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dc.contributor.authorMohsenzadeh, Amin
dc.contributor.authorPang, Chengzong
dc.contributor.authorHaghifam, Mahmoudreza
dc.date.accessioned2018-12-19T20:38:20Z
dc.date.available2018-12-19T20:38:20Z
dc.date.issued2018-12
dc.identifier.citationA. Mohsenzadeh, C. Pang and M. Haghifam, "Determining Optimal Forming of Flexible Microgrids in the Presence of Demand Response in Smart Distribution Systems," in IEEE Systems Journal, vol. 12, no. 4, pp. 3315-3323, Dec. 2018en_US
dc.identifier.issn1932-8184
dc.identifier.otherWOS:000451262300027
dc.identifier.urihttps://doi.org/10.1109/JSYST.2017.2739640
dc.identifier.urihttp://hdl.handle.net/10057/15715
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractImplementing microgrids in power systems will improve the network reliability and reduce the impact of outages on end-users. Determining the most efficient boundaries of microgrids under contingencies is one of the main challenges for utilities from reliability and economics points of view. Currently, most research works have been focused on predefined boundary or static microgrids regardless system conditions and priority or importance of customers. In this paper, a novel concept for designing and operation of flexible microgrids in order to improve the reliability of a power distribution system is proposed. Compared to current approaches, boundaries of the proposed flexible microgrids can be extended or shrunk based on generation and demand levels, technical constraints, and customers' comfort. Furthermore, a demand response (DR) program is performed to maintain a balance between generation and consumption in the microgrid. In this paper, genetic algorithm (GA) and mixed-integer linear programming (MILP) are simultaneously applied to a model and solve two-stage optimization considering utilities' profits and customers' satisfaction. In planning level, GA is utilized for sitting and sizing of distributed generations and placement of switches. In operation level, MILP is used to select target switches as boundaries of optimal microgrids, model priority of customers, and determine the contribution of each load in the DR program. The case study is also presented and final results show the superiority of the proposed method compared with the traditional fixed boundaries method in microgrids.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesIEEE Systems Journal;v.12:no.4
dc.subjectDemand response (DR)en_US
dc.subjectDistributed generationen_US
dc.subjectMicrogriden_US
dc.subjectSmart griden_US
dc.subjectSwitch placementen_US
dc.titleDetermining optimal forming of flexible microgrids in the presence of demand response in smart distribution systemsen_US
dc.typeArticleen_US
dc.rights.holder© 2018, IEEEen_US


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