A New Hybrid Optimization Algorithm for Solving Economic Load Dispatch Problem with Valve-Point Effect

被引:0
作者
Elyas, Seyyed H. [1 ]
Mandal, Paras [1 ]
Haque, Ashraf U. [2 ]
Giani, Annarita [3 ]
Tseng, Tzu-Liang [4 ]
机构
[1] Univ Texas El Paso, Dept Elect & Comp Engn, El Paso, TX 79968 USA
[2] Teshmont Consultants LP, Power Study Grp, Calgary, AB, Canada
[3] Los Alamos Natl Lab, Energy & Infrastructure Decis Grp, Los Alamos, NM 87545 USA
[4] Univ Texas El Paso, Dept Ind Mfg & Syst Engn, El Paso, TX 79968 USA
来源
2014 NORTH AMERICAN POWER SYMPOSIUM (NAPS) | 2014年
基金
美国国家科学基金会;
关键词
Clonal selection algorithm; economic load dispatch; gases Brownian motion optimization; particle swarm optimization; valve-point effect; POWER;
D O I
暂无
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
This paper presents an efficient approach for solving the economic load dispatch (ELD) problem with valve-point effect using a new hybrid optimization algorithm. The main aim for solving ELD problem is to schedule the output of the committed generating units in order to meet the system load under various operating constraints. Since ELD is a non-linear and non-convex problem, stochastic search algorithms are considered as appropriate solutions. In this paper, the proposed new hybrid optimization algorithm is based on Clonal Selection Algorithm (CSA) that uses the positive features of two other optimization techniques, Gases Brownian Motion Optimization (GBMO) and Particle Swarm Optimization (PSO), for local search and improving the quality of initial population, respectively. To validate the efficiency of the proposed hybrid method, termed as PG-Clonal in this paper, we tested it on two systems considering different constraints, and the obtained results are compared with the results of existing stochastic search algorithms available in the literature. The test results demonstrate the effectiveness of the proposed new hybrid PG-Clonal method in solving the ELD problem efficiently.
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页数:6
相关论文
共 15 条
[1]   Gases Brownian Motion Optimization: an Algorithm for Optimization (GBMO) [J].
Abdechiri, Marjan ;
Meybodi, Mohammad Reza ;
Bahrami, Helena .
APPLIED SOFT COMPUTING, 2013, 13 (05) :2932-2946
[2]  
Abdullah M. N., 2012, P U POW ENG C UPEC
[3]   Nonconvex Economic Dispatch With AC Constraints by a New Real Coded Genetic Algorithm [J].
Amjady, Nima ;
Nasiri-Rad, Hadi .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2009, 24 (03) :1489-1502
[4]   A fast particle swarm algorithm for solving smooth and non-smooth economic dispatch problems [J].
Cecilia Cagnina, Leticia ;
Cecilia Esquivel, Susana ;
Coello Coello, Carlos A. .
ENGINEERING OPTIMIZATION, 2011, 43 (05) :485-505
[5]   Solving economic load dispatch problem with valve-point effects using a hybrid quantum mechanics inspired particle swarm optimisation [J].
Chakraborty, S. ;
Senjyu, T. ;
Yona, A. ;
Saber, A. Y. ;
Funabashi, T. .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2011, 5 (10) :1042-1052
[6]   A REVIEW OF RECENT ADVANCES IN ECONOMIC-DISPATCH [J].
CHOWDHURY, BH ;
RAHMAN, S .
IEEE TRANSACTIONS ON POWER SYSTEMS, 1990, 5 (04) :1248-1259
[7]   Learning and optimization using the clonal selection principle [J].
de Castro, LN ;
Von Zuben, FJ .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (03) :239-251
[8]   Dynamic adaptive bacterial foraging algorithm for optimum economic dispatch with valve-point effects and wind power [J].
Farhat, I. A. ;
El-Hawary, M. E. .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2010, 4 (09) :989-999
[9]   Power System Harmonics Estimation Using Clonal Selection Algorithm [J].
Holanda, L. ;
Rabelo, R. ;
Lemos, M. ;
Barbosa, D. .
IEEE LATIN AMERICA TRANSACTIONS, 2013, 11 (01) :525-530
[10]  
Karakasis V. K., 2008, IEEE T EVOLUTIONARY, V12, P463