基于残差卷积自编码的风光荷场景生成方法

随着风、光等新能源的发展,对含大量风、光电网的调控和运行提出了更高的要求.典型场景是处理该问题的主要方式之一.针对传统聚类生成典型场景的方法易产生数据信息损失、特征提取不够精确等问题,提出了一种基于残差卷积自编码聚类的风光荷不确定性源场景生成方法.首先,利用残差卷积自编码器网络提取风光荷数据的特征,在减少数据信息损失并...

Typical Wind-PV-Load Scenario Generation Based on Residual Convolutional Auto-encoders

With the development of new energy sources, higher requirements are put forward for the regulation and operation of grids with PV and wind power. The typical scenario is one of the main ways to deal with this problem. The traditional method for generating typical scenarios is prone to data information loss and feature extraction inaccuracy. This paper proposes an uncertain wind-PV-load typical scenario generation method based on residual convolution auto-encoders. First, the residual convolution auto-encoders network is used to extract the characteristics of wind-PV-load data to reduce the data dimension while reducing the loss of data information and taking into account the coupling of wind and solar power. Then, reducing the influence of noise data on the experimental results, k-medoids is used for clustering to generate typical scenarios. The actual data collected from a power grid in northwest China is taken as the research object. Comparison with traditional clustering methods such as DBI (Davies-Bouldin Index), CHI (Calinski-Harabasz Index) and other indicators, the feasibility of the proposed method is verified.