A Data-driven Distribution System Scenario Generation Method with Probabilistic Assessment of PV Station Generation

A data-driven distribution system scenario generation method is proposed in this paper. The model is based on local density based clustering (LDC) algorithm and copula functions, with considering the change of correlation of PV station’s output. LDC is used to classify the historical data into several clusters. In each cluster, copula function is utilized to build the joint probabilistic density functions (PDF) to reflect the inherent relations between PV stations. The scenario data are generated by applying Latin Hypercube Sampling (LHS) to the PDF of PVs’ output power. The proposed method aims to obtain the joint probabilistic distribution of multiple PV outputs more accurately. The proposed method is evaluated in the probabilistic power flow calculation of an distribution network with 30 nodes. The experiment results are compared with the traditional K-means clustering method to validate its performance.