Supervised Learning for Distribution Secondary Systems Modeling: Improving Solar Interconnection Processes

Wenbo Wang, Jeremy Keen, Julieta Giraldez, Kyle Baranko, Brent Lunghino, Dan Morris, Frances Bell, Aram Shumavon, Kaiya Levine, Teddy Ward, Adarsh Dave, Jason Bank

Research output: Contribution to journalArticlepeer-review

6 Scopus Citations

Abstract

The current interconnection process and hosting capacity analysis for distributed energy resources (DERs), such as photovoltaics (PV) and battery energy storage systems, are based on analyzing grid network constraints (voltage and thermal) using only medium-voltage distribution network models. This is because most utilities do not have secondary low-voltage system models that connect service transformers and residential customers. This is important because in many cases the main impact of interconnecting DERs could occur on the low-voltage distribution systems. This paper proposes a supervised learning method to approximate local secondary models to improve the interconnection process. The proposed supervised learning method includes a decision tree model that predicts the secondary topology and a logistic regression model that predicts conductor types. The case studies demonstrate the benefits of including secondary low-voltage circuits in the interconnection process. The proposed modeling methodology is readily scalable and thus can reduce the cost and effort of PV interconnection for the industry and stakeholders.

Original languageAmerican English
Pages (from-to)948-956
Number of pages9
JournalIEEE Transactions on Sustainable Energy
Volume13
Issue number2
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2010-2012 IEEE.

NREL Publication Number

  • NREL/JA-5C00-80234

Keywords

  • decision tree
  • Distribution secondary
  • logistic regression
  • solar interconnection
  • supervised learning

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