Applications of the genetic algorithm to the unit commitment problem in power generation industry

This paper proposes an innovative genetic algorithm (GA) approach to solve the thermal unit commitment (UC) problem in power generation industry through a constraint satisfaction technique. Due to a large variety of constraints to be satisfied, the solution space of the UC problem is highly nonconvex, and therefore the UC problem can not be solved efficiently by the standard GA. To effectively deal with the constraints of the problem and greatly reduce the search space of the GA, the minimum up- and down-time constraints are embedded in the binary strings that are coded to represent the on-off states of the generating units. The violations of the other constraints are handled by integrating penalty factors into the cost function. Numerical results on the practical Taiwan Power (Taipower) system of 38 thermal units over a 24-hour period show that the features of easy implementation, fast convergence, and highly near-optimal solution in solving the UC problem can be achieved by the proposed GA approach.<<ETX>>

[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.  Unit commitment by simulated annealing , 1990 .

[3]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[4]  Gary Boone,et al.  Optimal capacitor placement in distribution systems by genetic algorithm , 1993 .

[5]  Vladimiro Miranda,et al.  Genetic algorithms in optimal multistage distribution network planning , 1994 .

[6]  Anil Pahwa,et al.  Optimal selection of capacitors for radial distribution systems using a genetic algorithm , 1994 .

[7]  S. M. Shahidehpour,et al.  A heuristic short-term unit commitment , 1991 .

[8]  J. Bubenko,et al.  Application of Decomposition Techniques to Short-Term Operation Planning of Hydrothermal Power System , 1986, IEEE Transactions on Power Systems.

[9]  S. Ruzc,et al.  A new approach for solving extended unit commitment problem , 1991, IEEE Power Engineering Review.

[10]  Yahia Baghzouz,et al.  Implementation of the unit commitment problem on supercomputers , 1994 .

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

[12]  M. Kitagawa,et al.  Implementation of genetic algorithm for distribution systems loss minimum re-configuration , 1992 .

[13]  G. Sheblé,et al.  Genetic algorithm solution of economic dispatch with valve point loading , 1993 .

[14]  K. W. Edwin,et al.  Integer Programming Approach to the Problem of Optimal Unit Commitment with Probabilistic Reserve Determination , 1978, IEEE Transactions on Power Apparatus and Systems.

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