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Two-Stage Distributionally Robust Optimization for Energy Hub Systems

dc.rights.licenseopenen_US
dc.contributor.authorZHAO, Pengfei
dc.contributor.authorGU, Chenghong
dc.contributor.authorHUO, Da
dc.contributor.authorSHEN, Yichen
hal.structure.identifierESTIA - Institute of technology [ESTIA]
dc.contributor.authorHERNANDO GIL, Ignacio
ORCID: 0000-0002-6868-0685
IDREF: 257382976
dc.date.accessioned2023-04-06T16:31:46Z
dc.date.available2023-04-06T16:31:46Z
dc.date.issued2020-05
dc.identifier.issn1551-3203en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/172866
dc.description.abstractEnEnergy hub system (EHS) incorporating multiple energy carriers, storage and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation.This paper proposes an innovative distributionally robust optimization model to operate EHS with an energy storage system (ESS), considering the multimodal forecast errors of photovoltaic (PV) power. Both battery and heat storage are utilized to smooth PV output fluctuation and improve the energy efficiency of EHS. This paper proposes a novel multimodal ambiguity set to capture the stochastic characteristics of PV multimodality. A two-stage scheme is adopted, where i) the first stage optimizes EHS operation cost, and ii) the second stage implements real-time dispatch after the realization of PV output uncertainty. The aim is to overcome the conservatism of multimodal distribution uncertainties modelled by typical ambiguity sets and reduce the operation cost of EHS. The presented model is reformulated as a tractable semidefinite programming problem and solved by a constraint generation algorithm. Its performance is extensively compared with widely used normal and unimodal ambiguity sets. The results from this paper justify the effectiveness and performance of the proposed method compared to conventional models, which can help EHS operators to economically consume energy and use ESS wisely through the optimal coordination of multi-energy carriers.
dc.language.isoENen_US
dc.subject.enConstraint generation algorithm
dc.subject.endistributionally robust optimization
dc.subject.enenergy hub system
dc.subject.enmultimodal ambiguity set
dc.subject.enrenewable energy
dc.title.enTwo-Stage Distributionally Robust Optimization for Energy Hub Systems
dc.typeArticle de revueen_US
dc.identifier.doi10.1109/TII.2019.2938444en_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.journalIEEE Transactions on Industrial Informaticsen_US
bordeaux.page3460 - 3469en_US
bordeaux.volume16en_US
bordeaux.hal.laboratoriesESTIA - Rechercheen_US
bordeaux.issue5en_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-02353406
hal.version1
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
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