  This paper proposes group-based distributed optimization algorithms on top of
intelligent partitioning for the optimal power flow (OPF) problem. Radial
partitioning of the graph of a network is introduced as a systematic way to
split a large-scale problem into more tractable sub-problems, which can
potentially be solved efficiently with methods such as convex relaxations. The
simple implementation of a Distributed Consensus Algorithm (DiCA) with very few
parameters makes it viable for different parameter selection methods, which are
crucial for the fast convergence of the distributed algorithms. The DiCA
algorithm returns more accurate solutions to the tested problems with fewer
iterations than component-based algorithms. Our numerical results show the
performance of the algorithms for different power network instances and the
effect of parameter selection. A software package DiCARP is created, which is
implemented in Python using the Pyomo optimization package.

Optimization and Control;Systems and Control;Systems and Control