A Voltage Inference Framework for Real-Time Observability in Active Distribution Grids

Active distribution grids are gaining traction to meet the growing environmental, socio-economic, and sustain ability targets. Various advanced smart grid technologies have been deployed to facilitate the integration of distributed energy resources (DERs) by supporting the bi-directional power flow. However, the spatial-temporal sparse measurements from sensors cause the limited observability of distribution grids and bring challenges to optimal grid management, especially at the grid edge. This paper presents a novel inference framework, referred to as "Voltage Inference" to overcome the observability issues. The proposed framework employs a prediction step based on the Multivariate Taylor series approximation, followed by a corrector step that minimizes the estimation error to infer the otherwise unknown voltages from the available measurements. Numerical results on the IEEE 13-bus test feeder validate the accuracy and computational performance of the proposed framework.