Skip to main content

Assessment of Metaheuristic Techniques Applied to the Optimal Reactive Power Dispatch

  • Conference paper
  • First Online:
Applied Computer Sciences in Engineering (WEA 2019)

Abstract

The optimal reactive power dispatch (ORPD) problem consists of finding the optimal settings of several reactive power resources in order to minimize system power losses. The ORPD is a complex combinatorial optimization problem that involves discrete and continuous variables as well as a nonlinear objective function and nonlinear constraints. From the point of view of computational complexity, the ORPD problem is NP-complete. Several techniques have been reported in the specialized literature to approach this problem in which modern metaheuristics stand out. This paper presents a comparison of such techniques with a Mean-Variance Mapping Optimization (MVMO) algorithm implemented by the authors with two different constraint handling approaches. Several tests with the IEEE 30 bus test system show the effectiveness of the proposed approach which outperforms results of previously reported methods.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mohseni-Bonab, S.M., Rabiee, A.: Optimal reactive power dispatch: a review, and a new stochastic voltage stability constrained multi-objective model at the presence of uncertain wind power generation. Transm. Distrib. IET Gener. 11, 815–829 (2017). https://doi.org/10.1049/iet-gtd.2016.1545

    Article  Google Scholar 

  2. Ela, A.A.A.E., Abido, M.A., Spea, S.R.: Differential evolution algorithm for optimal reactive power dispatch. Electr. Power Syst. Res. 81, 458–464 (2011). https://doi.org/10.1016/j.epsr.2010.10.005

    Article  Google Scholar 

  3. Mukherjee, A., Mukherjee, V.: Solution of optimal reactive power dispatch by chaotic krill herd algorithm. Transm. Distrib. IET Gener. 9, 2351–2362 (2015). https://doi.org/10.1049/iet-gtd.2015.0077

    Article  Google Scholar 

  4. Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17, 4831–4845 (2012). https://doi.org/10.1016/j.cnsns.2012.05.010

    Article  MathSciNet  MATH  Google Scholar 

  5. Yoshida, H., Kawata, K., Fukuyama, Y., Takayama, S., Nakanishi, Y.: A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans. Power Syst. 15, 1232–1239 (2000). https://doi.org/10.1109/59.898095

    Article  Google Scholar 

  6. Esmin, A.A.A., Lambert-Torres, G., de Souza, A.C.Z.: A hybrid particle swarm optimization applied to loss power minimization. IEEE Trans. Power Syst. 20, 859–866 (2005). https://doi.org/10.1109/TPWRS.2005.846049

    Article  Google Scholar 

  7. Gutiérrez, D., Villa, W.M., López-Lezama, J.M.: Flujo Óptimo Reactivo mediante Optimización por Enjambre de Partículas. Inf. Tecnológica. 28, 215–224 (2017). https://doi.org/10.4067/S0718-07642017000500020

    Article  Google Scholar 

  8. Mahadevan, K., Kannan, P.S.: Comprehensive learning particle swarm optimization for reactive power dispatch. Appl. Soft Comput. 10, 641–652 (2010). https://doi.org/10.1016/j.asoc.2009.08.038

    Article  Google Scholar 

  9. Singh, R.P., Mukherjee, V., Ghoshal, S.P.: Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers. Appl. Soft Comput. 29, 298–309 (2015). https://doi.org/10.1016/j.asoc.2015.01.006

    Article  Google Scholar 

  10. Duman, S., Sonmez, Y., Guvenc, U., Yorukeren, N.: Optimal reactive power dispatch using a gravitational search algorithm. Transm. Distrib. IET Gener. 6, 563–576 (2012). https://doi.org/10.1049/iet-gtd.2011.0681

    Article  Google Scholar 

  11. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009). https://doi.org/10.1016/j.ins.2009.03.004

    Article  MATH  Google Scholar 

  12. Chen, G., Liu, L., Zhang, Z., Huang, S.: Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints. Appl. Soft Comput. 50, 58–70 (2017). https://doi.org/10.1016/j.asoc.2016.11.008

    Article  Google Scholar 

  13. Shaw, B., Mukherjee, V., Ghoshal, S.P.: Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm. Int. J. Electr. Power Energy Syst. 55, 29–40 (2014). https://doi.org/10.1016/j.ijepes.2013.08.010

    Article  Google Scholar 

  14. Rajan, A., Malakar, T.: Optimal reactive power dispatch using hybrid Nelder-Mead simplex based firefly algorithm. Int. J. Electr. Power Energy Syst. 66, 9–24 (2015). https://doi.org/10.1016/j.ijepes.2014.10.041

    Article  Google Scholar 

  15. Bhattacharya, A., Chattopadhyay, P.K.: Biogeography-based optimization for solution of optimal power flow problem. In: ECTI-CON2010: The 2010 ECTI International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp. 435–439 (2010)

    Google Scholar 

  16. Mei, R.N.S., Sulaiman, M.H., Mustaffa, Z., Daniyal, H.: Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique. Appl. Soft Comput. 59, 210–222 (2017)

    Article  Google Scholar 

  17. Rojas, D.G., Lezama, J.L., Villa, W.: Metaheuristic techniques applied to the optimal reactive power dispatch: a review. IEEE Lat. Am. Trans. 14, 2253–2263 (2016). https://doi.org/10.1109/TLA.2016.7530421

    Article  Google Scholar 

  18. Villa-Acevedo, W.M., López-Lezama, J.M., Valencia-Velásquez, J.A.: A novel constraint handling approach for the optimal reactive power dispatch problem. Energies 11, 2352 (2018). https://doi.org/10.3390/en11092352

    Article  Google Scholar 

  19. Erlich, I., Venayagamoorthy, G.K., Worawat, N.: A mean-variance optimization algorithm. In: IEEE Congress on Evolutionary Computation, pp. 1–6 (2010). https://doi.org/10.1109/CEC.2010.5586027

  20. Erlich, I.: Mean-variance mapping optimization algorithm home page. https://www.uni-due.de/mvmo/

  21. Rueda, J.L., Erlich, I.: Optimal dispatch of reactive power sources by using MVMOs optimization. In: 2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG), pp. 29–36 (2013). https://doi.org/10.1109/CIASG.2013.6611495

  22. Cepeda, J.C., Rueda, J.L., Erlich, I.: Identification of dynamic equivalents based on heuristic optimization for smart grid applications. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8 (2012). https://doi.org/10.1109/CEC.2012.6256493

  23. Rueda, J.L., Erlich, I.: Evaluation of the mean-variance mapping optimization for solving multimodal problems. In: 2013 IEEE Symposium on Swarm Intelligence (SIS), pp. 7–14 (2013). https://doi.org/10.1109/SIS.2013.6615153

Download references

Acknowledgements

The authors acknowledge the sustainability project of Universidad de Antioquia and Colciencias (Project code 1115-745-54929; contract 056-2017) for the economic support in the development of this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jesús María López-Lezama .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Londoño, D.C., Villa-Acevedo, W.M., López-Lezama, J.M. (2019). Assessment of Metaheuristic Techniques Applied to the Optimal Reactive Power Dispatch. In: Figueroa-García, J., Duarte-González, M., Jaramillo-Isaza, S., Orjuela-Cañon, A., Díaz-Gutierrez, Y. (eds) Applied Computer Sciences in Engineering. WEA 2019. Communications in Computer and Information Science, vol 1052. Springer, Cham. https://doi.org/10.1007/978-3-030-31019-6_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-31019-6_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31018-9

  • Online ISBN: 978-3-030-31019-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics