Solution of unit commitment problem using Shuffled Frog Leaping Algorithm

This paper presents a solution to Unit commitment (UC) of thermal units based on a new evolutionary algorithm known as Shuffled Frog Leaping Algorithm(SFLA). The integer coded algorithm is based on the behavior of group of frogs searching for a location that has the maximum amount of available food. This algorithm involves local search and shuffling process and these are repeated until a required convergence is reached. In this proposed method of SFLA for the UC problem, the scheduling variables are coded as integers, so that the minimum up/down time constraints can be handled directly. To verify the performance of the proposed algorithm it is applied to the 10 unit system for a one day scheduling period.

[1]  G B Gharehpetian,et al.  Unit Commitment Problem Solution Using Shuffled Frog Leaping Algorithm , 2011, IEEE Transactions on Power Systems.

[2]  N.P. Padhy,et al.  Unit commitment-a bibliographical survey , 2004, IEEE Transactions on Power Systems.

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

[4]  D. P. Kothari,et al.  Power system optimization , 2004, 2012 2nd National Conference on Computational Intelligence and Signal Processing (CISP).

[5]  Yanfeng Wang,et al.  An improved shuffled frog leaping algorithm with cognitive behavior , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[6]  Antonio J. Conejo,et al.  A Parallel Repair Genetic Algorithm to Solve the Unit Commitment Problem , 2002, IEEE Power Engineering Review.

[7]  Thai-Hoang Huynh,et al.  A modified shuffled frog leaping algorithm for optimal tuning of multivariable PID controllers , 2008, 2008 IEEE International Conference on Industrial Technology.

[8]  Muzaffar Eusuff,et al.  Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization , 2006 .

[9]  B. Vahidi,et al.  Bacterial Foraging-Based Solution to the Unit-Commitment Problem , 2009, IEEE Transactions on Power Systems.

[10]  Walter L. Snyder,et al.  Dynamic Programming Approach to Unit Commitment , 1987, IEEE Transactions on Power Systems.

[11]  Qin Jinlei.  An Improved Dual Particle Swarm Optimization Algorithm for Unit Commitment Problem , 2012 .

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

[13]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

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

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

[16]  Wei Xiong,et al.  An Improved Particle Swarm Optimization Algorithm for Unit Commitment , 2008, 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA).

[17]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[18]  R. C. Desai,et al.  Modern power system analysis , 1982, Proceedings of the IEEE.