Distributed Dual Subgradient Methods with Averaging and Applications to Grid Optimization

TL;DR

This paper analyzes the finite-time performance of a distributed dual subgradient method, improving convergence rates and demonstrating its effectiveness on various power grid optimization problems through numerical experiments.

Contribution

It provides improved convergence guarantees for the DDSG algorithm with constant step-size and applies it to complex grid optimization problems.

Findings

Enhanced convergence rate from O(log T/√T) to O(1/√T)

Successful application to multiple power grid optimization scenarios

Insights into the limitations of Nesterov acceleration in DDSG

Abstract

We study finite-time performance of a recently proposed distributed dual subgradient (DDSG) method for convex constrained multi-agent optimization problems. The algorithm enjoys performance guarantees on the last primal iterate, as opposed to those derived for ergodic means for vanilla DDSG algorithms. Our work improves the recently published convergence rate of $\Ocal(\log T/\sqrt{T})$ with decaying step-sizes to $\Ocal(1/\sqrt{T})$ with constant step-size on a metric that combines suboptimality and constraint violation. We then numerically evaluate the algorithm on three grid optimization problems. Namely, these are tie-line scheduling in multi-area power systems, coordination of distributed energy resources in radial distribution networks, and joint dispatch of transmission and distribution assets. The DDSG algorithm applies to each problem with various relaxations and linearizations of the power flow equations. The numerical experiments illustrate various properties of the DDSG algorithm--comparison with vanilla DDSG, impact of the number of agents, and why Nesterov-style acceleration fails in DDSG settings.