[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5370/JEET.2013.8.1.053

A Study on Multi-objective Optimal Power Flow under Contingency using Differential Evolution

Mahdad, Belkacem (Dept. of Electrical Engineering, Biskra University)
Srairi, Kamel (Dept. of Electrical Engineering, Biskra University)

Publication Information

Journal of Electrical Engineering and Technology / v.8, no.1, 2013 , pp. 53-63 More about this Journal

Abstract

To guide the decision making of the expert engineer specialized in power system operation and control; the practical OPF solution should take in consideration the critical situation due to severe loading conditions and fault in power system. Differential Evolution (DE) is one of the best Evolutionary Algorithms (EA) to solve real valued optimization problems. This paper presents simple Differential Evolution (DE) Optimization algorithm to solving multi objective optimal power flow (OPF) in the power system with shunt FACTS devices considering voltage deviation, power losses, and power flow branch. The proposed approach is examined and tested on the standard IEEE-30Bus power system test with different objective functions at critical situations. In addition, the non smooth cost function due to the effect of valve point has been considered within the second practical network test (13 generating units). The simulation results are compared with those by the other recent techniques. From the different case studies, it is observed that the results demonstrate the potential of the proposed approach and show clearly its effectiveness to solve practical OPF under contingent operation states.

Keywords

Differential evolution; Multi objective function; Optimal power flow; Valve point effect; FACTS; SVC; Contingency;

Citations & Related Records

Times Cited By KSCI : 1 (Citation Analysis)

Reference
Cited By KSCI

1	P. Pezzinia, O. Gomis-Bellmunt, and A. Sudrià- Andreua, "Optimization techniques to improve energy efficiency in power systems," Renewable and Sustainable Energy Reviews, pp. 2029-2041, 2011.
2	B. Sttot and J. L. Marinho, "Linear programming for power system network security applications," IEEE Trans. Power Apparat. Syst., Vol. PAS-98, pp. 837- 848, May/June 1979. DOI ScienceOn
3	S. Frank, I. Steponavice, and S. Rebennak, "Optimal power flow: a bibliographic survey I, formulations and deterministic methods," Int. J. Energy. System (Springer-Verlag), Vol. 3, No. 3, pp. 221-258, 2012. DOI
4	J. Wood and B. F. Wollenberg, Power Generation, Operation, and Control, 2nd ed. New York: Wiley, 1984.
5	J. A. Momoh and J. Z. Zhu, "Improved interior point method for OPF problems," IEEE Trans. Power Syst., Vol. 14, pp. 1114-1120, Aug. 1999. DOI ScienceOn
6	Goldberg, D.E., Genetic algorithms in search, optimisation and machine learning. Massachusetts: Addison- Wesley, 1989.
7	Chen, P. H., Chang, H. C., "Large-scale economic dispatch by genetic algorithm," IEEE Trans. Power Systems, 10, 1919-1926. 1995. DOI ScienceOn
8	Glover, F., 1986. Tabu search - part i. ORSA Journal on Computing, 1(3), 190-206.
9	Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P., 'Optimisation by simulated annealing. Science, 220, 671-679, 1983. DOI ScienceOn
10	Yuryevich, J., Wong, K. P. "Evolutionary programming based optimal power flow algorithm," IEEE Trans. Power Systems, 14(4), 1245-1250, 1999. DOI ScienceOn
11	Z. L. Gaing, "Particle swarm optimization to solving the economic dispatch considering the generator constraints," IEEE Trans. Power Systems, Vol. 18, No. 3, pp. 1187-1195, 2003. DOI ScienceOn
12	K. Price, R. Storn, and J. Lampinen, Differential Evolution: A Practical Approach to Global Optimization. Berlin, Germany: Springer-Verlag, 2005.
13	S. Sivasubramani, K.S. Swarup, "Multi-objective harmony search algorithm for optimal power flow problem," Electrical Power & Energy Systems (Elsevier), Vol. 33, pp.745-752, 2011. DOI ScienceOn
14	D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Technical Report-TR06, Erciyes University of Engineering, Faculty of Computer Engineering Department, 2005.
15	D. Simon, "Biogeography-based optimization," IEEE Trans. Evol.Comput., Vol. 12, No. 6, pp. 702-713, Dec. 2008. DOI ScienceOn
16	A. Bhattacharya, and P. k, Chattopadhyay "Biogeaography- based optimization for different economic load dispatch problems," IEEE Trans. Power Syst., Vol. 25, No. 2, pp. 1064-1077, May. 2010. DOI ScienceOn
17	B. Akay , D. Karaboga, "A modified Artificial Bee Colony algorithm for real-parameter optimization," Journal of Information Sciences, 2010.
18	M. M. Eusuff, K. E. Lansey, "Optimization of water distribution network design using the shuffled frog leaping algorithm," Journal of Water Resources Planning and Management. Vol. 129, No.3, pp. 210- 225, June 2003. DOI ScienceOn
19	H. Nobahari, M. Nikusokhan, P. Siarry," Nondominated Sorting Gravitational Search Algorithm," ICSI 2011: International conference on swarm intelligence, 2011.
20	S. Frank, I. Steponavice, and S. Rebennak, "Optimal power flow: a bibliographic survey II, nondeterministic and hybrid methods," Int. J. Energy. System (Springer-Verlag), 2012.
21	R. Storn and K. Price, "Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces," International Computer Science Institute, Berkeley, CA, 1995, Tech. Rep. TR-95-012.
22	L. S. Coelho, R. C. Thom Souza, and V. Cocco mariani, "Improved differential evoluation approach based on clutural algorithm and diversity measure applied to solve economic load dispatch problems," Journal of Mathemtics and Computers in Simulation, Elsevier, Vol. 79, No. 10, pp. 3136-3147, 2009. DOI ScienceOn
23	B. Mahdad, K. Srairi "Differential evolution based dynamic decomposed strategy for solution of large practical economic dispatch," 10th EEEIC International Conference on Environment and Electrical Engineering, Italy, 2011.
24	B. Mahdad, T. Bouktir, K. Srairi, and M. EL. Benbouzid "Optimal Power Flow with Discontinous Fuel Cost Functions Using Decomposed GA Coordinated with Shunt FACTS," International Journal of Electrical Engineering & Technology (IJEET), Korea, Vol. 4, No. 4, pp. 315-322, 2009. DOI ScienceOn
25	A. Gupta, "Economic emission load dispatch using interval differential evolution algorithm," 4th International Workshop on reliable Engineering Computing (REC 2010).
26	J. S. Al-Sumait, J. K. Sykulski, A. K. Al-Othman, "Solution of different types of economic load dispatch problems using a pattern search method," Int. J. Electr. Power Components and Systems, Vol. 36, No. 3, pp. 250-265, 2008. DOI ScienceOn

1	Pan Wang. (2014) IEEE Systems Journal Determination of Weights for Multiobjective Decision Making or Machine Learning / 8 (1) , 63
	Mojtaba Ghasemi. (2016) Applied Soft Computing Colonial competitive differential evolution: An experimental study for optimal economic load dispatch / 40 , 342
3	Belkacem Mahdad. (2014) International Journal of System Assurance Engineering and Management Solving practical economic dispatch using hybrid GA–DE–PS method / 5 (3) , 391
	Boubakeur Hadji. (2015) Energy Procedia Multi-objective Economic Emission Dispatch Solution Using Dance Bee Colony with Dynamic Step Size / 74 , 65
4	Belkacem Mahdad. (2013) Frontiers in Energy Interactive DE for solving combined security environmental economic dispatch considering FACTS technology / 7 (4) , 429
	Jingrui Zhang. (2016) Applied Soft Computing A modified MOEA/D approach to the solution of multi-objective optimal power flow problem / 47 , 494
	Belkacem Mahdad. (2015) Applied Soft Computing Security optimal power flow considering loading margin stability using hybrid FFA–PS assisted with brainstorming rules / 35 , 291
	S. Surender Reddy. (2017) Electrical Engineering Solution of multi-objective optimal power flow using efficient meta-heuristic algorithm /