  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.
