Joint Research Centre. The views expressed are purely those of the author and may not in any circumstances be regarded as stating an official position of the European Commission.
Author to whom correspondence should be addressed.
Academic Editor: Frede Blaabjerg
Received: 19 January 2017 / Revised: 8 March 2017 / Accepted: 9 March 2017 / Published: 16 March 2017
PDF [9522 KB, uploaded 16 March 2017]


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 regression wake model that can be used to predict production losses of existing and potential wind farms. Motivated by simple engineering wake models, the predicting variables are wind speed, the turbine alignment angle, and distance. By utilizing data from two wind farms in Germany, we show that our models can compete with the standard Jensen model in predicting wake effect losses. A scenario analysis reveals that a distance between turbines can be reduced by up to three times the rotor size, without entailing substantial production losses. In contrast, an unfavorable configuration of turbines with respect to the main wind direction can result in production losses that are much higher than in an optimal case. View Full-Text
Keywords: wind energy; wake modeling; wind farm design wind energy; wake modeling; wind farm design

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Ritter, M.; Pieralli, S.; Odening, M. Neighborhood Effects in Wind Farm Performance: A Regression Approach. Energies 2017, 10, 365.

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