Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/93971
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Electrical Engineeringen_US
dc.creatorZhou, Ben_US
dc.creatorXu, Den_US
dc.creatorChan, KWen_US
dc.creatorLi, Cen_US
dc.creatorCao, Yen_US
dc.creatorBu, Sen_US
dc.date.accessioned2022-08-03T08:49:37Z-
dc.date.available2022-08-03T08:49:37Z-
dc.identifier.issn0360-5442en_US
dc.identifier.urihttp://hdl.handle.net/10397/93971-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.rights© 2017 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.en_US
dc.rightsThe following publication Zhou, B., Xu, D., Chan, K. W., Li, C., Cao, Y., & Bu, S. (2017). A two-stage framework for multiobjective energy management in distribution networks with a high penetration of wind energy. Energy, 135, 754-766 is available at https://doi.org/10.1016/j.energy.2017.06.178.en_US
dc.subjectDistribution networksen_US
dc.subjectEnergy managementen_US
dc.subjectEnergy storage systemen_US
dc.subjectVolt/var optimizationen_US
dc.subjectWind energyen_US
dc.titleA two-stage framework for multiobjective energy management in distribution networks with a high penetration of wind energyen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage754en_US
dc.identifier.epage766en_US
dc.identifier.volume135en_US
dc.identifier.doi10.1016/j.energy.2017.06.178en_US
dcterms.abstractThe integration of renewable energy sources (RESs) in distribution networks has brought great challenges to the volt/var management due to their intermittency and volatility. This paper proposes a two-stage energy management framework of distribution networks to facilitate the accommodation of high wind energy penetration. In the proposed framework, the volt/var management problem is formulated and decomposed as a two-stage energy scheduling optimization model with different time frames considering the uncertainties of wind energy and load forecasts. In the first stage, a scenario-based stochastic day-ahead scheduling model is formulated to optimize the 24-h charging/discharging scheme of energy storage system (ESS) and power generation of diesel generator (DG) in order to minimize the expected operation cost. Based on the stochastic optimal scheduling results in the first stage, the second stage implements the multiobjective volt/var optimization (VVO) to determine the optimal real-time operation of volt/var control devices, considering the costs of adjusting the control devices (CACDs). The proposed method has been fully evaluated and benchmarked on a 69-bus distribution network under various operational scenarios to demonstrate its superiority on various performance metrics and further confirm its effectiveness and efficiency for distribution networks to accommodate a high penetration of wind energy.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEnergy, 15 Sept. 2017, v. 135, p. 754-766en_US
dcterms.isPartOfEnergyen_US
dcterms.issued2017-09-15-
dc.identifier.scopus2-s2.0-85021778318-
dc.identifier.eissn1873-6785en_US
dc.description.validate202205 bchyen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberEE-0596-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of China; China Postdoctoral Science Foundation; Hunan Natural Science Foundation of China; The Hong Kong Polytechnic Universityen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS6758398-
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Chan_Two-Stage_Framework_Multiobjective.pdfPre-Published version905.59 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

45
Last Week
1
Last month
Citations as of May 12, 2024

Downloads

122
Citations as of May 12, 2024

SCOPUSTM   
Citations

25
Citations as of May 16, 2024

WEB OF SCIENCETM
Citations

23
Citations as of May 16, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.