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Windmill Farm Pattern Optimization using Evolutionary Algorithms

Abstract : When designing a wind farm layout, we can reduce the number of variables by optimizing a pattern instead of considering the position of each turbine. In this paper we show that when reducing the problem to only two variables defining a grid, we can gain up to 3% of energy output on simple examples of wind farms dealing with many turbines (up to 1000) while dramatically reducing computation time. To achieve these results, we compared both a genetic algorithm and a differential evolution algorithm to previous results from the literature. These preliminary results should be extended to examples involving non-rectangular farm layouts and wind distributions that may require pattern deformation variables in order to increase solution diversity.
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Submitted on : Friday, September 5, 2014 - 5:16:00 PM
Last modification on : Tuesday, October 19, 2021 - 2:24:13 PM
Long-term archiving on: : Saturday, December 6, 2014 - 10:10:29 AM


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Charlie Vanaret, Nicolas Durand, Jean-Marc Alliot. Windmill Farm Pattern Optimization using Evolutionary Algorithms. GECCO 2014, Genetic and Evolutionary Computation Conference, Jul 2014, Vancouver, Canada. pp 181-182, ⟨10.1145/2598394.2598506⟩. ⟨hal-00996716⟩



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