A novel approach for wind farm micro-siting in complex terrain based on an improved genetic algorithm

Weicheng Hu, Qingshan Yang, Hua-Peng Chen, Kunpeng Guo, Tong Zhou, Min Liu, Jian Zhang and Ziting Yuan

Energy, 2022, vol. 251, issue C

Abstract: This study presents a novel approach for wind farm micro-siting in complex terrain based on an improved Genetic Algorithm (GA). Firstly, computational fluid dynamics (CFD) simulations validated by wind tunnel tests, are combined with meteorological measurements to determine the distribution of wind energy potential and wind speed-up ratio. Then, an improved GA considering the wind power density and topographic acceleration effects is proposed for the layout optimization of wind turbines. In this improved algorithm, calculation of the objective functions of the population is programmed uniquely and parallelized to boost efficiency, and a variable mutation rate is adopted to avoid from local optimum. Finally, the proposed approach is applied for the wind farm micro-siting in a real terrain in Changsha, China. Performance of the proposed approach is compared with the Greedy algorithm and the original GA in the case of various wake models and objective functions, and the uncertainties of results are analyzed in-depth. The results indicate that the proposed approach independent of wake models and objective functions is more effective in terms of offering a reasonable wind turbine layout plan than the above two previously mentioned methods.

Keywords: Wind farm micro-siting; Wind energy potential; Complex terrain; Improved genetic algorithm; Computational fluid dynamics; Wind tunnel tests (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:251:y:2022:i:c:s0360544222008738

DOI: 10.1016/j.energy.2022.123970

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