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Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm

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dc.citedby15
dc.contributor.authorChong K.L.en_US
dc.contributor.authorLai S.H.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorWan Jaafar W.Z.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57208482172en_US
dc.contributor.authorid36102664300en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55006925400en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:07:01Z
dc.date.available2023-05-29T09:07:01Z
dc.date.issued2021
dc.descriptionAnt colony optimization; Hydroelectric power; Hydroelectric power plants; Investments; Particle swarm optimization (PSO); Reservoirs (water); Stream flow; Water supply; Ant colony algorithms; Hydro-power generation; Hydropower reservoirs; Optimization algorithms; Particle swarm optimization algorithm; Reservoir performance; Streamflow generations; Uncertainty and variability; Genetic algorithmsen_US
dc.description.abstractThe production and use of energy from hydropower generation play a vital role in the economy. Besides, the presence of uncertainty further increases the complexity in optimizing the reservoir operation. A synthetic streamflow generation based on historical inflow records was employed using the Thomas�Fiering model for handling the uncertainty and variability of reservoir inflows. However, under the circumstances of water deficiency, the hydropower output is significantly reduced. In this study, an investigation of a parameter free Jaya algorithm as an optimization method for reservoir operation was carried out. When deriving the optimal operational rule a hedging strategy is introduced to attenuate the impact of reduced water supply. This strategy can effectively counterbalance the lack of water supply with reservoir storage requirements. The higher amount of hydropower generated by the proposed algorithm than the other algorithms used in this study, such as genetic algorithm (GA), the ant colony algorithm (ACO), the bat algorithm (BA), the particle swarm optimization (PSO) algorithm, chicken swarm optimization (CSO) algorithm, grasshopper optimization algorithm (GOA), equilibrium optimizer (EO) and firefly algorithm (FA), has shown its efficiency in the reservoir system. Several reservoir performance indices, such as total hydropower generation, reliability, and resilience, were used to access the proposed algorithm and other algorithms efficiency � 2021 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo107325
dc.identifier.doi10.1016/j.asoc.2021.107325
dc.identifier.scopus2-s2.0-85102972043
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85102972043&doi=10.1016%2fj.asoc.2021.107325&partnerID=40&md5=1f02981b444d5d48df6d904afa2b8f6c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26128
dc.identifier.volume106
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleApplied Soft Computing
dc.titleOptimization of hydropower reservoir operation based on hedging policy using Jaya algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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