Optimizing the non cascaded short term hydrothermal scheduling using accelerated particle swarm optimization (APSO) algorithm

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dc.contributor.authorZaheer Hussain, Hafiz
dc.date.accessioned2018-01-08T05:13:26Z
dc.date.available2018-01-08T05:13:26Z
dc.date.issued2015
dc.descriptionSupervised by:Dr. Aun Haideren_US
dc.description.abstractEfficient planning and optimal economic operation of power generation systems have played a major role in the growth of electrical power industry. Future energy demand not only depends upon increase of power generation units, but also requires the optimal operation of existing power systems. The cost of hydroelectric power generation is minimum but is not enough to fulfill the consumer’s electric power demand. Hence hydroelectric power system is used in conjunction with the thermal power system. A hybrid of both these electrical generation processes increase the overall power generation cost. In this thesis a meta-heuristic Accelerated Particle Swarm Optimization (APSO) algorithm has been proposed for hydro-thermal scheduling problem. The performance of the APSO algorithm has better than the existing various optimization techniques such as Lagrange Multiplier, Gradient Search, Simulated Annealing, Genetic Algorithm, Evolutionary Programming and its variants, Canonical Particle Swarm Optimization and its variants. It takes extremely less execution time and minimum number of iterations required to reduce the overall production cost of short term hydrothermal scheduling problem while meeting all constraints with and without considering transmission losses.en_US
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/2398
dc.language.isoenen_US
dc.publisherUniversity of Management and Technology Lahoreen_US
dc.subjectLagrange Multiplieren_US
dc.subjectGenetic Algorithmen_US
dc.subjectMSC Thesisen_US
dc.titleOptimizing the non cascaded short term hydrothermal scheduling using accelerated particle swarm optimization (APSO) algorithmen_US
dc.typeThesisen_US
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