应用于负荷经济分配的改进差分进化算法  

A Modified Differential Evolution for Economic Dispatching

作　　者：赵昕[1] 李剑[2] 

机构地区：[1]三峡大学信息技术中心,湖北宜昌443002 [2]湖北第二师范学院计算机科学与工程系,湖北武汉430060

出　　处：《微电子学与计算机》2010年第2期159-162,166,共5页Microelectronics & Computer

摘　　要：为了求解电力系统负荷经济分配问题,提出一种改进差分进化算法.该算法考虑机组的爬坡约束、出力限制区约束等非光滑费用函数曲线等非线性特性,采用词典排序法处理系统约束来保证算法结果严格满足约束条件,保证了系统的稳定性和安全性.在差分进化算法的交叉算子计算中引入微粒群算法中的个体最优和全局最优的概念,并采用遗传微粒群算法的多点交叉机制,将两者以一定的比率引入试验向量增强算法的局部搜索能力.此算法被应用于一个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,改进的差分进化算法的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.To solve the economic dispatch problems in power systems, a modified differential evolution algorithm was introduced In the proposed algorithm the non- linear characteristics, such as ramp constraints of the generating units, output restricted zone and non- smooth cost functions are considered. To handle the constraints, the lexicographie order method was enaployed to ensure the stability and the security of the system by providing feasible solutions. In the algorithm, the concepts of the individual best （pbest） and the global best （gbest） were introduced to DE, which were derived from the particle swarm optimization （PSO）. Moreover, based on the crossover mechanism from the genetic particle swarm optimization, pbest and gbest were used to generate the trail vector with user defined ratios to enhance the local search performance. The simulation results for a practical system with 6 units have shown the effectiveness and the stability of the proposed approach for economic dispatch problems, which are better than those of the genetic algorithm, PSO, and traditional differential evolution.

关 键 词：差分进化 负荷经济分配 电力系统 微粒群算法 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]