Yasak İşletim Bölgeli Ekonomik Güç Dağıtım Problemlerine Geliştirilmiş Parçacık Sürü Optimizasyonu Yaklaşımı

Abstract

In this study, prohibited operating zone economic power dispatch problem which considers ramp rate limit, has been solved by improved particle swarm optimization algorithm (GPSO). The transmission line losses used in the solution of the problem have been computed by B-loss matrix. GPSO method has been applied to two different test systems in literature which consist of 6 and 15 generators. The attained optimum solution values have been compared with the optimum results in literature and have been discussed.

Keywords

• Economic power dispatch,prohibited operating zones,ramp rate limits,improved particle swarm optimization

References

1. [1] T. Niknam, H. D. Mojarrad and H. Z. Meymand, Non-smooth economic dispatch computation by fuzzy and self adaptive particle swarm optimization, Applied Soft Computing 11 (2011), 2805–2817.
2. [2] M. Neyestani, M. M. Farsangi and H. Nezamabadi-pour, A modified particle swarm optimization for economic dispatch with non-smooth cost functions, Engineering Applications of Artificial Intelligence 23 (2010), 1121–1126.
3. [3] A. Safari and H. Shayeghi, Iteration particle swarm optimization procedure for economic load dispatch with generator constraints, Expert Systems with Applications 38 (2011), 6043–6048.
4. [4] A. I. Selvakumar and K. Thanushkodi, A new particle swarm optimization solution to nonconvex economic dispatch problems, IEEE Transaction on Power Systems 22 (2007), 41–51.
5. [5] Z. L. Gaing, Particle swarm optimization to solving the economic dispatch considering the generator constraints, IEEE Transaction on Power Systems 18 (2003), 1187–1195.
6. [6] T. Niknam, H. D. Mojarrad, H. Z. Meymand and B. B. Firouzi, A new honey bee mating optimization algorithm for non-smooth economic dispatch, Energy 36 (2011), 896–908.
7. [7] S. O. Orero and M. R. Irving, Economic dispatch of generators with prohibited operating zones: A genetic algorithm approach, IEE Proceedings Generation, Transmission & Distribution 143 (1996), 529–534.
8. [8] J. X. V. Neto, D. L. A. Bernert and L. S. Coelho, Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones, Energy Conversion and Management 52 (2011), 8–14.

9. [9] P. Somasundaram, K. Kuppusamy and R. P. Kumuudini Devi, Economic dispatch with prohibited operating zones using fast computation evolutionary programming algorithm, Electric Power Systems Research 70 (2004), 245–252.
10. [10] T. Javabarathi, G. Sadasivam and V. Ramachandran, Evolutionary programming based economic dispatch of generators with prohibited operating zones, Electric Power Systems Research 52 (1999), 261–266.
11. [11] L. G. Papageorgiou and E. S. Fraga, A mixed integer quadratic programming formulation for the economic dispatch of generators with prohibited operating zones, Electric Power Systems Research 77 (2007), 1292–1296.
12. [12] B. K. Panigrahi, S. R. Yadav, S. Agrawal and M. K. Tiwari, A clonal algorithm to solve economic load dispatch, Electric Power Systems Research 77 (2007), 1381–1389.
13. [13] A. Bhattacharya and P. K. Chattopadhyay, Solving complex economic load dispatch problems using biogeography based optimization, Expert Systems with Applications 37 (2010), 3605– 3615.
14. [14] S. Pothiya, I. Ngamroo and W. Kongprawechnon, Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints, Energy Conversion and Management 49 (2008), 506–516.
15. [15] C. L. Chen, Non-convex economic dispatch : A direct search approach, Energy Conversion and Management 48 (2007), 219–225.
16. [16] A. J. Wood and B. F. Wollenberg, Power Generation Operation and Control, New York-Wiley, 1996.
17. [17] L. Wang and C. Singh, Environmental/economic power dispatch using a fuzzified multiobjective particle swarm optimization algorithm, Electric Power Systems Research 77 (2007), 1654-1664.
18. [18] M. A. Abido, Multiobjective particle swarm optimization for environmental/economic dispatch problem, Electric Power Systems Research 79 (2009), 1105–1113.
19. [19] L. Wang and C. Singh, Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search, Engineering Applications of Artificial Intelligence 22 (2009), 298-307.
20. [20] A. Mahor, V. Prasad and S. Rangnekar, Economic dispatch using particle swarm optimization: A review, Electric Power Systems Research 13 (2009), 2134–2141.
21. [21] J. Cai, X. Ma, Q. Li and H. Peng, A multi-objective chaotic particle swarm optimization for environmental/economic dispatch, Energy Conversion and Management 50 (2009), 1318– 1325.
22. [22] L. Wang and C. Singh, Stochastic combined heat and power dispatch based on multi-objective particle swarm optimization, Electric Power and Energy Systems 30 (2008), 226–234.
23. [23] T. Cura, Modern Sezgisel Teknikler ve Uygulamaları, Papatya Yayıncılık E˘gitim, 2008.
24. [24] D. Karabo˘ga, Yapay Zeka Optimizasyon Algoritmaları, Atlas Yayın Da˘gıtım, 2004.
25. [25] S. Ozy¨on, C. Ya¸sar and H. Temurta¸s, Particle swarm optimization algorithm applied to environmental economic power dispatch problems consisting of thermal units, 6th International Advanced Technologies Symposium (IATS’11), Electrical & Electronics Technologies Papers 4 (2011), 175–180.