Distributed Real-time State Estimation for Power Distribution Grid

Distribution grid is used to provide electric power to end-customers, where voltages from transmission grid are reduced and regulated through series of transformers to customer usable levels. In contrast to transmission grids, distribution grids of today are sparsely measured and much larger and complex than transmission networks, often approaching 10’s of millions of electrical nodes. Traditionally, assessment of the current system state for distribution networks has been attempted using power flow-based approaches and a combination of limited measurements and historical load profiles coupled with heuristics. As the increasing deployment of distributed energy resources (DERs) is causing significant variation in power flow, such approaches are inadequate to provide robust and accurate estimation. This has a direct impact on real-time situational awareness and subsequent control and optimization that rely on an accurate estimate of the current system state.

The team is developing and testing more advanced methods based on dynamic state estimation that employ non-linear Kalman filtering to provide robustness to measurement errors and utilize time-series data from limited measurements. While centralized estimation approaches are very mature and used across multiple industrial assets for online monitoring and control,  a fundamental challenge for the application of state estimation to electrical distribution networks is the sheer size of the problem. The team has developed a novel distributed estimation solution that is practical, scalable and can achieve the same general level of accuracy as attainable by a centralized estimator. The technology development leverages a mature, state-of-the art, in-house estimation library implemented in MATLAB®/Simulink® for rapid prototyping and testing in conjunction with automated tools to produce validated and portable software that can be deployed on a target platform using containerized microservices.