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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Communication dans un congrès
GECCO 2014, Genetic and Evolutionary Computation Conference, Jul 2014, Vancouver, Canada. pp 181-182, 2014, 〈10.1145/2598394.2598506〉
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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, 2014, 〈10.1145/2598394.2598506〉. 〈hal-00996716〉

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