文献类型：期刊文献

中文题名：基于混合DE-PSO多目标算法的动态环境经济调度

英文题名：Dynamic economic emission dispatch based on hybrid DE-PSO multi-objective algorithm

作者：刘刚 朱永利 蒋伟

第一作者：刘刚

机构：[1]华北电力大学电气与电子工程学院;[2]贵州理工学院电气与信息工程学院

第一机构：华北电力大学电气与电子工程学院,河北保定071003

年份：2018

卷号：38

期号：8

起止页码：1-7

中文期刊名：电力自动化设备

外文期刊名：Electric Power Automation Equipment

收录：CSTPCD;;EI(收录号：20184606062874);Scopus;北大核心:【北大核心2017】；CSCD:【CSCD2017_2018】；

基金：国家自然科学基金资助项目(51677072)~~

语种：中文

中文关键词：动态环境经济调度;多目标优化;微分进化;粒子群优化;最佳折中解

外文关键词：dynamic economic emission dispatch;multi-objective optimization;differential evolution;particle swarm optimization;best compromise solution

摘要：电力系统中的动态环境经济调度(DEED)是一个多变量、强约束、非凸的多目标优化问题,传统方法很难进行求解。基于微分进化(DE)算法的快速收敛性和粒子群优化(PSO)算法的搜索多样性,提出一种融合2种算法优点的混合DE-PSO多目标优化算法来求解DEED问题,该算法基于外部存档集和Pareto占优原则,采用自适应参数的DE和PSO双种群更新策略以及一种改进的Pareto解集裁剪方法。引入3种指标评价算法的性能,并采用模糊决策技术从Pareto前沿中提取折中解以供决策者进行选择。经典算例的仿真结果表明所提方法能同时优化成本和排放这2个冲突的目标,且获得了比其他算法更为宽广和均匀的Pareto前沿,体现了所提方法的可行性和优越性。
The DEED （Dynamic Economic Emission Dispatch） in power system is a muhivariable, strongly constrained, non-convex and multi-objective optimization problem, which is difficult to be solved by the traditional methods. Based on the fast convergence of DE （Differential Evolution） algorithm and the search diversity of PSO （Particle Swarm Optimization） algorithm, a hybrid DE-PSO multi-objective optimization algorithm combined with the advantages of the two algorithms is proposed to solve the DEED problem. The algorithm is based on the external archiving set and Pareto domination principle, and adopts a bi-population update strategy of DE and PSO with adaptive para- meters and an improved Pareto solution clipping method. Three indexes are introduced to evaluate the performance of the algorithm, and the fuzzy decision technology is adopted to extract the best compromise solution from the Pareto front for decision makers to choose. The simulative results of a classic case show that the proposed algorithm can optimize the two conflicting objectives simultaneously, i.e. the cost and emission, and obtain wider and uniform Pareto front than other algorithms ,which shows the feasibility and superiority of the proposed method.