• Dr.K.Lenin Professor, Department of EEE Prasad V.Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh -520007, India



Optimal Reactive Power, Transmission Loss, Charged Particle, Adaptive Charged System Search Algorithm

Abstract [English]

This paper presents a new optimization algorithm called Adaptive Charged System Search Algorithm (ACA) for solving optimal power problem. Coulomb law from electrostatics and the Newtonian laws of mechanics are forming the basics of the proposed algorithm. Adaptive Charged System Search Algorithm (ACA) is a multi-agent approach in which each agent is a Charged Particle (CP) & they affect each other based on their fitness values, separation of distances. The quantity of the resultant force is determined by using the electrostatics laws and the quality of the movement is determined using Newtonian mechanics laws. Proposed Adaptive Charged System Search Algorithm (ACA) has been tested in Standard IEEE 57,118 bus systems & real power loss has been comparatively reduced with voltage profiles are within the limits.


Download data is not yet available.


O.Alsac,and B. Scott, “Optimal load flow with steady state security”,IEEE Transaction. PAS -1973, pp. 745-751. DOI:

Lee K Y ,Paru Y M , Oritz J L –A united approach to optimal real and reactive power dispatch , IEEE Transactions on power Apparatus and systems 1985: PAS-104 : 1147-1153 DOI:

A.Monticelli , M .V.F Pereira ,and S. Granville , “Security constrained optimal power flow with post contingency corrective rescheduling” , IEEE Transactions on Power Systems :PWRS-2, No. 1, pp.175-182.,1987. DOI:

DeebN ,Shahidehpur S.M ,Linear reactive power optimization in a large power network using the decomposition approach. IEEE Transactions on power system 1990: 5(2) : 428-435 DOI:

E. Hobson ,’Network consrained reactive power control using linear programming, ‘ IEEE Transactions on power systems PAS -99 (4) ,pp 868=877, 1980 DOI:

K.Y Lee, Y.M Park, and J.L Oritz, “Fuel –cost optimization for both real and reactive power dispatches”, IEE Proc; 131C,(3), pp.85-93. DOI:

M.K. Mangoli, and K.Y. Lee, “Optimal real and reactive power control using linear programming” ,Electr.PowerSyst.Res, Vol.26, pp.1-10,1993. DOI:

C.A. Canizares , Souza and V.H. Quintana , “ Comparison of performance indices for detection of proximity to voltage collapse ,’’ vol. 11. no.3 , pp.1441-1450, Aug 1996 .

S.R.Paranjothi ,andK.Anburaja, “Optimal power flow using refined genetic algorithm”, Electr.PowerCompon.Syst , Vol. 30, 1055-1063,2002. DOI:

D. Devaraj, and B. Yeganarayana, “Genetic algorithm based optimal power flow for security enhancement”, IEE proc-Generation.Transmission and. Distribution; 152, 6 November 2005. DOI:

Berizzi, C. Bovo, M. Merlo, and M. Delfanti, “A ga approach to compare orpf objective functions including secondary voltage regulation,” Electric Power Systems Research, vol. 84, no. 1, pp. 187 – 194, 2012. DOI:

C.-F. Yang, G. G. Lai, C.-H. Lee, C.-T. Su, and G. W. Chang, “Optimal setting of reactive compensation devices with an improved voltage stability index for voltage stability enhancement,” International Journal of Electrical Power and Energy Systems, vol. 37, no. 1, pp. 50 – 57, 2012. DOI:

P. Roy, S. Ghoshal, and S. Thakur, “Optimal var control for improvements in voltage profiles and for real power loss minimization using biogeography based optimization,” International Journal of Electrical Power and Energy Systems, vol. 43, no. 1, pp. 830 – 838, 2012. DOI:

B. Venkatesh, G. Sadasivam, and M. Khan, “A new optimal reactive power scheduling method for loss minimization and voltage stability margin maximization using successive multi-objective fuzzy lp technique,” IEEE Transactions on Power Systems, vol. 15, no. 2, pp. 844 – 851, may 2000. DOI:

W. Yan, S. Lu, and D. Yu, “A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique,” IEEE Transactions on Power Systems, vol. 19, no. 2, pp. 913 – 918, may 2004. DOI:

W. Yan, F. Liu, C. Chung, and K. Wong, “A hybrid genetic algorithminterior point method for optimal reactive power flow,” IEEE Transactions on Power Systems, vol. 21, no. 3, pp. 1163 –1169, aug. 2006.

J. Yu, W. Yan, W. Li, C. Chung, and K. Wong, “An unfixed piecewiseoptimal reactive power-flow model and its algorithm for ac-dc systems,” IEEE Transactions on Power Systems, vol. 23, no. 1, pp. 170 –176, feb. 2008. DOI:

F. Capitanescu, “Assessing reactive power reserves with respect to operating constraints and voltage stability,” IEEE Transactions on Power Systems, vol. 26, no. 4, pp. 2224–2234, nov. 2011.

Z. Hu, X. Wang, and G. Taylor, “Stochastic optimal reactive power dispatch: Formulation and solution method,” International Journal of Electrical Power and Energy Systems, vol. 32, no. 6, pp. 615 – 621, 2010. DOI:

Kargarian, M. Raoofat, and M. Mohammadi, “Probabilistic reactive power procurement in hybrid electricity markets with uncertain loads,” Electric Power Systems Research, vol. 82, no. 1, pp. 68 – 80, 2012. DOI:

D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Tech. Rep. TR06, Erciyes Univ. Press, Erciyes, 2005.

D. Karaboga and B. Basturk, “A powerful and efficient algorithm fornumerical function optimization: Artificial bee colony (ABC) algorithm,” Journal of Global Optimization, Vol. 39, No. 3, pp. 459-471, 2007. DOI:

D. Karaboga and B. Basturk, “On the performance of artificial beecolony (ABC) algorithm,” Applied Soft computing, Vol. 8, pp. 687-697, 2008. DOI:

D. Karaboga and B. Akay, “A comparative study of artificial Beecolony algorithm,” Applied Mathematics and Computation, Vol. 214,No. 1, pp. 108-132, 2009. DOI:

D. Whitley, “A genetic Algorithm tutorial,” Statistics and Computing, Vol. 4, pp. 65-85, 1994. DOI:

D. Karaboga and B. Akay, “artificial bee colony (abc) algorithm ontraining artificial neural networks 15th IEEE Signal Processing andCommunications Applications, pp.1-4, 2007. DOI:

S. K. Udgata, S. L. Sabat and S. Mini, “Sensor deployment in irregularterrain using artificial bee colony algorithm,” IEEE Congress on Nature& Biologically Inspired Computing, pp. 1309-1314, 2009.

B. Akay and D. Karaboga, “Artificial bee colony algorithm for largescaleproblems and engineering design optimization,” Journal ofIntelligent Manufacturing, Vol. 23, No. 4, pp. 1001-1014, 2010.

B. Alatas, “Chaotic bee colony algorithm for global numericaloptimization,” Expert Systems with Applications, Vol. 37, pp. 5682-5687, 2010.

G. Zhu and S. Kwong, “Gbest-guided artificial bee colony algorithm fornumerical function optimization,” Applied Mathematics andComputation , Vol. 217, pp. 3166-3173, 2010.

Banharnsakun, T. Achalakul and B. Sirinaovakul, “The best-so-farselection in artificial bee colony algorithm,” Applied Soft Computing,Vol. 11, No. 2, pp. 2888-2901, 2011.

D. Karaboga and B. Gorkemli, “A combinatorial artificial bee colonyalgorithm for traveling salesman problem,” International Symposium onInnovation in Intelligent Systems and Applications (INISTA) , pp. 50-53, 2011. DOI:

W. Gao and S. Liu, “A modified artificial bee colony algorithm,”Computers & Operations Research, Vol. 39. pp. 687-697, 2012. DOI:

Kaveh, A., and Talatahari, S. (2010 a). A novel heuristic optimization method: charged system search. Acta Mechanica, vol. 213, no. 3-4, pp. 267- 289. DOI:

Kaveh, A., and Talatahari, S. (2010b). Optimal design of skeletal structures via the charged system search algorithm. Structural and Multidisciplinary Optimization, vol. 41, no. 6, pp. 893-911. DOI:

Kaveh, A., and Laknejadi, K. (2011a). A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization. Expert Systems with Applications, vol. 38, no. 12, pp. 15475-15488. DOI:

Kaveh, A., and Talatahari S. (2011b). An enhanced charged system search for configuration optimization using the concept of fields of forces. Structural and Multidisciplinary Optimization, vol. 43, no. 3, pp. 339-351. DOI:

Kaveh, A., Talatahari, S.: Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput. Struct. 87(5–6), 267–283 (2009) DOI:

Chaohua Dai, Weirong Chen, Yunfang Zhu, and Xuexia Zhang, “Seeker optimization algorithm for optimal reactive power dispatch,” IEEE Trans. Power Systems, Vol. 24, No. 3, August 2009, pp. 1218-1231. DOI:

J. R. Gomes and 0. R. Saavedra, “Optimal reactive power dispatch using evolutionary computation: Extended algorithms,” IEE Proc.-Gener. Transm. Distrib.. Vol. 146, No. 6. Nov. 1999. DOI:

IEEE, “The IEEE 30-bus test system and the IEEE 118-test system”, (1993),

Jiangtao Cao, Fuli Wang and Ping Li, “An Improved Biogeography-based Optimization Algorithm for Optimal Reactive Power Flow” International Journal of Control and Automation Vol.7, No.3 (2014), pp.161-176.




How to Cite


Most read articles by the same author(s)

1 2 3 4 5 6 7 > >>