Photovoltaic (PV) systems are considered an important pillar in the energy transition because they are usually located near the consumers. In order to provide accurate PV system models, e.g. for microgrid simulation or hybrid-physical forecast models, it is of high importance to know the underlying PV system parameters, such as location, panel orientation and peak power. In most open PV generation databases, these parameters are missing or are inaccurate.In this paper, we present a framework based on particle swarm optimisation and the PVWatts model to estimate PV system parameters using only power feed-in measurements and satellite-based ERA5 climate reanalysis data. Our sensitivity analysis points out the most relevant PV system parameters, which are panel and inverter peak power, panel orientation, system location and a small but not negligible influence of ambient temperature and albedo. The detailed evaluation on one exemplary PV system shows an acceptable accuracy in panel azimuth and tilt for the use in microgrid PV system simulation. The extracted location has less than 25 km of positioning error in the best case, which is more than satisfying with respect to the underlying data resolution of the ERA5 dataset. Similar results are observed for 10 systems in Europe and the USA.

中文翻译：

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从功率测量时间序列中提取位置和太阳系参数

光伏 (PV) 系统被认为是能源转型的重要支柱，因为它们通常位于消费者附近。为了提供准确的光伏系统模型，例如用于微电网模拟或混合物理预测模型，了解潜在的光伏系统参数（例如位置、面板方向和峰值功率）非常重要。在大多数开放的光伏发电数据库中，这些参数缺失或不准确。在本文中，我们提出了一个基于粒子群优化和 PVWatts 模型的框架，以仅使用馈电测量和基于卫星的 ERA5 气候来估计光伏系统参数再分析数据。我们的敏感性分析指出了最相关的光伏系统参数，即面板和逆变器峰值功率、面板方向、系统位置和环境温度和反照率的影响很小但不可忽略。对一个示例性光伏系统的详细评估表明，在微电网光伏系统模拟中使用的面板方位角和倾斜度具有可接受的精度。在最佳情况下，提取的位置具有小于 25 公里的定位误差，这对于 ERA5 数据集的基础数据分辨率而言是令人满意的。在欧洲和美国的 10 个系统中观察到类似的结果。这在 ERA5 数据集的基础数据分辨率方面非常令人满意。在欧洲和美国的 10 个系统中观察到类似的结果。这在 ERA5 数据集的基础数据分辨率方面非常令人满意。在欧洲和美国的 10 个系统中观察到类似的结果。