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Article: An Efficient Approach to Power System Uncertainty Analysis With High-Dimensional Dependencies

TitleAn Efficient Approach to Power System Uncertainty Analysis With High-Dimensional Dependencies
Authors
KeywordsCopula
correlation
dependent
discrete convolution
high dimension
renewable energy
uncertainty
Issue Date2018
Citation
IEEE Transactions on Power Systems, 2018, v. 33, n. 3, p. 2984-2994 How to Cite?
AbstractThe integration of high penetration of renewable energy brings greater uncertainties for the operation of future power systems due to its intermittency and lack of predictability. The uncertainties brought by wide scale renewables might have dependencies with each other because their outputs are mainly influenced by weather. However, an analysis of such uncertainties with complex dependencies faces the "curse of dimensionality". This challenges the power system uncertainty analysis in probabilistic forecasting, power system operation optimization, and power system planning. This paper proposes an efficient approach that is able to handle high-dimensional dependencies. The approach uses the high-dimensional Copula theory and discrete convolution method to conduct a high-dimensional dependent discrete convolution (DDC) calculation. A recursive algorithm is proposed to decompose the computation of DDC into multiple convolutions between each pair of stochastic variables so that the "curse of dimensionality" is solved. The computational complexity of the proposed method is linear with respect to the number of dimensions and guarantees computational efficiency. Finally, illustrative examples of power system reserve requirement evaluation and wind power capacity credit assessment analysis are used to verify the effectiveness and superiority of the proposed approach.
Persistent Identifierhttp://hdl.handle.net/10722/308733
ISSN
2023 Impact Factor: 6.5
2023 SCImago Journal Rankings: 3.827
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Yi-
dc.contributor.authorZhang, Ning-
dc.contributor.authorKang, Chongqing-
dc.contributor.authorMiao, Miao-
dc.contributor.authorShi, Rui-
dc.contributor.authorXia, Qing-
dc.date.accessioned2021-12-08T07:50:01Z-
dc.date.available2021-12-08T07:50:01Z-
dc.date.issued2018-
dc.identifier.citationIEEE Transactions on Power Systems, 2018, v. 33, n. 3, p. 2984-2994-
dc.identifier.issn0885-8950-
dc.identifier.urihttp://hdl.handle.net/10722/308733-
dc.description.abstractThe integration of high penetration of renewable energy brings greater uncertainties for the operation of future power systems due to its intermittency and lack of predictability. The uncertainties brought by wide scale renewables might have dependencies with each other because their outputs are mainly influenced by weather. However, an analysis of such uncertainties with complex dependencies faces the "curse of dimensionality". This challenges the power system uncertainty analysis in probabilistic forecasting, power system operation optimization, and power system planning. This paper proposes an efficient approach that is able to handle high-dimensional dependencies. The approach uses the high-dimensional Copula theory and discrete convolution method to conduct a high-dimensional dependent discrete convolution (DDC) calculation. A recursive algorithm is proposed to decompose the computation of DDC into multiple convolutions between each pair of stochastic variables so that the "curse of dimensionality" is solved. The computational complexity of the proposed method is linear with respect to the number of dimensions and guarantees computational efficiency. Finally, illustrative examples of power system reserve requirement evaluation and wind power capacity credit assessment analysis are used to verify the effectiveness and superiority of the proposed approach.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Power Systems-
dc.subjectCopula-
dc.subjectcorrelation-
dc.subjectdependent-
dc.subjectdiscrete convolution-
dc.subjecthigh dimension-
dc.subjectrenewable energy-
dc.subjectuncertainty-
dc.titleAn Efficient Approach to Power System Uncertainty Analysis With High-Dimensional Dependencies-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TPWRS.2017.2755698-
dc.identifier.scopuseid_2-s2.0-85030699012-
dc.identifier.volume33-
dc.identifier.issue3-
dc.identifier.spage2984-
dc.identifier.epage2994-
dc.identifier.isiWOS:000430733300059-

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