A Method for Extracting Typical Scenes of Photovoltaic and Load Considering Temporal and Spatial Distribution Characteristics
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.
typical scenarioLaplacian eigenmapsWasserstein distanceGaussian mixture modelscenario description matri
万方数据
维普
