Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain
Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain
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DOI：
10.1016/j.renene.2014.10.024
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发表时间：
2015-03-01
期刊：
影响因子：
8.7
通讯作者：
Lenaghan, D.
中科院分区：
文献类型：
--
作者：
Cannon, D. J.;Brayshaw, D. J.;Lenaghan, D.
随着风力发电的比例迅速增加，极端风力发电事件，如长时间的低（或高）发电和发电斜坡，对国家电力系统的有效和安全运行越来越关注。由于极端事件发生的频率较低，需要长期和可靠的气象记录来准确估计其特征。最近的出版物已经开始调查使用全球气象“再分析”数据集的电力系统应用，其中许多侧重于长期平均统计，如月平均发电量。在这里，我们证明，再分析数据也可以用来估计相对短暂的极端事件的频率（包括次日常时间尺度上的斜坡）。对英国328个地面观测站的验证表明，再分析可以忠实地再现时空尺度大于300 km和6 h的近地面风变率，而不需要昂贵的动力降尺度。（MERRA，来自NASA-GMAO），用于构建英国（GB）全国累计风力发电的小时时间序列，假设风电场的固定，现代分布。由此产生的发电量估计值与最近一段时间内国家电网记录的数据高度相关，包括瞬时小时值和大于约6小时的时间间隔内的变化。然后，使用这33年的时间序列来量化不同极端GB级风力发电事件发生的频率，以及它们的季节性和年际变化。描述了对极端风力发电事件性质的几种新颖见解，包括（i）长时间低或高发电事件的数量很好地近似于类泊松随机过程，以及（ii）虽然一般存在较大的季节性变化，最极端的斜坡的大小在夏季和冬季都是相似的。GB案例研究数据的最新版本以及基础模型可从我们的网站www.example.com免费下载http://www.mareading.ac.uk/similar到energymet/data/Cannon 2014/。(C)2014作者由Elsevier Ltd.发布。这是CC BY许可下的开放获取文章（http：//creativecommons.org/licenses/by/3.0/）。
With a rapidly increasing fraction of electricity generation being sourced from wind, extreme wind power generation events such as prolonged periods of low (or high) generation and ramps in generation, are a growing concern for the efficient and secure operation of national power systems. As extreme events occur infrequently, long and reliable meteorological records are required to accurately estimate their characteristics.Recent publications have begun to investigate the use of global meteorological "reanalysis" data sets for power system applications, many of which focus on long-term average statistics such as monthly-mean generation. Here we demonstrate that reanalysis data can also be used to estimate the frequency of relatively short-lived extreme events (including ramping on sub-daily time scales). Verification against 328 surface observation stations across the United Kingdom suggests that near-surface wind variability over spatiotemporal scales greater than around 300 km and 6 h can be faithfully reproduced using reanalysis, with no need for costly dynamical downscaling.A case study is presented in which a state-of-the-art, 33 year reanalysis data set (MERRA, from NASA-GMAO), is used to construct an hourly time series of nationally-aggregated wind power generation in Great Britain (GB), assuming a fixed, modern distribution of wind farms. The resultant generation estimates are highly correlated with recorded data from National Grid in the recent period, both for instantaneous hourly values and for variability over time intervals greater than around 6 h. This 33 year time series is then used to quantify the frequency with which different extreme GB-wide wind power generation events occur, as well as their seasonal and inter-annual variability. Several novel insights into the nature of extreme wind power generation events are described, including (i) that the number of prolonged low or high generation events is well approximated by a Poission-like random process, and (ii) whilst in general there is large seasonal variability, the magnitude of the most extreme ramps is similar in both summer and winter.An up-to-date version of the GB case study data as well as the underlying model are freely available for download from our website: http://www.mareading.ac.uk/similar to energymet/data/Cannon2014/. (C) 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).