Next Article in Journal
Electric Field Simulations and Analysis for High Voltage High Power Medium Frequency Transformer
Previous Article in Journal
A Novel High Step-Up DC-DC Converter with Coupled Inductor and Switched Clamp Capacitor Techniques for Photovoltaic Systems
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Energies 2017, 10(3), 365; doi:10.3390/en10030365

Neighborhood Effects in Wind Farm Performance: A Regression Approach

1
Department of Agricultural Economics, Faculty of Life Sciences, Humboldt-Universität zu Berlin, Philippstr. 13, 10115 Berlin, Germany
2
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
View Full-Text   |   Download PDF [9522 KB, uploaded 16 March 2017]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top