基于PSO-BBO混合优化算法的动态经济调度问题  

A modified hybrid PSO-BBO algorithm for dynamic economic dispatch

作　　者：陈珍[1] 胡志坚[1] 

机构地区：[1]武汉大学电气工程学院,湖北武汉430072

出　　处：《电力系统保护与控制》2014年第18期44-49,共6页Power System Protection and Control

基　　金：高等学校博士学科点专项科研基金项目(20110141110032);西安交通大学电力设备电气绝缘国家重点实验室资助项目(EIPE13205)~~

摘　　要：动态经济调度(Dynamic Economic Dispatch,DED)问题是电力系统运行与控制领域比较经典的多变量、非线性、强约束优化问题。为解决该问题,提出了将粒子群优化算法(Particle Swarm Optimization,PSO)和基本生物地理学优化算法(Biogeography-Based Optimization,BBO)相结合的改进生物地理学优化算法,并将该改进方法应用于一天24时段10机39节点标准算例。在考虑网损与不考虑网损两种情况下分别进行仿真分析,并将仿真结果与PSO和基本BBO算法以及参考文献中提出的六种智能算法进行对比,验证了该改进算法的有效性及在寻优能力上的提高。Dynamic economic dispatch（DED） problem is the classic multivariable, nonlinear, strong constrained optimization problem in the area of power system operation and control. To solve this problem, a hybrid Particle Swarm Optimization（PSO） and biogeography-based optimization（BBO） new algorithm is proposed. And the improved method is applied in 10 unit systems for a period of 24 hours. The simulation is done in the two cases with and without considering the net loss, and the simulation results are compared with the results of particle swarm optimization algorithm and the biogeography-based optimization algorithm and the results of other six intelligent algorithms＇ referred in the literatures, which verify the validity and the improvement in the capability of finding the best of the improved algorithm.

关 键 词：动态经济调度 生物地理学优化算法 PSO-BBO混合优化算法 阀点效应 约束处理 

分 类 号：TM73[电气工程—电力系统及自动化]