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Real option‐based network investment assessment considering energy storage systems under long‐term demand uncertainties

dc.rights.licenseopenen_US
dc.contributor.authorCHENG, Shuang
dc.contributor.authorGU, Chenghong
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorHERNANDO GIL, Ignacio
ORCID: 0000-0002-6868-0685
IDREF: 257382976
dc.contributor.authorLI, Shuangqi
dc.contributor.authorLI, Furong
dc.date.accessioned2023-04-03T13:38:50Z
dc.date.available2023-04-03T13:38:50Z
dc.date.issued2022-10-05
dc.identifier.issn1752-1416en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/172690
dc.description.abstractEnThis paper proposes a novel real option (RO)-based network investment assessment method to quantify the flexibility value of battery energy storage systems (BESS) in distribution network planning (DNP). It applied geometric Brownian motion (GBM) to simulate the long-term load growth uncertainty. Compared with commonly used stochastic models (e.g. normal probability model) that assume a constant variance, it reflects the fact that from the point of prediction, uncertainty would increase as time elapses. Hence, it avoids the bias of traditional net present value (NPV) frameworks towards lumpy investments that cannot provide strategic flexibility relative to more flexible alternatives. It is for the first time to adopt the option pricing method to evaluate the flexibility value of distribution network planning strategies. To optimize the planning scheme, this paper compares the static NPVs and flexibility values of different investment strategies. A 33-bus system is used to verify the effectiveness of the formulated model. Results indicate that flexibility values of BESS are of utmost importance to DNP under demand growth uncertainties. It provides an analytical tool to quantify the flexibility of planning measures and evaluate the well-timed investment of BESS, thus supporting network operators to facilitate flexibility services and hedge risks from the negative impact of long-term uncertainty.
dc.language.isoENen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.title.enReal option‐based network investment assessment considering energy storage systems under long‐term demand uncertainties
dc.typeArticle de revueen_US
dc.identifier.doi10.1049/rpg2.12532en_US
dc.subject.halSciences de l'ingénieur [physics]en_US
dc.subject.halSciences de l'environnement/Ingénierie de l'environnementen_US
dc.subject.halSciences de l'ingénieur [physics]/Automatique / Robotiqueen_US
dc.subject.halSciences de l'ingénieur [physics]/Electromagnétismeen_US
dc.subject.halSciences de l'ingénieur [physics]/Energie électriqueen_US
dc.subject.halSciences de l'ingénieur [physics]/Electroniqueen_US
bordeaux.journalIET Renewable Power Generationen_US
bordeaux.page2778-2792en_US
bordeaux.volume16en_US
bordeaux.hal.laboratoriesESTIA - Rechercheen_US
bordeaux.issue13en_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-03952738
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=2022-10-05&rft.volume=16&rft.issue=13&rft.spage=2778-2792&rft.epage=2778-2792&rft.eissn=1752-1416&rft.issn=1752-1416&rft.au=CHENG,%20Shuang&GU,%20Chenghong&HERNANDO%20GIL,%20Ignacio&LI,%20Shuangqi&LI,%20Furong&rft.genre=article


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