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  4. Wind Farm Layout Optimization using Genetic Algorithms with a Gaussian Wake Model
 
conference poster not in proceedings

Wind Farm Layout Optimization using Genetic Algorithms with a Gaussian Wake Model

Kirchner Bossi, Nicolas  
•
Porté-Agel, Fernando  
October 22, 2018
SCCER-FURIES 2018

In this work we have designed and implemented different Genetic Algorithms especially adapted to the Wind Farm Layout Optimization (WFLO) problem, with the goal to optimize the overall power output and electricity cable length of different Wind Farms currently in operation in Europe. The used flow dynamics framework relies on a wind turbine wake model (EPFL, 2014) that has shown a higher accuracy representing the turbine wakes, compared to the traditionally used wake models. We obtained a 16-18% reduction of the electricity cable length, when increasing the overall wind power output 0.24-0.89%. In addition, the methodology is shown to outperform the other optimization methods in literature applied to similar cases.

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Type
conference poster not in proceedings
Author(s)
Kirchner Bossi, Nicolas  
Porté-Agel, Fernando  
Date Issued

2018-10-22

Total of pages

1

Subjects

wind farm

•

layout optimization

•

Gaussian wake model

•

Genetic Algorithms

Written at

EPFL

EPFL units
WIRE  
Event nameEvent placeEvent date
SCCER-FURIES 2018

Swiss Tech Center

2018-10-22

Available on Infoscience
February 21, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/154645
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