Differential Evolution Algorithm for Solving Unit Commitment with Ramp Constraints

Abstract This article proposes a differential evolution algorithm to solve the unit commitment problem with ramp constraints. Two implementations of the proposed algorithm—the first one using the binary code, and the second one using the integer code—have been developed. Both of these implementations have been found to converge to the same optimum solution requiring a different number of generations and a different CPU time. The proposed algorithm shows competitive performance with the best of the similar methods proposed earlier.

[1]  Rainer Storn,et al.  Minimizing the real functions of the ICEC'96 contest by differential evolution , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

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

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

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

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

[6]  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.

[7]  C.K. Pang,et al.  Optimal short-term thermal unit commitment , 1976, IEEE Transactions on Power Apparatus and Systems.

[8]  Yong Fu,et al.  Security-constrained unit commitment with AC constraints , 2005, IEEE Transactions on Power Systems.

[9]  Arthur I. Cohen,et al.  A Branch-and-Bound Algorithm for Unit Commitment , 1983, IEEE Transactions on Power Apparatus and Systems.

[10]  C. A. Dortolina,et al.  An approach to solve the unit commitment problem using genetic algorithm , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

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

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

[13]  K. Price.  Differential evolution vs. the functions of the 2/sup nd/ ICEO , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

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

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

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

[17]  Narayana Prasad Padhy,et al.  An ant colony system approach for unit commitment problem , 2006 .

[18]  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).

[19]  F.D. Galiana,et al.  Unit commitment with primary frequency regulation constraints , 2005, IEEE Transactions on Power Systems.

[20]  S. M. Shahidehpour,et al.  A multi-stage intelligent system for unit commitment , 1992 .

[21]  C.D. Vournas,et al.  Unit Commitment by an Enhanced Simulated Annealing Algorithm , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[22]  A. H. Mantawy,et al.  Unit commitment by tabu search , 1998 .