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Modified particle swarm optimization for nonconvex economic dispatch problems
Affiliation: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;1. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, P.O. Box 50, Al-Huson, Irbid, Jordan;2. Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine;3. School of Computer Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, Malaysia;4. Department of Computer Science, Faculty of Pure and Applied Sciences, Federal University Wukari, P. M. B. 1029, Wukari, Taraba State, Nigeria;1. School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, Jiangsu 221116, China;2. Graduate School of Business and Law, RMIT University, 379-405 Russell St, Melbourne, VIC 3000, Australia;3. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China;1. University of El-Oued, Department of Electrical Engineering, El-Oued, Algeria;2. Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, UOIT, ON, Canada;3. Department of Electrical Engineering, University of Biskra, Biskra, Algeria
Abstract:This paper presents modified particle swarm optimization to solve economic dispatch problems with non-smooth/non-convex cost functions. Particle swarm optimization performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes Gaussian random variables in velocity term which improves search efficiency and guarantees a high probability of obtaining the global optimum without considerably worsening the speed of convergence and the simplicity of the structure of particle swarm optimization. The efficacy of the proposed method has been demonstrated on four test problems and four different non-convex economic dispatch problems with valve-point effects, prohibited operating zones with transmission losses, multiple fuels with valve point effects and the large-scale Korean power system with valve-point effects and prohibited operating zones. The results of the proposed approach are compared with those obtained by other evolutionary methods reported in the literature. It is found that the proposed modified particle swarm optimization based approach is able to provide better solution.
Keywords:Modified particle swarm optimization  Economic dispatch  Prohibited operating zones  Valve-point loading  Multi-fuel option
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