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A robust model for aggregated bidding of energy storages and wind resources in the joint energy and reserve markets

dc.contributor.authorKhojasteh, Meysam
dc.contributor.authorFaria, Pedro
dc.contributor.authorVale, Zita
dc.date.accessioned2023-02-01T10:20:16Z
dc.date.available2023-02-01T10:20:16Z
dc.date.issued2022
dc.description.abstractThe high reliability and flexibility of Battery Energy Storage (BES) resources in comparison with other renewable technologies promote the development of this technology in smart grids. The fast response of BES to load variations could help the power system operators to maintain the balance of generation and consumption in real-time, and improve the flexibility of the smart grid, effectively. In this work, a new model is presented that determines the aggregated scheduling of BES and Wind Power Resource (WPR) in the joint energy and reserve markets. To evaluate the performance of BES in different markets, the proposed model is divided into day-ahead and real-time planning horizons. According to market prices, ramp rates, marginal costs, and technical constraints of units, the optimal participation levels in different markets are determined. The deployed power in real-time and wind power are considered as the uncertain parameters and the Robust Optimization (RO) framework is proposed to manage the related financial risk based on the worst-case realizations of uncertain parameters. The robust strategy is formulated based on the Mixed Integer Linear Programming (MILP) technique, which can be solved via the branch-and-bound method. Finally, the performance and effectiveness of the model are analyzed via different case studies. Simulation results show that the day-ahead and real-time markets are the best options for buying and selling the energy of BESs, and participation in the reserve market and regulation service increases their profit, significantly. Furthermore, the expected profit greatly depends on the risk preferences of decision-makers, and reducing the variation interval of wind generation by 40 % leads to an increase of 74.65 % in revenues.pt_PT
dc.description.sponsorshipThe present work has received funding from the European Regional Development Fund (FEDER) through the Northern Regional Operational Program, under the PORTUGAL 2020 Partnership Agreement and the terms of the NORTE-45-2020-75 call - Support System for Scientific and Technological Research - "Structured R&D&I Projects" - Horizon Europe, within project RETINA (NORTE 01-0145-FEDER-000062), we also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/ 00760/2020) to the project team.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.energy.2021.121735pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/22046
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationNORTE-45-2020-75pt_PT
dc.relationNORTE 01-0145-FEDER-000062pt_PT
dc.relationUIDB/ 00760/2020pt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0360544221019836?via%3Dihubpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectBattery energy storagept_PT
dc.subjectEnergy marketpt_PT
dc.subjectReserve marketpt_PT
dc.subjectRegulation servicept_PT
dc.subjectRobust optimizationpt_PT
dc.subjectUncertaintypt_PT
dc.subjectWind powerpt_PT
dc.titleA robust model for aggregated bidding of energy storages and wind resources in the joint energy and reserve marketspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage121735pt_PT
oaire.citation.titleEnergypt_PT
oaire.citation.volume238pt_PT
person.familyNameFaria
person.familyNameVale
person.givenNamePedro
person.givenNameZita
person.identifier632184
person.identifier.ciencia-idB212-2309-F9C3
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-5982-8342
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication35e6a4ab-f644-4bc5-b6fc-9fd89c23d6c6
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscovery35e6a4ab-f644-4bc5-b6fc-9fd89c23d6c6

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