  The limitations of centralized optimization methods for power systems
operation have led to the distributed computing paradigm, particularly in power
distribution systems. The existing techniques reported in recent literature for
solving distributed optimization problems are not viable for power distribution
systems applications. The essential drawback remains a large number of required
communication rounds, i.e., macro-iterations among the computing agents to
solve one instance of the optimization problem; the typical number of
macro-iterations are in the order of 10^2~10^3. In this paper, a new and
scalable distributed optimization method based on Equivalent Network
Approximation (ENApp) is proposed to solve optimal power flow (OPF) for a
balanced radial distribution system. Specifically, the distribution system's
radial topology is leveraged to reduce the decomposed systems into upstream and
downstream network equivalents. The proposed innovations reduce the required
number of macro-iterations/communication-rounds for convergence by order of
magnitude. The approach is validated using IEEE 123-bus and IEEE 8500-node test
systems and is shown to converge to the same optimal solution as obtained using
an equivalent centralized OPF (C-OPF) model.
