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Title: Distribution System Parameter and Topology Estimation Applied to Resolve Low-Voltage Circuits on Three Real Distribution Feeders

Abstract

Accurate distribution secondary low-voltage circuit models are needed to enhance overall distribution system operations and planning, including effective monitoring and coordination of distributed energy resources located in the secondary circuits. We present a full-scale demonstration across three real feeders of a computationally efficient approach for estimating the secondary circuit topologies and parameters using historical voltage and power measurements provided by smart meters. The method is validated against several secondary configurations, and compares favorably to satellite imagery and the utility secondary model. Feeder-wide results show how much parameters can vary from simple assumptions. In conclusion, model sensitivities are tested, demonstrating only modest amounts of data and resolutions of data measurements are needed for accurate parameter and topology results.

Authors:
 [1];  [1];  [2]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Electric Power Research Institute, Palo Alto, CA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
OSTI Identifier:
1529289
Report Number(s):
SAND-2018-8168J
Journal ID: ISSN 1949-3029; 666198
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Sustainable Energy
Additional Journal Information:
Journal Volume: 10; Journal Issue: 3; Journal ID: ISSN 1949-3029
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; electrical engineering; energy management; resistance

Citation Formats

Lave, Matthew Samuel, Reno, Matthew J., and Peppanen, Jouni. Distribution System Parameter and Topology Estimation Applied to Resolve Low-Voltage Circuits on Three Real Distribution Feeders. United States: N. p., 2019. Web. doi:10.1109/TSTE.2019.2917679.
Lave, Matthew Samuel, Reno, Matthew J., & Peppanen, Jouni. Distribution System Parameter and Topology Estimation Applied to Resolve Low-Voltage Circuits on Three Real Distribution Feeders. United States. https://doi.org/10.1109/TSTE.2019.2917679
Lave, Matthew Samuel, Reno, Matthew J., and Peppanen, Jouni. Mon . "Distribution System Parameter and Topology Estimation Applied to Resolve Low-Voltage Circuits on Three Real Distribution Feeders". United States. https://doi.org/10.1109/TSTE.2019.2917679. https://www.osti.gov/servlets/purl/1529289.
@article{osti_1529289,
title = {Distribution System Parameter and Topology Estimation Applied to Resolve Low-Voltage Circuits on Three Real Distribution Feeders},
author = {Lave, Matthew Samuel and Reno, Matthew J. and Peppanen, Jouni},
abstractNote = {Accurate distribution secondary low-voltage circuit models are needed to enhance overall distribution system operations and planning, including effective monitoring and coordination of distributed energy resources located in the secondary circuits. We present a full-scale demonstration across three real feeders of a computationally efficient approach for estimating the secondary circuit topologies and parameters using historical voltage and power measurements provided by smart meters. The method is validated against several secondary configurations, and compares favorably to satellite imagery and the utility secondary model. Feeder-wide results show how much parameters can vary from simple assumptions. In conclusion, model sensitivities are tested, demonstrating only modest amounts of data and resolutions of data measurements are needed for accurate parameter and topology results.},
doi = {10.1109/TSTE.2019.2917679},
journal = {IEEE Transactions on Sustainable Energy},
number = 3,
volume = 10,
place = {United States},
year = {Mon May 20 00:00:00 EDT 2019},
month = {Mon May 20 00:00:00 EDT 2019}
}

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Cited by: 18 works
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