International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.5, No.10, Sep. 2013

Optimal Reactive Power Dispatch Using Differential Evolution Algorithm with Voltage Profile Control

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Messaoudi Abdelmoumene, Belkacemi Mohamed, Azoui Boubakeur

Index Terms

Active Power Loss Minimization, Differential Evolution, Load Flow, Voltage Profile Improvement


This paper proposes an efficient differential evolution (DE) algorithm for the solution of the optimal reactive power dispatch (ORPD) problem. The main objective of ORPD is to minimize the total active power loss with optimal setting of control variables. The continuous control variables are generator bus voltage magnitudes. The discrete control variables are transformer tap settings and reactive power of shunt compensators. In DE algorithm the other form of differential mutation operator is used. It consists to add the global best individual in the differential mutation operator to improve the solution. The DE algorithm solution has been tested on the standard IEEE 30-Bus test system to minimize the total active power loss without and with voltage profile improvement. The results have been compared to the other heuristic methods such as standard genetic algorithm and particle swarm optimization method. Finally, simulation results show that this method converges to better solutions.

Cite This Paper

Messaoudi Abdelmoumene, Belkacemi Mohamed, Azoui Boubakeur,"Optimal Reactive Power Dispatch Using Differential Evolution Algorithm with Voltage Profile Control", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.10, pp.28-34, 2013.DOI: 10.5815/ijisa.2013.10.04


[1]H.W.Dommel, W.F.Tinney. Optimal power flow solutions. IEEE, Trans. On power Apparatus and Systems, VOL. PAS-87, octobre 1968,pp.1866-1876.

[2]Lee K, Park Y, Ortiz J. A. United approach to optimal real and reactive power dispatch. IEEE Trans Power Appar. Syst. 1985; 104(5):1147-53.

[3]Y. Y.Hong, D.I. Sun, S. Y. Lin and C. J.Lin. Multi-year multi-case optimal AVR planning. IEEE Trans. Power Syst., vol.5 , no.4, pp.1294-1301,Nov.1990.

[4]J. A. Momoh, S. X. GUO, E .C. Ogbuobiri, and R. Adapa. The quadratic interior point method solving power system optimization problems. IEEE Trans. Power Syst. vol. 9, no. 3, pp. 1327-1336,Aug.1994.

[5]S. Granville. Optimal Reactive Dispatch Through Interior Point Methods. IEEE Trans. Power Syst. vol. 9, no. 1, pp. 136-146, Feb. 1994.

[6]J.A.Momoh, J.Z.Zhu. Improved interior point method for OPF problems. IEEE Trans. On power systems; Vol. 14, No. 3, pp. 1114-1120, August 1999. 

[7]Y.C.Wu, A. S. Debs, and R.E. Marsten. A Direct nonlinear predictor-corrector primal-dual interior point algorithm for optimal power flows. IEEE Transactions on power systems Vol. 9, no. 2, pp 876-883, may 1994. 

[8]L.L.Lai, J.T.Ma, R. Yokoma, M. Zhao. Improved genetic algorithms for optimal power flow under both normal and contingent operation states. Electrical Power & Energy System, Vol. 19, No. 5, p. 287-292, 1997.

[9]Q.H. Wu, Y.J.Cao, and J.Y. Wen. Optimal reactive power dispatch using an adaptive genetic algorithm. Int. J. Elect. Power Energy Syst. Vol 20. Pp. 563-569; Aug 1998. 

[10]B. Zhao, C. X. Guo, and Y.J. CAO. Multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Trans. Power Syst. Vol. 20, no. 2, pp. 1070-1078, May 2005.

[11]J. G. Vlachogiannis, K.Y. Lee. A Comparative study on particle swarm optimization for optimal steady-state performance of power systems. IEEE trans. on Power Syst., vol. 21, no. 4, pp. 1718-1728, Nov. 2006. 

[12]M. Varadarajan, K.S. Swarup. network loss minimization with voltage security using differential evolution. Electric Power Sys. Research 78(2008), pp. 815-823.

[13]M. Varadarajan, K.S. Swarup. Differential evolution approach for optimal reactive power dispatch. Applied Soft Computing, 8 (2008); pp. 1549–1561

[14]R.Storn and K.Price. Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. Journal Global Optimization. 11, 1997,pp. 341-359.

[15]H. A. Hejazi, H. R. Mohabati, S. H. Hosseinian, M. Abedi. Differential evolution algorithm for security-constrained energy and reserve optimization considering credible contingencies. IEEE trans. on power syst., vol. 26, no. 3, pp 1145-1155,aug. 2011.

[16]M.A. Abido. Optimal power flow using particle swarm optimization. Elec.Power Energy Syst.,24 (2002) pp.563-571.