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Optimal home energy management under hybrid photovoltaic-storage uncertainty: a distributionally robust chance-constrained approach

dc.rights.licenseopenen_US
dc.contributor.authorZHAO, Pengfei
dc.contributor.authorWU, Han
dc.contributor.authorGU, Chenghong
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorHERNANDO GIL, Ignacio
ORCID: 0000-0002-6868-0685
IDREF: 257382976
dc.date.accessioned2023-04-19T08:58:44Z
dc.date.available2023-04-19T08:58:44Z
dc.date.issued2019-08-19
dc.identifier.issn1752-1416en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/173093
dc.description.abstractEnEnergy storage and demand response (DR) resources, in combination with intermittent renewable generation, are expected to provide domestic customers with the ability to reducing their electricity consumption. This study highlights the role that an intelligent battery control, in combination with solar generation, could play to increase renewable uptake while reducing customers’ electricity bills without intruding on people's daily life. The optimal performance of a home energy management system (HEMS) is investigated through a range of interventions, leading to different levels of customer weariness and consumption patterns. Thus, the DR is applied with efficient and specific control of domestic appliances through load shifting and curtailment. Regarding the uncertainty associated with the photovoltaic generation, a chance-constrained (CC) optimal scheduling is considered subject to the operation constraints from each power component in the HEMS. By applying distributionally robust optimisation, the ambiguity set is accurately built for this distributionally robust CC (DRCC) problem without the need for any probability distribution associated with uncertainty. Based on the greatly altered consumption profiles in this study, the proposed DRCC-HEMS is proven to be optimally effective and computationally efficient while considering uncertainty.
dc.language.isoENen_US
dc.subject.enEnergy storage
dc.subject.enDemand response
dc.subject.enHome energy management
dc.subject.enOptimization
dc.title.enOptimal home energy management under hybrid photovoltaic-storage uncertainty: a distributionally robust chance-constrained approach
dc.typeArticle de revueen_US
dc.identifier.doi10.1049/iet-rpg.2018.6169en_US
dc.subject.halSciences de l'ingénieur [physics]/Energie électriqueen_US
dc.subject.halSciences de l'ingénieur [physics]/Electroniqueen_US
dc.subject.halSciences de l'ingénieur [physics]/Electromagnétismeen_US
dc.subject.halSciences de l'ingénieur [physics]/Automatique / Robotiqueen_US
dc.subject.halSciences de l'environnement/Ingénierie de l'environnementen_US
bordeaux.journalIET Renewable Power Generationen_US
bordeaux.page1911-1919en_US
bordeaux.volume13en_US
bordeaux.hal.laboratoriesESTIA - Rechercheen_US
bordeaux.issue11en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionBordeaux Sciences Agroen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcehal
hal.identifierhal-02353361
hal.version1
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=IET%20Renewable%20Power%20Generation&rft.date=2019-08-19&rft.volume=13&rft.issue=11&rft.spage=1911-1919&rft.epage=1911-1919&rft.eissn=1752-1416&rft.issn=1752-1416&rft.au=ZHAO,%20Pengfei&WU,%20Han&GU,%20Chenghong&HERNANDO%20GIL,%20Ignacio&rft.genre=article


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