Distributed State Estimation for AC Power Systems using Gauss-Newton ALADIN

Abstract

This paper proposes a structure exploiting algorithm for solving non-convex power system state estimation problems in distributed fashion. Because the power flow equations in large electrical grid networks are non-convex equality constraints, we develop a tailored state estimator based on Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method, which can handle the nonlinearities efficiently. Here, our focus is on using Gauss-Newton Hessian approximations within ALADIN in order to arrive at at an efficient (computationally and communicationally) variant of ALADIN for network maximum likelihood estimation problems. Analyzing an IEEE 30-Bus system we illustrate how the proposed algorithm can be used to solve highly nontrivial network state estimation problems. We also compare the method with existing distributed parameter estimation codes in order to illustrate its performance.2 Sometimes distributed approaches are also called hierarchical depending on the amount of central coordination [11].

Rapid Scalable Distributed Power Flow with Open-Source Implementation

“…In addition, Houska et al (2016) proposed the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN) that is devised for non-convex problems with local convergence guarantee. It has found widespread application for power flow analysis of small-and mediumsized power systems (Engelmann et al, 2018;Meyer-Huebner et al, 2019;Du et al, 2019). ALADIN shares the same idea with ADMM-update primal variables in an alternating fashion.…”

ALADIN‐—An open‐source MATLAB toolbox for distributed non‐convex optimization

“…Distributed non‐convex optimization is of significant interest in various engineering domains. These domains range from electrical power systems, 1‐4 transportation problems, 5 via machine learning, 6 to distributed control, 5,7‐9 and distributed estimation 10‐13 . However, only few software toolboxes for distributed optimization are currently available.…”

“…These domains range from electrical power systems, [1][2][3][4] transportation problems, 5 via machine learning, 6 to distributed control, 5,[7][8][9] and distributed estimation. [10][11][12][13] However, only few software toolboxes for distributed optimization are currently available. Moreover, these toolboxes are typically tailored to specific applications and often focus on convex problems.…”