International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 5, No. 5, 8 Nov. 2013
Cover page and Table of Contents: PDF (size: 155KB)
A Novel Hybrid PSO-GSA Method for Non-convex Economic Dispatch Problems
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Author(s)
Hardiansyah
1,*
Index Terms
Particle Swarm Optimization, Gravitational Search Algorithm, Non-convex Economic Dispatch, Ramp Rate Limits, Prohibited Operating Zones, Valve-Point Effect
Abstract
This paper proposes a novel and efficient hybrid algorithm based on combining particle swarm optimization (PSO) and gravitational search algorithm (GSA) techniques, called PSO-GSA. The core of this algorithm is to combine the ability of social thinking in PSO with the local search capability of GSA. Many practical constraints of generators, such as power loss, ramp rate limits, prohibited operating zones and valve point effect, are considered. The new algorithm is implemented to the non-convex economic dispatch (ED) problem so as to minimize the total generation cost when considering the linear and non linear constraints. In order to validate of the proposed algorithm, it is applied to two cases with six and thirteen generators, respectively. The results show that the proposed algorithms indeed produce more optimal solution in both cases when compared results of other optimization algorithms reported in literature.
Cite This Paper
Hardiansyah, "A Novel Hybrid PSO-GSA Method for Non-convex Economic Dispatch Problems", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.5, no.5, pp.1-9, 2013. DOI:10.5815/ijieeb.2013.05.01
Reference
[1]A. J Wood and B. F. Wollenberg, “Power Generation, Operation, and Control,” 2nd ed., John Wiley and Sons, New York, 1996.
[2]Z. X. Liang and J. D. Glover, “A zoom feature for a dynamic programming solution to economic dispatch including transmission losses,” IEEE Transactions on Power Systems, 7(2): 544-550, May 1992.
[3]C. L. Chiang, “Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels,” IEEE Transactions on Power Systems, 20(4): 1690-1699, 2005.
[4]W. M. Lin, F. S. Cheng and M. T. Tsay, “An improved tabu search for economic dispatch with multiple minima,” IEEE Transactions on Power Systems, 17(1): 108-112, 2002.
[5]K. P. Wong and C. C. Fung, “Simulated annealing based economic dispatch algorithm,” Proc. Inst. Elect. Eng. C, 140(6): 509-515, 1993.
[6]K. Y. Lee, A. Sode-Yome and J. H. Park, “Adaptive Hopfield neural network for economic load dispatch,” IEEE Transactions on Power Systems, 13(2): 519-526, 1998.
[7]T. Jayabarathi and G. Sadasivam, “Evolutionary programming-based economic dispatch for units with multiple fuel options,” European Transactions on Electrical Power, 10(3): 167-170, 2000.
[8]N. Sinha, R. Chakrabarti, and P. K. Chattopadhyay, “Evolutionary programming techniques for economic load dispatch,” IEEE Transactions on Evolutionary Computation, 7(1): 83-94, 2003.
[9]H. T. Yang, P. C. Yang and C. L. Huang, “Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions,” IEEE Transactions on Power Systems, 11(1): 112-118, 1996.
[10]A. Bhattacharya and P. K. Chattopadhyay, “Biogeography-based optimization for different economic load dispatch problems,” IEEE Transactions on Power Systems, 25(2): 1064-1077, 2010.
[11]J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Networks (ICNN'95), Perth, Australia, IV: 1942-1948, 1995.
[12]Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” Proceedings of IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, 69-73, 1998.
[13]Z. L. Gaing, “Particle swarm optimization to solving the economic dispatch considering the generator constraints,” IEEE Transactions on Power Systems, 18(3): 1187-1195, 2003.
[14]J. B. Park, K. S. Lee, J. R. Shin and K. Y. Lee, “A particle swarm optimization for economic dispatch with nonsmooth cost functions,” IEEE Transactions on Power Systems, 20(1): 34-42, 2005.
[15]Hardiansyah, Junaidi and M. S. Yohannes, “Solving economic load dispatch problem using particle swarm optimization technique,” International Journal of Intelligent Systems and Applications (IJISA), 4(12): 12-18, November 2012.
[16]Shi Yao Lim, Mohammad Montakhab and Hassan Nouri, “Economic dispatch of power system using particle swarm optimization with constriction factor,” International Journal of Innovations in Energy Systems and Power, 4(2): 29-34, 2009.
[17]N. Noman and H. Iba, “Differential evolution for economic load dispatch problems,” Electric Power Systems Research, 78(8): 1322-1331, 2008.
[18]S. Duman, U. Guvenc and N. Yorukeren, “Gravitational search algorithm for economic dispatch with valve-point effects,” International Review of Electrical Engineering, 5(6): 2890-2895, 2010.
[19]E. Rashedi, H. Nezamabadi-pour and S. Saryazdi, “GSA: A gravitational search algorithm,” Information Sciences, 179: 2232–2248, 2009.
[20]S. Mirjalili and Siti Zaiton Mohd Hashim, “A new hybrid PSOGSA algorithm for function optimization,” IEEE International Conference on Computer and Information Application (ICCIA 2010), 374-377, 2010.
[21]R. Balamurugan and S. Subramanian, “An improved dynamic programming approach to economic power dispatch with generator constraints and transmission losses,” Journal of Electrical Engineering & Technology, 3(3): 320-330, 2008.
[22]U. Guvenc, S. Duman, B. Saracoglu and A. Ozturk, “A hybrid GA-PSO approach based on similarity for various types of economic dispatch problems,” Electronics and Electrical Engineering, 108(2): 109-114, 2011.
[23]S. Muthu Vijaya Pandian and K. Thanushkodi, “An evolutionary programming based efficient particle swarm optimization for economic dispatch problem with valve-point loading,” European Journal of Scientific Research, 52(3): 385-397, 2011.