Evaluation of Optimal Energy Productıon Usıng Deterministic, Probabilistic and Risky Cases In a Multi-Reservoir System

MakaleAçık ErişimScopus SJRQ1
  • SDG 6 · Temiz Su ve Sanitasyon
  • SDG 7 · Erişilebilir ve Temiz Enerji
  • SDG 9 · Sanayi, Yenilikçilik ve Altyapı

Özet

In a multi-reservoir system, the stochastic nature of basin data resulting from rainfall introduces risk into water management operations. Effective management that accounts for these risks can obtain maximum benefits from the system. This study presents a description of a multi-reservoir water resources system with hydroelectric power plants, utilizing the energy optimization model developed by OPAN in 2007. The model was applied to reservoirs located successively on the Lower Kızılırmak River in the Kızılırmak Basin, with the objective function being the maximization of firm power by using drought period inflows and total energy by using monthly average inflows. The study considered three scenarios: deterministic, probabilistic, and risky (critical cases), with probabilities of inflows from the basin being determined for the latter. Monthly inflows with determined probabilities were used to obtain data for the risky case. Optimum operating levels were determined based on this data to maximize firm power and total energy. According to the operating levels, the reservoir with the largest useful volume manages the operation. The values obtained from the optimization were then used in multivariate regression analysis using the Statistical Package for Social Scientists (SPSS), a statistical analysis program. The analysis explored the effects of monthly operating levels in the reservoirs, the amount of inflow released from the spillway, the amount of inflow released for energy production, the monthly average inflow to the reservoirs, and firm power values on energy production.

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RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/bayburt/11830Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.1007/s11269-023-03633-7Yayıncı resmi DOI adresi
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6
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7
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Tam Kayıt
AlanDeğerDil
dc.contributor.authorBacaksız, Efsun
dc.contributor.authorOpan, Mücahit
dc.contributor.authorKara Dilek, Zuhal Elif
dc.contributor.authorKaradeniz, Murat
dc.date.accessioned2026-10-29T19:08:51.160007755Z
dc.date.available2026-10-29T19:08:51.160007755Z
dc.date.issued2023
dc.description.abstractIn a multi-reservoir system, the stochastic nature of basin data resulting from rainfall introduces risk into water management operations. Effective management that accounts for these risks can obtain maximum benefits from the system. This study presents a description of a multi-reservoir water resources system with hydroelectric power plants, utilizing the energy optimization model developed by OPAN in 2007. The model was applied to reservoirs located successively on the Lower Kızılırmak River in the Kızılırmak Basin, with the objective function being the maximization of firm power by using drought period inflows and total energy by using monthly average inflows. The study considered three scenarios: deterministic, probabilistic, and risky (critical cases), with probabilities of inflows from the basin being determined for the latter. Monthly inflows with determined probabilities were used to obtain data for the risky case. Optimum operating levels were determined based on this data to maximize firm power and total energy. According to the operating levels, the reservoir with the largest useful volume manages the operation. The values obtained from the optimization were then used in multivariate regression analysis using the Statistical Package for Social Scientists (SPSS), a statistical analysis program. The analysis explored the effects of monthly operating levels in the reservoirs, the amount of inflow released from the spillway, the amount of inflow released for energy production, the monthly average inflow to the reservoirs, and firm power values on energy production.
dc.identifier.doi10.1007/s11269-023-03633-7
dc.identifier.eissn1573-1650
dc.identifier.endpage5848
dc.identifier.issn0920-4741
dc.identifier.issue15
dc.identifier.scopus2-s2.0-85174217070
dc.identifier.startpage5829
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/bayburt/11830
dc.identifier.volume37
dc.identifier.wosWOS:001083605000001
dc.language.isoen
dc.publisherSpringer Science and Business Media B.V.
dc.relation.ispartofWater Resources Management
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCritical Cases
dc.subjectDynamic Programming
dc.subjectEnergy Production
dc.subjectMulti-Reservoir Systems
dc.subjectMultivariate Regression Analysis
dc.subjectSPSS
dc.subject.classificationEngineering, Civil
dc.subject.classificationWater Resources
dc.subject.classificationEngineering
dc.subject.otherKizilirmak River
dc.subject.otherTurkey
dc.subject.otherHydroelectric power
dc.subject.otherHydroelectric power plants
dc.subject.otherInformation management
dc.subject.otherMultivariant analysis
dc.subject.otherRegression analysis
dc.subject.otherReservoir management
dc.subject.otherReservoirs (water)
dc.subject.otherStochastic systems
dc.subject.otherWater management
dc.subject.otherCritical case
dc.subject.otherDeterministics
dc.subject.otherEnergy productions
dc.subject.otherFirm power
dc.subject.otherMulti-reservoir systems
dc.subject.otherMultivariate regression analysis
dc.subject.otherOperating levels
dc.subject.otherSocial scientists
dc.subject.otherStatistical package for social scientist
dc.subject.otherStatistical packages
dc.subject.otherelectricity generation
dc.subject.otherenergy resource
dc.subject.otherenergy storage
dc.subject.othermultivariate analysis
dc.subject.otherprobability
dc.subject.otherstochasticity
dc.subject.otherDynamic programming
dc.titleEvaluation of Optimal Energy Productıon Usıng Deterministic, Probabilistic and Risky Cases In a Multi-Reservoir System
dc.typeArticle
rdl.citation.openalex7
rdl.citation.scopus7
rdl.citation.wos6
rdl.oa.statusgreen
rdl.oa.urlhttps://www.researchsquare.com/article/rs-2693552/latest.pdf
rdl.scopus.quartileQ1
rdl.sdgSDG07
rdl.sdgSDG06
rdl.sdgSDG09
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.wos.indexScience Citation Index Expanded (SCI-EXPANDED)