Validation of Synthetic U.S. Electric Power Distribution System Data Sets

Venkat Krishnan, Bruce Bugbee, Tarek Elgindy, Carlos Mateo, Pablo Duenas, Fernando Postigo, Jean Sebastien Lacroix, Tomas Gomez San Roman, Bryan Palmintier

Research output: Contribution to journalArticlepeer-review

34 Scopus Citations

Abstract

There is a strong need for synthetic yet realistic distribution system test data sets that are as diverse, large, and complex to solve as real systems. Such data sets can facilitate the development of advanced algorithms and the assessment of emerging distributed energy resources while avoiding the need to acquire proprietary critical infrastructure or private data. Such synthetic data sets, however, are useful only if they are realistic enough to look and behave similarly to actual systems. This paper presents a comprehensive framework for validating synthetic distribution data sets using a three-pronged statistical, operational, and expert validation approach. It also presents a set of statistical and operational metric targets for achieving realistic data sets based on detailed characterization of more than 10,000 real U.S. utility feeders. The paper demonstrates the use of the proposed validation approach to validate three large-scale synthetic data sets developed by the authors representing Santa Fe, New Mexico; Greensboro, North Carolina; and the San Francisco Bay Area, California.

Original languageAmerican English
Article number9043892
Pages (from-to)4477-4489
Number of pages13
JournalIEEE Transactions on Smart Grid
Volume11
Issue number5
DOIs
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© 2010-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-72584

Keywords

  • Electric distribution test feeders
  • power flow
  • smart grid use case
  • statistical metrics
  • synthetic data~sets
  • validation

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