Skip to main content

Advertisement

SpringerLink
Log in

What metaheuristic solves the economic dispatch faster? A comparative case study

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

The economic dispatch (ED) is one of the most important short-term problems in power systems, and solving it quickly is essential. However, classical optimization tools are often too computationally demanding to be considered satisfactory. This has motivated the application of metaheuristic methods, which offer a good compromise in terms of solution quality and computation time. However, these methods have been applied in an isolated way and on different problem definitions and case studies, so that there were no clear insights on how they compared to each other. This paper fills this gap by performing an objective comparison of six metaheuristics solving the ED in several case studies under different conditions. Although mixed-integer programming performs best for small case studies, our results confirm that metaheuristics are able to efficiently solve the ED problem. Genetic algorithms emerge as the best performers in terms of solution quality and computation time, followed by PSO and TLBO.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Krishnamurthy S, Tzoneva R (2013) Economic dispatch solution using different algorithms and softwares. In: 2013 International conference on green computing, communication and conservation of energy (ICGCE)

  2. Hosseini SSS, Yang X-S, Gandomi AH, Nemati A (2015) Solutions of non-smooth economic dispatch problems by swarm intelligence. Adaptation and hybridization in computational intelligence, pp 129–146. Springer

  3. Jadhav H, Raj S, Roy R (2013) Solution to economic emission load dispatch problem using modified artificial bee colony algorithm. In: 2013 3rd international conference on electric power and energy conversion systems (EPECS)

  4. Chowdhury BH, Rahman S (1990) A review of recent advances in economic dispatch. IEEE Trans Power Syst 5(4):1248–1259

    Article  MathSciNet  Google Scholar 

  5. Wood A, Wollenberg B (1984) Power generation, control, and operation. Wiley, New York

    Google Scholar 

  6. Glover F, Sörensen K (2015) Metaheuristics. Scholarpedia 10(4):6532

    Article  Google Scholar 

  7. Mahor A, Prasad V, Rangnekar S (2009) Economic dispatch using particle swarm optimization: a review. Renew Sustain Energy Rev 13(8):2134–2141

    Article  Google Scholar 

  8. Barros RS, Cortes OAC, Lopes RF, Da Silva JC (2013) A hybrid algorithm for solving the economic dispatch problem. In: 2013 BRICS congress on computational intelligence and 11th Brazilian congress on computational intelligence (BRICS-CCI & CBIC)

  9. Nasiruzzaman A, Rabbani M (2008) Implementation of genetic algorithm and fuzzy logic in economic dispatch problem. In: International conference on paper presented at the electrical and computer engineering, 2008. ICECE 2008

  10. Su C-T, Lin C-T (2000) New approach with a Hopfield modeling framework to economic dispatch. IEEE Trans Power Syst 15(2):541–545

    Article  MathSciNet  Google Scholar 

  11. Gaing Z-L (2003) Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans Power Syst 18(3):1187–1195

    Article  Google Scholar 

  12. Elyas SH, Mandal P, Haque AU, Giani A, Tseng TLB (2014) A new hybrid optimization algorithm for solving economic load dispatch problem with valve-point effect. Paper presented at the North American power symposium (NAPS)

  13. Kannan G, Karthik N (2014) Application of fireflies algorithm to solve economic load dispatch. In: 2014 International conference on paper presented at the green computing communication and electrical engineering (ICGCCEE)

  14. Basu M (2014) Teaching–learning-based optimization algorithm for multi-area economic dispatch. Energy 68:21–28

    Article  Google Scholar 

  15. Niknam T, Azizipanah-Abarghooee R, Aghaei J (2013) A new modified teaching–learning algorithm for reserve constrained dynamic economic dispatch. IEEE Trans Power Syst 28(2):749–763

    Article  Google Scholar 

  16. Banerjee S, Maity D, Chanda CK (2015) Teaching learning based optimization for economic load dispatch problem considering valve point loading effect. Int J Electr Power Energy Syst 73:456–464

    Article  Google Scholar 

  17. Narimani MR (2011) A new modified shuffle frog leaping algorithm for non-smooth economic dispatch. World Appl Sci J 12(6):803–814

    Google Scholar 

  18. Roy P, Roy P, Chakrabarti A (2013) Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect. Appl Soft Comput 13(11):4244–4252

    Article  Google Scholar 

  19. Chen G (2009) Combined economic emission dispatch using SFLA. In: International conference on paper presented at the information engineering and computer science, 2009. ICIECS 2009

  20. Barisal A, Prusty R (2015) Large scale economic dispatch of power systems using oppositional invasive weed optimization. Appl Soft Comput 29:122–137

    Article  Google Scholar 

  21. Hemmati R (2018) Optimal design and operation of energy storage systems and generators in the network installed with wind turbines considering practical characteristics of storage units as design variable. J Clean Prod 185:680–693

    Article  Google Scholar 

  22. Mahdi FP, Vasant P, Kallimani V, Watada J, Fai PYS, Abdullah-Al-Wadud M (2018) A holistic review on optimization strategies for combined economic emission dispatch problem. Renew Sustain Energy Rev 81(2):3006–3020

    Article  Google Scholar 

  23. Arunachalam S, Saranya R, Sangeetha N (2013) Hybrid artificial bee colony algorithm and simulated annealing algorithm for combined economic and emission dispatch including valve point effect. Paper presented at the international conference on swarm, evolutionary, and memetic computing

  24. Chen P-H, Chang H-C (1995) Large-scale economic dispatch by genetic algorithm. IEEE Trans Power Syst 10(4):1919–1926

    Article  Google Scholar 

  25. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99

    Article  Google Scholar 

  26. Kennedy R, Eberhart R (1995) Particle swarm optimization. In: Paper presented at the proceedings of ICNN’95—international conference on neural networks, Perth, WA, Australia

  27. Karaboga D (2010) Artificial bee colony algorithm. Scholarpedia 5(3):6915

    Article  Google Scholar 

  28. Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154

    Article  MathSciNet  Google Scholar 

  29. Sharma R, Nayak N, Krishnanand K, Rout P (2011) Modified invasive weed optimization with dual mutation technique for dynamic economic dispatch. In: 2011 International conference on energy, automation, and signal (ICEAS)

  30. Rao RV, Savsani VJ, Vakharia D (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Article  Google Scholar 

  31. Toğan V (2012) Design of planar steel frames using teaching–learning based optimization. Eng Struct 34:225–232

    Article  Google Scholar 

  32. Yoshida H, Kawata K, Fukuyama Y, Takayama S, Nakanishi Y (2000) A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans Power Syst 15(4):1232–1239

    Article  Google Scholar 

  33. Duman S, Yorukeren N, Altas IH (2015) A novel modified hybrid PSOGSA based on fuzzy logic for non-convex economic dispatch problem with valve-point effect. Int J Electr Power Energy Syst 64:121–135

    Article  Google Scholar 

  34. Sinha N, Chakrabarti R, Chattopadhyay P (2003) Evolutionary programming techniques for economic load dispatch. IEEE Trans Evol Comput 7(1):83–94

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran

    Hamdi Abdi

  2. Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

    Hamid Fattahi

  3. Universidad Pontificia Comillas, Madrid, Spain

    Sara Lumbreras

Authors
  1. Hamdi Abdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Fattahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sara Lumbreras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamdi Abdi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdi, H., Fattahi, H. & Lumbreras, S. What metaheuristic solves the economic dispatch faster? A comparative case study. Electr Eng 100, 2825–2837 (2018). https://doi.org/10.1007/s00202-018-0750-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-018-0750-4

Keywords

Search

Navigation