A novel approach for wind farm micro-siting in complex terrain based on an improved genetic algorithm. (15th July 2022)

• Record Type: Journal Article
• Authors: Hu, Weicheng; Yang, Qingshan; Chen, Hua-Peng; Guo, Kunpeng; Zhou, Tong; Liu, Min; Zhang, Jian; Yuan, Ziting
• Is Part Of: Energy. Volume 251(2022)
• Publication Date: 2022-07-15
• Subjects: Wind farm micro-siting -- Wind energy potential -- Complex terrain -- Improved genetic algorithm -- Computational fluid dynamics -- Wind tunnel tests; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.123970
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: Novel three-stage approach for wind farm micro-siting in complex terrain. Improved Genetic algorithm to optimize the layout of wind turbines. Evaluations of the effectiveness compared to existing models. Detailed analysis of uncertainties of the proposed approach.