Global Sensitivity Analysis of Large Distribution System with PVs Using Deep Gaussian Process

Ketian Ye, Junbo Zhao, Fei Ding, Rui Yang, Xiao Chen, George Dobbins

Research output: Contribution to journalArticlepeer-review

6 Scopus Citations


Global sensitivity analysis (GSA) of voltage to uncertain power injection variations plays an important role for appropriate Volt-VAR optimization. This paper proposes a data-driven GSA method for large-scale distribution systems with a large number of uncertain sources. Specifically, the deep Gaussian process is used to identify the mapping relationship between uncertain power injections and voltages. This allows resorting to the analysis of variance framework to calculate the Sobol indices for GSA. Unlike the existing polynomial chaos expansion and Gaussian process-based approaches, our proposed method has much better scalability. Test results on the EPRI 1747-node K1 circuit with different numbers of uncertain sources with various uncertain levels and different PV distributions demonstrate that the proposed method can achieve accurate GSA.

Original languageAmerican English
Article number9442807
Pages (from-to)4888-4891
Number of pages4
JournalIEEE Transactions on Power Systems
Issue number5
StatePublished - Sep 2021

Bibliographical note

Publisher Copyright:
© 1969-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-80340


  • deep Gaussian process
  • Distribution system analysis
  • global sensitivity analysis
  • PVs
  • Sobol indices


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