Optimal Scheduling of Cascaded Hydrothermal Systems Using a New Improved Particle Swarm Optimization Technique
Kamal K. Mandal, Niladri Chakraborty
DOI: 10.4236/sgre.2011.23032   PDF    HTML     7,616 Downloads   13,134 Views   Citations


Optimum scheduling of hydrothermal plants generation is of great importance to electric utilities. Many evolutionary techniques such as particle swarm optimization, differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This paper presents a new improved particle swarm optimization technique called self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) for solving short-term economic generation scheduling of hydrothermal systems to avoid premature convergence. A multi-reservoir cascaded hydrothermal system with nonlinear relationship between water discharge rate, power generation and net head is considered here. The performance of the proposed method is demonstrated on two test systems comprising of hydro and thermal units. The results obtained by the proposed methods are compared with other methods. The results show that the proposed technique is capable of producing better results.

Share and Cite:

K. Mandal and N. Chakraborty, "Optimal Scheduling of Cascaded Hydrothermal Systems Using a New Improved Particle Swarm Optimization Technique," Smart Grid and Renewable Energy, Vol. 2 No. 3, 2011, pp. 282-292. doi: 10.4236/sgre.2011.23032.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] H. Habibollahzadeh and J. A. Bubenko, “Application of Decomposition Techniques to Short Term Operation Planning of Hydro-Thermal Power System,” IEEE Transactions on Power Systems, Vol. 1, No. 1, February 1986, pp. 41-47. doi:org/10.1109/TPWRS.1986.4334842
[2] S. Chang, C. Chen, I. Fong and P. B. Luh, ‘Hydroelectric Generation Scheduling with an Effective Differential Dynamic Programming,” IEEE Transactions on Power Systems, Vol. 5, No. 3, 1990, pp. 737-743. doi:org/10.1109/59.65900
[3] R. F. Loyola and V. H. Quintana, “Medium-Term Hydrothermal Coordination by Semi Definite Programming”, IEEE Transactions on Power Systems, Vol. 18, No. 4, November 2003, pp. 1515-1522. doi:org/10.1109/TPWRS.2003.811006
[4] H. Brannud, J. A. Bubenko and D. Sjelvgren, “Optimal Short Term Operation Planning of a Large Hydrothermal Power System Based on a Non Linear Network Flow Concept,” IEEE Transactions on Power Systems, Vol. 1, No. 4, November 1986, pp. 75-82. http://dx.doi.org/10.1109/TPWRS.1986.4335019
[5] E. Gil, J. Bustos and H. Rudnick, “Short-Term Hydrothermal Generation Scheduling Model Using a Genetic Algorithm,” IEEE Transaction on Power Systems, Vol. 18, No. 4, November 2003, pp. 1256-1264. doi:org/10.1109/TPWRS.2003.819877
[6] K. P. Wong and Y. W. Wong, “Short-Term Hydrothermal Scheduling Part 1: Simulated Annealing Approach,” IEE Proceedings Generation, Transmission and Distribution, Vol. 141, No. 5, 1994, pp. 497-501. http://dx.doi.org/10.1049/ip-gtd:19941350
[7] P. C. Yang, H. T. Yang and C. L. Huang, “Scheduling Short-Term Hydrothermal Generation Using Evolutionary Programming Techniques,” IEE Proceedings Generation Transmission and Distribution, Vol. 143, No. 4, July 1996, pp. 371-376. doi:org/10.1049/ip-gtd:19960463
[8] B. H. Yu, X. H. Yuan and J. W. Wang, “Short-Term Hydro-Thermal Scheduling Using Particle Swarm Optimization Method,” Energy Conversion & Management, Vol. 48, No. 7, 2007, pp. 1902-1908. doi:org/10.1016/j.enconman.2007.01.034
[9] L. Lakshminarasimman and S. Subramanian, “A Modified Hybrid Differential Evolution for Short-Term Scheduling of Hydrothermal Power Systems with Cascaded Reservoirs,” Energy Conversion & Management, Vol. 49, No. 10, 2008, pp. 2513-2521. doi:org/10.1016/j.enconman.2008.05.021
[10] C.-T. Cheng, S.-L. Liao, Z.-T. Tang and M.-Y. Zhao, “Comparison of Particle Swarm Optimization and Dynamic Programming for Large Scale Hydro Unit Load Dispatch,” Energy Conversion & Management, Vol. 50, 2009, pp. 3007-3014. doi:org/10.1016/j.enconman.2009.07.020
[11] J. P. S. Catal?o, H. M. I. Pousinho and V. M. F. Mendes, “Scheduling of Head-Dependent Cascaded Hydro Systems: Mixed-Integer Quadratic Programming Approach,” Energy Conversion & Management, Vol. 51, No. 3, 2010, pp. 524-530.
[12] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks (ICNN’95), Perth, Australia, 27 November-1 December 1995, Vol. 5, pp. 2753-2756.
[13] Z.-L. Gaing, “Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constraints,” IEEE Transaction on Power Systems, Vol. 18, No. 3, August 2003, pp. 1187-1195. doi:org/10.1109/TPWRS.2003.814889
[14] M. A. Abido, “Optimal Design of Power-System Stabilizers Using Particle Swarm Optimization,” IEEE Transactions on Energy Conversion, Vol. 17, No. 3, September 2002, pp. 406-413. doi:org/10.1109/TEC.2002.801992
[15] J. B. Park, K.S. Lee, J. R. Shin and K.Y. Lee, “A Particle Swarm Optimization for Solving the Economic Dispatch With Non-smooth Cost Functions,” IEEE Transaction on Power Systems, Vol. 20, No.1, February 2005, pp. 34-42. doi:org/10.1109/TPWRS.2004.831275
[16] J. K. Wu, J. Q. Zhu, G. T. Chen and H. L. Zhang, “A Hybrid Method for Optimal Scheduling of Short-Term Electric Power Generation of Cascaded Hydroelectric Plants Based on Particle Swarm Optimization and Chance-Constrained Programming,” IEEE Transaction on Power Systems, Vol. 23, No. 4, November 2008, pp. 1570-1579. doi:org/10.1109/TPWRS.2008.2004822
[17] A. Ratnaweera, S. K. Halgamuge and H. C. Watson, “Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients,” IEEE Trans- actions on Evolutionary Computation, Vol. 8, No. 3, June 2004, pp. 240-255. doi:org/10.1109/TEVC.2004.826071
[18] Y. Shi and R. C. Eberhart, “Parameter Selection in Particle Swarm Optimization, Lecture Notes in Computer Science—Evolutionary Programming,” Proceedings of the 7th International Conference on Evolutionary Programming, San Diego, 25-27 March 1998, Vol. 1447, pp. 591- 600.
[19] P. K. Hota, A. K. Barisal and R. Chakrabarti, “An Improved PSO Technique for Short-Term Optimal Hydrothermal Scheduling,” Electric Power Systems Research, Vol. 79, No. 7, 2009, pp. 1047-1053. doi:org/10.1016/j.epsr.2009.01.001

Copyright © 2023 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.