Solar energy is envisaged as a major pillar of the global transition to a climate-friendly energy system. Variability of solar radiation requires additional balancing measures to ensure a stable and secure energy supply. In order to analyze this issue in detail, solar radiation time series data of appropriate temporal and spatial resolution is necessary. Common weather models and satellites are only delivering solar surface irradiance with temporal resolutions of up to 15 min. Significant short-term variability in irradiances within seconds to minutes however is induced by clouds. Ground-based measurements typically used to capture this variability are costly and only sparsely available. Hence, a method to synthetically generate time series from currently available satellite imagery is of value for researchers, grid operators, and project developers. There are efforts to increase satellite resolution to 1 min, but this is not planned everywhere and will not change the spatial resolution. Therefore, the fundamental question remains if there are alternative strategies to obtain high temporal resolution observations at a pinpoint. This paper presents a method to generate 1 min resolved synthetic time series of global and direct normal irradiances for arbitrary locations. A neural network based on satellite image derived cloud structure parameters enables to classify high-frequency solar radiation variability. Combined with clear-sky radiation data, 1 min time series which reflect the typical variability characteristics of a site are reproduced. Testing and validation against ground observations (BSRN) show that the method can accurately reproduce characteristics such as frequency and ramp distributions. An application case demonstrates the usage in low-voltage grid studies.

中文翻译：

---

基于卫星图像的高频太阳辐射时间序列生成，用于评估太阳能系统

太阳能被视为全球向气候友好型能源系统过渡的主要支柱。太阳辐射的可变性要求采取额外的平衡措施，以确保稳定和安全的能源供应。为了详细分析此问题，需要具有适当的时间和空间分辨率的太阳辐射时间序列数据。常见的天气模型和卫星仅以最高15分钟的时间分辨率提供太阳表面辐照度。然而，几秒钟至几分钟内辐照度的短期显着变化是由云引起的。通常用于捕获这种可变性的基于地面的测量是昂贵的，并且很少有。因此，从当前可用的卫星图像综合生成时间序列的方法对于研究人员，电网运营商，和项目开发人员。我们正在努力将卫星分辨率提高到1分钟，但这并不是到处都计划，并且不会改变空间分辨率。因此，基本问题仍然存在，如果有其他策略可以准确地获得高时间分辨率的观测值。本文提出了一种方法，可为任意位置生成1分钟的全局和直接法向辐照度的合成时间序列。基于卫星图像导出的云结构参数的神经网络可以对高频太阳辐射的变异性进行分类。结合晴空辐射数据，再现了反映站点典型变异性特征的1分钟时间序列。针对地面观测（BSRN）的测试和验证表明，该方法可以准确地再现频率和斜坡分布等特征。一个应用案例演示了低压电网研究中的用法。