协同差分进化算法求解负荷经济分配问题  

Economic dispatching via a co-evolutionary differential evolution method

作　　者：雷建军[1] 李剑[1] 

机构地区：[1]湖北第二师范学院计算机学院,武汉430205

出　　处：《华中师范大学学报（自然科学版）》2011年第2期223-226,共4页Journal of Central China Normal University：Natural Sciences

基　　金：2011年度湖北省教育厅科学研究计划重点项目(D20113006);2010年度湖北省教育厅科学技术研究项目(B20103002)

摘　　要：提出了一种协同差分进化算法求解电力系统负荷经济分配问题(Economic Dispatching,ED).该算法考虑了机组的爬坡约束、出力限制区约束等非光滑费用函数曲线这样的非线性特征,并根据ED中可行域被分割为多个独立的区域的特点,采用协同进化策略处理约束条件.将种群分为保守和激进两种策略的子种群,子种群最优个体分别对另一个子种群中部分个体进行吞并和更新,以引导算法搜索新可行域.算法应用于一个6台机组的算例,与遗传算法、微粒群算法和标准差分进化算法相比较,本文算法结果质量更好并且更稳定,是求解负荷经济分配问题的一种有效方法.A co-evolutionary differential evolution algorithm （Co-DE） was employed to solve the economic dispatch problems （ED） in power systems. In the proposed algorithm the non-linear characteristics, such as ramp constraints of the generating units, output restricted zone and nonsmooth cost functions are considered. The co-evolutionary strategy was employed to handle ED problems with a number of isolated feasible regions, where the population was divided into two groups, the conservative group and the hazardous group. The best individual of each group was employed to annex and update a part of individuals of the other group which leaded the algorithm fly across infeasible regions to find new feasible regions. The proposed algorithm was implemented to a system with 6 units, and the simulation results have shown the effectiveness and the stability of the proposed approach for economic dispatch problems, which are better than those of the genetic algorithm, traditional particle swarm optimization, and differential evolution.

关 键 词：协同进化算法 负荷经济分配 电力系统 差分进化 

分 类 号：O415[理学—理论物理]