Evaluating the Performance of Distributed Optimal Power Flow Algorithms with Nonideal Communication.

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), Analytic Target Cascading (ATC), and Auxiliary Principal Problem (APP) algorithms in the context of DC Optimal Power Flow (DC OPF) problems. Using several test cases, 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) false data, and (3) intermittent communication failure.