一种新的基于ranss的风电场参数化及入流模型用于风电场集群建模

IF 3.6 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
M. P. van der Laan, O. García-Santiago, M. Kelly, A. M. Meyer Forsting, C. Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, A. Peña, N. Sørensen, P. Réthoré
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引用次数: 2

摘要

摘要海上风电场通常安装在风电场集群中,风电场的相互作用可能导致能源损失;因此,需要能够正确模拟风电场相互作用的数值模型。这项工作提出了一种雷诺平均Navier-Stokes(RANS)方法,以有效模拟相邻风电场对风电场功率和年能源生产的影响。首先,提出了一种新的稳态大气入流,并对其在大型风电场RANS模拟中的应用进行了测试。其次,引入了一种基于RANS的风电场参数化,即致动器风电场(AWF)模型,该模型将风电场表示为林冠,与将所有风力涡轮机建模为致动器盘(AD)相比,可以使用更粗糙的网格。当RANS-AWF模型的水平分辨率增加时,模型结果接近RANS-AD模型的结果。用RANS模拟了一个双风电场的情况,表明用AWF模型代替上游风电场只会导致小于1的偏差 % 就下游风电场的风电场功率而言。最重要的是,CPU小时数减少了75.1 % 假设AWF输入是已知的,即风电场推力和功率系数。当所有风电场都用AWF模型表示时,CPU小时数的减少进一步减少,即92.3 % 和99.9 % 对于双风电场情况和对于分别由三个风电场组成的风电场集群情况。如果风电场推力和功率系数输入来自RANS-AD模拟,则CPU时间减少仍然为82.7 % 风电场集群案例。对于双风电场的情况,与中尺度天气研究和预测模型进行的模拟输出相比,RANS模型预测了不同的风速流场,但这些模型与下游风电场的流入风速一致。RANS-AD-AWF模型也通过风电场尾流形状的测量进行了验证;该模型捕捉了大范围风向的测量趋势,尽管测量结果表明某些风向的风电场尾流形状更明显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new RANS-based wind farm parameterization and inflow model for wind farm cluster modeling
Abstract. Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier–Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed and tested for the application of RANS simulations of large wind farms. Second, a RANS-based wind farm parameterization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation of less than 1 % in terms of the wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 75.1 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models, namely, 92.3 % and 99.9 % for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. If the wind farm thrust and power coefficient inputs are derived from RANS-AD simulations, then the CPU time reduction is still 82.7 % for the wind farm cluster case. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model, but the models are in agreement with the inflow wind speed of the downstream wind farm. The RANS-AD-AWF model is also validated with measurements in terms of wind farm wake shape; the model captures the trend of the measurements for a wide range of wind directions, although the measurements indicate more pronounced wind farm wake shapes for certain wind directions.
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来源期刊
Wind Energy Science
Wind Energy Science GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
6.90
自引率
27.50%
发文量
115
审稿时长
28 weeks
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