Assessing the Impacts of Nonideal Communications on Distributed Optimal Power Flow Algorithms

TL;DR

This paper evaluates how nonideal communication conditions, such as noise and failures, affect the performance of distributed optimization algorithms in power system operations, focusing on convergence and solution quality.

Contribution

It provides a comparative analysis of ADMM, ATC, and APP algorithms under various nonideal data conditions in DC optimal power flow problems.

Findings

ADMM shows robust convergence under noisy data.

Communication failures significantly impact solution accuracy.

Algorithm performance varies with data quality issues.

Abstract

Power system operators are increasingly looking toward distributed optimization to address various challenges facing electric power systems. To assess their capabilities in environments with nonideal communications, this paper investigates the impacts of data quality on the performance of distributed optimization algorithms. Specifically, this paper compares the performance of the Alternating Direction Method of Multipliers (ADMM), Analytical Target Cascading (ATC), and Auxiliary Problem Principle (APP) algorithms in the context of DC Optimal Power Flow (DC OPF) problems. Using several test systems, this paper characterizes the performance of these algorithms in terms of their convergence rates and solution quality under three data quality nonidealities: (1) additive Gaussian noise, (2) bad data (large error), and (3) intermittent communication failure.