Urban MV and LV distribution grid topology estimation via group Lasso

Yizheng Liao, Yang Weng, Guangyi Liu, Ram Rajagopal

Research output: Contribution to journalArticle

15 Scopus citations

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 medium voltage (MV) and low voltage (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 Pacific Gas and Electric Company residential smart meter data.

Original languageEnglish (US)
Article number8456535
Pages (from-to)12-27
Number of pages16
JournalIEEE Transactions on Power Systems
Volume34
Issue number1
DOIs
StatePublished - Jan 2019

Keywords

  • Graphical model
  • Lasso
  • Power distribution grid
  • Structure learning
  • Topology learning
  • Voltage measurement

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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