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Optimal operation of hydropower reservoirs under climate change

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dc.citedby3
dc.contributor.authorEhteram M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorChow M.F.en_US
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorChau K.-W.en_US
dc.contributor.authorChong K.L.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid7202674661en_US
dc.contributor.authorid57208482172en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:17:30Z
dc.date.available2024-10-14T03:17:30Z
dc.date.issued2023
dc.description.abstractThe current research aims to optimize the water release to generate optimal hydropower generation for the future up to the year 2039. The study�s novelty is the adaptive and nonadaptive rule curves for power production using optimization algorithms under the climate change model. In addition, the study used the RCP 8.5 scenario based on seven climate change models. A weighting method was used to select the best climate change models. The method can allocate more weights to more accurate models. The results revealed that the temperature increased by about 26% in the future, while precipitation would decreased by around 3%. The bat algorithm was also used, given it is a powerful method in solving optimization problems in water resources management. The results indicated that less power could be generated during the future period in comparison with the base period as there will be less inflow to the reservoir and released water for hydropower generation. However, by applying adaptive rule curves, the hydropower generation may be improved even under the climate change conditions. For example, the volumetric reliability index obtained when using adaptive rule curves (92%) was higher than when nonadaptive rule curves (90%) were applied. Also, the adoption of adaptive rule curves decreased the vulnerability index for the future period. Therefore, the bat algorithm with adaptive rule curves has a high potential for optimizing reservoir operations under the climate change conditions. � 2022, The Author(s), under exclusive licence to Springer Nature B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s10668-022-02497-y
dc.identifier.epage10659
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85134348570
dc.identifier.spage10627
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85134348570&doi=10.1007%2fs10668-022-02497-y&partnerID=40&md5=9a3005aa421cf8a25f7ef85e2b9f7bef
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33954
dc.identifier.volume25
dc.pagecount32
dc.publisherSpringer Science and Business Media B.V.en_US
dc.sourceScopus
dc.sourcetitleEnvironment, Development and Sustainability
dc.subjectAdaptive rule curves
dc.subjectHydropower generation
dc.subjectMetaheuristic algorithm
dc.subjectNonadaptive rule curves
dc.subjectalgorithm
dc.subjectclimate change
dc.subjectclimate conditions
dc.subjectclimate modeling
dc.subjectheuristics
dc.subjectpower generation
dc.subjectprecipitation (climatology)
dc.subjectvulnerability
dc.titleOptimal operation of hydropower reservoirs under climate changeen_US
dc.typeArticleen_US
dspace.entity.typePublication
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