Multi-Stage Planning of Active Distribution Network Considering Correlation and Time Sequential Features

This paper presents a multi-stage active distribution network (ADN) expansion planning model. The proposed model considers the phased construction of both the distributed power generations (DGs) and the feeders, and the correlation between DGs and loads in the same area. Correlation and time sequential feature samples are generated by Latin hypercube sampling (LHS) technique and the Cholesky decomposition technique, and scene models are generated by K-means method. The ADN investment model is an investment-operation co-optimization one, which includes multi-stage distribution network planning at the upper layer and operation optimization in the lower layer considering active management. According to the changing trend of China's power consumption level, the phase of planning is divided into several stage scenarios based on the medium and long-term load data. The proposed co-optimization model in this paper was solved by particle swarm algorithm. The simulation results of the 33-bus distribution network discussed the benefits of the operational scenarios with correlation between uncertain factors in the distribution network planning. The effectiveness of the proposed multi-stage scenarios model was demonstrated by using the numerical results.