Share This Article:

Multi-Area Unit Commitment Using Hybrid Particle Swarm Optimization Technique with Import and Export Constraints

Abstract Full-Text HTML Download Download as PDF (Size:577KB) PP. 140-150
DOI: 10.4236/eng.2009.13017    4,909 Downloads   9,313 Views   Citations

ABSTRACT

This paper presents a novel approach to solve the Multi-Area unit commitment problem using particle swarm optimization technique. The objective of the multi-area unit commitment problem is to determine the optimal or a near optimal commitment strategy for generating the units. And it is located in multiple areas that are interconnected via tie lines and joint operation of generation resources can result in significant operational cost savings. The dynamic programming method is applied to solve Multi-Area Unit Commitment problem and particle swarm optimization technique is embedded for computing the generation assigned to each area and the power allocated to all committed unit. Particle Swarm Optimization technique is developed to derive its Pareto-optimal solutions. The tie-line transfer limits are considered as a set of constraints during the optimization process to ensure the system security and reliability. Case study of four areas each containing 26 units connected via tie lines has been taken for analysis. Numerical results are shown comparing the cost solutions and computation time obtained by using the Particle Swarm Optimization method is efficient than the conventional Dynamic Programming and Evolutionary Programming Method.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

S. SELVI, R. DEVI and C. RAJAN, "Multi-Area Unit Commitment Using Hybrid Particle Swarm Optimization Technique with Import and Export Constraints," Engineering, Vol. 1 No. 3, 2009, pp. 140-150. doi: 10.4236/eng.2009.13017.

References

[1] S. Salam, “Unit commitment solution methods,” Proceedings of World Academy of Science, Engineering and Technology, Vol. 26, December 2007.
[2] B. Lu and M. shahidehpour, “Short term scheduling of combined cycle units,” IEEE Transaction on Power System, Vol. 19, pp. 1616–1625, August 2004.
[3] F. Gao, “Economic dispatch algorithms for thermal unit system involving combined cycle units,” IEEE and Gerald Bushel IEEE Lowa State University Ames, IA, USA, IEEE Transaction on Power Systems, pp. 1066–1072, November 2003.
[4] E. Fan, X. H. Guan, and Q. Z. Zhai, “A new method for unit commitment with ramping con straints,” IEEE Transaction on Power Systems, March 2001.
[5] H. T. Yang and C. L. Huang, “Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions,” IEEE Transactions on power system, Vol. 11, No. 2, pp. 112–118, 1996.
[6] Z. Ouyang and S. M. Shahidehpour, “Heuristic multi-area unit commitment with economic dispatch,” IEEE Proceedings, Vol. 138, No. 3, pp. 242–252, 1991.
[7] C. L. Tseng, “Multi-area unit commitment for large scale power system,” IEEE Proceedings - Generation and Distribution, Vol. 145, No. 41, pp. 415–421, 1999.
[8] C. Wang and M. Shahidehpour, “A decomposition approach to non-linear multi-area generation scheduling with tie-line constraints using expert systems,” IEEE Transactions on Power System, Vol. 7, No. 4, pp. 1409 –1418, 1992.
[9] 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 System, Vol. 100, No. 3, pp. 1212–1218, 1981.
[10] F. N. Lee, J. Huang, and R. Adapa, “Multi-area unit commitment via sequential method and a DC power flow network model,” IEEE Transactions on Power System, Vol. 9, No. 1, pp. 279– 284, 1994.
[11] C. Yingvivatanapong, W. J. Lee, and E. Liu, “Multi-area power generation dispatch in competitive markets,” IEEE Transactions on Power Systems, pp. 196–203, 2008.
[12] U. B. Fogel, “On the philosophical differences between evolutionary algorithms and genetic algorithms,” IEEE Proceedindings in Second Annual Conference on Evolutionary Programming, pp. 23–29, 1993.
[13] T. Biick and H. P. Schwefel, “An overview of evolutionary algorithm for parameter optimization,” Evolutionary Computation, Vol. 1, No. 1, pp. 1–24. 1993.
[14] C. C. Asir Rajan and M. R. Mohan, “An evolutionary programming-based tabu search method for solving the unit commitment problem,” IEEE Transactions on Power System, Vol. 19, No. 1, pp. 577–585, 2004.
[15] D. Srinivasan, F. Wen, and C. S. Chang, “A survey of applications evolutionary computing to power systems,” IEEE Proceedings, USA, pp. 35–41, 1996.
[16] J. Kennedy, “The particle swarm: Social adaptation of knowledge,” Proceedings in International Conference on Evolutionary Computation, Indianapolis, pp. 303–308, 1997.

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.