Optimal Micro-Siting of Wind Turbines in an Offshore Wind Farm Using Frandsen-Gaussian Wake Model

• Published in: IEEE transactions on power systems Vol. 34; no. 6; pp. 4944 - 4954
• Main Authors: Tao, Siyu, Kuenzel, Stefanie, Xu, Qingshan, Chen, Zhe
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-11-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer simulation; Frandsen-Gaussian wake model; hybrid grey wolf optimization algorithm; Load modeling; Mathematical model; micro-siting; Offshore; Offshore drilling rigs; Offshore energy sources; Offshore installations; offshore wind farm planning; Optimization; wake effect; Wakes; Wind farms; Wind power; Wind power generation; Wind turbines
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2019.2916906

This paper proposes a method to obtain the optimal placement of wind turbines (WTs) in an offshore wind farm (WF). The optimization objective is to minimize the levelized average cost per net electric power generated by a WF with a fixed number of WTs while the distance between WTs is not less than the allowed minimal distance in the far wake region. The WT wake losses have been taken into account, with the Frandsen-Gaussian (F-G) wake model and the optimization problem is subsequently solved by the hybrid grey wolf optimization (HGWO) algorithm. Synthesis methods that contain a special WT ranking strategy for multiple WTs are described in detail. Both the F-G model and Jensen's model are applied in the offshore WF optimization simulation platform for comparison. Simulation results demonstrate that the F-G model is more consistent with real wakes and thus the optimization result is more accurate than the commonly used Jensen's model.