Urban MV and LV Distribution Grid Topology Estimation via Group Lasso

TL;DR

This paper introduces a data-driven method using group lasso regularization to accurately estimate urban distribution grid topology from smart meter data, applicable to both radial and mesh structures.

Contribution

It develops a novel topology estimation approach based on probabilistic graphical models and group lasso, suitable for complex urban grid configurations without requiring bus connectivity monitoring.

Findings

Achieves high accuracy in topology estimation across eight networks.

Effective in both radial and mesh grid structures.

Validated with real smart meter data from PG&E.

Abstract

The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution grid. The topology estimation is a critical step to ensure the robustness of distribution grid operation. However, the bus connectivity and grid topology estimation are usually hard in distribution grids. For example, it is technically challenging and costly to monitor the bus connectivity in urban grids, e.g., underground lines. It is also inappropriate to use the radial topology assumption exclusively because the grids of metropolitan cities and regions with dense loads could be with many mesh structures. To resolve these drawbacks, we propose a data-driven topology estimation method for MV and LV distribution grids by only utilizing the historical smart meter measurements. Particularly, a probabilistic graphical model is utilized to capture the statistical dependencies amongst bus voltages. We prove that the bus connectivity and grid topology estimation problems, in radial and mesh structures, can be formulated as a linear regression with a least absolute shrinkage regularization on grouped variables (\textit{group lasso}). Simulations show highly accurate results in eight MV and LV distribution networks at different sizes and 22 topology configurations using PG\&E residential smart meter data.