A Robust Parallel Distributed State Estimation for Large Scale Distribution Systems

The growing need and interest in real-time monitoring of large distribution networks motivated by the rapid population of renewable sources, EVs and etc. demand a computationally efficient state estimation framework. This article presents an improved computational framework for implementing a robust state estimator using a multi-core processor. The main contribution of the article is the proposed computational framework along with two partitioning strategies which enable fast and robust state estimation for large scale radial and/or meshed distribution systems. Formulation of the proposed method and its implementation are described in detail. Performance of the estimator is tested by simulations first using a small 84-bus radial distribution system. Then the method's scalability is demonstrated by simulations on two very large scale distribution networks one configured radially and the other meshed each containing over 12 500 buses.