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

TL;DR

This paper introduces a distributed computing method with a novel decomposition technique for efficiently solving large-scale, radial power system optimal power flow problems, achieving faster convergence and network-level optimal solutions.

Contribution

It presents a new distributed algorithm leveraging radial network properties to significantly reduce iteration count in solving OPF problems.

Findings

Achieves faster convergence compared to traditional methods.

Ensures solutions are close to network-level optimal solutions.

Reduces computational complexity for large-scale systems.

Abstract

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