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Article: An Adaptive Distributionally Robust Model for Three-Phase Distribution Network Reconfiguration
Title | An Adaptive Distributionally Robust Model for Three-Phase Distribution Network Reconfiguration |
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Authors | |
Keywords | Distribution networks Probability distribution Optimization Adaptation models Stochastic processes |
Issue Date | 2021 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411 |
Citation | IEEE Transactions on Smart Grid, 2021, v. 12 n. 2, p. 1224-1237 How to Cite? |
Abstract | Distributed generator (DG) volatility has a great impact on system operation, which should be considered beforehand due to the slow time scale of distribution network reconfiguration (DNR). However, it is difficult to derive accurate probability distributions (PDs) for DG outputs and loads analytically. To remove the assumptions on accurate PD knowledge, a deep neural network is first devised to learn the reference joint PD from historical data in an adaptive way. The reference PD along with the forecast errors are enveloped by a distributional ambiguity set using Kullback-Leibler divergence. Then a distributionally robust model for three-phase unbalanced DNR is proposed to obtain the optimal configuration under the worst-case PD of DG outputs and loads within the ambiguity set. The result inherits the advantages of stochastic optimization and robust optimization. Finally, a modified column-and-constraint generation method with efficient scenario decomposition is investigated to solve this model. Numerical tests are carried out using an IEEE unbalanced benchmark and a practical-scale system in Shandong, China. Comparison with the deterministic, stochastic and robust DNR methods validates the effectiveness of the proposed method. |
Persistent Identifier | http://hdl.handle.net/10722/306148 |
ISSN | 2023 Impact Factor: 8.6 2023 SCImago Journal Rankings: 4.863 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zheng, W | - |
dc.contributor.author | HUANG, W | - |
dc.contributor.author | Hill, DJ | - |
dc.contributor.author | Hou, Y | - |
dc.date.accessioned | 2021-10-20T10:19:28Z | - |
dc.date.available | 2021-10-20T10:19:28Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Transactions on Smart Grid, 2021, v. 12 n. 2, p. 1224-1237 | - |
dc.identifier.issn | 1949-3053 | - |
dc.identifier.uri | http://hdl.handle.net/10722/306148 | - |
dc.description.abstract | Distributed generator (DG) volatility has a great impact on system operation, which should be considered beforehand due to the slow time scale of distribution network reconfiguration (DNR). However, it is difficult to derive accurate probability distributions (PDs) for DG outputs and loads analytically. To remove the assumptions on accurate PD knowledge, a deep neural network is first devised to learn the reference joint PD from historical data in an adaptive way. The reference PD along with the forecast errors are enveloped by a distributional ambiguity set using Kullback-Leibler divergence. Then a distributionally robust model for three-phase unbalanced DNR is proposed to obtain the optimal configuration under the worst-case PD of DG outputs and loads within the ambiguity set. The result inherits the advantages of stochastic optimization and robust optimization. Finally, a modified column-and-constraint generation method with efficient scenario decomposition is investigated to solve this model. Numerical tests are carried out using an IEEE unbalanced benchmark and a practical-scale system in Shandong, China. Comparison with the deterministic, stochastic and robust DNR methods validates the effectiveness of the proposed method. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411 | - |
dc.relation.ispartof | IEEE Transactions on Smart Grid | - |
dc.rights | IEEE Transactions on Smart Grid. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Distribution networks | - |
dc.subject | Probability distribution | - |
dc.subject | Optimization | - |
dc.subject | Adaptation models | - |
dc.subject | Stochastic processes | - |
dc.title | An Adaptive Distributionally Robust Model for Three-Phase Distribution Network Reconfiguration | - |
dc.type | Article | - |
dc.identifier.email | Hill, DJ: dhill@HKUCC-COM.hku.hk | - |
dc.identifier.email | Hou, Y: yhhou@hku.hk | - |
dc.identifier.authority | Hill, DJ=rp01669 | - |
dc.identifier.authority | Hou, Y=rp00069 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TSG.2020.3030299 | - |
dc.identifier.scopus | eid_2-s2.0-85101956695 | - |
dc.identifier.hkuros | 327344 | - |
dc.identifier.volume | 12 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 1224 | - |
dc.identifier.epage | 1237 | - |
dc.identifier.isi | WOS:000623420700027 | - |
dc.publisher.place | United States | - |