基于混沌遗传和模糊决策算法的多目标负荷经济调度  

Multi-objective economic load dispatching based on chaos genetic algorithm and fuzzy decision

作　　者：张秀霞 王爽心[2] 吴冠玮[2] 

机构地区：[1]中国水利水电建设集团公司,北京100044 [2]北京交通大学机械与电子控制工程学院,北京100044

出　　处：《电力自动化设备》2009年第1期94-99,共6页Electric Power Automation Equipment

基　　金：国家自然科学基金项目(50776005);北京交通大学"十一.五"科技专项基金(2006xm029)~~

摘　　要：提出一种可同时得到电力系统最优机组组合和多目标负荷分配结果的混沌遗传和模糊决策算法。结合改进优先顺序法、启发式遗传算法、混沌优化和模糊决策的优点,按改进的优先顺序法确定各时段运行的机纽序列,用启发式遗传算法确定机组组合状态,并对交叉率和变异率进行模糊决策。在负荷分配中,考虑单一经济目标和多目标优化2种决策模型,用遗传算法进行并行搜索,同时在最优点附近利用混沌优化的遍历性进行局部寻优,避免遗传算法陷入局部最优,有效提高了收敛速度。将所提算法分别应用于10机和30机系统中,结果表明,该算法较好地处理了电力系统负荷经济调度的各种约束条件,减少了不可行解,加快了收敛速度。A chaos genetic and fuzzy decision algorithm is presented for power system to obtain optimal unit commitment and 10ad dispatch. It combines the advantages of improved priority list, heuristic genetic algorithm, chaotic optimization and fuzzy decision. The unit commitment sequence in different periods of time is set by improved priority list and the optimal unit commitment is determined by heuristic genetic algorithm. The crossover rate and mutation rate of genetic algorithm is controlled by fuzzy decision. Two economic load dispatch models are considered： pure economic objective and multiple objectives of economy and speediness. The parallel searching is used in heuristic genetic algorithm while chaotic optimization searching is used around the best point to avoid the local minimum of genetic algorithm and effectively accelerate the convergence speed. Tests on 10-machine ＆ 30-machine systems show that, the algorithm treats various constraints of economic load dispatch very well, reduces the number of unfeasible solutions, and improves convergence speed.

关 键 词：机组组合 负荷分配 多目标优化 混沌遗传算法 模糊决策 

分 类 号：TM73[电气工程—电力系统及自动化]