Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem |
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| Affiliation: | 1. Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;2. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, No. 19 Nguyen Huu Tho Street, Tan Phong Ward, District 7, Ho Chi Minh City, Viet Nam;1. UM Power Energy Dedicated Advanced Center (UMPEDAC), Level 4, Wisma R&D University of Malaya, JalanPantai Baharu, 59990 Kuala Lumpur, Malaysia;2. Renewable Energy Research Group, King Abdulaziz University, Jeddah 21589, Saudi Arabia;3. Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;1. Department of Electrical and Electronics Engineering, Technology Faculty, Duzce University, Duzce, Turkey;2. Department of Electrical Engineering, Engineering Faculty, Kocaeli University, Kocaeli, Turkey;3. Department of Electrical and Electronics Engineering, Engineering Faculty, Karadeniz Technical University, 61080 Trabzon, Turkey;1. Dr. B.C. Roy Engineering College, Durgapur, West Bengal 713206, India;2. National Institute of Technology-Agartala, Tripura 799055, India;3. Department of Electrical Engineering, Jadavpur University, Kolkata, West Bengal 700032, India;1. School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, Jiangsu 221116, PR China;2. Graduate School of Business and Law, RMIT University, 379-405 Russell St, Melbourne, VIC 3000, Australia;3. School of Computer Science & Technology, Jiangsu Normal University, Xuzhou, Jiangsu 221116, PR China;4. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China;5. School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, Guangdong 510006, PR China |
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| Abstract: | This paper proposes an efficient optimization approach, namely quasi-oppositional teaching learning based optimization (QOTLBO) for solving non-linear multi-objective economic emission dispatch (EED) problem of electric power generation with valve point loading. In this article, a non-dominated sorting QOTLBO is employed to approximate the set of Pareto solution through the evolutionary optimization process. The proposed approach is carried out to obtain EED solution for 6-unit, 10-unit and 40-unit systems. For showing the superiority of this optimization technique, numerical results of the four test systems are compared with several other EED based recent optimization methods. The simulation results show that the proposed algorithm gives comparatively better operational fuel cost and emission in less computational time compared to other optimization techniques. |
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| Keywords: | Economic dispatch Emission Valve point loading Pareto front Opposition based learning Teaching learning based optimization |
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