skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Representative Day Selection Using Statistical Bootstrapping for Accelerating Annual Distribution Simulations

Abstract

Capturing technical and economic impacts of solar photovoltaics (PV) and other distributed energy resources (DERs) on electric distribution systems can require high-time resolution (e.g. 1 minute), long-duration (e.g. 1 year) simulations. However, such simulations can be computationally prohibitive, particularly when including complex control schemes in quasi-steady-state time series (QSTS) simulation. Various approaches have been used in the literature to down select representative time segments (e.g. days), but typically these are best suited for lower time resolutions or consider only a single data stream (e.g. PV production) for selection. We present a statistical approach that combines stratified sampling and bootstrapping to select representative days while also providing a simple method to reassemble annual results. We describe the approach in the context of a recent study with a utility partner. This approach enables much faster QSTS analysis by simulating only a subset of days, while maintaining accurate annual estimates.

Authors:
ORCiD logo [1];  [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1423545
Report Number(s):
NREL/CP-5D00-67605
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 23-26 April 2017, Washington, D.C.
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; power system simulation; distributed power generation; solar power generation; statistical selection; quasi-steady-state-time-series simulation

Citation Formats

Palmintier, Bryan S, Bugbee, Bruce, and Gotseff, Peter. Representative Day Selection Using Statistical Bootstrapping for Accelerating Annual Distribution Simulations. United States: N. p., 2017. Web. doi:10.1109/ISGT.2017.8086066.
Palmintier, Bryan S, Bugbee, Bruce, & Gotseff, Peter. Representative Day Selection Using Statistical Bootstrapping for Accelerating Annual Distribution Simulations. United States. doi:10.1109/ISGT.2017.8086066.
Palmintier, Bryan S, Bugbee, Bruce, and Gotseff, Peter. Tue . "Representative Day Selection Using Statistical Bootstrapping for Accelerating Annual Distribution Simulations". United States. doi:10.1109/ISGT.2017.8086066.
@article{osti_1423545,
title = {Representative Day Selection Using Statistical Bootstrapping for Accelerating Annual Distribution Simulations},
author = {Palmintier, Bryan S and Bugbee, Bruce and Gotseff, Peter},
abstractNote = {Capturing technical and economic impacts of solar photovoltaics (PV) and other distributed energy resources (DERs) on electric distribution systems can require high-time resolution (e.g. 1 minute), long-duration (e.g. 1 year) simulations. However, such simulations can be computationally prohibitive, particularly when including complex control schemes in quasi-steady-state time series (QSTS) simulation. Various approaches have been used in the literature to down select representative time segments (e.g. days), but typically these are best suited for lower time resolutions or consider only a single data stream (e.g. PV production) for selection. We present a statistical approach that combines stratified sampling and bootstrapping to select representative days while also providing a simple method to reassemble annual results. We describe the approach in the context of a recent study with a utility partner. This approach enables much faster QSTS analysis by simulating only a subset of days, while maintaining accurate annual estimates.},
doi = {10.1109/ISGT.2017.8086066},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Oct 03 00:00:00 EDT 2017},
month = {Tue Oct 03 00:00:00 EDT 2017}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: