多尺度耦合模型:捕捉风电场-大气相互作用和全球阻塞效应的新框架

Sebastiano Stipa, Arjun Ajay, D. Allaerts, J. Brinkerhoff
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引用次数: 3

摘要

摘要过去十年间,风能项目的数量和规模不断增长,这暴露出风能行业目前采用的评估潜在风电场选址方法存在结构性局限。这些局限性是由于忽略了大型风电场与热分层大气边界层的相互影响。虽然目前可用的分析模型足够精确,可以对孤立的转子或小型风力涡轮机集群进行选址评估,但对于大型阵列而言,风电场与大气层的相互作用不容忽视。具体来说,风电场会使边界层垂直位移,引发大气重力波,从而导致大范围的水平压力梯度。这些压力扰动会改变涡轮机位置的速度场,最终影响全球风电场的发电量。这种动力学的影响还可能在第一台涡轮机上游产生一个扩展的阻塞区域,并在风电场内部产生有利的压力梯度。在本文中,我们介绍了多尺度耦合(MSC)模型,这是一种新颖的方法,可同时预测发生在风力涡轮机尺度的微尺度效应(如单个尾流相互作用和转子感应)和发生在风电场尺度及更大尺度的中尺度现象(如大气重力波)。这是通过评估从简化中尺度模型获得的空间异质背景速度场上的尾流模型来实现的。MSC 模型的验证是根据两个大涡度模拟(LES)进行的,这两个模拟具有相似的平均流入速度剖面和不同的封顶反演强度,因此产生了两种不同的界面重力波状态,即亚临界和超临界。界面波在第一种情况下会产生较高的阻塞,因为它们被允许向上游传播。另一方面,在超临界状态下,它们的传播速度小于平流速度,上游阻塞只能由内波产生。事实证明,MSC 模型不仅能成功捕捉到两种分析模式下的局部感应和全局阻塞效应,还能捕捉到风电场与重力波之间的相互作用,与 LES 结果相比,MSC 模型仅低估了风电场功率约 2%。相反,即使结合局部感应模型,单独的唤醒模型也无法区分热分层的差异。具体来说,它们受到第一排过预测偏差的影响,导致风场功率在模型模式下被高估了 13% 到 20%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects
Abstract. The growth in the number and size of wind energy projects in the last decade has revealed structural limitations in the current approach adopted by the wind industry to assess potential wind farm sites. These limitations are the result of neglecting the mutual interaction of large wind farms and the thermally stratified atmospheric boundary layer. While currently available analytical models are sufficiently accurate to conduct site assessments for isolated rotors or small wind turbine clusters, the wind farm's interaction with the atmosphere cannot be neglected for large-size arrays. Specifically, the wind farm displaces the boundary layer vertically, triggering atmospheric gravity waves that induce large-scale horizontal pressure gradients. These perturbations in pressure alter the velocity field at the turbine locations, ultimately affecting global wind farm power production. The implication of such dynamics can also produce an extended blockage region upstream of the first turbines and a favorable pressure gradient inside the wind farm. In this paper, we present the multi-scale coupled (MSC) model, a novel approach that allows the simultaneous prediction of micro-scale effects occurring at the wind turbine scale, such as individual wake interactions and rotor induction, and meso-scale phenomena occurring at the wind farm scale and larger, such as atmospheric gravity waves. This is achieved by evaluating wake models on a spatially heterogeneous background velocity field obtained from a reduced-order meso-scale model. Verification of the MSC model is performed against two large-eddy simulations (LESs) with similar average inflow velocity profiles and a different capping inversion strength, so that two distinct interfacial gravity wave regimes are produced, i.e. subcritical and supercritical. Interfacial waves can produce high blockage in the first case, as they are allowed to propagate upstream. On the other hand, in the supercritical regime their propagation speed is less than their advection velocity, and upstream blockage is only operated by internal waves. The MSC model not only proves to successfully capture both local induction and global blockage effects in the two analyzed regimes, but also captures the interaction between the wind farm and gravity waves, underestimating wind farm power by about only 2 % compared with the LES results. Conversely, wake models alone cannot distinguish between differences in thermal stratification, even if combined with a local induction model. Specifically, they are affected by a first-row overprediction bias that leads to an overestimation of the wind farm power by 13 % to 20 % for the modeled regimes.
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