A Data-Driven Global Sensitivity Analysis Framework for Three-Phase Distribution System with PVs

Ketian Ye, Junbo Zhao, Can Huang, Nan Duan, Yingchen Zhang, Thomas Field

Research output: Contribution to journalArticlepeer-review

21 Scopus Citations

Abstract

Global sensitivity analysis (GSA) of distribution systems with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution systems. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of PV and load power injections. The sampling-based method approximates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-node distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework.
Original languageAmerican English
Pages (from-to)4809-4819
Number of pages11
JournalIEEE Transactions on Power Systems
Volume36
Issue number5
DOIs
StatePublished - 2021

NREL Publication Number

  • NREL/JA-5D00-79767

Keywords

  • ANOVA kernel
  • distribution system analysis
  • Gaussian process
  • global sensitivity analysis
  • PVs
  • Sobol indices

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