Improved Artificial Bee Colony Algorithm for Non-Convex Economic Power Dispatch Problem
Authors: Badr M. Alshammari, T. Guesmi
Abstract:
This study presents a modified version of the artificial bee colony (ABC) algorithm by including a local search technique for solving the non-convex economic power dispatch problem. The local search step is incorporated at the end of each iteration. Total system losses, valve-point loading effects and prohibited operating zones have been incorporated in the problem formulation. Thus, the problem becomes highly nonlinear and with discontinuous objective function. The proposed technique is validated using an IEEE benchmark system with ten thermal units. Simulation results demonstrate that the proposed optimization algorithm has better convergence characteristics in comparison with the original ABC algorithm.
Keywords: Economic power dispatch, artificial bee colony, valve-point loading effects, prohibited operating zones.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316556
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 760References:
[1] C.L. Chen, and C.L. Wang, “Branch-and-bound scheduling for thermal generating units,” International Journal of Industrial Mathematics, vol. 4, no. 3, pp. 163-170, 2012.
[2] A.J. Wood, and B.F. Wollenberg, “Power Generation, Operation and Control,” Wiley, New York-USA, 1994.
[3] C.E. Lin, S.T. Chen, and C.L. Huang, “A direct Newton-Raphson economic dispatch,” IEEE Transactions on Power Systems, vol. 7, no. 3, pp. 1149-1154, 1992.
[4] 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 Transaction on Power Systems, Vol. 20, pp. 34-42, 2005.
[5] C.W. Gar, J.G. Aganagic, B. Tony Meding Jose, and S. Reeves, “Experience with mixed integer linear programming based approach on short term hydrothermal scheduling,” IEEE Transaction on Power Systems, vol. 16, no.4, pp. 743-749, 2001.
[6] Z. Yang, K. Li, Q. Niu, Y. Xue, and A. Foley, “A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads,” Journal of Modern Power Systems and Clean Energy, vol. 2, no. 4, pp. 298-307, 2014.
[7] P. Jain, K.K. Swarnkar, S. Wadhwani, and A.K. Wadhwani, “Prohibited operating zones constraint with economic load dispatch using genetic algorithm,” International Journal of Engineering and Innovative Technology, vol. 1, no. 3, pp. 179-183, 2012.
[8] A. Bakirtzis, V. Petrides, and S. Kazarlis, “Genetic algorithm solution to the economic dispatch problem,” IEE Proceedings Generation Transmission and Distribution, vol. 141, no. 4, pp. 377-382, 1994.
[9] R.L. Kherfane, M. Younes, N. Kherfane & F. Khodja, “Solving economic dispatch problem using hybrid GA-MGA,” Energy Procedia, vol. 50, pp. 937-944, 2014.
[10] H. Bouzeboudja, A. Chaker, A. Allali, and B. Naama, “Economic dispatch solution using a real-coded genetic algorithm,” Acta Electrotechnica et Informatica, vol. 5, no. 4, pp. 1-5, 2005.
[11] A.M. Elaiw, X. Xia, and A.M. Shehata, “Combined heat and power dynamic economic dispatch with emission limitations using hybrid DE-SQP method,” Abstract and Applied Analysis, pp. 1-11, 2013.
[12] J. Jasper, and A.V.T. Aruldoss, “Deterministically guided differential evolution for constrained power dispatch with prohibited operating zones,” Archives of Electrical Engineering, vol. 62, no. 4, pp. 593-603, 2013.
[13] V. Ramesh, T. Jaybarathi, S. Mital, S. Asthana, and S. Basu, “Combined hybrid differential particle swarm optimization approach for economic dispatch problems,” Electric Power Components and Systems, vol. 38, no. 5, pp. 545-557, 2010.
[14] V.K. Jadoun, N. Gupta, K.R. Niazi, and A. Swarnkar, “Nonconvex economic dispatch using particle swarm optimization with time varying operators,” Advances in Electrical Engineering, pp. 1-13, 2014.
[15] S. Jianga, Z. Jia, and Y. Shen, “A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints,” Electrical Power and Energy Systems, vol. 55, pp. 628-644, 2014.
[16] T. Sen, and H.D. Mathur, “A new approach to solve Economic Dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm,” Electrical Power and Energy Systems, vol. 78, pp. 735-744, 2016.
[17] “D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Tech. Rep TR06, 2005, Turkey.
[18] Y. Labbi, J. Ben Attous, and B. Mahdad, “Artificial bee colony optimization for economic dispatch with valve point effect,” Frontier Energy, vol. 8, no. 4, pp. 449-458, 2014.
[19] K. Tlijani, T. Guesmi, and H. Hadj Abdallah, “Dynamic coupled active–reactive dispatch including SVC devices with limited switching operations,” Arab J Sci Eng, Online September 2016.
[20] M. Basu, “Particle Swarm Optimization Based Goal-Attainment Method for Dynamic Economic Emission Dispatch,” Electric Power Components and Systems, vol. 34, pp. 1015-1025, 2006.
[21] M. Basu, “Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II,” Electric Power and Energy Systems, vol. 30, pp. 140-149, 2008.