A Distributed Photovoltaic Aggregation Uncertainty Characterization Method Based on Quantile Spatial-temporal Graph Neural Network
The quantification of uncertainty in aggregated distributed photovoltaic power generation is crucial for decision control in power systems.A data-driven aggregation quantification method for power uncertainty in distributed photovoltaic power generation was proposed.Firstly,considering the spatial-temporal distribution characteristics of distributed photovoltaics,an uncertainty aggregation model based on spatial-temporal graph convolutional neural networks was proposed,which can effectively mine the spatial-temporal characteristics of data.Secondly,aggregated uncertainty was quantified and characterized without assuming its probability distribution through the combination of spatial-temporal graph convolutional neural networks with quantile regression models.In addition,considering the cross problem of quantiles,a quantile mapping method to avoid the cross problem of quantized results with aggregated uncertainty was proposed.Finally,the effectiveness of the proposed method was verified based on the IEEE-33 bus system.
万方数据
维普
