Solving Ill-Conditioned State-Estimation Problems in Distribution Grids With Hidden-Markov Models of Load Dynamics

Future smart grid operations must rely upon accurate state estimation (SE). Estimation at the distribution level is very challenging since the customary monitoring infrastructure inhibits access to real-time widespread information, precluding redundancy. To overcome the lack of redundancy, we propose to use Markov information on load dynamics instead of load pseudo-measurements directly. The proposed SE methodology Combines a (i) regularization-based method for solving ill-conditioned load-estimation problems with (ii) a Markov model for restricting load variations to probable time-varying load changes. Two Markov approaches are implemented. First, Markov information is used to estimate load one-step ahead, allowing a simple forward dynamic estimation. Then, the SE problem is formulated as a Hidden Markov problem and solved with the Viterbi algorithm to estimate load evolution. Several monitoring contexts are finally analyzed to illustrate the approaches proposed and to discuss the limitations of their application in realistic contexts.