Multiobjective Optimal Power Flow Using Hybrid Evolutionary Algorithm
This paper solves the environmental/ economic dispatch
power system problem using the Non-dominated Sorting Genetic
Algorithm-II (NSGA-II) and its hybrid with a Convergence Accelerator
Operator (CAO), called the NSGA-II/CAO. These multiobjective
evolutionary algorithms were applied to the standard IEEE 30-bus
six-generator test system. Several optimization runs were carried out
on different cases of problem complexity. Different quality measure
which compare the performance of the two solution techniques were
considered. The results demonstrated that the inclusion of the CAO
in the original NSGA-II improves its convergence while preserving
the diversity properties of the solution set.
[1] J. S. Dhillon, S. C. Parti, and D. P. Kothari, "Stochastic economic
emission load dispatch," Elect. Power Syst. Res., vol. 26, pp. 179-186,
1993.
[2] R. Yokoyama, S. Bae, T. Morita, and H. Sasaki, "Multiobjective generation
dispatch based on probability security criteria," IEEE Trans. Power
Syst., vol. 3, no. 1, pp. 317-324, 1988.
[3] M. A. Abido, "A niched pareto genetic algorithm for multiobjective
environmental/ economic dispatch," Int. J. Electr. Power Energy Syst.,
vol. 25, no. 2, pp. 97-105, 2003.
[4] ÔÇöÔÇö, "A novel multiobjective evolutionary algorithm for environmental/
economic power dispatch," Electr. Power Syst. Res., vol. 65, no. 1,
pp. 71-81, 2003.
[5] ÔÇöÔÇö, "Environmental/economic power dispatch using multiobjective
evolutionary algorithms," IEEE Trans. Power Syst., vol. 18, no. 4, pp.
1529-1537, 2003.
[6] ÔÇöÔÇö, "Multiobjective evolutionary algorithms for electric power dispatch
problem," IEEE Trans.Evol. Comput., vol. 10, no. 3, pp. 315-329,
2006.
[7] R. T. F. A. King and H. C. S. Rughooputh, "Elitist multiobjective
evolutionary algorithm for environmental/economic dispatch," in IEEE
Congress on Evolutionary Computation, vol. 2, Canberra, Australia,
2003, pp. 1108-1114.
[8] R. T. F. A. King, H. C. S. Rughooputh, and K. Deb, "Evolutionary multiobjective
environmental/ economic dispatch: Stochastic vs deterministic
approaches," KanGAL, Tech. Rep. 2004019, 2004.
[9] M. Laumanns, L. Thiele, K. Deb, and E. Zitzler, "On the convergence
and diversity-preserving properties of multi-objective evolutionary algorithms,"
TIK, Tech. Rep. 108, 2001.
[10] S. F. Adra, I. Griffin, and P. Fleming, Multiobjective Memetic Algorithm.
Berlin Heidelberg: Springer-Verlag, 2009, vol. SCI 171, ch. 9, pp. 183-
205.
[11] S. F. Adra, "Improving convergence, diversity and pertinency in multiobjective
optimization," Ph.D. dissertation, The University of Sheffield,
2007.
[12] K. Deb, A. Pratab, S. Agarwal, and T. Meyarivan, "A fast and elitist
multiobjective genetic algorithm: NSGA-II," IEEE Trans. Evol. Comput.,
vol. 6, no. 2, pp. 182-197, 2002.
[13] K. Deb, Multiobjective Optimization using Evolutionary Algorithms.
John Wiley and Sons, Ltd., 2001.
[14] E. Talbi, Metaheuristics- From Design to Implementation. New Jersey:
John Wiley and Sons, Inc., 2009.
[15] C. A. C. Coello, Evolutionary Multiobjective Optimization: Theoretical
Advances and Applications. Springer-Verlag, 2005, ch. 2, pp. 7-32.
[16] C. A. C. Coello, G. B. Lamont, and D. A. V. Veldhuizen, Evolutionary
Algorithms for Solving Multi-Objective Problems. New York: Springer,
2007.
[17] J. D. L. Silva and E. K. Burke, Applications of Multi-Objective
Evolutionary Algorithms, Advances in Natural Computation. World
Scientific, 2004, ch. 30, pp. 727-751.
[1] J. S. Dhillon, S. C. Parti, and D. P. Kothari, "Stochastic economic
emission load dispatch," Elect. Power Syst. Res., vol. 26, pp. 179-186,
1993.
[2] R. Yokoyama, S. Bae, T. Morita, and H. Sasaki, "Multiobjective generation
dispatch based on probability security criteria," IEEE Trans. Power
Syst., vol. 3, no. 1, pp. 317-324, 1988.
[3] M. A. Abido, "A niched pareto genetic algorithm for multiobjective
environmental/ economic dispatch," Int. J. Electr. Power Energy Syst.,
vol. 25, no. 2, pp. 97-105, 2003.
[4] ÔÇöÔÇö, "A novel multiobjective evolutionary algorithm for environmental/
economic power dispatch," Electr. Power Syst. Res., vol. 65, no. 1,
pp. 71-81, 2003.
[5] ÔÇöÔÇö, "Environmental/economic power dispatch using multiobjective
evolutionary algorithms," IEEE Trans. Power Syst., vol. 18, no. 4, pp.
1529-1537, 2003.
[6] ÔÇöÔÇö, "Multiobjective evolutionary algorithms for electric power dispatch
problem," IEEE Trans.Evol. Comput., vol. 10, no. 3, pp. 315-329,
2006.
[7] R. T. F. A. King and H. C. S. Rughooputh, "Elitist multiobjective
evolutionary algorithm for environmental/economic dispatch," in IEEE
Congress on Evolutionary Computation, vol. 2, Canberra, Australia,
2003, pp. 1108-1114.
[8] R. T. F. A. King, H. C. S. Rughooputh, and K. Deb, "Evolutionary multiobjective
environmental/ economic dispatch: Stochastic vs deterministic
approaches," KanGAL, Tech. Rep. 2004019, 2004.
[9] M. Laumanns, L. Thiele, K. Deb, and E. Zitzler, "On the convergence
and diversity-preserving properties of multi-objective evolutionary algorithms,"
TIK, Tech. Rep. 108, 2001.
[10] S. F. Adra, I. Griffin, and P. Fleming, Multiobjective Memetic Algorithm.
Berlin Heidelberg: Springer-Verlag, 2009, vol. SCI 171, ch. 9, pp. 183-
205.
[11] S. F. Adra, "Improving convergence, diversity and pertinency in multiobjective
optimization," Ph.D. dissertation, The University of Sheffield,
2007.
[12] K. Deb, A. Pratab, S. Agarwal, and T. Meyarivan, "A fast and elitist
multiobjective genetic algorithm: NSGA-II," IEEE Trans. Evol. Comput.,
vol. 6, no. 2, pp. 182-197, 2002.
[13] K. Deb, Multiobjective Optimization using Evolutionary Algorithms.
John Wiley and Sons, Ltd., 2001.
[14] E. Talbi, Metaheuristics- From Design to Implementation. New Jersey:
John Wiley and Sons, Inc., 2009.
[15] C. A. C. Coello, Evolutionary Multiobjective Optimization: Theoretical
Advances and Applications. Springer-Verlag, 2005, ch. 2, pp. 7-32.
[16] C. A. C. Coello, G. B. Lamont, and D. A. V. Veldhuizen, Evolutionary
Algorithms for Solving Multi-Objective Problems. New York: Springer,
2007.
[17] J. D. L. Silva and E. K. Burke, Applications of Multi-Objective
Evolutionary Algorithms, Advances in Natural Computation. World
Scientific, 2004, ch. 30, pp. 727-751.
@article{"International Journal of Electrical, Electronic and Communication Sciences:51146", author = "Alawode Kehinde O. and Jubril Abimbola M. Komolafe Olusola A.", title = "Multiobjective Optimal Power Flow Using Hybrid Evolutionary Algorithm", abstract = "This paper solves the environmental/ economic dispatch
power system problem using the Non-dominated Sorting Genetic
Algorithm-II (NSGA-II) and its hybrid with a Convergence Accelerator
Operator (CAO), called the NSGA-II/CAO. These multiobjective
evolutionary algorithms were applied to the standard IEEE 30-bus
six-generator test system. Several optimization runs were carried out
on different cases of problem complexity. Different quality measure
which compare the performance of the two solution techniques were
considered. The results demonstrated that the inclusion of the CAO
in the original NSGA-II improves its convergence while preserving
the diversity properties of the solution set.", keywords = "optimal power flow, multiobjective power dispatch,evolutionary algorithm", volume = "4", number = "3", pages = "419-6", }