Traditionally the power grid has been a one-way street with power flowing from large transmission-connected generators through the distribution network to consumers. This paradigm is changing with the introduction of distributed renewable energy resources (DERs), and with it, the way the grid is managed. There is currently a dearth of high fidelity solar irradiance datasets available to help grid researchers understand how expansion of DERs could affect future power system operations. Realistic simulations of by-the-second solar irradiances are needed to study how DER variability affects the grid. Irradiance data are highly non-stationary and non-Gaussian, and even modern time series models are challenged by their distributional properties. We develop a subordinated non-Gaussian stochastic model whose simulations realistically capture the distribution and dependence structure in measured irradiance. We illustrate our approach on a fine resolution dataset from Hawaii, where our approach outperforms standard nonlinear time series models.

中文翻译：

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太阳辐照度的从属高斯过程

传统上，电网是一条单向街道，电力从大型输电连接发电机通过配电网络流向消费者。随着分布式可再生能源 (DER) 的引入以及电网的管理方式，这种范式正在发生变化。目前缺乏高保真太阳辐照度数据集来帮助电网研究人员了解分布式能源的扩展如何影响未来电力系统的运行。需要对每秒太阳辐照度进行真实模拟，以研究分布式能源变化如何影响电网。辐照度数据是高度非平稳和非高斯的，即使是现代时间序列模型也受到其分布特性的挑战。我们开发了一个从属非高斯随机模型，其模拟真实地捕捉了测量辐照度的分布和依赖性结构。我们在夏威夷的高分辨率数据集上说明了我们的方法，我们的方法优于标准非线性时间序列模型。