Summary
The optimal scheduling of power generation is a major problem in Electricity generating industry. This scheduling problem is complex because of many constraints which can not be violated while finding optimal or near-optimal scheduling. A real-time scheduling of generating units involves the selection of units for for operation to meet the consumer requirement. These decisions are to be taken so as to minimize the sum of startup, banking and expected running costs subject to the demand and spinning_reserve, and satisfying the minimum down-time and up-time constraints of generating units. So the objective of the scheduling is to generate power to meet the load with minimum cost of generation. We tackled the problem with heuristic knowledge (load forecast) and genetic search technique. The paper examines the feasibility of using genetic algorithms, and reports results in determining a near-optimal commitment order of thermal units in a studied power system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
L. F. B. Baptistella and J. C. Geromel. A decomposition approach to problem of unit commitment schedule for hydrothermal systems. IEE Proceedings- Part-C, 127(6):250–258, November 1980.
Jonathan F. Bard. Short-Term Scheduling of Thermal-Electric Generators using Lagrangian Relaxation. Operations Research, 36(5):756–766, Sept/Oct. 1988.
L. B. Booker. Intelligent behavior as an adaptation to the task environment. PhD thesis, Computer Science, University of Michigan, Ann Arbor, U. S. A, 1982.
Arthur I. Cohen and Miki Yoshimura. A Branch-and-Bound Algorithm for Unit Commitment. IEEE Transactions on Power Apparatus and Systems., PAS102(2):444–449, February 1983.
Yuval Davidor. Epistasis Variance: Suitability of a Representation to Genetic Algorithms. Complex Systems, 4:369–383, 1990.
David E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley., first edition, 1989.
David E. Goldberg. Sizing populations for serial and parallel genetic algorithms. In International Conference on Genetic Algorithms (ICGA-89), pages 70–79, 1989.
A. R. Hamdam and K. Mohamed-Nor. Integrating an expert system into a thermal unit-commitment algorithm. IEE Proceedings-C, 138(6):553–559, November 1991.
W. J. Hobbs, G. Hermon, S. Warner, and G. B. Sheble. An Advanced Dynamic Programming approach for Unit Commitment. IEEE Transactions on Power Systems, 3(3):1201–1205, August 1988.
John H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan press, Ann Arbor, 1975.
Fred N. Lee. The application of commitment utilization factor (CUF) to thermal unit commitment. IEEE Transactions on Power Systems., 6(2):691–698, May 1991.
P. G. Lowery. Generating Unit Commitment by Dynamic Programming. IEEE Transactions on Power Apparatus and Systems, PAS-85(5):422–426, May 1966.
A. Merlin and P. Sandrin. A New Method for Unit Commitment at Electricité de France. IEEE Transactions on Power Apparatus and Systems, PAS102(5):1218–1225, May 1983.
John A. Muckstadt and Sherri A. Koenig. An Application of Lagrangian Relaxation to Scheduling in Power Generation Systems. Operations Research, 25(1):387–403, Jan/Feb 1977.
Sasan Mukhtari, Jagjit Singh, and Bruce Wollenberg. A Unit Commitment Expert System. IEEE Transactions on Power Systems, 3(1):272–277, February 1988.
Z. Ouyang and S. M. Shahidehpour. An Intelligent Dynamic Programming for Unit Commitment Application. IEEE Transactions on Power Systems, August 6(3):1203–1209, Augus 1991.
Z. Ouyang and S. M. Shahidehpour. A Hybrid Artificial Neural Network. Dynamic Programming approach to Unit Commitment. IEEE Transactions on Power Systems, 7(1):236–242, February 1992.
C. K. Pang and H. C. Chen. Optimal Short-term Thermal Unit Commitment. IEEE Transaction on Power Apparatus and Systems, PAS-95(4):1336–1346, July/August 1976.
C. K. Pang, G. B. Sheble’, and F. Albuyeh. Evaluation of dynamic programming based methods and multiple area representation for thermal unit commitments. IEEE Transactions on Power Apparatus and Systems, PAS-100(3):1212–1218, March 1981.
S. K. Tong, S. M. Shahidehpour, and Z. Ouyang. A Heuristic Short-term Unit Commitment. IEEE Transactions on Power Systems, 6(3):1210–1216, August 1991.
F. Zhuang and F. D. Galiana. Unit Commitment by Simulated Annealing. IEEE Transactions on Power Systems, 5(1):311–317, February 1990.
Fulin Zhuang and F. D. Galiana. Towards a more rigorous and practical Unit Commitment by Lagrangian Relaxation. IEEE Transactions on Power Systems, 3(2):763–773, May 1988.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Dasgupta, D. (1997). Optimal Scheduling of Thermal Power Generation Using Evolutionary Algorithms. In: Dasgupta, D., Michalewicz, Z. (eds) Evolutionary Algorithms in Engineering Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03423-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-662-03423-1_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-08282-5
Online ISBN: 978-3-662-03423-1
eBook Packages: Springer Book Archive