高渗透率分布式光伏接入的新型电力系统净功率预测

在双碳背景下,分布式光伏发电的大规模增加以及并网接入,对新型电力系统带来了巨大的挑战。高渗透率分布式光伏出力与电力负荷因受天气因素的影响,具有较强的不确定性和波动性,这在一定程度上增加了配电网净功率的预测难度。为了提高配电网净功率的预测精度,文章提出了Attention-双向GRU神经网络配电网净功率预测方法。文章首先对光伏出力特性、用户侧负荷特性、以及配电网净功率影响因素进行分析,充分掌握净功率受分布式光伏出力和用户侧负荷变化规律的影响。然后将Attention机制融入到双向GRU神经网络中建立了配电网净功率预测模型。其中,Attention机制赋予输入特征不同的关注度,双向GRU神经网络能够学习到净功率的时序特征,二者的完美结合,大大提升了净功率预测模型的表示能力和泛化能力。实验结果表明,文章提出的方法大大提高了配电网净功率预测精度,且性能优于对比模型。

Net power prediction for new power systems with extremely high permeability distributed energy access

In the dual carbon background, the large-scale increase of distributed photovoltaic power generation and grid-connected access have brought great challenges to the new power system. Due to the influence of weather factors, the high permeability distributed photovoltaic output and power load have strong uncertainty and volatility, which to a certain extent increases the difficulty of predicting the net power of distribution network. In order to improve the prediction accuracy of net power of distribution network, this paper puts forward the net power prediction method of attention-Bidirectional GRU neural network. Firstly, the paper analyzes the photovoltaic output characteristics, user-side load characteristics and the influencing factors of the net power of distribution network, and fully grasps the influence of the net power on the variation law of distributed photovoltaic output and user-side load. Then the net power prediction model of distribution network is established by integrating Attention mechanism into Bidirectional GRU neural network. Among them, The Attention mechanism gives different Attention to the input features, and the Bidirectional GRU neural network can learn the temporal characteristics of net power. The perfect combination of the two greatly improves the representation and generalization ability of net power prediction model. Experimental results show that the proposed method greatly improves the accuracy of net power prediction of