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

Title: Data-Driven Load Diversity and Variability Modeling for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint

Abstract

This paper presents a data-driven load modeling methodology for distribution system quasi-static time-series (QSTS) simulation considering both diversity and variability characteristics of distribution loads. Based on our previous work in [1]-[2], a variability library and diversity library have been established based on the realistic high-resolution data collected from actual utility feeders. Given the load profile for the start-of-circuit load of a feeder, the loads on the feeder nodes can be modeled with both diversity and variability instead of being directly scaled from the substation load profile according to the distribution allocation factors. With diversified load models, the load-induced impact on the feeder operation characteristics, such as voltage ramp and regulator operations, can be better considered in QSTS simulation. The proposed modeling methodology has been tested on both the IEEE 123-bus feeder and an actual utility feeder model, and the simulation results have demonstrated the merits of deploying the proposed load modeling methodology.

Authors:
 [1]; ORCiD logo [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:
1572643
Report Number(s):
NREL/CP-5D00-73146
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2019 IEEE Power and Energy Society General Meeting (IEEE PES GM), 4-8 August 2019, Atlanta, Georgia
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; data-driven; load modeling; diversity; variability; quasi-static time series (QSTS); distribution system

Citation Formats

Zhu, Xiangqi, and Mather, Barry A. Data-Driven Load Diversity and Variability Modeling for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint. United States: N. p., 2019. Web.
Zhu, Xiangqi, & Mather, Barry A. Data-Driven Load Diversity and Variability Modeling for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint. United States.
Zhu, Xiangqi, and Mather, Barry A. Fri . "Data-Driven Load Diversity and Variability Modeling for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint". United States. https://www.osti.gov/servlets/purl/1572643.
@article{osti_1572643,
title = {Data-Driven Load Diversity and Variability Modeling for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint},
author = {Zhu, Xiangqi and Mather, Barry A},
abstractNote = {This paper presents a data-driven load modeling methodology for distribution system quasi-static time-series (QSTS) simulation considering both diversity and variability characteristics of distribution loads. Based on our previous work in [1]-[2], a variability library and diversity library have been established based on the realistic high-resolution data collected from actual utility feeders. Given the load profile for the start-of-circuit load of a feeder, the loads on the feeder nodes can be modeled with both diversity and variability instead of being directly scaled from the substation load profile according to the distribution allocation factors. With diversified load models, the load-induced impact on the feeder operation characteristics, such as voltage ramp and regulator operations, can be better considered in QSTS simulation. The proposed modeling methodology has been tested on both the IEEE 123-bus feeder and an actual utility feeder model, and the simulation results have demonstrated the merits of deploying the proposed load modeling methodology.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {10}
}

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: