Community-Detection-Based Approaches for Distribution Network Partition

A rational partition is the key prerequisite for the application of distributed algorithms in distribution networks. This paper proposes community-detection-based approaches to a distribution network partition, including a non-overlapping partition and a border-node partitioning method. First, a novel electrical distance is defined to quantify the coupling relationships between buses and it is further used as the edge weight in a transformed equivalent graph. Then, a vertex/link partition community detection approach is applied to over-partition the network into high intra-cohesive and low inter-coupled subregions. Following this, a greedy algorithm and a tabu search method are combined to merge these subregions into target numbers according to the scale similarity principle. The proposed approaches take the influence of three-phase imbalance into consideration and they are decoupled from the power flow. Finally, the approaches are tested on an IEEE 123-bus distribution system and the results verify the effectiveness and the credibility of our proposed methods.