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Efficient Evolutionary Particle Swarm Optimization Approach for Nonconvex Economic Load Dispatch Problem

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Warianty tytułu
PL
Zagadnienie ekonomicznie uzasadnionego określenia wytwarzanej energii elektrycznej o charakterystyce niewypukłej – wykorzystanie metody optymalizacji roju cząstek
Języki publikacji
EN
Abstrakty
EN
The main objective of economic load dispatch (ELD) is to allocate the output power generator at minimum cost while satisfying all the operation constraints. This paper presents a new hybrid method by integrating particle swarm optimization with time varying acceleration coefficients and evolutionary programming (TVAC-EPSO) for solving nonconvex ELD problem. The competition, sorting and selection in EP method are used to determine the best particle in PSO for finding the optimum solution efficiently. The proposed TVAC-EPSO has been tested on three different power system benchmarks. The simulation results have demonstrated the effectiveness of the proposed method in solving nonconvex ELD problem.
PL
W artykule przedstawiono hybrydową metodę ekonomicznie uzasadnionego określenia założeń dotyczących generowanej energii elektrycznej (ang. Economic Load Dispatch - ELD). Algorytm oparty jest na wykorzystaniu metody optymalizacji roju cząstek ze współczynnikami zmiennymi w czasie i programowaniu ewolucyjnym. (ang. TVAC-EPSO). Proponowana metoda została poddana weryfikacji na trzech różnych systemach energetycznych. Wyniki symulacyjne potwierdzają jej efektywność w analizie problemu ELD.
Rocznik
Strony
139--143
Opis fizyczny
Bibliogr. 17 poz., tab., wykr.
Twórcy
  • Faculty of Electrical and Electronic Engineering, Universiti Tunn Hussien Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia
autor
  • University of Malaya Power Energy Dedicated Advanced Centre (UMPEDAC), University of Malaya, 50603 Kuala Lumpur, Malaysia
  • University of Malaya Power Energy Dedicated Advanced Centre (UMPEDAC), University of Malaya, 50603 Kuala Lumpur, Malaysia
autor
  • Department of Electrical Engineering,University of Malaya, 50603 Kuala Lumpur, Malaysia
autor
  • Department of Electrical Engineering,University of Malaya, 50603 Kuala Lumpur, Malaysia
autor
  • Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bharu, Johor, Malaysia
Bibliografia
  • [1] B. H. Chowdhury and S. Rahman, "A review of recent advances in economic dispatch," IEEE Transactions on Power Systems, vol. 5, pp. 1248-1259, 1990.
  • [2] A. Mahor, V. Prasad, and S. Rangnekar, "Economic dispatch using particle swarm optimization: A review," Renewable and Sustainable Energy Reviews, vol. 13, pp. 2134-2141, 2009.
  • [3] A. Safari and H. Shayeghi, "Iteration particle swarm optimization procedure for economic load dispatch with generator constraints," Expert Systems with Applications, vol. 38, pp. 6043-6048, 2011.
  • [4] K. T. Chaturvedi, M. Pandit, and L. Srivastava, "Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch," IEEE Transactions on Power Systems, vol. 23, pp. 1079-1087, 2008.
  • [5] P. Subbaraj, R. Rengaraj, S. Salivahanan, and T. R. Senthilkumar, "Parallel particle swarm optimization with modified stochastic acceleration factors for solving large scale economic dispatch problem," International Journal of Electrical Power and Energy Systems, vol. 32, pp. 1014-1023, 2010.
  • [6] M. Younes and F. Benhamida, "Genetic Algorithm-Particle Srawrm Optimization (GA-PSO) for Economic Load Dispatch," Przeglad Elektrotechniczny, p. 4, 2011.
  • [7] J. Cai, Q. Li, L. Li, H. Peng, and Y. Yang, "A hybrid CPSO–SQP method for economic dispatch considering the valve-point effects," Energy Conversion and Management, vol. 53, pp. 175-181, 2012.
  • [8] J. Kennedy and R. Eberhart, "Particle swarm optimization," IEEE International Conference on in Neural Networks, 1995, pp. 1942-1948 vol.4.
  • [9] P. Jong-Bae, L. Ki-Song, S. Joong-Rin, and K. Y. Lee, "A particle swarm optimization for economic dispatch with nonsmooth cost functions," IEEE Transactions on Power Systems, vol. 20, pp. 34-42, 2005.
  • [10] P. J. Angeline, "Using selection to improve particle swarm optimization," IEEE International Conference on Evolutionary Computation, 1998, pp. 84-89.
  • [11] J. J. Jamian, M. W. Mustafa, H. Mokhlis, and M. N. Abdullah, "Comparative Study on Distributed Generator Sizing Using Three Types of Particle Swarm Optimization," 2012 Third International Conference on Intelligent Systems, Modelling and Simulation (ISMS), 2012, pp. 131-136.
  • [12] A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, "Selforganizing hierarchical particle swarm optimizer with timevarying acceleration coefficients," IEEE Transactions on Evolutionary Computation, vol. 8, pp. 240-255, 2004.
  • [13] N. Sinha, R. Chakrabarti, and P. K. Chattopadhyay, "Evolutionary programming techniques for economic load dispatch," IEEE Transactions on Evolutionary Computation, vol. 7, pp. 83-94, 2003.
  • [14] T. A. A. Victoire and A. E. Jeyakumar, "Hybrid PSO–SQP for economic dispatch with valve-point effect," Electric Power Systems Research, vol. 71, pp. 51-59, 2004.
  • [15] G. Zwe-Lee, "Particle swarm optimization to solving the economic dispatch considering the generator constraints," IEEE Transactions on Power Systems, vol. 18, pp. 1187-1195, 2003.
  • [16] A. I. Selvakumar and K. Thanushkodi, "A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems," IEEE Transactions on Power Systems, vol. 22, pp. 42-51, 2007.
  • [17] I. Ciornei and E. Kyriakides, "A GA-API Solution for the Economic Dispatch of Generation in Power System Operation," IEEE Transactions on Power Systems, vol. 27, pp. 233-242, 2012.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3054576c-724d-456c-ae49-42a39c4e400b
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