DOI QR코드

DOI QR Code

A Novel Binary Ant Colony Optimization: Application to the Unit Commitment Problem of Power Systems

  • Jang, Se-Hwan (Dept. of Electrical Engineering, Konkuk University) ;
  • Roh, Jae-Hyung (Dept. of Electrical Engineering, Konkuk University) ;
  • Kim, Wook (Korea Southern Power Co.) ;
  • Sherpa, Tenzi (Dept. of Electrical Engineering, Konkuk University) ;
  • Kim, Jin-Ho (Dept. of Electrical Engineering, Kyungwon University) ;
  • Park, Jong-Bae (Dept. of Electrical Engineering, Konkuk University)
  • Received : 2010.06.11
  • Accepted : 2010.09.01
  • Published : 2011.03.01

Abstract

This paper proposes a novel binary ant colony optimization (NBACO) method. The proposed NBACO is based on the concept and principles of ant colony optimization (ACO), and developed to solve the binary and combinatorial optimization problems. The concept of conventional ACO is similar to Heuristic Dynamic Programming. Thereby ACO has the merit that it can consider all possible solution sets, but also has the demerit that it may need a big memory space and a long execution time to solve a large problem. To reduce this demerit, the NBACO adopts the state probability matrix and the pheromone intensity matrix. And the NBACO presents new updating rule for local and global search. The proposed NBACO is applied to test power systems of up to 100-unit along with 24-hour load demands.

Keywords

References

  1. M. Dorigo, “Optimization, learning, and natural algorithms,” Ph.D. dissertation (in Italian), Dipartimento di Elettronica, Politecnico di Milano, Milano, Italy, 1992.
  2. M. Dorigo and L.M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Trans. on Evol. Compiut., Vol. 1, pp. 53-66, Apr. 1997. https://doi.org/10.1109/4235.585892
  3. Christian Blum and Marco Dorigo, “The Hyper-Cube Framework for Ant Colony Optimization”, IEEE Trans. Systems, Man and Cybernetics, Vol. 34, No. 2, pp. 1161-1172, April. 2004 https://doi.org/10.1109/TSMCB.2003.821450
  4. G. Wu and H. Huang, “Theoretical Framework of Binary Ant Colony Optimization Algorithm”, IEEE Computer society, 2008
  5. S.-J. Huang, “Enhancement of hydroelectric generation scheduling using ant colony system based optimization approaches”, IEEE Trans. Energy Conv., Vol. 16, No. 3, pp. 296-301, Mar. 2001. https://doi.org/10.1109/60.937211
  6. Y. H. Hou, Y. W. Wu, L. J. Lu and X. Y. Xiong, “Generlized Ant Colony Optimization for Economic Dispatch of Power Systems”, IEEE, 2002.
  7. L. Shi, J. Hao, J. Zhou and G. Xu, “Ant colony optimisation algorithm with random perturbation behaviour to the problem of optimal unit commitment with probabilistic spinning reserve determination”, Electric Power Syst. Res., Vol. 69, pp. 295-303, 2004. https://doi.org/10.1016/j.epsr.2003.10.008
  8. S. P. Simon, N. P. Padhy and R. S. Anand, “An ant colony system for unit commitment problem,” Elect. Power Energy Syst., 2006
  9. S. Chusanapiputt, D. Nualhong and S. Phoomvuthisarn, “Relativity Pheromone Updating Strategy in Ant Colony Optimization for Constrained Unit Commitment Problem”, Power System Technology, IEEE, 2006
  10. Ahmed Yousuf Saber and Tomomobu Senjyu, “Memory-Bounded Ant Colony Optimization with Dynamic Programming and $A^{*}$ Local Search for Generator Planning”, IEEE Trans. on Power Systems, Vol. 22, No. 4, pp. 1965-1973, Nov. 2007 https://doi.org/10.1109/TPWRS.2007.907382
  11. A. J. Wood and B. F. Wollenberg, Power Generation, Operation, and Control. New York: John Wiley & Sons, Inc., 1984.
  12. R. M. Burns and C. A. Gibson, “Optimization of priority lists for a unit commitment program,” in Proc. IEEE Power Engineering Society Summer Meeting, Paper A, 75 453-1, 1975.
  13. G. B. Sheble, “Solution of the unit commitment problem by the method of unit periods,” IEEE Trans. on Power Systems, Vol. 5, No. 1, pp. 257-260, Feb. 1990. https://doi.org/10.1109/59.49114
  14. Z. Ouyang and S. M. Shahidehpour, “An intelligent dynamic programming for unit commitment application,” IEEE Trans. on Power Systems, Vol. 6, No. 3, pp. 1203-1209, Aug. 1991. https://doi.org/10.1109/59.119267
  15. F. Zhuang and F. D. Galiana, “Toward a more rigorous and practical unit commitment by Lagrangian relaxation,” IEEE Trans. on Power Systems, Vol. 3, No. 2, pp. 763-770, May 1988. https://doi.org/10.1109/59.192933
  16. A. I. Cohen and M. Yoshimura, “A branch-and-bound algorithm for unit commitment,” IEEE Trans. on Power Apparatus and Systems, Vol. PAS-102, pp. 444-451, Feb. 1983. https://doi.org/10.1109/TPAS.1983.317714
  17. J. A. Muckstadt and R. C. Wilson, “An application of mixed-integer programming duality to scheduling thermal generating systems,” IEEE Trans. on Power Apparatus and Systems, pp. 1968-1978, 1968 https://doi.org/10.1109/TPAS.1968.292156
  18. S. A. Kazarlis, A. G. Bakirtzis, and V. Petridis, “A genetic algorithm solution to the unit commitment problem,” IEEE Trans. on Power Systems, Vol. 11, No. 1, pp. 83-92, Feb. 1996. https://doi.org/10.1109/59.485989
  19. K. A. Juste, H. Kita, E. Tanaka, and J. Hasegawa, “An evolutionary programming solution to the unit commitment problem,” IEEE Trans. on Power Systems, Vol. 14, pp. 1452-1459, Nov. 1999. https://doi.org/10.1109/59.801925
  20. D. N. Simopoulos, S. D. Kavatza, and C. D. Vournas, “Unit commitment by an enhanced simulated annealing algorithm,” IEEE Trans. on Power Systems, Vol. 21, No. 1, pp. 68-76, Feb. 2006. https://doi.org/10.1109/TPWRS.2005.860922
  21. B. Zhao, C. X. Guo, B. R. Bai and Y. J. Cao, “An improved particle swarm optimization algorithm for unit commitment,” Electrical Power & Energy Systems, Vol. 28, Issue 7, pp. 482-490, Sep. 2006. https://doi.org/10.1016/j.ijepes.2006.02.011
  22. Y.-W. Jeong, J.-B. Park, J.-R. Shin, and K. Y. Lee, “A thermal unit commitment approach using an improved quantum evolutionary algorithm”, Electric Power Components and Systems, Vol. 37, No. 7, pp. 770-786, July 2009. https://doi.org/10.1080/15325000902762331

Cited by

  1. A feature selection method based on modified binary coded ant colony optimization algorithm vol.49, 2016, https://doi.org/10.1016/j.asoc.2016.08.011
  2. Binary ant colony optimization applied to variable screening in the Mahalanobis–Taguchi System vol.40, pp.2, 2013, https://doi.org/10.1016/j.eswa.2012.07.058
  3. Resolution of the unit commitment problems by using the hybrid Taguchi-ant colony system algorithm vol.49, 2013, https://doi.org/10.1016/j.ijepes.2013.01.007
  4. Mahalanobis–Taguchi system applied to variable selection in automotive pedals components using Gompertz binary particle swarm optimization vol.40, pp.7, 2013, https://doi.org/10.1016/j.eswa.2012.10.049
  5. Optimal identification of impact variables in a welding process for automobile seats mechanism by MTS-GBPSO approach vol.90, pp.1-4, 2017, https://doi.org/10.1007/s00170-016-9395-5
  6. Binary Accelerated Particle Swarm Algorithm (BAPSA) for discrete optimization problems vol.57, pp.2, 2013, https://doi.org/10.1007/s10898-012-0006-1
  7. Consumers' Price Elasticity of Demand Modeling With Economic Effects on Electricity Markets Using an Agent-Based Model vol.4, pp.1, 2013, https://doi.org/10.1109/TSG.2012.2234487
  8. Classifier System and Co-evolutionary Hybrid Approach to Restoration Service of Electric Power Distribution Networks vol.7, pp.3, 2012, https://doi.org/10.5370/JEET.2012.7.3.288
  9. Application Research of Inner-plant Economical Operation by Multi-colony Ant Optimization vol.32, pp.13, 2018, https://doi.org/10.1007/s11269-018-2048-8
  10. Fast unsupervised feature selection based on the improved binary ant system and mutation strategy pp.1433-3058, 2019, https://doi.org/10.1007/s00521-018-03991-z