基于遗传粒子群混合算法的机组组合优化  

Unit commitment optimization based on genetic algorithm and particle swarm optimization hybrid algorithm

作　　者：张炯[1] 刘天琪[1] 苏鹏[1] 张鑫 

机构地区：[1]四川大学电气信息学院,四川成都610065 [2]成都电业局,四川成都610100

出　　处：《电力系统保护与控制》2009年第9期25-29,共5页Power System Protection and Control

摘　　要：节能发电调度的目标是实现能耗量最小,合理安排机组发电计划则更为至关重要。在参考文献的基础上,提出了一种用于机组组合优化的遗传粒子群混合优化算法。先用遗传算法求解机组组合,再用粒子群优化算法求解负荷经济分配。按照节能调度思路对遗传算法进行了改进,提高了优化性能。给出了10机算例系统优化结果,验证了该混合算法的可行性和有效性。The purpose of energy-efficient scheduling is to realize the minimum of energy consumption, therefore, short-term generation scheduling is of vital importance. On the basis of the references, a hybrid algorithm of genetic algorithm（GA） and particle swarm optimization（PSO） is presented. The first step is to perform the optimization of unit commitment using GA, and the second step is to dispatch economic load in these units using PSO. At last, the example of ten-unit system shows that the hybrid algorithm is feasible and effective.

关 键 词：机组组合 负荷经济分配 遗传算法(GA) 粒子群优化(PSO) 

分 类 号：TM73[电气工程—电力系统及自动化]