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A high-performance democratic political algorithm for solving multi-objective optimal power flow problem

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dc.citedby8
dc.contributor.authorAhmadipour M.en_US
dc.contributor.authorAli Z.en_US
dc.contributor.authorOthman M.M.en_US
dc.contributor.authorBo R.en_US
dc.contributor.authorJavadi M.S.en_US
dc.contributor.authorRidha H.M.en_US
dc.contributor.authorAlrifaey M.en_US
dc.contributor.authorid57203964708en_US
dc.contributor.authorid25824589000en_US
dc.contributor.authorid35944613200en_US
dc.contributor.authorid35823727400en_US
dc.contributor.authorid56902737600en_US
dc.contributor.authorid59513348300en_US
dc.contributor.authorid57206778104en_US
dc.date.accessioned2025-03-03T07:44:18Z
dc.date.available2025-03-03T07:44:18Z
dc.date.issued2024
dc.description.abstractThe optimal power flow (OPF) is one of the most noticeable and integral tools in the power system operation and control and aims to obtain the most economical combination of power plants to exactly serve the total demand of the system without any load shedding or islanding through adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, the successful implementation of an expeditious and reliable optimization algorithm is crucial. To solve this issue, this research proposes an enhanced democratic political algorithm (DPA), which can effectively solve multi-objective optimum power flow problems. The proposed method is a version of the democratic political optimization algorithm in which the search capability of this method to cover the borders of the Pareto frontier is enhanced. For the sake of practicality, the objectives with innate differences such as total emission, active power loss, and fuel cost are selected. Due to the practical limitations in real power systems, additional restrictions including valve-point effect, multi-fuel characteristics, and forbidden operational zones, are also considered. The proposed approach is tested and validated on IEEE 57-bus and IEEE 118-bus systems with different case studies. Simulation results are analyzed and compared with two popular and commonly used multi-objective-evolutionary algorithms namely, non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) on the problem. The study results illustrate the effectiveness of the proposed method in handling different scales, non-convex, and multi-objective optimization problems. ? 2023 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo122367
dc.identifier.doi10.1016/j.eswa.2023.122367
dc.identifier.scopus2-s2.0-85176091164
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85176091164&doi=10.1016%2fj.eswa.2023.122367&partnerID=40&md5=a1a1f48c79971f93b7b1b97f8304fc18
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36739
dc.identifier.volume239
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleExpert Systems with Applications
dc.subjectAcoustic generators
dc.subjectElectric load flow
dc.subjectElectric load shedding
dc.subjectEmission control
dc.subjectGenetic algorithms
dc.subjectPareto principle
dc.subjectParticle swarm optimization (PSO)
dc.subjectScreening
dc.subjectEmissions control
dc.subjectEnhanced political optimizer
dc.subjectMulti-objectives optimization
dc.subjectOptimal power flow problem
dc.subjectOptimization algorithms
dc.subjectOptimizers
dc.subjectPareto optimal technique
dc.subjectPareto-optimal
dc.subjectPerformance
dc.subjectPractical constraint
dc.subjectMultiobjective optimization
dc.titleA high-performance democratic political algorithm for solving multi-objective optimal power flow problemen_US
dc.typeArticleen_US
dspace.entity.typePublication
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