类进化算法驱动的动态电力经济调度优化

Cluster Evolutionary Algorithm Driven Dynamic Economic Dispatch Optimization

动态电力经济调度(Dynamic Economic Dispatch,DED)属于一种在时间和空间上相互耦合的多阶段动态决策问题,一般被转化为一个高维的约束数值优化问题来求解.本文提出了一种新型全局优化算法--类进化算法(Cluster Evolutionary Algorithm,CEA),并将其应用于DED问题的计算.CEA通过聚类过程在进化个体间构建一定结构的连接关系,并利用这种虚拟的簇类化组织来协调和控制群体的优化计算过程,提高算法对高维问题空间的搜索效率和抗早熟能力.在仿真实验中2个DED测试系统被用于对CEA的性能进行检验,其所得最佳计算结果要好于目前已报道的最优解,而实验统计数据则显示CEA是一种求解DED问题可行且有效的方法.

Dynamic economic dispatch（ DED） is a multi-stage decision problem with space and time coupling. In order to get the global optimal solution,a DED problem generally has been transformed into a high-dimensional constrained numerical optimization problem to solve. In this study,a novel global optimization algorithm,cluster evolutionary algorithm（ CEA）,is proposed to solve DED problem. In CEA,a virtual cluster organization is constructed among individuals so as to dynamically adjust the searching process of simulated evolutionary system while improving the optimization efficiency of population. In simulations,CEA is applied to 2 DED testing systems for verifying its feasibility. Meanwhile,a comparative study is carried out with other existing methods. Results clarify the significance of the proposed algorithm and verify its performance. Considering the quality of the solution obtained,CEA seems to be a promising alternative approach for solving the DED problem.