%0 Journal Article %1 Wagner2013Fast %A Wagner, Markus %A Day, Jareth %A Neumann, Frank %D 2013 %J Renewable Energy %K windfarm renewables wind %P 64--70 %R 10.1016/j.renene.2012.09.008 %T A fast and effective local search algorithm for optimizing the placement of wind turbines %U http://dx.doi.org/10.1016/j.renene.2012.09.008 %V 51 %X The placement of wind turbines on a given area of land such that the wind farm produces a maximum amount of energy is a challenging optimization problem. In this article, we tackle this problem, taking into account wake effects that are produced by the different turbines on the wind farm. We significantly improve upon existing results for the minimization of wake effects by developing a new problem-specific local search algorithm. One key step in the speed-up of our algorithm is the reduction in computation time needed to assess a given wind farm layout compared to previous approaches. Our new method allows the optimization of large real-world scenarios within a single night on a standard computer, whereas weeks on specialized computing servers were required for previous approaches. ► We use a local search method to optimize the placement of wind turbines in large wind farms. ► We consider the Park wake model and significantly reduce its computation time. ► The produced layouts are superior to those created by state-of-the-art academic and industrial optimizers. ► Our approach allows the optimization of large wind farm layouts within hours on regular desktop computers.

@article{Wagner2013Fast, abstract = {The placement of wind turbines on a given area of land such that the wind farm produces a maximum amount of energy is a challenging optimization problem. In this article, we tackle this problem, taking into account wake effects that are produced by the different turbines on the wind farm. We significantly improve upon existing results for the minimization of wake effects by developing a new problem-specific local search algorithm. One key step in the speed-up of our algorithm is the reduction in computation time needed to assess a given wind farm layout compared to previous approaches. Our new method allows the optimization of large real-world scenarios within a single night on a standard computer, whereas weeks on specialized computing servers were required for previous approaches. \^{a}–º We use a local search method to optimize the placement of wind turbines in large wind farms. \^{a}–º We consider the Park wake model and significantly reduce its computation time. \^{a}–º The produced layouts are superior to those created by state-of-the-art academic and industrial optimizers. \^{a}–º Our approach allows the optimization of large wind farm layouts within hours on regular desktop computers.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Wagner, Markus and Day, Jareth and Neumann, Frank}, biburl = {https://www.bibsonomy.org/bibtex/225fe5b5cbf539472ce0f9e11bd000683/pbett}, citeulike-article-id = {11449788}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.renene.2012.09.008}, comment = {(private-note)Not got.}, doi = {10.1016/j.renene.2012.09.008}, interhash = {db1e3106248cb22372444791763377f4}, intrahash = {25fe5b5cbf539472ce0f9e11bd000683}, issn = {09601481}, journal = {Renewable Energy}, keywords = {windfarm renewables wind}, month = mar, pages = {64--70}, posted-at = {2012-12-18 09:27:48}, priority = {2}, timestamp = {2018-06-22T18:32:57.000+0200}, title = {A fast and effective local search algorithm for optimizing the placement of wind turbines}, url = {http://dx.doi.org/10.1016/j.renene.2012.09.008}, volume = 51, year = 2013 }