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Title: DWT-Based Aggregated Load Modeling and Evaluation for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint

Abstract

This paper presents an extension of a previously reported discrete wavelet transform (DWT) based load modeling methodology which targets at modeling time-series load profiles to enable the more effective and realistic quasi-static time-series simulation of distribution feeders. The time-series load model is composed of two major parts: 1) The low-resolution field-measured load data which usually have a resolution of 30 or 15 min., 2) The high-resolution load variability model data extracted from the established variability database which can have resolution up to 1 sec. A load aggregation methodology is developed to aggregate the load profiles so that load profiles at different transformer ratings can be effectively modeled. Validation, evaluation, and analysis of the developed load modeling approach has been performed on the IEEE-123 feeder and an actual utility feeder from California. The analysis completed on the two feeders have demonstrated the effectiveness and revealed the value of the developed model for distribution feeder quasi-static time-series simulation at high temporal resolution.

Authors:
 [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:
1467551
Report Number(s):
NREL/CP-5D00-70975
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the IEEE Power and Energy Society General Meeting, 5-9 August 2018, Portland, Oregon
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; load modeling; quasi-static time-series; GSTS; high-resolution load models; distribution system simulation

Citation Formats

Zhu, Xiangqi, and Mather, Barry A. DWT-Based Aggregated Load Modeling and Evaluation for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint. United States: N. p., 2018. Web.
Zhu, Xiangqi, & Mather, Barry A. DWT-Based Aggregated Load Modeling and Evaluation for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint. United States.
Zhu, Xiangqi, and Mather, Barry A. Thu . "DWT-Based Aggregated Load Modeling and Evaluation for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint". United States. https://www.osti.gov/servlets/purl/1467551.
@article{osti_1467551,
title = {DWT-Based Aggregated Load Modeling and Evaluation for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint},
author = {Zhu, Xiangqi and Mather, Barry A},
abstractNote = {This paper presents an extension of a previously reported discrete wavelet transform (DWT) based load modeling methodology which targets at modeling time-series load profiles to enable the more effective and realistic quasi-static time-series simulation of distribution feeders. The time-series load model is composed of two major parts: 1) The low-resolution field-measured load data which usually have a resolution of 30 or 15 min., 2) The high-resolution load variability model data extracted from the established variability database which can have resolution up to 1 sec. A load aggregation methodology is developed to aggregate the load profiles so that load profiles at different transformer ratings can be effectively modeled. Validation, evaluation, and analysis of the developed load modeling approach has been performed on the IEEE-123 feeder and an actual utility feeder from California. The analysis completed on the two feeders have demonstrated the effectiveness and revealed the value of the developed model for distribution feeder quasi-static time-series simulation at high temporal resolution.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {8}
}

Conference:
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