Title: Robust and Scalable Power System State Estimation via Composite Optimization,
Abstract: In today's cyber-enabled smart grids, high penetration of uncertain
renewables, purposeful manipulation of meter readings, and the need for
wide-area situational awareness, call for fast, accurate, and robust power
system state estimation. The least-absolute-value (LAV) estimator is known for
its robustness relative to the weighted least-squares (WLS) one. However, due
to nonconvexity and nonsmoothness, existing LAV solvers based on linear
programming are typically slow, hence inadequate for real-time system
monitoring. This paper develops two novel algorithms for efficient LAV
estimation, which draw from recent advances in composite optimization. The
first is a deterministic linear proximal scheme that handles a sequence of
convex quadratic problems, each efficiently solvable either via off-the-shelf
algorithms or through the alternating direction method of multipliers.
Leveraging the sparse connectivity inherent to power networks, the second
scheme is stochastic, and updates only \emph{a few} entries of the complex
voltage state vector per iteration. In particular, when voltage magnitude and
(re)active power flow measurements are used only, this number reduces to one or
two, \emph{regardless of} the number of buses in the network. This
computational complexity evidently scales well to large-size power systems.
Furthermore, by carefully \emph{mini-batching} the voltage and power flow
measurements, accelerated implementation of the stochastic iterations becomes
possible. The developed algorithms are numerically evaluated using a variety of
benchmark power networks. Simulated tests corroborate that improved robustness
can be attained at comparable or markedly reduced computation times for medium-
or large-size networks relative to the "workhorse" WLS-based Gauss-Newton
iterations.