Stochastic optimization energy and reserve scheduling model application for alaçatı, Turkey

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dc.authorscopusid57189221354
dc.authorscopusid25722917300
dc.contributor.authorOzay C.
dc.contributor.authorCeliktas M.S.
dc.date.accessioned2023-01-12T20:22:55Z
dc.date.available2023-01-12T20:22:55Z
dc.date.issued2021
dc.departmentN/A/Departmenten_US
dc.description.abstractStochastic programming is a popular method that is used to model systems that contain a high level of uncertainty. This paper presents a new model for energy and reserve scheduling for microgrid systems using stochastic programming to optimize the energy and reserve planning in order to minimize the operational costs of a microgrid structure with renewable energy resources. Purchased power from the main grid and demand response programs are used for both energy and reserve scheduling. Uncertainties related to renewable energy generation, day-ahead market prices as well as costs related to unbalanced energy are considered and integrated into the model via scenarios. The model is formulated as a two-stage stochastic linear programming problem, solved using the L-shaped method, subjected to a numerical analysis and compared with a deterministic scheduling and a no-reserve approach in order to emphasize its efficiency. The results of the optimization show that 9.448 kW's of the total 14.300 kW DSR bid is accepted by the operator and 4.254 kW (45% of total) is used as direct energy purchase while the rest 5.194 kW is used as stand by reserve. When the results of deterministic and stochastic models are considered, 70% of all realization outcomes of the stochastic model yielded lower costs when compared to both the deterministic and no-reserve approaches. Stochastic planning cost is %5 lower than deterministic planning across all realization outcomes. The proposed model has clear economic advantages, which can be translated into the energy sector. © 2021 The Authorsen_US
dc.identifier.doi10.1016/j.segy.2021.100045
dc.identifier.issn26669552
dc.identifier.issn2666-9552en_US
dc.identifier.scopus2-s2.0-85123939745en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.segy.2021.100045
dc.identifier.urihttps://hdl.handle.net/11454/79598
dc.identifier.volume3en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofSmart Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMicrogriden_US
dc.subjectRenewable energyen_US
dc.subjectScenario analysisen_US
dc.subjectSmart griden_US
dc.subjectStochasticen_US
dc.subjectCostsen_US
dc.subjectElectric power transmission networksen_US
dc.subjectEnergy policyen_US
dc.subjectLinear programmingen_US
dc.subjectNumerical methodsen_US
dc.subjectSchedulingen_US
dc.subjectSmart power gridsen_US
dc.subjectStochastic modelsen_US
dc.subjectStochastic programmingen_US
dc.subjectStochastic systemsen_US
dc.subjectDeterministicsen_US
dc.subjectEnergyen_US
dc.subjectMicrogriden_US
dc.subjectRenewable energiesen_US
dc.subjectScenarios analysisen_US
dc.subjectSmart griden_US
dc.subjectStochastic optimizationsen_US
dc.subjectStochastic-modelingen_US
dc.subjectStochasticsen_US
dc.subjectUncertaintyen_US
dc.subjectRenewable energy resourcesen_US
dc.titleStochastic optimization energy and reserve scheduling model application for alaçatı, Turkeyen_US
dc.typeArticleen_US

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