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dc.contributor.authorLuo, Fengji
dc.contributor.authorRanzi, Gianluca
dc.contributor.authorLiang, Gaoqi
dc.contributor.authorDong, Zhao Yang
dc.date.accessioned2020-01-30
dc.date.available2020-01-30
dc.date.issued2018-02-01
dc.identifier.citationF. Luo, G. Ranzi, G. Liang and Z. Y. Dong, "Stochastic residential energy resource scheduling by multi-objective natural aggregation algorithm," 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, 2017, pp. 1-5. doi: 10.1109/PESGM.2017.8274308en_AU
dc.identifier.issn1944-9933
dc.identifier.urihttps://hdl.handle.net/2123/21760
dc.description.abstractThis paper studies the coordinated scheduling of residential energy resources in a smart home environment. The particularity of this paper is to consider the uncertainties of the must-run appliance load demand forecast errors and to addresses the residential energy resource scheduling through a multi-objective optimization approach. Multiple 1-day must-run appliance power demand scenarios are firstly generated from the house’s historical energy consumption data. Based on this, a stochastic day-ahead appliance scheduling model is formulated, aiming to minimize the 1-day energy costs while maximizing the preference of the homeowner simultaneously. A new multi-objective optimization tool, i.e. Multi-Objective Natural Aggregation Algorithm (MONAA), is proposed to solve the stochastic day-ahead appliance scheduling model. Simulations are designed for the validation of the proposed method.en_AU
dc.description.sponsorshipAustralian Research Councilen_AU
dc.language.isoen_AUen_AU
dc.publisherIEEEen_AU
dc.relationARC FT140100130en_AU
dc.subjectdemand side managementen_AU
dc.subjectdemand responseen_AU
dc.subjectsmart griden_AU
dc.subjectenergy management systemen_AU
dc.subjectsmart homeen_AU
dc.subjectevolutionary computationen_AU
dc.titleStochastic residential energy resource scheduling by multi-objective natural aggregation algorithmen_AU
dc.typeArticleen_AU
dc.subject.asrc090607en_AU
dc.type.pubtypePost-printen_AU


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