基于MDPA算法的火电厂多目标负荷优化分配模型

发布时间：2018-04-27 07:45

  本文选题：火电厂 + 负荷优化分配　； 参考：《热力发电》2014年12期

【摘要】：在传统的火电厂经济负荷分配模型基础上,综合考虑全厂供电煤耗率、污染物排放量以及全厂负荷升、降时间3个目标,构建了厂级负荷优化分配的多目标模型。将差分粒子群混合算法发展为一种新型的多目标进化(MDPA)算法,即利用擂台赛法和凝聚层次聚类分析方法分别构造和修剪非支配集,同时加入精英保留策略,保留进化过程中的极值点。将该算法应用于以经济、环保、快速3个目标为多目标的厂级负荷优化分配,并与基于非支配排序的多目标优化(NSGA-Ⅱ)算法进行对比。结果表明,MDPA算法较NSGA-Ⅱ算法收敛速度更快,解集分布更均匀。
[Abstract]:On the basis of the traditional economic load distribution model of thermal power plant, a multi-objective model for optimal load distribution of power plant was constructed by considering three objectives: coal consumption rate of power supply, pollutant emission and load increase and drop time of the whole plant. The differential particle swarm optimization (DPSO) hybrid algorithm is developed into a new multi-objective evolutionary MDPA algorithm, in which the non-dominated sets are constructed and pruned by using the beating table method and the condensed hierarchical cluster analysis method, and the elite retention strategy is added. Preserve the extremum of evolution. The proposed algorithm is applied to plant load optimal allocation with three objectives of economy, environmental protection and speed, and is compared with the NSGA- 鈪，

本文编号：1809870