The simulation of photovoltaic installations is a major issue for their sizing, their smart grid operation, and their fault detection and diagnosis. In this article, we study in detail every step of the simulation chain, either from the global horizontal irradiance and the ambient temperature (i.e., 4 steps of simulation) or considering the global in-plane irradiance and the module operating temperature (i.e., 1 step of simulation). The average quality estimation of the models is made through the calculations of average annual error between estimations and measurements, from 2016 to 2020. We have shown that the most uncertain step is the conversion of the global irradiance in its diffuse and direct components (17.2%, 2 models tested). If the model goes up to the in-plane irradiance, the average annual error decreases to 5.3% (6 models tested). The photovoltaic module temperature calculation induces an error of less than 2°C (4 models tested with 2 configurations). Meanwhile, the photoelectrical conversion shows a 3.5% error, similar to the measurement uncertainties, considering as input, the modules temperature, and the in-plane irradiance. If the simulation goes from the global irradiance and the ambient temperature measured locally, the estimation leads to a 6.7% average annual error. If the local measurements are not available, we can use the closest meteorological station’s records (13 for our study), and the error becomes 12.1%. Finally, we can also use the satellite images that lead to a 15.2% error, for average per year. The impact of available input shows that modeling the DC photovoltaic production, using global horizontal irradiance and ambient temperature, gives rise to an error of 6.6% for local measurements, 12.1% for weather station measurements, and 15.2% for satellite images estimations. This article thus draws up a review of the existing models, allowing to calculate the DC production of a photovoltaic module, depending on the atmospheric conditions, and highlights the most precise or most critical steps, considering in situ and weather station ground-based measurements, and also estimation from satellite images.

中文翻译：

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真实运行条件下模拟光伏转换每一步的不确定度估计

光伏装置的模拟是其规模、智能电网运行以及故障检测和诊断的主要问题。在本文中，我们从全局水平辐照度和环境温度（即模拟的 4 个步骤）或考虑全局面内辐照度和模块工作温度（即 1模拟步骤）。模型的平均质量估计是通过计算 2016 年至 2020 年估计和测量之间的年平均误差进行的。我们已经表明，最不确定的步骤是全球辐照度在其漫射和直接分量（17.2% , 测试了 2 个模型）。如果模型上升到面内辐照度，则平均年误差降低至 5.3%（测试了 6 个模型）。光伏模块温度计算引起的误差小于 2°C（4 个模型用 2 种配置进行测试）。同时，光电转换显示出 3.5% 的误差，类似于测量不确定度，将模块温度和面内辐照度视为输入。如果模拟来自全球辐照度和本地测量的环境温度，则估计会导致 6.7% 的年均误差。如果当地测量不可用，我们可以使用最近的气象站的记录（我们研究的 13 个），误差变为 12.1%。最后，我们还可以使用导致平均每年 15.2% 误差的卫星图像。可用输入的影响表明，使用全球水平辐照度和环境温度对直流光伏生产进行建模，导致本地测量误差为 6.6%，气象站测量误差为 12.1%，卫星图像估计误差为 15.2%。因此，本文对现有模型进行了回顾，允许根据大气条件计算光伏模块的直流产生，并强调最精确或最关键的步骤，考虑到现场和气象站地面测量，以及卫星图像的估计。