Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/612344
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dc.contributor.authorSeyedmahdi Moghadasien_US
dc.contributor.authorSukumar Kamalasadanen_US
dc.date.accessioned2020-05-20T09:10:44Z-
dc.date.available2020-05-20T09:10:44Z-
dc.date.issued2016en_US
dc.identifier.issn0093-9994en_US
dc.identifier.issn1939-9367en_US
dc.identifier.other10.1109/TIA.2016.2531623en_US
dc.identifier.urihttp://localhost/handle/Hannan/154381en_US
dc.identifier.urihttp://localhost/handle/Hannan/612344-
dc.description.abstractIn this paper, a convex optimal power flow (OPF) formulation integrated within receding horizon control (RHC) architecture using second-order conic programming (SOCP) is proposed. The main advantages of the proposed method are 1) global optimal scheduling with faster computation time; 2) dynamic models with online control within optimization routine; and 3) integration of uncertain resources and measurements. The effectiveness of this method is evaluated using modified IEEE 32-bus and IEEE 119-bus distribution test systems considering network constraints such as energy market interactions, storage dynamics, and uncertain model of wind generation. The efficiency of the proposed method compared to RHC ac optimal power flow (RHC-ACOPF) is also evaluated using real-time simulator. The results show that the proposed method outperforms the RHC-ACOPF and guarantees global optimal solution. The proposed method also provides effective usage of energy storage system since dynamic modeling of energy storage within the optimization algorithm is possible using RHC integration.en_US
dc.publisherIEEEen_US
dc.relation.haspart7410031.pdfen_US
dc.subjectRadial Distribution System|Convex Optimization|Real-time Optimizationen_US
dc.titleOptimal Fast Control and Scheduling of Power Distribution System Using Integrated Receding Horizon Control and Convex Conic Programmingen_US
dc.typeArticleen_US
dc.journal.volume52en_US
dc.journal.issue3en_US
dc.journal.titleIEEE Transactions on Industry Applicationsen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorSeyedmahdi Moghadasien_US
dc.contributor.authorSukumar Kamalasadanen_US
dc.date.accessioned2020-05-20T09:10:44Z-
dc.date.available2020-05-20T09:10:44Z-
dc.date.issued2016en_US
dc.identifier.issn0093-9994en_US
dc.identifier.issn1939-9367en_US
dc.identifier.other10.1109/TIA.2016.2531623en_US
dc.identifier.urihttp://localhost/handle/Hannan/154381en_US
dc.identifier.urihttp://localhost/handle/Hannan/612344-
dc.description.abstractIn this paper, a convex optimal power flow (OPF) formulation integrated within receding horizon control (RHC) architecture using second-order conic programming (SOCP) is proposed. The main advantages of the proposed method are 1) global optimal scheduling with faster computation time; 2) dynamic models with online control within optimization routine; and 3) integration of uncertain resources and measurements. The effectiveness of this method is evaluated using modified IEEE 32-bus and IEEE 119-bus distribution test systems considering network constraints such as energy market interactions, storage dynamics, and uncertain model of wind generation. The efficiency of the proposed method compared to RHC ac optimal power flow (RHC-ACOPF) is also evaluated using real-time simulator. The results show that the proposed method outperforms the RHC-ACOPF and guarantees global optimal solution. The proposed method also provides effective usage of energy storage system since dynamic modeling of energy storage within the optimization algorithm is possible using RHC integration.en_US
dc.publisherIEEEen_US
dc.relation.haspart7410031.pdfen_US
dc.subjectRadial Distribution System|Convex Optimization|Real-time Optimizationen_US
dc.titleOptimal Fast Control and Scheduling of Power Distribution System Using Integrated Receding Horizon Control and Convex Conic Programmingen_US
dc.typeArticleen_US
dc.journal.volume52en_US
dc.journal.issue3en_US
dc.journal.titleIEEE Transactions on Industry Applicationsen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7410031.pdf2.87 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSeyedmahdi Moghadasien_US
dc.contributor.authorSukumar Kamalasadanen_US
dc.date.accessioned2020-05-20T09:10:44Z-
dc.date.available2020-05-20T09:10:44Z-
dc.date.issued2016en_US
dc.identifier.issn0093-9994en_US
dc.identifier.issn1939-9367en_US
dc.identifier.other10.1109/TIA.2016.2531623en_US
dc.identifier.urihttp://localhost/handle/Hannan/154381en_US
dc.identifier.urihttp://localhost/handle/Hannan/612344-
dc.description.abstractIn this paper, a convex optimal power flow (OPF) formulation integrated within receding horizon control (RHC) architecture using second-order conic programming (SOCP) is proposed. The main advantages of the proposed method are 1) global optimal scheduling with faster computation time; 2) dynamic models with online control within optimization routine; and 3) integration of uncertain resources and measurements. The effectiveness of this method is evaluated using modified IEEE 32-bus and IEEE 119-bus distribution test systems considering network constraints such as energy market interactions, storage dynamics, and uncertain model of wind generation. The efficiency of the proposed method compared to RHC ac optimal power flow (RHC-ACOPF) is also evaluated using real-time simulator. The results show that the proposed method outperforms the RHC-ACOPF and guarantees global optimal solution. The proposed method also provides effective usage of energy storage system since dynamic modeling of energy storage within the optimization algorithm is possible using RHC integration.en_US
dc.publisherIEEEen_US
dc.relation.haspart7410031.pdfen_US
dc.subjectRadial Distribution System|Convex Optimization|Real-time Optimizationen_US
dc.titleOptimal Fast Control and Scheduling of Power Distribution System Using Integrated Receding Horizon Control and Convex Conic Programmingen_US
dc.typeArticleen_US
dc.journal.volume52en_US
dc.journal.issue3en_US
dc.journal.titleIEEE Transactions on Industry Applicationsen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7410031.pdf2.87 MBAdobe PDFThumbnail
Preview File