Bacterial Foraging-Based Solution to the Unit-Commitment Problem

The unit commitment (UC) problem is one of the most difficult optimization problems in power system, because this problem has many variables and constraints. The objective is the minimization of the total production cost over the scheduling horizon while the constraints must be satisfied, too. This paper employs a new evolutionary algorithm known as bacterial foraging (BF) for solving the UC problem. This new integer-code algorithm is on the base of foraging behavior of E-coli Bacteria in the human intestine. By integer coding of the problem, computation time decreases and the minimum up/down-time constraints may be coded directly, and therefore, there is no need to use penalty functions for these constraints. From simulation results, satisfactory solutions are obtained in comparison with previously reported results.

[1]  F. Albuyeh,et al.  Evaluation of Dynamic Programming Based Methods and Multiple area Representation for Thermal Unit Commitments , 1981, IEEE Transactions on Power Apparatus and Systems.

[2]  Francisco D. Galiana,et al.  Towards a more rigorous and practical unit commitment by Lagrangian relaxation , 1988 .

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

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

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

[6]  A. Conejo,et al.  A parallel repair genetic algorithm to solve the unit commitment problem , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[7]  Robert E. Smith,et al.  A genetic algorithm based approach to thermal unit commitment of electric power systems , 1995 .

[8]  F. Lee.  A Fuel-Constrained Unit Commitment Method , 1989, IEEE Power Engineering Review.

[9]  Mohammad Shahidehpour,et al.  Market operations in electric power systems , 2002 .

[10]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[11]  S. Mishra.  Bacteria foraging based solution to optimize both real power loss and voltage stability limit , 2007, 2007 IEEE Power Engineering Society General Meeting.

[12]  A. Rudolf,et al.  A genetic algorithm for solving the unit commitment problem of a hydro-thermal power system , 1999 .

[13]  Tomonobu Senjyu,et al.  Fast solution technique for large-scale unit commitment problem using genetic algorithm , 2003 .

[14]  K. M. Dale,et al.  A Study of the Economic Shutdown of Generating Units in Daily Dispatch , 1959, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems.

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

[16]  C. N. Bhende,et al.  Bacterial Foraging Technique-Based Optimized Active Power Filter for Load Compensation , 2007, IEEE Transactions on Power Delivery.

[17]  Felix F. Wu,et al.  Genetic algorithm based unit commitment with energy contracts , 2002 .

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

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

[20]  H. H. Happ,et al.  Large Scale Hydro-Thermal Unit Commitment-Method and Results , 1971 .

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

[22]  Sukumar Mishra,et al.  A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation , 2005, IEEE Transactions on Evolutionary Computation.

[23]  Gerald B. Sheblé,et al.  Unit commitment by genetic algorithm and expert system , 1994 .