This paper focuses on the estimation of annual energy production (AEP) by simulating the flowfield on a complex terrain located in the northeast region of Brazil using different numerical approaches: CFD RANS with k-ε and k-ω turbulence models (WindSim), simple mass-conserving (WindMap), and refined mesoscale (SiteWind). The last two are run through OpenWind software. Wind observations from five meteorological masts are used to adjust the models. Optimal layouts for a hypothetical wind farm with 50 wind turbines are obtained over each of the four wind fields to predict the power generation. As non-negligible differences are found on the spatial distribution of the winds simulated by the different models, the layouts are also substantially different. The AEP is calculated to compare scenarios varying the layouts over the wind fields. The distinct micrositing generate differences of up to 13 % on AEP prediction, which could mean the impact of an improper siting on the wind farm profitability. It is not plausible to categorically claim the superiority of accuracy of one model over the others. Nonetheless, the observed data provide an indicative that the refined mesoscale model was able to better capture the wind acceleration in the western region of the studied site.

本文侧重于通过使用不同数值方法模拟巴西东北部复杂地形上的流场来估算年能源生产 (AEP)：CFD RANS 与 k-ε 和 k-ω 湍流模型 (WindSim)，简单质量守恒 (WindMap) 和精细中尺度 (SiteWind)。最后两个是通过 OpenWind 软件运行的。来自五个气象桅杆的风观测用于调整模型。在四个风场中的每一个风场上获得具有 50 台风力涡轮机的假设风电场的最佳布局，以预测发电量。由于不同模型模拟的风的空间分布存在不可忽视的差异，因此布局也有很大不同。计算 AEP 以比较不同风场布局的场景。不同的微型选址对 AEP 预测产生高达 13% 的差异，这可能意味着选址不当对风电场盈利能力的影响。断然声称一种模型的准确性优于其他模型是不合理的。尽管如此，观察到的数据表明，改进的中尺度模型能够更好地捕捉研究地点西部地区的风加速度。