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Article: Dependent Discrete Convolution Based Probabilistic Load Flow for the Active Distribution System

TitleDependent Discrete Convolution Based Probabilistic Load Flow for the Active Distribution System
Authors
Keywordsactive distribution system
Copula
correlation
dependent
discrete convolution
probabilistic load flow
sequence operation theory
uncertainty
Issue Date2017
Citation
IEEE Transactions on Sustainable Energy, 2017, v. 8, n. 3, p. 1000-1009 How to Cite?
AbstractActive distribution system (ADS) plays a significant role in enabling the integration of distributed generation. The stochastic nature of renewable energy resources injects the complex uncertainties of power flow into ADS. This paper proposes a discrete convolution methodology for probabilistic load flow (PLF) of ADS considering correlated uncertainties. First, the uncertainties of load and renewable energy are modeled using the distribution of the corresponding forecasting error, and the correlation is formulated using a Copula function. A novel reactive power-embedded DC power flow model with high accuracy in both branch flow and node voltage is introduced into ADS. Finally, the distribution of power flow is calculated using dependent discrete convolution, which is capable of handling nonanalytical probability distribution functions. In addition, a reduced dimension approximation method is proposed to further reduce the computational burden. The proposed PLF algorithm is tested on the IEEE 33-nodes system and 123-nodes system, and the results show that the proposed methodology requires less computation and produces higher accuracy compared with current methods.
Persistent Identifierhttp://hdl.handle.net/10722/308730
ISSN
2023 Impact Factor: 8.6
2023 SCImago Journal Rankings: 4.364
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Yi-
dc.contributor.authorZhang, Ning-
dc.contributor.authorChen, Qixin-
dc.contributor.authorYang, Jingwei-
dc.contributor.authorKang, Chongqing-
dc.contributor.authorHuang, Junhui-
dc.date.accessioned2021-12-08T07:50:00Z-
dc.date.available2021-12-08T07:50:00Z-
dc.date.issued2017-
dc.identifier.citationIEEE Transactions on Sustainable Energy, 2017, v. 8, n. 3, p. 1000-1009-
dc.identifier.issn1949-3029-
dc.identifier.urihttp://hdl.handle.net/10722/308730-
dc.description.abstractActive distribution system (ADS) plays a significant role in enabling the integration of distributed generation. The stochastic nature of renewable energy resources injects the complex uncertainties of power flow into ADS. This paper proposes a discrete convolution methodology for probabilistic load flow (PLF) of ADS considering correlated uncertainties. First, the uncertainties of load and renewable energy are modeled using the distribution of the corresponding forecasting error, and the correlation is formulated using a Copula function. A novel reactive power-embedded DC power flow model with high accuracy in both branch flow and node voltage is introduced into ADS. Finally, the distribution of power flow is calculated using dependent discrete convolution, which is capable of handling nonanalytical probability distribution functions. In addition, a reduced dimension approximation method is proposed to further reduce the computational burden. The proposed PLF algorithm is tested on the IEEE 33-nodes system and 123-nodes system, and the results show that the proposed methodology requires less computation and produces higher accuracy compared with current methods.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Sustainable Energy-
dc.subjectactive distribution system-
dc.subjectCopula-
dc.subjectcorrelation-
dc.subjectdependent-
dc.subjectdiscrete convolution-
dc.subjectprobabilistic load flow-
dc.subjectsequence operation theory-
dc.subjectuncertainty-
dc.titleDependent Discrete Convolution Based Probabilistic Load Flow for the Active Distribution System-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TSTE.2016.2640340-
dc.identifier.scopuseid_2-s2.0-85028847071-
dc.identifier.volume8-
dc.identifier.issue3-
dc.identifier.spage1000-
dc.identifier.epage1009-
dc.identifier.isiWOS:000404251100010-

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