In view of the problem of insufficient consideration of the spatiotemporal correlation between photovoltaic(PV)and load in typical scenarios generated in the distribution networks with PV,this paper proposes a method for extracting typical PV and load scenarios based on Laplace matrix dimensionality reduction and Gaussian mixture model(GMM).Firstly,it uses the historical operating data of PV and load to construct a daily scenario description matrix for PV and load.Then,the probability density function of the description matrix is obtained through dimensionality reduction of Laplacian eigenmaps(LE)and GMM.Finally,after filtering through Wasserstein distance,the extraction of typical solar and load daily scenarios is completed.The operation data from a PV distribution network in Guangdong province is uses for the experiment.The paper compares the scenario generation method based on k-means clustering,Latin hypercube sampling method and the proposed scenario joint method.The results show that the typical scenarios generated by the proposed method have smaller errors,faster speed,and fewer quantities compared to the actual operating scenarios of the distribution network.
关键词
典型场景/拉普拉斯映射/Wasserstein距离/高斯混合模型/场景描述矩阵
Key words
typical scenario/Laplacian eigenmaps/Wasserstein distance/Gaussian mixture model/scenario description matri
维普
万方数据