000264040 001__ 264040
000264040 005__ 20190619220205.0
000264040 037__ $$aPOST_TALK
000264040 245__ $$aWind Farm Layout Optimization using Genetic Algorithms with a Gaussian Wake Model
000264040 260__ $$c2018-10-22
000264040 269__ $$a2018-10-22
000264040 300__ $$a1
000264040 336__ $$aPosters
000264040 513__ $$aPosters
000264040 520__ $$aIn 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.
000264040 6531_ $$awind farm
000264040 6531_ $$alayout optimization
000264040 6531_ $$aGaussian wake model
000264040 6531_ $$aGenetic Algorithms
000264040 700__ $$g264522$$aKirchner Bossi, Nicolas$$0249481
000264040 700__ $$0243661$$aPorté-Agel, Fernando$$g168244
000264040 7112_ $$aSCCER-FURIES 2018$$cSwiss Tech Center$$d22-10-2018
000264040 8560_ $$fnicolas.kirchnerbossi@epfl.ch
000264040 909C0 $$zCharbonnier, Valérie$$xU12172$$pWIRE$$mfernando.porte-agel@epfl.ch$$0252260
000264040 909CO $$ppresentation$$pENAC$$ooai:infoscience.epfl.ch:264040$$pposter
000264040 960__ $$anicolas.kirchnerbossi@epfl.ch
000264040 961__ $$afantin.reichler@epfl.ch
000264040 973__ $$aEPFL$$sPUBLISHED
000264040 980__ $$aPOST_TALK
000264040 981__ $$aoverwrite