Alam, Mohammad Khurshed and Mohd Herwan, Sulaiman and Ferdowsi, Asma and Sayem, Md. Shaoran and Khair, Nazmus Sakib (2022) Moth Flame Optimization Algorithm including Renewable Energy for Minimization of Generation & Emission Costs in Optimal Power Flow. In: IEEE 5th Asia Conference on Energy and Electrical Engineering (ACEEE) , 8-10 July 2022 , Kuala Lumpur, Malaysia. pp. 41-45.. ISBN 978-1-6654-9933-0
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Abstract
Optimal power flow is an approach for enhancing power system performance, scheduling, and energy management. Because of its adaptability in a variety of settings, optimum power flow is becoming increasingly vital. The demand for optimization is driven by the need for cost-effective, efficient, and optimum solutions. Optimization is useful in a variety of fields, including science, economics, and engineering. This problem must be overcome to achieve the goals while keeping the system stable. Moth Flame Optimization (MFO), a recently developed metaheuristic algorithm, will be used to solve objective functions of the OPF issue for combined cost and emission reduction in IEEE 57-bus systems with thermal and stochastic wind-solar-small hydropower producing systems. According to the data, the MFO generated the best results across all simulated research conditions. MFO, for example, offers a total cost and emission of power generation of 248.4547 $/h for IEEE 57-bus systems, providing a 1.5 percent cost savings per hour above the worst values obtained when comparing approaches. According to the statistics, MFO beats the other algorithms and is a viable solution to the OPF problem
| Item Type: | Conference or Workshop Item (Lecture) |
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| Uncontrolled Keywords: | Wind energy generation, Renewable energy sources, Costs, Wind energy, Stochastic processes, Fires, Solar energy |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Noorul Farina Arifin |
| Date Deposited: | 25 Oct 2022 02:40 |
| Last Modified: | 25 Oct 2022 02:40 |
| URI: | http://umpir.ump.edu.my/id/eprint/35508 |
| Download Statistic: | View Download Statistics |
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