采用基于分解的多目标进化算法的电力环境经济调度  

EnvironmentalEconomic Dispatch Adopting Multi-Objective Evolutionary Algorithm Based on Decomposition

作　　者：朱永胜[1] 王杰[1] 瞿博阳[2] P.N.Suganthan 

机构地区：[1]郑州大学电气工程学院,河南省郑州市450001 [2]中原工学院电子信息学院,河南省郑州市450007 [3]南洋理工大学电气电子工程学院,新加坡南洋道50号639798

出　　处：《电网技术》2014年第6期1577-1584,共8页Power System Technology

基　　金：国家自然科学基金项目(61305080);河南省科技攻关计划项目(132102210521)~~

摘　　要：为了准确、快速地求解电力系统环境经济调度(environmental economic dispatching,EED)问题,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)应用于电力调度领域,提出了基于MOEA/D的多目标环境经济调度算法。该算法首先采用Tchebycheff法将整个EED Pareto最优前沿的逼近问题分解为一定数量的单目标优化子问题,然后利用差分进化同时求解这些子问题,并在算法中加入约束处理及归一化操作,以获得最优的带约束EED问题的调度方案。最后,应用模糊集理论为决策者提供最优折中解。对IEEE 30节点测试系统进行仿真计算,并与其它智能优化算法的调度方案对比。结果表明,该算法有效可行,且具有很好的收敛速度和求解精度。To solve the environmental economic dispatching （EED） problem quickly and accurately, the multi-objective evolutionary algorithm based on decomposition （MOEA/D） is applied in the field of power dispatching and a multi-objective EED dispatch method based on MOEA/D is proposed. In the proposed method, firstly the approximation of the entire EEl） Pareto-optimal front is decomposed into a certain amount of single-objective sub-problems using Tchebycheff algorithm; then these sub-problems are solved simultaneously utilizing differential evolution （DE） algorithm, and the constraint handling method and normalization operation are addedto achieve an optimal scheme of EED with constraints; finally, the fuzzy set theory is used to offer decision-makers an optimal compromise solution. Simulation of IEEE 30-bus system is performed and the simulation results are compared with dispatching schemes obtained by other intelligent optimization algorithms, and comparison result shows that the proposed method is effective and feasible, and both convergence speed and accuracy of the solution are satisfied.

关 键 词：环境经济调度 多目标进化算法 MOEA D PARETO最优前沿 

分 类 号：TM73[电气工程—电力系统及自动化]