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This chapter aspires to glean some of the recent advances in power systemstate estimation (PSSE), though our collection is not exhaustive by any means.The Cram{\'e}r-Rao bound, a lower bound on the (co)variance of any unbiasedestimator, is first derived for the PSSE setup. After reviewing the classicalGauss-Newton iterations, contemporary PSSE solvers leveraging relaxations toconvex programs and successive convex approximations are explored. Adisciplined paradigm for distributed and decentralized schemes is subsequentlyexemplified under linear(ized) and exact grid models. Novel bad data processingmodels and fresh perspectives linking critical measurements to cyber-attacks onthe state estimator are presented. Finally, spurred by advances in onlineconvex optimization, model-free and model-based state trackers are reviewed.

PSSE Redux: Convex Relaxation, Decentralized, Robust, and Dynamic Approaches