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research article

Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model

Kirchner Bossi, Nicolas  
•
Porté-Agel, Fernando  
December 1, 2018
Energies

Wind Farm Layout Optimization (WFLO) can be useful to minimize power losses associated with turbine wakes in wind farms. This work presents a new evolutionary WFLO methodology integrated with a recently developed and successfully validated Gaussian wake model (Bastankhah and Porte-Agel model). Two different parametrizations of the evolutionary methodology are implemented, depending on if a baseline layout is considered or not. The proposed scheme is applied to two real wind farms, Horns Rev I (Denmark) and Princess Amalia (the Netherlands), and two different turbine models, V80-2MW and NREL-5MW. For comparison purposes, these four study cases are also optimized under the traditionally used top-hat wake model (Jensen model). A systematic overestimation of the wake losses by the Jensen model is confirmed herein. This allows it to attain bigger power output increases with respect to the baseline layouts (between 0.72% and 1.91%) compared to the solutions attained through the more realistic Gaussian model (0.24-0.95%). The proposed methodology is shown to outperform other recently developed layout optimization methods. Moreover, the electricity cable length needed to interconnect the turbines decreases up to 28.6% compared to the baseline layouts.

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Type
research article
DOI
10.3390/en11123268
Web of Science ID

WOS:000455358300029

Author(s)
Kirchner Bossi, Nicolas  
Porté-Agel, Fernando  
Date Issued

2018-12-01

Published in
Energies
Volume

11

Issue

12

Article Number

3268

Subjects

Energy & Fuels

•

Energy & Fuels

•

wind farm layout optimization

•

gaussian wake model

•

genetic algorithms

•

evolutionary computation

•

horns rev

•

princess amalia

•

turbine wakes

•

optimal placement

•

turbulence

•

system

•

design

•

flow

•

speed

Note

This is an open access article under the terms of the Creative Commons Attribution License

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
WIRE  
FunderGrant Number

CTI/Innosuisse

1155002544

FNS

200021_172538

Swiss federal funding

SI/501337-01

Available on Infoscience
January 25, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/154111
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