An Improved Particle Swarm Optimization Technique for Combined Economic and Environmental Power Dispatch Including Valve Point Loading Effects
In recent years, the combined economic and emission power dispatch is one of the main problems of electrical power system. It aims to schedule the power generation of generators in order to minimize cost production and emission of harmful gases caused by fossil-fueled thermal units such as CO, CO2, NOx, and SO2. To solve this complicated multi-objective problem, an improved version of the particle swarm optimization technique that includes non-dominated sorting concept has been proposed. Valve point loading effects and system losses have been considered. The three-unit and ten-unit benchmark systems have been used to show the effectiveness of the suggested optimization technique for solving this kind of nonconvex problem. The simulation results have been compared with those obtained using genetic algorithm based method. Comparison results show that the proposed approach can provide a higher quality solution with better performance.
[1] K. S. Damodaran, and T. K. Sunil Kumar, “Optimal Environmental Economic Dispatch Using a Classical Technique,” International review of automatic control (IREACO), vol. 7, no. 3, pp. 300 – 306, 2014.
[2] M. A. Abido, “Multiobjective evolutionary algorithms for electric power dispatch problem,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 315 – 329, 2006.
[3] S. Sivasubramani, and K. S. Swarup, “Environmental/economic dispatch using multi-objective harmony search algorithm, Electric Power Systems Research,” vol. 81, no. 9, pp. 1778 – 1785, 2011.
[4] G. Irisarri, L. M. Kimball, K. A. Clements, A. Bagchi, and P. W. Davis, “Economic dispatch with network and ramping constraints via interior point methods,” IEEE Transactions on Power Systems, vol. 13, no. 1, pp. 236 – 242, 1998.
[5] 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.
[6] C. W. Gar, J. G. Aganagic, T. M. B. 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.
[7] 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.
[8] 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.
[9] B. Zhao, and Y. J. Cao, “Multiple objective particle swarm optimization technique for economic load dispatch,” Journal of Zhejiang University Science, vol. 6A, no. 5, pp. 420 – 427, 2005.
[10] A. Mahor, V. Prasadb, and S. Rangnekar, “Economic dispatch using particle swarm optimization:A review,” Renewable and Sustainable Energy Reviews, vol. 13, pp. 2134 – 2141, 2009.
[11] J. Kennedy, and R. Eberhart, “Particle swarm optimization,” Proc. IEEE Int Conference on Neural Networks, pp. 1942 – 1948, 1995.
[12] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6 no.2, pp. 182 – 197, 2002.
[13] M. Basu, “Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II,” Electric Power and Energy Systems, vol. 30, pp. 140 – 149, 2008.
[14] M. Reyes-Sierra, and C. A. C. Coello, “Multiobjective particle swarm optimizers: a survey of the state-of-the-art,” International Journal of Computational intelligence Research, vol. 2, no. 3, pp. 287 – 308, 2006.
[15] Y. A. Gherbi, H. Bouzeboudja, and F. Z. Gherbi, “The combined economic environmental dispatch using new hybrid metaheuristic,” Energy, vol. 115, pp. 468-477, 2016.
[16] N. Pandit, A. Tripathi, S. Tapaswi, and M. Pandit, “An improved bacterial foraging algorithm for combined static/dynamic,” Applied Soft Computing, vol. 12, pp. 3500–3513, 2012.
[1] K. S. Damodaran, and T. K. Sunil Kumar, “Optimal Environmental Economic Dispatch Using a Classical Technique,” International review of automatic control (IREACO), vol. 7, no. 3, pp. 300 – 306, 2014.
[2] M. A. Abido, “Multiobjective evolutionary algorithms for electric power dispatch problem,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 315 – 329, 2006.
[3] S. Sivasubramani, and K. S. Swarup, “Environmental/economic dispatch using multi-objective harmony search algorithm, Electric Power Systems Research,” vol. 81, no. 9, pp. 1778 – 1785, 2011.
[4] G. Irisarri, L. M. Kimball, K. A. Clements, A. Bagchi, and P. W. Davis, “Economic dispatch with network and ramping constraints via interior point methods,” IEEE Transactions on Power Systems, vol. 13, no. 1, pp. 236 – 242, 1998.
[5] 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.
[6] C. W. Gar, J. G. Aganagic, T. M. B. 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.
[7] 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.
[8] 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.
[9] B. Zhao, and Y. J. Cao, “Multiple objective particle swarm optimization technique for economic load dispatch,” Journal of Zhejiang University Science, vol. 6A, no. 5, pp. 420 – 427, 2005.
[10] A. Mahor, V. Prasadb, and S. Rangnekar, “Economic dispatch using particle swarm optimization:A review,” Renewable and Sustainable Energy Reviews, vol. 13, pp. 2134 – 2141, 2009.
[11] J. Kennedy, and R. Eberhart, “Particle swarm optimization,” Proc. IEEE Int Conference on Neural Networks, pp. 1942 – 1948, 1995.
[12] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6 no.2, pp. 182 – 197, 2002.
[13] M. Basu, “Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II,” Electric Power and Energy Systems, vol. 30, pp. 140 – 149, 2008.
[14] M. Reyes-Sierra, and C. A. C. Coello, “Multiobjective particle swarm optimizers: a survey of the state-of-the-art,” International Journal of Computational intelligence Research, vol. 2, no. 3, pp. 287 – 308, 2006.
[15] Y. A. Gherbi, H. Bouzeboudja, and F. Z. Gherbi, “The combined economic environmental dispatch using new hybrid metaheuristic,” Energy, vol. 115, pp. 468-477, 2016.
[16] N. Pandit, A. Tripathi, S. Tapaswi, and M. Pandit, “An improved bacterial foraging algorithm for combined static/dynamic,” Applied Soft Computing, vol. 12, pp. 3500–3513, 2012.
@article{"International Journal of Electrical, Electronic and Communication Sciences:75666", author = "Badr M. Alshammari and T. Guesmi", title = "An Improved Particle Swarm Optimization Technique for Combined Economic and Environmental Power Dispatch Including Valve Point Loading Effects", abstract = "In recent years, the combined economic and emission power dispatch is one of the main problems of electrical power system. It aims to schedule the power generation of generators in order to minimize cost production and emission of harmful gases caused by fossil-fueled thermal units such as CO, CO2, NOx, and SO2. To solve this complicated multi-objective problem, an improved version of the particle swarm optimization technique that includes non-dominated sorting concept has been proposed. Valve point loading effects and system losses have been considered. The three-unit and ten-unit benchmark systems have been used to show the effectiveness of the suggested optimization technique for solving this kind of nonconvex problem. The simulation results have been compared with those obtained using genetic algorithm based method. Comparison results show that the proposed approach can provide a higher quality solution with better performance.
", keywords = "Power dispatch, valve point loading effects, multiobjective optimization, Pareto solutions.", volume = "11", number = "6", pages = "660-5", }
