Wake model selection in offshore wind energy: balancing efficiency and cost in Indian offshore wind farms

Abstract This study addresses the critical need for efficient offshore wind energy utilization in India, focusing on the impact of different wake models on turbine performance and financial viability. By evaluating models such as TurbOPark and Deep Array Wake Loss (DAWL), we examined their effectiveness in predicting wake losses and optimizing turbine layouts in offshore subzones. The findings reveal that higher wind farm capacity densities lead to significant differences in performance across models. The TurbOPark model predicts the highest array losses, resulting in the lowest capacity utilization factors (CUF) and highest levelized cost of energy (LCoE), reflecting its conservative nature. In contrast, the Modified Park and Eddy Viscosity models consistently estimate lower array losses, leading to lower LCoE and reduced financial burdens on the government, particularly when LCoE is fixed. These results underscore the importance of selecting appropriate wake models that balance cost efficiency with accurate performance predictions. The study highlights the need for refining wake models with high-resolution data and complex environmental factors to optimize wind farm design and enhance energy production, especially in emerging markets like India.