Consensus Clustering for Bi-objective Power Network Partition

Partitioning a complex power network into a number of sub-zones can help realize a divide-and-conquer management structure for the whole system, such as voltage and reactive power control, coherency identification, power system restoration, etc. Extensive partitioning methods have been proposed by defining various distances, applying different clustering methods, or formulating varying optimization models for one specific objective. However, a power network partition may serve two or more objectives, where a trade-off among these objectives is required. This paper proposes a novel weighted consensus clustering-based approach for bi-objective power network partition. By varying the weights of different partitions for different objectives, Pareto improvement can be explored based on the node-based and subset-based consensus clustering methods. Case studies on the IEEE 300-bus test system are conducted to verify the effectiveness and superiority of our proposed method.

Automated summary

A novel weighted consensus clustering-based approach is proposed for bi-objective power network partition, allowing for Pareto improvement. Case studies on the IEEE 300-bus test system demonstrate the effectiveness and superiority of the proposed method.