Distributed Interior Point Methods for Optimization in Energy Networks

TL;DR

This paper introduces a decentralized interior point method tailored for energy network optimization, offering guaranteed convergence, low communication needs, and high accuracy, demonstrated on a large-scale power flow problem.

Contribution

It presents a novel decentralized interior point algorithm with guaranteed convergence and low communication overhead for energy network optimization problems.

Findings

Effective on a 708-bus power flow problem

Achieves high solution accuracy with limited iterations

Exhibits fast local convergence even with non-convex constraints

Abstract

This note discusses an essentially decentralized interior point method, which is well suited for optimization problems arising in energy networks. Advantages of the proposed method are guaranteed and fast local convergence also for problems with non-convex constraints. Moreover, our method exhibits a small communication footprint and it achieves a comparably high solution accuracy with a limited number of iterations, whereby the local subproblems are of low computational complexity. We illustrate the performance of the proposed method on a problem from energy systems, i.e., we consider an optimal power flow problem with 708 buses.