wind farm layout optimization, variance, standard deviance, risk reward tradeoff, multiobjective, Pareto front, optimization under uncertainty, OUU
This paper investigates reducing power variance caused by different wind directions by using wind farm layout optimization. The problem was formulated as a multi-objective optimization. The ε−constraint method was used to solve the bi-objective problem in a two-step optimization framework where two sequential optimizations were performed. The first was maximizing the mean wind farm power alone and the second was minimizing the power variance with a constraint on the mean power. The results show that the variance in power estimates can be greatly reduced, by as much as 30%, without sacrificing mean plant power for the different farm sizes and wind conditions studied. This reduction is attributed to the multi-modality of the design space which allows for unique solutions of high mean plant power with different power variances due to varying wind direction. Thus, wind farms can be designed to maximize power capture with greater confidence.
Original Publication Citation
Gagakuma, B., Stanley, A. P. J., and Ning, A., “Reducing Wind Farm Power Variance from Wind Direction using Wind Farm Layout Optimization,” Wind Engineering, Jan. 2021. doi:10.1177/0309524X20988288
BYU ScholarsArchive Citation
Gagakuma, Bertelsen; Stanley, Andrew P.J.; and Ning, Andrew, "Reducing Wind Farm Power Variance from Wind Direction using Wind Farm Layout Optimization" (2021). Faculty Publications. 4580.
Ira A. Fulton College of Engineering and Technology
Copyright by the authors. CC BY-NC-ND. The publisher version can be found at: https://doi.org/10.1177/0309524X20988288
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