Wind farm layout optimization using a Gaussian-based wake model. (July 2017)

• Record Type: Journal Article
• Authors: Parada, Leandro; Herrera, Carlos; Flores, Paulo; Parada, Victor
• Is Part Of: Renewable energy. Volume 107(2017)
• Page Start: 531
• Page End: 541
• Publication Date: 2017-07
• Subjects: Wind farm -- Wind turbine -- Layout optimization -- Micro-siting -- Operations research -- Genetic algorithms; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2017.02.017
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: The wind farm layout optimization problem has received considerable attention over the past two decades. The objective of this problem is to determine the wind farm layout that maximizes the annual energy generated. The majority of studies that have solved this problem simulated the velocity deficit using the Jensen wake model. However, this model is not in agreement with field measurements and computational fluid dynamics simulations. In this study, an approach to solve the wind farm layout optimization problem based on a Gaussian wake model is proposed. The Gaussian wake model uses an exponential function to evaluate the velocity deficit, in contrast to the Jensen wake model that assumes a uniform velocity profile inside the wake. The proposed approach minimizes the annual cost of energy of a wind farm using a genetic algorithm. The application of the proposed approach yields higher annual generation and a lower computational time for all wind scenarios under study. Under a more complex wind scenario, the improvement was relatively small. This suggests that the use of a more robust wake model in the WFLO problem, does not lead to greater efficiency in real wind cases. Highlights: A robust approach for the wind farm layout optimization problem is proposed. The performance of a Gaussian-based wake model is compared to a top-hat wake model. The proposed approach minimizes the annual cost of energy using a genetic algorithm. Under a constant wind scenario, more efficient wind farms are obtained. The improvement is small under more complex wind scenarios.