Solving unit commitment problem by a binary shuffled frog leaping algorithm

Shuffled frog leaping (SFL) algorithm is one of the heuristic algorithms which is classified in swarm intelligence area. The standard version of the SFL and the improved versions of the algorithm operate in continuous space and is being researched and utilised in different subjects by researches around the world. The results obtained show that the improved versions of the algorithm perform well. But many optimisation problems are set in discrete space and there is no binary version of SFL to deal with these problems. Thus, an SFL algorithm is presented for optimising binary encoded problems called as binary SFL (BSFL). To show the effectiveness of the proposed algorithm, BSFL is tested on unit commitment problem, which is one of the most important problems to be solved in the operation and planning of a power system. The results obtained by the proposed algorithm are compared with the previous approaches reported in the literature. The results show that the proposed algorithm produces optimal solution for the study system.

[1]  Tomonobu Senjyu,et al.  A fast technique for unit commitment problem by extended priority list , 2003 .

[2]  Chuangxin Guo,et al.  An improved particle swarm optimization algorithm for unit commitment , 2006 .

[3]  Xiaohong Guan,et al.  Unit Commitment with Identical Units: Successive Subproblem Solving Method Based on Lagrangian Relaxation , 2002, IEEE Power Engineering Review.

[4]  M. M. Farsangi,et al.  A modified Shuffled Frog Leaping algorithm for nonconvex Economic Dispatch problem , 2012, 2012 IEEE Power and Energy Society General Meeting.

[5]  Kevin E Lansey,et al.  Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm , 2003 .

[6]  W. Ongsakul,et al.  Unit commitment by enhanced adaptive Lagrangian relaxation , 2004, IEEE Transactions on Power Systems.

[7]  A. Bakirtzis,et al.  A solution to the unit-commitment problem using integer-coded genetic algorithm , 2004, IEEE Transactions on Power Systems.

[8]  Anastasios G. Bakirtzis,et al.  A genetic algorithm solution to the unit commitment problem , 1996 .

[9]  Tomonobu Senjyu,et al.  A unit commitment problem by using genetic algorithm based on unit characteristic classification , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[10]  Chuan-Ping Cheng,et al.  Unit commitment by Lagrangian relaxation and genetic algorithms , 2000 .

[11]  Yanbin Yuan,et al.  An improved binary particle swarm optimization for unit commitment problem , 2009, Expert Syst. Appl..

[12]  S. M. Shahidehpour,et al.  Short-term generation scheduling with transmission and environmental constraints using an augmented Lagrangian relaxation , 1995 .

[13]  D. Srinivasan,et al.  A priority list-based evolutionary algorithm to solve large scale unit commitment problem , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[14]  Francisco D. Galiana,et al.  Unit commitment by simulated annealing , 1990 .

[15]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[16]  L. Jenkins,et al.  Simulated annealing with local search-a hybrid algorithm for unit commitment , 2002 .

[17]  H. H. Balci,et al.  Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method , 2004 .

[18]  Tomonobu Senjyu,et al.  Emerging solution of large-scale unit commitment problem by Stochastic Priority List , 2006 .

[19]  T.O. Ting,et al.  A novel approach for unit commitment problem via an effective hybrid particle swarm optimization , 2006, IEEE Transactions on Power Systems.

[20]  Chun-Lung Chen,et al.  Unit commitment with probabilistic reserve: An IPSO approach , 2007 .

[21]  W. Ongsakul,et al.  Ant colony search algorithm for unit commitment , 2003, IEEE International Conference on Industrial Technology, 2003.

[22]  Li Liu,et al.  The Chaos-Based Shuffled Frog Leaping Algorithm and Its Application , 2008, 2008 Fourth International Conference on Natural Computation.

[23]  Z. Gaing.  Discrete particle swarm optimization algorithm for unit commitment , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[24]  Xiaohui Yuan,et al.  Application of enhanced discrete differential evolution approach to unit commitment problem , 2009 .

[25]  Alice E. Smith,et al.  A Seeded Memetic Algorithm for Large Unit Commitment Problems , 2002, J. Heuristics.

[26]  W. J. Hobbs,et al.  An enhanced dynamic programming approach for unit commitment , 1988 .

[27]  Eiichi Tanaka,et al.  An Evolutionary Programming Solution to the Unit Commitment Problem , 1997 .

[28]  Chuanwen Jiang,et al.  A matrix real-coded genetic algorithm to the unit commitment problem , 2006 .

[29]  K. S. Swarp,et al.  Unit Connuitment Solution Methodology Using Genetic Algorithm , 2002, IEEE Power Engineering Review.

[30]  Yuanyuan Liu,et al.  Improved shuffled frog leaping algorithm for continuous optimization problem , 2009, 2009 IEEE Congress on Evolutionary Computation.

[31]  S. Virmani,et al.  Implementation of a Lagrangian Relaxation Based Unit Commitment Problem , 1989, IEEE Power Engineering Review.