Improved particle swarm optimization algorithm for multi-reservoir system operation  

作　　者：Jun ZHANG Zhen WU Chun-tian CHENG Shi-qin ZHANG 

机构地区：[1]Zhejiang Electric Power Dispatching and Communication Center, Hangzhou 310007, R R. China [2]Department of Civil and Hydraulic Engineering, Dalian University of Technology, Dalian 116024, P. R. China [3]Fujian Electric Power Dispatching and Communication Center, Fuzhou 350003, R R. China

出　　处：《Water Science and Engineering》2011年第1期61-73,共13页水科学与水工程（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No. 50679011)

摘　　要：In this paper, a hybrid improved particle swarm optimization （IPSO） algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization （PSO） algorithm is improved in two ways： （1） The linearly decreasing inertia weight coefficient （LDIWC） is replaced by a self-adaptive exponential inertia weight coefficient （SEIWC）, which could make the PSO algorithm more balanceable and more effective in both global and local searches. （2） The crossover and mutation idea inspired by the genetic algorithm （GA） is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation （DPSA） algorithm.In this paper, a hybrid improved particle swarm optimization （IPSO） algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization （PSO） algorithm is improved in two ways： （1） The linearly decreasing inertia weight coefficient （LDIWC） is replaced by a self-adaptive exponential inertia weight coefficient （SEIWC）, which could make the PSO algorithm more balanceable and more effective in both global and local searches. （2） The crossover and mutation idea inspired by the genetic algorithm （GA） is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation （DPSA） algorithm.

关 键 词：particle swarm optimization self-adaptive exponential inertia weight coefficient multi-reservoir system operation hydroelectric power generation Minjiang Basin 

分 类 号：O242.23[理学—计算数学] TP18[理学—数学]