Efficient Network Partitioning: Application for Decentralized State Estimation in Power Distribution Grids

Biswajeet Rout, Govind Saraswat, Balasubramaniam Natarajan

Research output: Contribution to conferencePaper

Abstract

Increase in the proliferation of distributed energy resources require real-time situational awareness for efficient grid operations. State estimation plays an important role for the real-time control and management of the power grid. As the sensing infrastructure grows, aggregating and handling high volumes of data at a centralized location is extremely difficult. To address this challenge, this paper first proposes a novel and efficient hier-archical spectral clustering-based network partitioning algorithm followed by a decentralized compressive sensing (DCS)-based state estimation. The applicability of the proposed network partitioning algorithm is tested on an IEEE 123-bus network, an IEEE 8,500-node system, and a 6,000+ node distribution network. The results shows that the proposed approach efficiently divides the network into multiple sub-networks with the minimum number of edge connections among the neighbors. Then, we perform DCS-based state estimation on the 6,000+ node distribution network after dividing the network into 18 optimal partitions. Simulation results show that the DCS-based state estimation recovers the system states with high accuracy and low complexity.
Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2023
Event2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) - Washington, D.C.
Duration: 16 Jan 202319 Jan 2023

Conference

Conference2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
CityWashington, D.C.
Period16/01/2319/01/23

Bibliographical note

See NREL/CP-5D00-83876 for preprint

NREL Publication Number

  • NREL/CP-5D00-86223

Keywords

  • alternating direction method of multipliers
  • compressive sensing
  • decentralized state estimation
  • network partition
  • power distribution network
  • spectral clustering

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