Distributed State Estimation for Multi-Feeder Distribution Grids

The real-time monitoring of electric distribution grids via state estimation is a fundamental requirement to deploy smart automation and control in the distribution system. Due to the large size of distribution networks and the poor coverage of measurement instrumentation on the field, designing fast state estimation algorithms and achieving accurate results are two major challenges associated to distribution system state estimation. In this paper, an efficient and accurate solution for performing state estimation in multi-feeder radial distribution grids is presented. The proposed algorithm is based on a two-step approach. In the first step, state estimation is performed in parallel on the different feeders suitably processing the available measurements and pseudo-measurements and taking into account their uncertainty characteristics. In the second step, the results on each feeder are post-processed to refine the estimations and to improve the accuracy performance. To this purpose, the second step considers how measurement uncertainties propagate towards the final estimates and how measurements shared among the feeders could adversely affect the final estimation. Performed tests show that the conceived design leads to accuracy performance very close to those achievable by running state estimation on the full grid. At the same time, the parallelization of the estimation process on the different feeders allows decentralizing the state estimation problem, with the associated benefits in terms of computation time and distribution of the communication and storage requirements.