A nonstationary and non‐Gaussian moving average model for solar irradiance
A nonstationary and non‐Gaussian moving average model for solar irradiance
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DOI：
10.1002/env.2712
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发表时间：
2021-12
期刊：
影响因子：
1.7
通讯作者：
Wenqi Zhang;W. Kleiber;B. Hodge;B. Mather
中科院分区：
文献类型：
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作者：
Wenqi Zhang;W. Kleiber;B. Hodge;B. Mather
从历史上看，电力一直从大型发电厂流向消费者。分布式能源（如屋顶光伏太阳能）的日益普及，使得配电网成为客户层面的双向供电。可再生能源的加入给电网带来了额外的不确定性和可变性。配电网业务研究正在进行调整，以纳入可再生能源；然而，这类研究需要高质量的太阳辐照度数据，以充分反映现实的气象变异性。来自卫星产品的数据在空间上是完整的，但在时间上是粗糙的，而太阳辐照度在非常精细的时间尺度上表现出高频变化。我们提出了一种新的随机方法来暂时降低全球水平辐照度（GHI）到1分钟分辨率，但由于原位辐照度测量的可用性有限，我们没有考虑空间方面。太阳辐照度的一阶和二阶结构随日和季节变化，我们的模型适应这种非平稳性。经验辐照度数据表现出高度的非高斯特性；我们开发了一个非平稳和非高斯移动平均模型，该模型被证明可以在多个时间尺度上捕捉真实的太阳变化。我们还提出了一种基于经验自协方差矩阵的Cholesky因子的新估计方案，绕过了困难和难以接近的基于似然的方法。该模型以位于美国不同气候的三个地点为例进行了演示。该模型与来自文献的竞争对手进行了比较，并显示出在测试数据上提供更好的不确定性和可变性量化。
Historically, power has flowed from large power plants to customers. Increasing penetration of distributed energy resources such as solar power from rooftop photovoltaic has made the distribution network a two‐way‐street with power being generated at the customer level. The incorporation of renewables introduces additional uncertainty and variability into the power grid. Distribution network operation studies are being adapted to include renewables; however, such studies require high quality solar irradiance data that adequately reflect realistic meteorological variability. Data from satellite‐based products are spatially complete, but temporally coarse, whereas solar irradiances exhibit high frequency variation at very fine timescales. We propose a new stochastic method for temporally downscaling global horizontal irradiance (GHI) to 1 min resolution, but we do not consider the spatial aspect due to limited availability of the in situ irradiance measurements. Solar irradiance's first and second‐order structures vary diurnally and seasonally, and our model adapts to such nonstationarity. Empirical irradiance data exhibits highly non‐Gaussian behavior; we develop a nonstationary and non‐Gaussian moving average model that is shown to capture realistic solar variability at multiple timescales. We also propose a new estimation scheme based on Cholesky factors of empirical autocovariance matrices, bypassing difficult and inaccessible likelihood‐based approaches. The model is demonstrated for a case study of three locations that are located in diverse climates through the United States. The model is compared against competitors from the literature and is shown to provide better uncertainty and variability quantification on testing data.