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Solving the non-convex optimal power flow (OPF) problem for large-scale powerdistribution systems is computationally expensive. An alternative is to solvethe relaxed convex problem or linear approximated problem, but these methodslead to sub-optimal or power flow infeasible solutions. In this paper, wepropose a fast method to solve the OPF problem using distributed computingalgorithms combined with a decomposition technique. The full network-level OPFproblem is decomposed into multiple smaller sub-problems defined for eachdecomposed area or node that can be easily solved using off-the-shelf nonlinearprogramming (NLP) solvers. Distributed computing approach is proposed via whichsub-problems achieve consensus and converge to network-level optimal solutions.The novelty lies in leveraging the nature of power flow equations in radialnetwork topologies to design effective decomposition techniques that reduce thenumber of iterations required to achieve consensus by an order of magnitude.

Distributed Computing for Scalable Optimal Power Flow in Large Radial Electric Power Distribution Systems with Distributed Energy Resources