采用基于分解的多目标进化算法的电力环境经济调度

发布时间：2018-01-16 17:15

本文关键词：采用基于分解的多目标进化算法的电力环境经济调度　出处：《电网技术》2014年06期 　论文类型：期刊论文

【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。
[Abstract]:In order to solve the power system environmental and economic dispatch economic dispatch problem accurately and quickly. Multiobjective evolutionary algorithm based on decomposition. Multi-objective evolutionary algorithm based on decomposition. MOEA / D) is used in the field of power dispatching. A multi-objective environmental economic scheduling algorithm based on MOEA/D is proposed. The Tchebycheff method is used to make the whole EED. The approximation problem of Pareto optimal frontier is decomposed into a certain number of single-objective optimization subproblems. Then the subproblems are solved simultaneously by differential evolution, and the constrained processing and normalized operation are added to the algorithm to obtain the optimal scheduling scheme of the constrained EED problem. Finally. The fuzzy set theory is applied to provide the optimal compromise solution for the decision makers. The IEEE 30-bus test system is simulated and calculated, and compared with the scheduling schemes of other intelligent optimization algorithms. The results show that. The algorithm is effective and feasible, and has good convergence speed and accuracy.
【作者单位】：郑州大学电气工程学院;中原工学院电子信息学院;南洋理工大学电气电子工程学院;
【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)~~
【分类号】：TM73
【正文快照】： 0引言目前,世界上绝大多数的电厂仍然是以煤、石油、天然气等不可再生能源作为发电的主要燃料,这会使得电厂排放大量的硫氧化物、氮氧化物及CO2等气体,不仅污染大气环境,还会导致温室效应[1]。随着全球环境问题的日益严峻,减少污染气体排放,保护生态环境,已成为全人类的共识。

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