Skip to main content
Log in

A multi-objective GRASP procedure for reactive power compensation planning

  • Published:
Optimization and Engineering Aims and scope Submit manuscript

Abstract

A multi-objective approach based on the GRASP (Greedy Randomized Adaptive Search Procedure) meta-heuristic is proposed to provide decision support in the problem of locating and sizing capacitors for reactive power compensation in electrical radial distribution networks. The installation of capacitors (local sources of reactive power) in the network is aimed at correcting the power factor to improve the quality of service, particularly the network voltage profile, and reduce energy losses and power peak. The mathematical model explicitly considers two conflicting objective functions: the minimization of the network active losses and the minimization of the capacitor installation cost. An algorithmic approach based on GRASP is presented for the characterization of the non-dominated solution set.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Antunes CH, Barrico C, Gomes A, Pires DF, Martins AG (2009) An evolutionary algorithm for reactive power compensation in radial distribution networks. Appl Energy 86(7–8):977–984

    Article  Google Scholar 

  • Antunes CH, Lima P, Oliveira E, Pires D (2011) A multi-objective simulated annealing approach to reactive power compensation. Eng Optim 43(10):1063–1077

    Article  MathSciNet  Google Scholar 

  • Arroyo J, Pereira A (2011) A GRASP heuristic for the multi-objective permutation flowshop scheduling problem. Int J Adv Manuf Technol 1:1–13

    Google Scholar 

  • Arroyo J, Vieira P, Vianna DS (2008) A GRASP algorithm for the multi-criteria minimum spanning tree problem. Ann Oper Res 159:125–133

    Article  MATH  MathSciNet  Google Scholar 

  • Delfanti M, Granelli G, Marannino P, Montagna M (2000) Optimal capacitor placement using deterministic and genetic algorithms. IEEE Trans Power Syst 15(3):1041–1046

    Article  Google Scholar 

  • Feo T, Resende M (1995) Greedy randomized adaptive search procedures. J Glob Optim 6:109–134

    Article  MATH  MathSciNet  Google Scholar 

  • Festa P, Resende M (2011) Effective application of GRASP. In: Cochran JJ, Cox LA Jr, Keskinocak P, Kharoufeh JP, Smith JC (eds) Wiley encyclopedia of operations research and management science, vol 3. Wiley, New York, pp 1609–1617

    Google Scholar 

  • Fonseca CM, Paquete L, López-Ibáñez M (2006) An improved dimension—sweep algorithm for the hypervolume indicator. In: Proceedings of the 2006 congress on evolutionary computation (CEC’06), pp 1157–1163

    Google Scholar 

  • Higgins AJ, Hajkowicz S, Bui E (2008) A multi-objective model for environmental investment decision making. Comput Oper Res 35:253–266

    Article  MATH  Google Scholar 

  • Iba K, Suzuki H, Suzuki KI, Suzuki K (1988) Practical reactive power allocation/operation planning using successive linear programming. IEEE Trans Power Syst 3(2):558–566

    Article  Google Scholar 

  • Ishida C, Pozo A, Goldbarg E, Goldbarg M (2009) Multiobjective optimization and rule learning: subselection algorithm or meta-heuristic algorithm? In: Nedjah N, Mourelle LM, Kacprzyk J (eds) Innovative applications in data mining, studies in computational intelligence, vol 169. Springer, Berlin, pp 47–70

    Chapter  Google Scholar 

  • Li H, Landa-Silva D (2009) An elitist GRASP metaheuristic for the multi-objective quadratic assignment problem. In: Ehrgott M, Fonseca CM, Gandibleux X, Hao J-K, Sevaux M (eds) Evolutionary multi-criterion optimization. Lecture notes in computer science, vol 5467. Springer, Berlin, pp 481–494

    Chapter  Google Scholar 

  • Martí R, Campos V, Resende MGC, Duarte A (2011) Multi-objective GRASP with path-relinking. AT&T Labs Research Technical Report. Available at: http://www2.research.att.com/~mgcr/papers.html

  • Pires DF, Antunes CH, Martins AG (2005) A multiobjective model for VAR planning in radial distribution networks based on tabu search. IEEE Trans Power Syst 20(2):1089–1094

    Article  Google Scholar 

  • Pires DF, Antunes CH, Martins AG (2012) NSGA-II with local search for a multi-objective reactive power compensation problem. Electr Power Energy Syst 43(1):313–324

    Article  Google Scholar 

  • Resende MGC, Ribeiro CC (2003) Greedy randomized adaptive search procedures. In: Glover F, Kochenberger G (eds) Handbook of metaheuristics. Kluwer Academic, Dordrecht, pp 219–249

    Google Scholar 

  • Resende MGC, Ribeiro CC (2010) Greedy randomized adaptive search procedures: advances, hybridizations, and applications. In: Gendreau M, Potvin J-Y (eds) Handbook of metaheuristics, 2nd edn. Springer, Berlin, pp 283–319

    Chapter  Google Scholar 

  • Resende MGC, Werneck RF (2004) A hybrid heuristic for the p-median problem. J Heuristics 10:59–88

    Article  MATH  Google Scholar 

  • Reynolds AP, de la Iglesia B (2009) A multi-objective GRASP for partial classification. Soft Comput 13:227–243

    Article  Google Scholar 

  • Reynolds AP, Corne DW, de la Iglesia B (2009) A multiobjective GRASP for rule selection. In: Proceedings of the 11th annual conference on genetic and evolutionary computation, Montreal, Quebec, Canada. ACM, New York, pp 643–650

    Google Scholar 

  • Vianna DS, Arroyo JEC (2004) A GRASP algorithm for the multi-objective knapsack problem. In: Proceedings of the XXIV international conference of the Chilean computer science society (SCCC04). IEEE Computer Society, Washington

    Google Scholar 

  • Zhang W, Li F, Tolbert L (2007) Review of reactive power planning: objectives, constraints, and algorithms. IEEE Trans Power Syst 22(4):2177–2186

    Article  Google Scholar 

  • Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Proceedings of 5th int conf parallel problem solving from nature. Springer, Berlin, pp 292–301

    Google Scholar 

Download references

Acknowledgements

This work has been framed under the Energy for Sustainability Initiative of the University of Coimbra and partially supported by R&D Project EMSURE—Energy and Mobility for Sustainable Regions (CENTRO 07 0224 FEDER 002004) and Fundação para a Ciência e a Tecnologia (FCT) under project grants PEst-C/EEI/UI0308/2011 and MIT/SET/0018/2009. The authors are also indebted to an anonymous reviewer for his/her very constructive comments on earlier versions of this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carlos Henggeler Antunes.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Antunes, C.H., Oliveira, E. & Lima, P. A multi-objective GRASP procedure for reactive power compensation planning. Optim Eng 15, 199–215 (2014). https://doi.org/10.1007/s11081-013-9228-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11081-013-9228-4

Keywords

Navigation