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Title: Reduced-Order Modeling of Aggregated Thermostatic Loads With Demand Response

Abstract

Demand Response is playing an increasingly important role in smart grid control strategies. Modeling the behavior of populations of appliances under demand response is especially important to evaluate the effectiveness of these demand response programs. In this paper, an aggregated model is proposed for a class of Thermostatically Controlled Loads (TCLs). The model efficiently includes statistical information of the population, systematically deals with heterogeneity, and accounts for a second-order effect necessary to accurately capture the transient dynamics in the collective response. However, an accurate characterization of the collective dynamics however requires the aggregate model to have a high state space dimension. Most of the existing model reduction techniques require the stability of the underlying system which does not hold for the proposed aggregated model. In this work, a novel model reduction approach is developed for the proposed aggregated model, which can significantly reduce its complexity with small performance loss. The original and the reducedorder aggregated models are validated against simulations of thousands of detailed building models using GridLAB-D, which is a realistic open source distribution simulation software. Index Terms – demand response, aggregated model, ancillary

Authors:
; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1076721
Report Number(s):
PNNL-SA-86238
TE1201000
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the IEEE 51st Annual Conference on Decision and Control (CDC), December 10-13, 2012, Maui, Hawaii, 5592-5597
Country of Publication:
United States
Language:
English

Citation Formats

Zhang, Wei, Lian, Jianming, Chang, Chin-Yao, Kalsi, Karanjit, and Sun, Yannan. Reduced-Order Modeling of Aggregated Thermostatic Loads With Demand Response. United States: N. p., 2012. Web. doi:10.1109/CDC.2012.6426010.
Zhang, Wei, Lian, Jianming, Chang, Chin-Yao, Kalsi, Karanjit, & Sun, Yannan. Reduced-Order Modeling of Aggregated Thermostatic Loads With Demand Response. United States. https://doi.org/10.1109/CDC.2012.6426010
Zhang, Wei, Lian, Jianming, Chang, Chin-Yao, Kalsi, Karanjit, and Sun, Yannan. 2012. "Reduced-Order Modeling of Aggregated Thermostatic Loads With Demand Response". United States. https://doi.org/10.1109/CDC.2012.6426010.
@article{osti_1076721,
title = {Reduced-Order Modeling of Aggregated Thermostatic Loads With Demand Response},
author = {Zhang, Wei and Lian, Jianming and Chang, Chin-Yao and Kalsi, Karanjit and Sun, Yannan},
abstractNote = {Demand Response is playing an increasingly important role in smart grid control strategies. Modeling the behavior of populations of appliances under demand response is especially important to evaluate the effectiveness of these demand response programs. In this paper, an aggregated model is proposed for a class of Thermostatically Controlled Loads (TCLs). The model efficiently includes statistical information of the population, systematically deals with heterogeneity, and accounts for a second-order effect necessary to accurately capture the transient dynamics in the collective response. However, an accurate characterization of the collective dynamics however requires the aggregate model to have a high state space dimension. Most of the existing model reduction techniques require the stability of the underlying system which does not hold for the proposed aggregated model. In this work, a novel model reduction approach is developed for the proposed aggregated model, which can significantly reduce its complexity with small performance loss. The original and the reducedorder aggregated models are validated against simulations of thousands of detailed building models using GridLAB-D, which is a realistic open source distribution simulation software. Index Terms – demand response, aggregated model, ancillary},
doi = {10.1109/CDC.2012.6426010},
url = {https://www.osti.gov/biblio/1076721}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Dec 12 00:00:00 EST 2012},
month = {Wed Dec 12 00:00:00 EST 2012}
}

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