Abstract
This paper presents a multi-objective differential-evolution-based approach to solve the optimal power flow (OPF) problem. The OPF problem has been treated as a true multi-objective constrained optimization problem. Different objective functions and operational constraints have been considered in the problem formulation. A clustering algorithm is applied to manage the size of the Pareto set. In addition, an algorithm based on fuzzy set theory is used to extract the best compromise solution. Simulation results on IEEE 30-bus and IEEE 118-bus standard test systems show the effectiveness of the proposed approach in solving true multi-objective OPF and also finding well-distributed Pareto-optimal solutions.
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Momoh J.A., El-Hawary M.E., Adapa R.: A review of selected optimal power flow literature to 1993, part I: non-linear and quadratic programming approaches. IEEE Trans. Power Syst. 14(1), 96–104 (1999)
Momoh J.A., El-Hawary M.E., Adapa R.: A review of selected optimal power flow literature to 1993, part II: Newton, linear programming and interior point methods. IEEE Trans. Power Syst. 14(1), 105–111 (1999)
Huneault M., Galiana F.D.: A survey of the optimal power flow literature. IEEE Trans. Power Syst. 6(2), 762–770 (1991)
Abido M.: Optimal power flow using particle swarm optimization. Int. J. Electr. Power Energy Syst. 24(7), 563–571 (2002)
Perez-Guerrero, R.E.; Cedeno-Maldonado, J.R.: Differential evolution based economic environmental power dispatch. In: Proceedings of the 37th Annual North American Power Symposium, pp. 191–197, 23–25 October 2005
Abido M.: Environmental/economic power dispatch using multiobjective evolutionary algorithms. IEEE Trans. Power Syst. 18(4), 1529–1537 (2003)
Abido, M.: Multiobjective optimal power flow using strength Pareto evolutionary algorithm. In: 39th International Universities Power Engineering Conference, 2004. UPEC 2004, vol. 1, pp. 457–461, 6–8 September 2004
Abido M.A.: Multiobjective evolutionary algorithms for electric power dispatch problem. IEEE Trans. Evol. Comput. 10(3), 315–329 (2006)
Abido, M.A.; Bakhashwain, J.M.: A novel multiobjective evolutionary algorithm for optimal reactive power dispatch problem. In: Proceedings of the 2003 10th IEEE International Conference on Electronics, Circuits and Systems, 2003. ICECS, vol. 3, pp. 1054–1057, 14–17 December 2003
Zhang, P.X.; Zhao, B.; Cao, Y.J.; Cheng, S.J.: A novel multi-objective genetic algorithm for economic power dispatch. In: 39th International Universities Power Engineering Conference, vol. 1, pp. 422–426, 6–8 September 2004
Zhao B., Cao Y.: Multiple objective particle swarm optimization technique for economic load dispatch. J. Zhejiang Univ. Sci. 6((5), 420–427 (2005)
Bansal, R.C.: Optimization methods for electric power systems: an overview. Int. J. Emerg. Electr Power Syst. 2(1), Article 1021 (2005)
Abido, M.A.: Multiobjective particle swarm optimization for optimal power flow problem. In: Proceedings of the 12th International Middle East Power Systems Conference (MEPCON’2008), 12–15 March 2008, Aswan, Egypt, pp. 392–396
Varadarajan M., Swarup K.S.: Solving multi-objective optimal power flow using differential evolution. IET Gener. Transm. Distrib. 2(5), 720–730 (2008)
Abido M. A.: Multiobjective optimal VAR dispatch considering control variable adjustment costs. Int. J. Power Energy Convers. 1(1), 90–104 (2009)
Dobson I., Glavitsch H., Liu C.-C., Tamura Y., Vu K.: Voltage collapse in power systems. IEEE Circuits Devices Mag. 8(3), 40–45 (1992)
Belhadj, C.A.; Abido, M.A.: An optimized fast voltage stability indicator. In: IEEE PowerTech’99 Conference, p. 79, 29 August–2 September 1999
Zitzler, E.; Laumannus, M.; Bleuler, S.: A tutorial on evolutionary multi-objective optimization. Swiss Federal Institute of Technology (ETH) Zurich, Computer Engineering and Networks Laboratory (TIK), Zurich, Switzerland. http://www.cs.cinvestav.mx/~emooworkgroup/zitzler04.pdf
Fonseca, C.; Fleming, P.: An overview of evolutionary algorithms in multiobjective optimization. Evol. Comput. 3(1):1–16 (Spring 1995). http://www.lania.mx/~ccoello/EMOO/fonseca95.ps.gz
Coello, C.A.C.: A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl. Inf. Syst. Int. J. 1(3), 269–308 (1999). http://www.lania.mx/~ccoello/EMOO/informationfinal.ps.gz
Storn, R.; Price, K.: Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012. ftp://ftp.icsi.berkeley.edu/pub/techreports/1995/tr-95-012.pdf
Storn, R.; Price, K.: Minimizing the real functions of the icec’96 contest by differential evolution. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 842–844, 20–22 May 1996
Vesterstrom, J.; Thomsen, R.: A comparative study of differential evolution, particle swarm optimization and evolutionary algorithms on numerical benchmark problems. In: Congress on Evolutionary Computation, 2004. CEC2004, vol. 2, pp. 1980–1987, 19–23 June 2004
Madavan, N.: Multiobjective optimization using a Pareto differential evolution approach. In: Proceedings of the 2002 Congress on Evolutionary Computation, 2002. CEC ’02, vol. 2, pp. 1145–1150, 12–17 May 2002
Xue, F.; Sanderson, A.C.; Graves R.J.: Pareto-based multi-objective differential evolution. In: The 2003 Congress on Evolutionary Computation, 2003. CEC ’03, vol. 2, pp. 862–869, 8–12 December 2003
Lee K., Park Y., Ortiz J.: A united approach to optimal real and reactive power dispatch. IEEE Trans. Power Apparatus Syst. PAS-104(5), 1147–1153 (1985)
Power Systems Test CaseArchive-UWEE: http://www.ee.washington.edu/research/pstca. Accessed 31 Jan 2009
Reid G.F., Hasdorff L.: Economic dispatch using quadratic programming. IEEE Trans. Power Apparatus Syst. PAS-92(6), 2015–2023 (1973)
Devaraj D., Yegnanarayana B.: Genetic-algorithm-based optimal power flow for security enhancement. IEE Proc. Gener. Transm. Distrib. 152(6), 899–905 (2005)
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Abido, M.A., Al-Ali, N.A. Multi-Objective Optimal Power Flow Using Differential Evolution. Arab J Sci Eng 37, 991–1005 (2012). https://doi.org/10.1007/s13369-012-0224-3
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DOI: https://doi.org/10.1007/s13369-012-0224-3