Short-term hydrothermal generation scheduling by a modified dynamic neighborhood learning based particle swarm optimization |
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| Affiliation: | 1. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China;2. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, PR China;1. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, 430074 Wuhan, China;2. School of Resource and Environmental Engineering, Wuhan University of Technology, 430070 Wuhan, China;3. College of Electrical Engineering and New Energy, China Three Gorges University, 443002 Yichang, China;1. Department of Electrical and Instrumentation Engineering, Thapar University, Patiala 147004, Punjab, India;2. School of Mathematics, Thapar University, Patiala 147004, Punjab, India;1. Department of Instrumental & Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen, 361005, China;2. Electric Power Research Institute, Fujian Electric Power Co., LTD, State Grid Corporation of China, Fuzhou, 350007, China;3. Shenzhen Engineering Laboratory of Geometry Measurement Technology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China;4. SYSU-CMU Joint Institute of Engineering, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, China |
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| Abstract: | The main objective of the short-term hydrothermal generation scheduling (SHGS) problem is to determine the optimal strategy for hydro and thermal generation in order to minimize the fuel cost of thermal plants while satisfying various operational and physical constraints. Usually, SHGS is assumed for a 1 day or a 1 week planing time horizon. It is viewed as a complex non-linear, non-convex and non-smooth optimization problem considering valve point loading (VPL) effect related to the thermal power plants, transmission loss and other constraints. In this paper, a modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO) is proposed to solve the SHGS problem. In the proposed approach, the particles in swarm are grouped in a number of neighborhoods and every particle learns from any particle which exists in current neighborhood. The neighborhood memberships are changed with a refreshing operation which occurs at refreshing periods. It causes the information exchange to be made with all particles in the swarm. It is found that mentioned improvement increases both of the exploration and exploitation abilities in comparison with the conventional PSO. The presented approach is applied to three different multi-reservoir cascaded hydrothermal test systems. The results are compared with other recently proposed methods. Simulation results clearly show that the MDNLPSO method is capable of obtaining a better solution. |
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| Keywords: | Short-term hydrothermal generation scheduling Dynamic neighborhood learning Particle swarm optimization (PSO) Non-convex optimization |
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