A Novel Node Flexibility Evaluation Method of Active Distribution Network for SNOP Integration

With the rising penetration levels of random resources such as renewable energy (RE) and electric vehicles (EVs), the uncertainty of active distribution network (ADN) is inherently increasing. To deal with disturbances and enhance stable operation, many power electronic devices such as Soft Normally Open Point (SNOP) have been integrated to ADN. This paper proposes a novel node flexibility evaluation method of ADN for SNOP integration. Firstly, operation scenarios of ADN are generated based on the k-medoids and k-means clustering method. A hybrid algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) is used in the scenarios. The above is the demand assessment for the flexibility evaluation. Then, RE consumption, load satisfaction, voltage deviation and netload fluctuation of each node are obtained from scenarios and weighted by the information entropy weight method (EWM) to form the comprehensive flexibility index. Nodes are classified according to the evaluation results, which provide the integrated location of SNOP. Finally, a case study showed the differences between the proposed method and others.