Wind Mill Pattern Optimization using Evolutionary Algorithms

Charlie Vanaret (1, 2) , Nicolas Durand (1, 2) , Jean-Marc Alliot (2)

1 ENAC - Ecole Nationale de l'Aviation Civile
2 IRIT-APO - Algorithmes Parallèles et Optimisation

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.

Mots clés: optimization; wind farm layout; Computer-aided software engineering; Computer-aided design; wind energy

Domaines: Machine Learning [stat.ML]

Charlie Vanaret, Nicolas Durand, Jean-Marc Alliot. Wind Mill Pattern Optimization using Evolutionary Algorithms. 2020. ⟨hal-02917483⟩