A hybrid harmony search with arithmetic crossover operation for economic dispatch

被引:59
|
作者
Niu, Qun [1 ]
Zhang, Hongyun [1 ]
Wang, Xiaohai [1 ]
Li, Kang [2 ]
Irwin, George W. [2 ]
机构
[1] Shanghai Univ, Shanghai Key Laboratoly Power Stn Automat Technol, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China
[2] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT9 5AH, Antrim, North Ireland
基金
英国工程与自然科学研究理事会; 中国国家自然科学基金;
关键词
Economic dispatch; Harmony search; Arithmetic crossover; Opposition based learning; PARTICLE SWARM OPTIMIZATION; BEE COLONY OPTIMIZATION; DIFFERENTIAL EVOLUTION; COMBINED HEAT; DYNAMIC DISPATCH; GENETIC ALGORITHM; NONCONVEX; UNITS; PSO; SQP;
D O I
10.1016/j.ijepes.2014.04.031
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
Economic dispatch (ED) problems often exhibit non-linear, non-convex characteristics due to the valve point effects. Further, various constraints and factors, such as prohibited operation zones, ramp rate limits and security constraints imposed by the generating units, and power loss in transmission make it even more challenging to obtain the global optimum using conventional mathematical methods. Meta-heuristic approaches are capable of solving non-linear, non-continuous and non-convex problems effectively as they impose no requirements on the optimization problems. However, most methods reported so far mainly focus on a specific type of ED problems, such as static or dynamic ED problems. This paper proposes a hybrid harmony search with arithmetic crossover operation, namely ACHS, for solving five different types of ED problems, including static ED with valve point effects, ED with prohibited operating zones, ED considering multiple fuel cells, combined heat and power ED, and dynamic ED. In this proposed ACHS, the global best information and arithmetic crossover are used to update the newly generated solution and speed up the convergence, which contributes to the algorithm exploitation capability. To balance the exploitation and exploration capabilities, the opposition based learning (OBL) strategy is employed to enhance the diversity of solutions. Further, four commonly used crossover operators are also investigated, and the arithmetic crossover shows its efficiency than the others when they are incorporated into HS. To make a comprehensive study on its scalability, ACHS is first tested on a group of benchmark functions with a 100 dimensions and compared with several state-of-the-art methods. Then it is used to solve seven different ED cases and compared with the results reported in literatures. All the results confirm the superiority of the ACHS for different optimization problems. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:237 / 257
页数:21
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