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A hybrid optimization technique for solving economic emission load dispatch problems

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dc.contributor.authorAisyah binti Nordinen_US
dc.date.accessioned2023-05-03T17:13:41Z
dc.date.available2023-05-03T17:13:41Z
dc.date.issued2020-02
dc.descriptionFYP 2 SEM 2 2019/2020en_US
dc.description.abstractThis project presents the application of Immune Evolutionary Programming (IEP) optimization technique in solving Economic Emission Load Dispatch (EED) problems in the power generation network. IEP algorithm is developed by using the MATLAB programming language and the IEEE 30-Bus Test System is used for its implementation. Additionally, the IEEE 30-Bus Test System consists of six different power generating units of PGs (PG1, PG2, PG3, PG4, PG5 and PG6). Once the IEP is developed successfully, the respective program’s performance will be tested based on two objective functions which are Total Cost and Total Emission. Each of these objectives will furthermore tested with two case studies based on the loading of the IEEE 30-Bus Test System. The conditions are Base Case and 100% Load Increment. Those results will be compared to the pre-optimized values and past research papers that uses the same parameters on the IEEE 30-Bus Test System. The optimization method used in past papers are Differential Evolution Immunized Ant Colony Optimization (DEIANT) and Particle Swarm Optimization (PSO) It was found that IEP optimization technique outperformed both DEIANT and PSO during the analysis of the results based on different case studies.en_US
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21549
dc.language.isoenen_US
dc.subjectImmune Evolutionary Programmingen_US
dc.subjectEconomic Emission Dispatchen_US
dc.subjectPower Generationen_US
dc.titleA hybrid optimization technique for solving economic emission load dispatch problems
dc.typeResource Types::text::Final Year Project
dspace.entity.typePublication
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