A framework for constrained static state estimation in unbalanced distribution networks

TL;DR

This paper introduces a flexible, constrained optimization framework for static state estimation in unbalanced distribution networks, addressing physical complexity and measurement limitations, with open-source implementation and numerical validation.

Contribution

It presents a unifying, adaptable framework for unbalanced static state estimation, enabling easy implementation and comparison of different models in distribution systems.

Findings

Framework effectively handles unbalanced conditions and limited measurements.

Numerical tests demonstrate robustness across various distribution feeders.

Open-source implementation facilitates adoption and further research.

Abstract

State estimation plays a key role in the transition from the passive to the active operation of distribution systems, as it allows to monitor these networks and, successively, to perform control actions. However, designing state estimators for distribution systems carries a significant amount of challenges. This is due to the physical complexity of the networks, e.g., phase unbalance, and limited measurements. Furthermore, the features of the distribution system present significant local variations, e.g., voltage level and number and type of customers, which makes it hard to design a "one-size-fits-all" state estimator. The present paper introduces a unifying framework that allows to easily implement and compare diverse unbalanced static state estimation models. This is achieved by formulating state estimation as a general constrained optimization problem. The advantages of this approach are described and supported by numerical illustration on a large set of distribution feeders. The framework is also implemented and made available open-source.