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Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approach

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dc.citedby18
dc.contributor.authorPremkumar M.en_US
dc.contributor.authorHashim T.J.T.en_US
dc.contributor.authorRavichandran S.en_US
dc.contributor.authorSin T.C.en_US
dc.contributor.authorChandran R.en_US
dc.contributor.authorAlsoud A.R.en_US
dc.contributor.authorJangir P.en_US
dc.contributor.authorid57191413142en_US
dc.contributor.authorid57217828276en_US
dc.contributor.authorid57219263030en_US
dc.contributor.authorid57212007867en_US
dc.contributor.authorid58873007200en_US
dc.contributor.authorid55711826000en_US
dc.contributor.authorid56857572500en_US
dc.date.accessioned2025-03-03T07:43:21Z
dc.date.available2025-03-03T07:43:21Z
dc.date.issued2024
dc.description.abstractThis paper delves into the increasingly complex domain of Optimal Power Flow (OPF) within modern power systems, enhanced by the integration of unpredictable renewable energy sources. The research originally integrates stochastic photovoltaic and wind energy sources, along with a suite of Flexible AC Transmission System (FACTS) components ? including thyristor-controlled series compensators, static VAR compensators, and thyristor-controlled phase shifters. The primary objective is to solve the OPF problem by reducing generation costs while accommodating the variable nature of renewable energy sources and load demands. This study prioritizes the examination of both constant and fluctuating load requirements. The inherent variability of PV and wind energy, along with load demand, is captured through the modelling of probability density functions. This approach enables a more detailed optimization process, incorporating not just the cost of thermal energy generation but also the scheduling costs of renewable sources and associated penalty costs. Moreover, the study examines the strategic placement and sizing of FACTS components, an aspect essential in minimizing the overall cost of power production. Employing both single- and multi-objective optimization algorithms, the research addresses the OPF problem in a modified IEEE-30 bus system through various case studies. The application of the recently developed flow direction algorithm, including its multi-objective variant with an �-based constraint-handling mechanism to OPF problem is the primary contributions of this work. The results, benchmarked against several advanced metaheuristic algorithms, reveal the proposed algorithm's superior performance. This comprehensive study not only underscores the potential of integrating renewable energy sources into the grid but also highlights the efficacy of intelligent optimization strategies in managing the complexities of modern power systems. ? 2024en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.aej.2024.02.069
dc.identifier.epage113
dc.identifier.scopus2-s2.0-85188029057
dc.identifier.spage90
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85188029057&doi=10.1016%2fj.aej.2024.02.069&partnerID=40&md5=59e7d50e21756340644bab3766fdd974
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36605
dc.identifier.volume93
dc.pagecount23
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleAlexandria Engineering Journal
dc.subjectConstraint handling
dc.subjectElectric load flow
dc.subjectElectric power system control
dc.subjectFlexible AC transmission systems
dc.subjectPower control
dc.subjectProbability density function
dc.subjectReactive power
dc.subjectStatic Var compensators
dc.subjectStochastic systems
dc.subjectThyristors
dc.subjectValue engineering
dc.subjectWind power
dc.subjectConstraint handling
dc.subjectEnergy
dc.subjectFlexible AC transmission system controller
dc.subjectFlow direction algorithms
dc.subjectMulti-objectives optimization
dc.subjectOptimal power flow analyse
dc.subjectOptimal power flows
dc.subjectPower flow analyze
dc.subjectSystem controllers
dc.subject?-based constraint-handling
dc.subjectMultiobjective optimization
dc.titleOptimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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