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Title: Holistic modeling framework of demand response considering multiti-mescale uncertainties for capacity value estimation

Abstract

Demand response (DR) is regarded as an effective tool to mitigate the operational uncertainties and enhance the reliability of power supply in smart grids. However, with the time-varying attribute, to what extent DR could be committed is a major concern for utilities. This paper proposes a new approach to assess the capacity value (CV) of DR with a methodological framework developed for the uncertainty modeling of DR. The novelty of this framework is its inclusion of both physical and human-related analyses in DR programs, which allows the characterization of DR variability to be accurate for the CV estimation. To achieve this, the demand-side activities in DR are disaggregated into several modules as load usage, contract selection and actual performance. Based on their intrinsic properties, different parametric models are proposed to represent the impact of each technical/social factor on the availability of DR. The parameters of these models are determined using learning based algorithms to adapt to various behavior patterns of consumers. The outputs of the framework will serve as the quantifiers of DR capability and are integrated into reliability-based CV evaluation. The results of case studies verify the effectiveness of the proposed methodology.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Natural Science Foundation of China (NNSFC); Fundamental Research Funds for the Central Universities
OSTI Identifier:
1571917
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 247
Country of Publication:
United States
Language:
English
Subject:
Capacity value; Demand response; Demand-side behavior; Regret matching; Reliability analysis; Uncertainty

Citation Formats

Zeng, Bo, Zhao, Dongbo, Singh, Chanan, Wang, Jianhui, and Chen, Chen. Holistic modeling framework of demand response considering multiti-mescale uncertainties for capacity value estimation. United States: N. p., 2019. Web. doi:10.1016/j.apenergy.2019.03.121.
Zeng, Bo, Zhao, Dongbo, Singh, Chanan, Wang, Jianhui, & Chen, Chen. Holistic modeling framework of demand response considering multiti-mescale uncertainties for capacity value estimation. United States. doi:10.1016/j.apenergy.2019.03.121.
Zeng, Bo, Zhao, Dongbo, Singh, Chanan, Wang, Jianhui, and Chen, Chen. Thu . "Holistic modeling framework of demand response considering multiti-mescale uncertainties for capacity value estimation". United States. doi:10.1016/j.apenergy.2019.03.121.
@article{osti_1571917,
title = {Holistic modeling framework of demand response considering multiti-mescale uncertainties for capacity value estimation},
author = {Zeng, Bo and Zhao, Dongbo and Singh, Chanan and Wang, Jianhui and Chen, Chen},
abstractNote = {Demand response (DR) is regarded as an effective tool to mitigate the operational uncertainties and enhance the reliability of power supply in smart grids. However, with the time-varying attribute, to what extent DR could be committed is a major concern for utilities. This paper proposes a new approach to assess the capacity value (CV) of DR with a methodological framework developed for the uncertainty modeling of DR. The novelty of this framework is its inclusion of both physical and human-related analyses in DR programs, which allows the characterization of DR variability to be accurate for the CV estimation. To achieve this, the demand-side activities in DR are disaggregated into several modules as load usage, contract selection and actual performance. Based on their intrinsic properties, different parametric models are proposed to represent the impact of each technical/social factor on the availability of DR. The parameters of these models are determined using learning based algorithms to adapt to various behavior patterns of consumers. The outputs of the framework will serve as the quantifiers of DR capability and are integrated into reliability-based CV evaluation. The results of case studies verify the effectiveness of the proposed methodology.},
doi = {10.1016/j.apenergy.2019.03.121},
journal = {Applied Energy},
number = ,
volume = 247,
place = {United States},
year = {2019},
month = {8}
}