Couple-based particle swarm optimization for short-term hydrothermal scheduling |
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Affiliation: | 1. Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, China;2. Shenzhen Research Institute of Xiamen University, Shenzhen 518057, China;1. Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;2. Department of Power Systems, Ho Chi Minh City University of Technology, Ho Chi Minh City, Viet Nam;3. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;1. Faculty of Electrical and Electronics Engineering, HCM City University of Technology and Education, 1 Vo Van Ngan str., Thu Duc dist., Ho Chi Minh City, Vietnam;2. Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam |
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Abstract: | A novel couple-based particle swarm optimization (CPSO) is presented in this paper, and applied to solve the short-term hydrothermal scheduling (STHS) problem. In CPSO, three improvements are proposed compared to the canonical particle swarm optimization, aimed at overcoming the premature convergence problem. Dynamic particle couples, a unique sub-group structure in maintaining population diversity, is adopted as the population topology, in which every two particles compose a particle couple randomly in each iteration. Based on this topology, an intersectional learning strategy using the partner learning information of last iteration is employed in every particle couple, which can automatically reveal useful history information and reduce the overly rapid evolution speed. Meanwhile, the coefficients of each particle in a particle couple are set as distinct so that the particle movement patterns can be described and controlled more precisely. In order to demonstrate the effectiveness of our proposed CPSO, the algorithm is firstly tested with four multimodal benchmark functions, and then applied to solve an engineering multimodal problem known as STHS, in which two typical test systems with four different cases are tested, and the results are compared with those of other evolutionary methods published in the literature. |
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Keywords: | Dynamic particle couples Intersectional learning strategy Distinct coefficients Particle swarm optimization Short-term hydrothermal scheduling |
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