Electricity‐heat‐gas integrated demand response dependency assessment based on BOXCOX‐Pair Copula model

Abstract With the continuous development of Regional Integrated Energy System (RIES), demand response (DR) is composed of diversified loads including electric load, heat load and gas load. Their cross‐dependencies reflecting the nonlinear coupling complementary relationship between each load type ar...

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Main Authors: Shuxin Tian, Wentao Huang, Taishan Yan, Xijun Yang, Yang Fu
Format: Article
Language:English
Published: Wiley 2022-03-01
Series:IET Energy Systems Integration
Subjects:
Online Access:https://doi.org/10.1049/esi2.12053
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author Shuxin Tian
Wentao Huang
Taishan Yan
Xijun Yang
Yang Fu
author_facet Shuxin Tian
Wentao Huang
Taishan Yan
Xijun Yang
Yang Fu
author_sort Shuxin Tian
collection DOAJ
description Abstract With the continuous development of Regional Integrated Energy System (RIES), demand response (DR) is composed of diversified loads including electric load, heat load and gas load. Their cross‐dependencies reflecting the nonlinear coupling complementary relationship between each load type are one of the key factors to improve multi‐energy flow optimisation modelling and utilisation efficiency on the demand side. Accordingly, this paper proposes a DR dependency assessment method considering electricity‐heat‐gas loads based on the BOXCOX‐Pair Copula model. BOXCOX transformation is introduced to convert various probability distribution statistics of electricity‐heat‐gas loads into Gaussian distributed variables. C‐vine Pair Copula is used to characterise an ensemble‐of‐trees of high‐dimensional dependency structure among multi‐energy demand modalities. Then the combined model of BOXCOX transformation and C‐vine Pair Copula can be employed to determine the complex coupling dependency among electricity‐heat‐gas loads according to different DR statistics. Some metrics between the original empirical distribution and BOXCOX‐Pair Copula distribution are introduced to assess the dependency evaluation precision of the proposed model. Finally, the novel dependency assessment model is numerically tested utilising the electricity load, heat load and gas load data sequences in a real RIES. The results illustrate that the cross‐dependency of electricity‐heat‐gas integrated DR based on the BOXCOX‐C‐vine copula model is closer to that of actual sample data, which verify the effectiveness and superiority of the proposed approach.
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spelling doaj.art-f1ab53046d0042d18c83a2609aaf40762022-12-22T02:57:27ZengWileyIET Energy Systems Integration2516-84012022-03-014113114210.1049/esi2.12053Electricity‐heat‐gas integrated demand response dependency assessment based on BOXCOX‐Pair Copula modelShuxin Tian0Wentao Huang1Taishan Yan2Xijun Yang3Yang Fu4Electrical Engineering College Shanghai University of Electric Power Shanghai ChinaKey Laboratory of Control of Power Transmission and Conversion (SJTU) Ministry of Education Shanghai ChinaState Grid Anhui Maintenance Company Hefei ChinaKey Laboratory of Control of Power Transmission and Conversion (SJTU) Ministry of Education Shanghai ChinaElectrical Engineering College Shanghai University of Electric Power Shanghai ChinaAbstract With the continuous development of Regional Integrated Energy System (RIES), demand response (DR) is composed of diversified loads including electric load, heat load and gas load. Their cross‐dependencies reflecting the nonlinear coupling complementary relationship between each load type are one of the key factors to improve multi‐energy flow optimisation modelling and utilisation efficiency on the demand side. Accordingly, this paper proposes a DR dependency assessment method considering electricity‐heat‐gas loads based on the BOXCOX‐Pair Copula model. BOXCOX transformation is introduced to convert various probability distribution statistics of electricity‐heat‐gas loads into Gaussian distributed variables. C‐vine Pair Copula is used to characterise an ensemble‐of‐trees of high‐dimensional dependency structure among multi‐energy demand modalities. Then the combined model of BOXCOX transformation and C‐vine Pair Copula can be employed to determine the complex coupling dependency among electricity‐heat‐gas loads according to different DR statistics. Some metrics between the original empirical distribution and BOXCOX‐Pair Copula distribution are introduced to assess the dependency evaluation precision of the proposed model. Finally, the novel dependency assessment model is numerically tested utilising the electricity load, heat load and gas load data sequences in a real RIES. The results illustrate that the cross‐dependency of electricity‐heat‐gas integrated DR based on the BOXCOX‐C‐vine copula model is closer to that of actual sample data, which verify the effectiveness and superiority of the proposed approach.https://doi.org/10.1049/esi2.12053BOXCOX‐Pair Copula modeldata transformationelectricity‐heat‐gas loadhigh‐dimensional dependencyintegrated demand response
spellingShingle Shuxin Tian
Wentao Huang
Taishan Yan
Xijun Yang
Yang Fu
Electricity‐heat‐gas integrated demand response dependency assessment based on BOXCOX‐Pair Copula model
IET Energy Systems Integration
BOXCOX‐Pair Copula model
data transformation
electricity‐heat‐gas load
high‐dimensional dependency
integrated demand response
title Electricity‐heat‐gas integrated demand response dependency assessment based on BOXCOX‐Pair Copula model
title_full Electricity‐heat‐gas integrated demand response dependency assessment based on BOXCOX‐Pair Copula model
title_fullStr Electricity‐heat‐gas integrated demand response dependency assessment based on BOXCOX‐Pair Copula model
title_full_unstemmed Electricity‐heat‐gas integrated demand response dependency assessment based on BOXCOX‐Pair Copula model
title_short Electricity‐heat‐gas integrated demand response dependency assessment based on BOXCOX‐Pair Copula model
title_sort electricity heat gas integrated demand response dependency assessment based on boxcox pair copula model
topic BOXCOX‐Pair Copula model
data transformation
electricity‐heat‐gas load
high‐dimensional dependency
integrated demand response
url https://doi.org/10.1049/esi2.12053
work_keys_str_mv AT shuxintian electricityheatgasintegrateddemandresponsedependencyassessmentbasedonboxcoxpaircopulamodel
AT wentaohuang electricityheatgasintegrateddemandresponsedependencyassessmentbasedonboxcoxpaircopulamodel
AT taishanyan electricityheatgasintegrateddemandresponsedependencyassessmentbasedonboxcoxpaircopulamodel
AT xijunyang electricityheatgasintegrateddemandresponsedependencyassessmentbasedonboxcoxpaircopulamodel
AT yangfu electricityheatgasintegrateddemandresponsedependencyassessmentbasedonboxcoxpaircopulamodel