An improved firefly algorithm for optimal microgrid operation with renewable energy
Lately, an electrical network in microgrid system becomes very important to rural or remote areas without connection from primary power grid system. Higher cost of fuels, logistic, spare parts and maintenance affect the cost for operation microgrid generation to supply electrical power for remote ar...
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| 格式: | Thesis |
| 語言: | English English English |
| 出版: |
2017
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| 主題: | |
| 在線閱讀: | http://eprints.uthm.edu.my/870/1/24p%20SYUKUR%20SALEH.pdf http://eprints.uthm.edu.my/870/2/SYUKUR%20SALEH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/870/3/SYUKUR%20SALEH%20WATERMARK.pdf |
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| 總結: | Lately, an electrical network in microgrid system becomes very important to rural or remote areas without connection from primary power grid system. Higher cost of fuels, logistic, spare parts and maintenance affect the cost for operation microgrid generation to supply electrical power for remote areas and rural community. This project proposes an Improved Firefly Algorithm (IFA), which is a improvement of classical Firefly Algorithm (FA) technique using characteristic approach of Lévy flights to solve the optimal microgrid operation. The IFA has been used for optimizing the cost of power generation in microgrid system where daily power balance constraints and generation limits are considered. The microgrid system for this case study considered both of renewable energy plant and conventional generator units. There are two test systems that have been considered as case study. The first test system is a simple microgrid system which consists of three generators. The second test system consists of seven generating units including two wind turbines, three fuel-cell plants and two diesel generators. The IFA method has been implemented using MATLAB software. The results obtained by IFA was compared to FA and other algorithms based on optimal cost, convergence characteristics and robustness to validate the effectiveness of the IFA. It shows that the IFA obtained better results in terms of operating costs compared to FA, Differential Evolution (DE), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA). |
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