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Enhancing optimization accuracy in power systems: Investigating correlation effects on objective function values

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dc.citedby6
dc.contributor.authorALAhmad A.K.en_US
dc.contributor.authorVerayiah R.en_US
dc.contributor.authorRamasamy A.en_US
dc.contributor.authorShareef H.en_US
dc.contributor.authorid58124002200en_US
dc.contributor.authorid26431682500en_US
dc.contributor.authorid16023154400en_US
dc.contributor.authorid57189691198en_US
dc.date.accessioned2025-03-03T07:42:59Z
dc.date.available2025-03-03T07:42:59Z
dc.date.issued2024
dc.description.abstractThis study addresses the critical yet often overlooked aspect of incorporating correlations among input stochastic variables in power system planning and scheduling optimization. While existing literature has extensively focused on uncertainty modelling, there remains a gap in fully assessing the consequences of disregarding correlations on objective function values across different power network sizes. To bridge this gap, we utilize Monte Carlo simulation with Cholesky decomposition, alongside Quasi-Monte Carlo sampling and Latin Hypercube Sampling, to effectively model uncertainty and capture correlation coefficients among input variables, including wind, solar photovoltaic, and load power. The most efficient technique is then integrated into our optimization model, which is applied to small, medium, and large power network models. Our proposed optimization model addresses conflicting objectives using a hybrid NSGAII-MOPSO, aiming to simultaneously minimize total operational cost, power loss, and voltage deviation. By implementing this model on selected power networks and comparing outcomes between cases with independent and correlated variables, we rigorously assess discrepancies in objective function values. We visualize and analyze these errors across systems of varying sizes, shedding light on the impact of neglecting variable correlations. Notably, the maximum discrepancies are observed at $3.26/h, $40.66/h, and $2754.04/h for the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems, respectively. Crucially, as the system size increases, so does the magnitude of these differences, underlining the escalating impact of neglecting variable correlations on optimization outcomes. We stress the importance of integrating such considerations into future planning and operational strategies to mitigate errors and enhance decision-making processes. ? 2024en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo102351
dc.identifier.doi10.1016/j.rineng.2024.102351
dc.identifier.scopus2-s2.0-85195165722
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85195165722&doi=10.1016%2fj.rineng.2024.102351&partnerID=40&md5=034fc43b38d9fdd94565bfd031afbae8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36542
dc.identifier.volume22
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleResults in Engineering
dc.subjectDecision making
dc.subjectElectric network analysis
dc.subjectIntelligent systems
dc.subjectMonte Carlo methods
dc.subjectSolar power generation
dc.subjectStochastic models
dc.subjectStochastic systems
dc.subjectUncertainty analysis
dc.subjectHybrid optimization
dc.subjectHybrid optimization technique
dc.subjectIndependent and correlated stochastic variable
dc.subjectMonte carlo simulation
dc.subjectMonte Carlo's simulation
dc.subjectOperations Modeling
dc.subjectOptimization operation
dc.subjectOptimization techniques
dc.subjectPower system optimization operation model
dc.subjectPower systems optimizations
dc.subjectStochastic variable
dc.subjectUncertainty models
dc.subjectOptimization
dc.titleEnhancing optimization accuracy in power systems: Investigating correlation effects on objective function valuesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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