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Consideration of Wind Speed Variability in Creating a Regional Aggregate Wind Power Time Series

机译:在创建区域性总风电时间序列时要考虑风速变异性

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For the purposes of understanding the impacts on the electricity network, estimates of hourly aggregate wind power generation for a region are required. However, the availability of wind production data for the UK is limited, and studies often rely on measured wind speeds from a network of meteorological (met) stations. Another option is to use historical wind speeds from a reanalysis dataset, with a resolution of around 40–50 km. Mesoscale models offer a potentially more desirable solution, with a homogeneous set of wind speeds covering a wide area at resolutions of 1–50 km, but they are computationally expensive to run at high resolution. An understanding of the most appropriate choice of data requires knowledge of the variability in time and space and how well that is represented by the choice of model. Here it is demonstrated that in regions offshore, or in relatively smooth terrain where variability in wind speeds is smaller, lower resolution models or single point records may suffice to represent aggregate power generation in a sub-region. The need for high resolution modelling in areas of complex terrain where spatial and temporal variability is higher is emphasised, particularly when the distribution of wind generation capacity is uneven over the region.
机译:为了了解对电网的影响,需要估算一个地区每小时的总风力发电量。但是,英国风力生产数据的可用性有限,研究通常依赖于气象(气象)站网络测得的风速。另一个选择是使用重新分析数据集中的历史风速,分辨率约为40-50 km。中尺度模型提供了一种可能更理想的解决方案,即在1-50 km的分辨率下,一组均匀的风速可以覆盖大范围的区域,但是以高分辨率运行在计算上却很昂贵。要了解最合适的数据选择,需要了解时间和空间的可变性以及模型选择所代表的程度。此处表明,在离岸地区或风速变化较小的相对平坦的地形中,较低分辨率的模型或单点记录可能足以表示该子区域的总发电量。强调了在空间和时间变化性较高的复杂地形区域中进行高分辨率建模的需求,尤其是当风力发电量在整个地区的分布不均匀时。

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