A parallel genetic algorithm approach to solving the unit commitment problem: implementation on the transputer networks

Through a constraint handling technique, this paper proposes a parallel genetic algorithm (GA) approach to solving the thermal unit commitment (UC) problem. The developed algorithm is implemented on an eight-processor transputer network, processors of which are arranged in master-slave and dual-direction ring structures, respectively. The proposed approach has been tested on a 38-unit thermal power system over a 24-hour period. Speed-up and efficiency for each topology with different number of processor are compared to those of the sequential GA approach. The proposed topology of dual-direction ring is shown to be well amenable to parallel implementation of the GA for the UC problem.

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

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

[3]  M. La Scala,et al.  Parallel-in-time implementation of transient stability simulations on a transputer network , 1994 .

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

[5]  Gerald B. Sheblé,et al.  Unit commitment literature synopsis , 1994 .

[6]  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.

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

[8]  W. L. Peterson,et al.  A capacity based Lagrangian relaxation unit commitment with ramp rate constraints , 1995 .

[9]  D. Dasgupta,et al.  Thermal unit commitment using genetic algorithms , 1994 .

[10]  Md. Sayeed Salam,et al.  Integrating an expert system into a thermal unit-commitment algorithm , 1991 .

[11]  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.

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

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

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

[15]  S. M. Shahidehpour,et al.  A hybrid artificial neural network-dynamic programming approach to unit commitment , 1992 .