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COMPARISON OF SOLAR IRRADIANCE SMOOTHING USING A 45-SENSOR NETWORK AND THE WAVELET VARIABILITY MODEL

机译:基于45传感器网络和小波变异性模型的太阳辐照平滑度比较

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With increasing penetrations of solar photovoltaic (PV) power in the electricity grid, the variability of the irra-diance, and therefore power, is important to understand because variable resources can challenge grid operations. Predicting PV variability using one irradiance sensor, as is commonly done, does not account for the smoothing of irradiance over the extent of the power plant. This smoothing is examined using two methods: averaging measurements from many irradiance sensors, and using a model developed by Lave, Kleissl, and Stein [1] called the Wavelet Variability Model. The results show the similarities and differences between two irradiance smoothing models. These two models both show that the smoothing effect is significant for large PV power plants, which means the power plant output has less variability and is easier to integrate into the electricity grid than might have been expected using a single point sensor measurement to predict variability.
机译:随着太阳能光伏(PV)功率在电网中的渗透率不断提高,因其可变的资源可能会挑战电网运行,因此,要了解电度差和功率的可变性非常重要。如通常所做的那样,使用一个辐照度传感器预测PV的变化性并不能解决整个电厂范围内辐照度的平滑问题。使用两种方法检查这种平滑:一种方法是对许多辐照度传感器的测量值求平均;另一种方法是使用由Lave,Kleissl和Stein [1]开发的称为小波变异性模型的模型。结果表明两种辐照度平滑模型之间的异同。这两个模型都表明,平滑效应对于大型PV电厂而言非常重要,这意味着与使用单点传感器测量来预测变异性所期望的相比,电厂输出的变异性较小,并且更易于集成到电网中。

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