Urban MV and LV Distribution Grid Topology Estimation via Group Lasso

Yizheng Liao, Yang Weng, Guangyi Liu, Ram Rajagopal

Research output: Contribution to journalArticle

4 Citations (Scopus)

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 (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.

Original languageEnglish (US)
JournalIEEE Transactions on Power Systems
DOIs
StateAccepted/In press - Sep 5 2018

Fingerprint

Topology
Smart meters
Energy resources
Electric power distribution
Linear regression
Electric potential

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

Urban MV and LV Distribution Grid Topology Estimation via Group Lasso. / Liao, Yizheng; Weng, Yang; Liu, Guangyi; Rajagopal, Ram.

In: IEEE Transactions on Power Systems, 05.09.2018.

Research output: Contribution to journalArticle

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