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Scenario based technique applied to photovoltaic sources uncertainty

机译：基于场景的技术应用于光伏源不确定性

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摘要

There is an increasing need to forecast power generated by photovoltaic sources in day-ahead power system operation. The electrical energy generated by these renewable sources is an uncertain variable and depends on solar irradiance, which is out of control and depends on climate conditions. The stochastic programming based on various scenarios is an efficient way to deal with such uncertainties. In this research paper, the long term hourly recorded irradiance data in 15 past years are applied to generate the next day's irradiance scenarios. Irradiance determines the operating point of PV panel as well as the generated electrical power. Also, the scenario generation method based on autoregressive and moving average time series is proposed. For decreasing the number of scenarios, backward reduction based on Kantorovich distance is applied. The obtained results confirm the accuracy and ability of the proposed method in forecasting the relevant data. ling ideal wind turbines, ideal rotating devices or ideal wind farms either numerically or experimentally and gives the maximum possible power extractions; thus, any improvement to the performance of a system can be made by this method.
机译：越来越需要在日前电力系统运行中预测由光伏电源产生的电力。这些可再生资源产生的电能是不确定的变量，并且取决于太阳辐照度，而太阳辐照度是不受控制的，并且取决于气候条件。基于各种场景的随机规划是解决此类不确定性的有效方法。在本研究论文中，将过去15年中长期每小时记录的辐照度数据应用于生成第二天的辐照情景。辐照度决定了光伏面板的工作点以及所产生的电能。此外，提出了一种基于自回归和移动平均时间序列的场景生成方法。为了减少场景数量，应用了基于Kantorovich距离的向后缩减。获得的结果证实了该方法在预测相关数据方面的准确性和能力。在数值上或实验上都安装理想的风力涡轮机，理想的旋转设备或理想的风电场，并能最大程度地提取功率;因此，可以通过此方法对系统性能进行任何改进。

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