A Novel Particle Swarm Optimization for Optimal Scheduling of Hydrothermal System

Wenping Chang

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DOI: 10.4236/epe.2010.24033                

Abstract

A fuzzy adaptive particle swarm optimization (FAPSO) is presented to determine the optimal operation of hydrothermal power system. In order to solve the shortcoming premature and easily local optimum of the standard particle swarm optimization (PSO), the fuzzy adaptive criterion is applied for inertia weight based on the evolution speed factor and square deviation of fitness for the swarm, in each iteration process, the inertia weight is dynamically changed using the fuzzy rules to adapt to nonlinear optimization process. The performance of FAPSO is demonstrated on hydrothermal system comprising 1 thermal unit and 4 hydro plants, the comparison is drawn in PSO, FAPSO and genetic algorithms (GA) in terms of the solution quality and computational efficiency. The experiment showed that the proposed approach has higher quality solutions and strong ability in global search.

Keywords

Hydrothermal Generation Scheduling, Particle Swarm Optimization, Fuzzy, Adaptability

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	R. Ringlee, “Sensitivity Methods for Economic Dispatch of Hydroelectric Plants,” IEEE Transactions on Automatic Control, Vol. 10, No. 3, 1965, pp. 315-322.
[2]	T. N. Saha and S. A. Khapade, “An Application of a Direct Method for the Optimal Scheduling of Hydrothermal Power Systems,” IEEE Transactions on Power Apparatus and Systems, Vol. 97, No. 3, 1978, pp. 977-985.
[3]	H. Habibollahzadeh and J. A. Bubenko, “Application of Decomposition Techniques to Short Term Operation Planning of Hydro-Thermal Power System,” IEEE Tran- sactions on PWRS, Vol. 1, No. 1, February 1986, pp. 41-47.
[4]	M. Heidari, V. T. Chow, P. V. Kokotovic and D. D. Meredith, “Discrete Differential Dynamic Programming Approach to Water Resources Systems Optimization,” Water Resources Research, Vol. 7, No. 2, 1971, pp. 273- 282.
[5]	H. Laabs and R. Harboe, “Generation of Operating Rules with Stochastic Dynamic Programming and Multiple Objectives,” Water Resources Management, Vol. 2, 1988, pp. 221-227.
[6]	S.-C. Chang, C.-H. Chen, I.-K. Fong and P. B. Luh, “Hydroelectric Generation Scheduling with an Effective Differential Dynamic Programming Algorithm,” IEEE Transactions on Power System, Vol. 5, No. 3, 1990, pp. 737-743.
[7]	D. P. Bertsekas, G. S. Lauer, N. R. Sandell and T. A. Posbergh, “Optimal Short-Term Scheduling of Large- Scale Power Systems,” IEEE Transactions on Automatic Contol, Vol. AC-28, No. 1, 1983, pp. 1-11.
[8]	X. H. Guan, P. B. Luh and L. Zhang, “Nonlinear Approximation Method in Lagrangian Relaxation-Based Algorithms for Hydrothermal Scheduling,” IEEE Transactions on Power System, Vol. 10, No. 2, May 1995, pp. 772-778.
[9]	J. M. Ngundam, F. Kenfack and T. T. Tatietse, “Optimal Scheduling of Large-Scale Hydrothermal Power Systems Using the Lagrangian Relaxation Technique,” International Journal of Electrical Power and Energy Systems, Vol. 22, No. 4, 2000, pp. 237-245.
[10]	X. M. Bai and S. M. Shahidehpour, “Hydro-Thermal Scheduling by Tabu Search and Decomposition Method,” IEEE Transactions on Power Systems, Vol. 11, No. 2, 1996, pp. 968-974.
[11]	S. Li, S. M. Shahidehpour and C. Wang, “Promoting the Application of Expect Systems in Short-Term Unit Commitment,” IEEE Transactions on Power System, Vol. 3, No. 1, May 1993, pp. 286-292.
[12]	H. Sasaki, M. Watanabe, J. Kubokawa, N. Yorina and R. Yokoyama, “A Solution Method of Unit Commitment by Artificial Neural Network,” Proceedings of 1991 IEEE Power Engineering Society Summer Meeting, Seattle, 1991.
[13]	S. O. Orero and M. R. Irving,“Large Scale Unit Commitment Using a Hybrid Genetic Algorithms,” International Journal of Electrical Power & Energy Systems, Vol. 19, No. 1, 1997, pp. 45-55.
[14]	W. P. Chang and X. J. Luo, “A Solution to the Unit Commitment Using Hybrid Genetic Algorithm,” TENCON2008, IEEE Region 10 Conference, 19-21 November 2008.
[15]	C.-T. Cheng, W.-C. Wang, D.-M. Xu and K. W. Chau, “Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos,” Water Resources Management, Vol. 22, No. 7, 2008, pp. 895-909.
[16]	J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of IEEE International Conference Neural Networks, Perth, Vol. 4, 1995, pp. 1942-1948.
[17]	M. A. Abido, “Optimal Design of Power-System Stabilizers Using Particle Swarm Optimization,” IEEE Transactions on Energy Conversion, Vol. 17, No. 3, pp. 406-413.
[18]	Z. L.Gaing, “A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System,” IEEE Transactions on Energy Conversion, Vol. 19, No. 2, June 2004, pp. 384-391.
[19]	B. Zhao, C. X. Guo and Y. J. Cao, “A Multiagent-Based Particle Swarm Optimization Approach for Optimal Reactive Power Dispatch,” IEEE Transactions on Power System, Vol. 20, No. 2, May 2005, pp. 1070-1078.
[20]	I. Selvakumar and K. Thanushkodi, “A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems,” IEEE Transactions on Power System, Vol. 22, No. 1, February 2007, pp. 42-51.
[21]	K. T. Chaturvedi, M. Pandit and L. Srivastava, “Self- Orgianizing Hierachical Particle Swarm Optimization for Nonconvex Econmic Dispatch,” IEEE Transactions on Power System, Vol. 23, No. 3, August 2008, pp. 1079- 1087.
[22]	Y. Shi and R. Eberhart, “A Modified Particle Swarm Optimize,” Evolutionary Computation Proceedings, IEEE Congress on Computational Intelligence, Brisbane, 1998, pp. 69-73.
[23]	Y. Shi and R. Eberhart, “Empirical Study of Particle Swarm Optimization,” Proceedings of the IEEE Congress on Evolutionary Computation, 1999, pp. 1949-1950.
[24]	J. Kenney and R. Eberhart, “Article Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks, Perth, Vol. 5, 1995, pp. 2753-2756. http://www.engr.iupui.edu/shi/Conference/psopap4.html
[25]	Z. Michalewicz and N. Attia, A. V. Sebald and L. J. Fogel, Eds., “Evolutionary Optimization of Constrained Problems,” Proceedings of 3rd Annual Conference on Evolutionary Programming, World Scientific, River Edge, 1994, pp. 98-108.
[26]	E. H. Mamdani, “Application of Fuzzy Logic to Appromaximate Reasoning Using Linguistic Synthesis,” IEEE Transactions on Computers, Vol. c-26, December 1977, pp. 1182-1191.
[27]	S. O. Orero and M. R. Irving, “A Genetic Algorithm Modelling Framework and Solution Technique for Short Term Optimal Hydrothermal Scheduling,” IEEE Transactions on Power System, Vol. 13, No. 2, May 1998.