  This chapter aspires to glean some of the recent advances in power system
state 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 unbiased
estimator, is first derived for the PSSE setup. After reviewing the classical
Gauss-Newton iterations, contemporary PSSE solvers leveraging relaxations to
convex programs and successive convex approximations are explored. A
disciplined paradigm for distributed and decentralized schemes is subsequently
exemplified under linear(ized) and exact grid models. Novel bad data processing
models and fresh perspectives linking critical measurements to cyber-attacks on
the state estimator are presented. Finally, spurred by advances in online
convex optimization, model-free and model-based state trackers are reviewed.
