On Optimal Grid Partitioning for Distributed Optimization of Reactive Power Dispatch

Distributed optimization has been shown to be one promising method for tackling reactive power dispatch, however the performance of distributed algorithms is known to be dependent on how the given problem is partitioned. The question of how to optimally partition a power grid for use in distributed optimization remains open in the literature. In the present paper, we test partitions generated by the graph partitioned KaFFPa, METIS, and spectral clustering using five edge-weighting metrics. The standard IEEE 14, 30, and 57 bus models are used as benchmark case studies and the Augmented Lagrangian Alternating Direction Inexact Newton algorithm is used as the distributed optimization algorithm. It is shown that performance varies drastically depending on which partitioner and weighting is used. Overall, KaFFPa with weightings given by the Y-bus matrix yields the best results.