  The convergence performance of distributed optimization algorithms is of
significant importance to solve optimal power flow (OPF) in a distributed
fashion. In this paper, we aim to provide some insights on how to partition a
power system to achieve a high convergence rate of distributed algorithms for
the solution of an OPF problem. We analyzed several features of the power
network to find a set of suitable partitions with the aim of convergence
performance improvement. We model the grid as a graph and decompose it based on
the edge betweenness graph clustering. This technique provides several
partitions. To find an effective partitioning, we merge the partitions obtained
by clustering technique and analyze them based on characteristics of tie-lines
connecting neighboring partitions. The main goal is to find the best set of
partitions with respect to the convergence speed. We deploy analytical target
cascading (ATC) method to distributedly solve optimization subproblems. We test
the proposed algorithm on the IEEE 118-bus system. The results show that the
algorithm converges faster with a proper partitioning, whereas improper
partitioning leads to a large number of iterations
