Fast Nonconvex SDP Solvers for Large-scale Power System State Estimation

TL;DR

This paper introduces fast nonconvex SDP-based algorithms for large-scale power system state estimation, achieving near-optimal accuracy with significantly reduced computational time.

Contribution

It develops gradient descent and accelerated gradient descent methods tailored for nonconvex SDP formulations in power system state estimation, ensuring fast convergence and robustness.

Findings

FGD-SE and AGD-SE approach SDP solution accuracy

Significant reduction in computational time compared to traditional SDP methods

Effective handling of outliers and integration of synchrophasor data

Abstract

Fast power system state estimation (SE) solution is of paramount importance for achieving real-time decision making in power grid operations. Semidefinite programming (SDP) reformulation has been shown effective to obtain the global optimum for the nonlinear SE problem, while suffering from high computational complexity. Thus, we leverage the recent advances in nonconvex SDP approach that allows for the simple first-order gradient-descent (GD) updates. Using the power system model, we can verify that the SE objective function enjoys nice properties (strongly convex, smoothness) which in turn guarantee a linear convergence rate of the proposed GD-based SE method. To further accelerate the convergence speed, we consider the accelerated gradient descent (AGD) extension, as well as their robust versions under outlier data and a hybrid GD-based SE approach with additional synchrophasor measurements. Numerical tests on the IEEE 118-bus, 300-bus and the synthetic ACTIVSg2000-bus systems have demonstrated that FGD-SE and AGD-SE, can approach the near-optimal performance of the SDP-SE solution at significantly improved computational efficiency, especially so for AGD-SE.