ally employed in large-scale interconnected power systems, where state estimation usually involves very tedious com- munications and computations. This paper presents 1) a modified coordination technique that is based on Kalman Filtering, derived from hierarchical state estimation; and 2) a time complexity analysis and experimental implementa- tion to compare central, distributed, and hierarchical state estimation algorithms in terms of computation power and communication bandwidth requirements. Analytical and ex- perimental results on the IEEE 118-bus test bed show that the presented approach, i.e., Hierarchical Kalman Filtering (HKF), needs about 34% communication bandwidth and O( 1 N3 ) computation power in subsystems compared to central state estimation, while giving approximately the same level of estimation precision.