Characterization of state estimation biases

Control and operation of electric power system is based on the ability to determine the state of the system in real time. State estimation (SE) has been introduced in the 60s to achieve this objective. The initial implementation was based on single phase measurements and a power system model that is assumed to operate under single frequency, balanced conditions and symmetric system model. These assumptions are still prevalent today. The single frequency, balanced and symmetric system assumptions have simplified the implementation, but have generated practical problems. The experience is that the state estimation problem does not have 100% performance, i.e. there are cases and time periods that the SE algorithm will not converge. There are practical and theoretical reasons for this and they are explained in the paper. Recent mergers and mandated RTOs as well as recent announcements for formation of mega RTOs will result in the application of the SE in systems of unprecedented size. We believe that these practical and theoretical issues will become of greater importance. There are scientists that believe that the SE problem is scalable meaning that it will work for the mega RTOs the same way as it performs now for medium-large systems. There are scientists that they believe this is not true. The fact of the matter is that no-one has investigated the problem, let alone perform numerical experiments to prove or disprove any claims. This paper identifies a number of issues relative to SE of mega RTOs and provides some preliminary results from numerical experiments for the relation between the SE algorithm performance and the power system size.