A multiobjective hybrid bat algorithm for combined economic/emission dispatch

被引:84
|
作者
Liang, Huijun [1 ]
Liu, Yungang [1 ]
Li, Fengzhong [1 ]
Shen, Yanjun [2 ]
机构
[1] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Shandong, Peoples R China
[2] China Three Gorges Univ, Coll Elect Engn & New Energy, Yichang 443002, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Multiobjective optimization; Economic/emission dispatch; Bat algorithm; Large-scale systems; ECONOMIC EMISSION DISPATCH; PARTICLE SWARM OPTIMIZATION; COLONY OPTIMIZATION; GENETIC ALGORITHM; LOAD DISPATCH; SYSTEM; FLOW; OPERATION; SEARCH;
D O I
10.1016/j.ijepes.2018.03.019
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, a multiobjective hybrid bat algorithm is proposed to solve the combined economic/emission dispatch problem with power flow constraints. In the proposed algorithm, an elitist nondominated sorting method and a modified crowding-distance sorting method are introduced to acquire an evenly distributed Pareto Optimal Front. A modified comprehensive learning strategy is used to enhance the learning ability of population. Through this way, each individual can learn not only from all individual best solutions but also from the global best solutions (nondominated solutions). A random black hole model is introduced to ensure that each dimension in current solution can be updated individually with a predefined probability. This is not only meaningful in enhancing the global search ability and accelerating convergence speed, but particularly key to deal with high dimensional systems, especially large-scale power systems. In addition, chaotic map is integrated to increase the diversity of population and avoid premature convergence. Finally, numerical examples on the IEEE 30-bus, 118-bus and 300-bus systems, are provided to demonstrate the superiority of the proposed algorithm.
引用
收藏
页码:103 / 115
页数:13
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