Hybrid Data-Driven and Model-Based Distribution Network Reconfiguration With Lossless Model Reduction

Abstract

Distribution network reconfiguration is an effective method to face the problem of power fluctuation in the power system. Previous studies have focused on mathematical optimization techniques with complex modeling processes and heuristic algorithms with time-consuming solving processes to obtain the optimal reconfiguration strategy. In this article, a hybrid data-driven and model-based distribution network reconfiguration (HDNR) framework is proposed, where the model-based module includes model reduction and goal-oriented clustering to cluster the identical reconfiguration strategies. Here, the data-driven module is implemented through a long short-term memory network to learn the mapping mechanism between load distribution and optimal reconfiguration strategies. The model-driven module and the data-driven module are coupled through the proposed hierarchical network recovery process, which presents the reconfiguration results layer by layer. Finally, the numerical case study on the IEEE 33-bus, IEEE 119-bus, and IEEE 123-bus network shows the validity of the proposed HDNR framework. It is shown that the solution space is reduced, which contributes to reducing computation time and resources. Moreover, the obtained accuracy of the reconfiguration strategy is higher than most existing research even with limited data samples.