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Title: Load Modeling – A Review

Abstract

Load modeling has significant impact on power system studies. This paper presents a review on load modeling and identification techniques. Load models can be classified into two broad categories: static and dynamic models, while there are two types of approaches to identify model parameters: measurement-based and component-based. Load modeling has received more attention in recent years because of the renewable integration, demand-side management, and smart metering devices. However, the commonly used load models are outdated, and cannot represent emerging loads. There is a need to systematically review existing load modeling techniques and suggest future research directions to meet the increasing interests from industry and academia. In this study, we provide a thorough survey on the academic research progress and industry practices, and highlight existing issues and new trends in load modeling.

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [2];  [3];  [4];  [5]
  1. Iowa State Univ., Ames, IA (United States)
  2. Southern Methodist Univ., Dallas, TX (United States)
  3. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  4. Siemens Power Technologies International, Distribution Planning and Microgrids, Schenectady, NY (United States)
  5. Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1435710
Report Number(s):
NREL/JA-5D00-68443
Journal ID: ISSN 1949-3053
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Volume: 9; Journal Issue: 6; Journal ID: ISSN 1949-3053
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; load modeling; dynamic models; static models; model identification; distributed generation; demand side management

Citation Formats

Arif, Anmar, Wang, Zhaoyu, Wang, Jianhui, Mather, Barry A., Bashualdo, Hugo, and Zhao, Dongbo. Load Modeling – A Review. United States: N. p., 2017. Web. doi:10.1109/TSG.2017.2700436.
Arif, Anmar, Wang, Zhaoyu, Wang, Jianhui, Mather, Barry A., Bashualdo, Hugo, & Zhao, Dongbo. Load Modeling – A Review. United States. doi:10.1109/TSG.2017.2700436.
Arif, Anmar, Wang, Zhaoyu, Wang, Jianhui, Mather, Barry A., Bashualdo, Hugo, and Zhao, Dongbo. Tue . "Load Modeling – A Review". United States. doi:10.1109/TSG.2017.2700436. https://www.osti.gov/servlets/purl/1435710.
@article{osti_1435710,
title = {Load Modeling – A Review},
author = {Arif, Anmar and Wang, Zhaoyu and Wang, Jianhui and Mather, Barry A. and Bashualdo, Hugo and Zhao, Dongbo},
abstractNote = {Load modeling has significant impact on power system studies. This paper presents a review on load modeling and identification techniques. Load models can be classified into two broad categories: static and dynamic models, while there are two types of approaches to identify model parameters: measurement-based and component-based. Load modeling has received more attention in recent years because of the renewable integration, demand-side management, and smart metering devices. However, the commonly used load models are outdated, and cannot represent emerging loads. There is a need to systematically review existing load modeling techniques and suggest future research directions to meet the increasing interests from industry and academia. In this study, we provide a thorough survey on the academic research progress and industry practices, and highlight existing issues and new trends in load modeling.},
doi = {10.1109/TSG.2017.2700436},
journal = {IEEE Transactions on Smart Grid},
number = 6,
volume = 9,
place = {United States},
year = {2017},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
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Cited by: 24 works
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