Abstract
The wind farm layout optimization (WFLO) is still regarded as an NP-hard problem involving a lot of constraints owing to the multiple wake effects. In order to efficiently cope with the problem, both a new optimization model and an improved differential evolution algorithm are developed. First of all, a new bilevel programming model is presented for the problems of this kind. Different from other existing models, the bilevel programming problem aims at the maximum profit at upper level as well as the maximum power output at lower level. Then, in the wind farm (WF) areas given, wind turbines (WTs) are placed at different locations and the wake effect among different wind turbines is taken into account. In addition, by designing a hybrid mutation operator, an improved differential evolution algorithm (IDE) is proposed for dealing with the bilevel programming model. Finally, the comparative analysis of maximal power output among the proposed algorithm and other four advanced methods is conducted under two wind conditions collected from commercial wind farms, and the results demonstrate that the proposed method is feasible and effective.
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Acknowledgments
This work is partially supported by the National Natural Science Foundation of China under Grant No. 61966030, the Natural Science Foundation of Qinghai Province of China under Grant No. 2018-ZJ-901 and the Key Laboratory of LoT of Qinghai province of china under Grant 2020-ZJ-Y16.
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Chen, L., Li, H., Shen, Y. (2022). A Differential Evolution Algorithm for Wind Farm Layout Optimization Using a New Bilevel Programming Model. In: Chu, SC., Lin, J.CW., Li, J., Pan, JS. (eds) Genetic and Evolutionary Computing. ICGEC 2021. Lecture Notes in Electrical Engineering, vol 833. Springer, Singapore. https://doi.org/10.1007/978-981-16-8430-2_53
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DOI: https://doi.org/10.1007/978-981-16-8430-2_53
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