Skip to main content

A novel hybrid PSO–GWO approach for unit commitment problem

Neural Computing and Applications volume 27, pages 1643–1655 (2016)Cite this article

Abstract

Particle swarm optimization algorithm is a inhabitant-based stochastic search procedure, which provides a populace-based search practice for getting the best solution from the problem by taking particles and moving them around in the search space and efficient for global search. Grey Wolf Optimizer is a recently developed meta-heuristic search algorithm inspired by Canis-lupus. This research paper presents solution to single-area unit commitment problem for 14-bus system, 30-bus system and 10-generating unit model using swarm-intelligence-based particle swarm optimization algorithm and a hybrid PSO–GWO algorithm. The effectiveness of proposed algorithms is compared with classical PSO, PSOLR, HPSO, hybrid PSOSQP, MPSO, IBPSO, LCA–PSO and various other evolutionary algorithms, and it is found that performance of NPSO is faster than classical PSO. However, generation cost of hybrid PSO–GWO is better than classical and novel PSO, but convergence of hybrid PSO–GWO is much slower than NPSO due to sequential computation of PSO and GWO.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

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

References

  1. Bhardwaj A, Tung NS, Shukla VK, Kamboj VK (2012) The important impacts of unit commitment constraints in power system planning. Int J Emerg Trends Eng Dev 5(2):301–306

    Google Scholar 

  2. Zhu J (2009) Unit commitment. In: EI-Hawary ME, Hanzo L (eds) Optimization of power system operation, 1st edn, chapter 7. Wiley-IEEE Press, Hoboken, pp 251–293

  3. Rajan CCA, Mohan MR, Manivannan K (2002) Neural based tabu search method for solving unit commitment problem. In: Proceedings of international conference on power system management and control (conference on publication no. 488), London, pp 180–185

  4. Kumar V, Bath SK (2013) Single area unit commitment problem by modern soft computing techniques. Int J Enhanc Res Sci Technol Eng 2(3). ISSN: 2319-7463

  5. Sriyanyong P, Song YH (2005) Unit commitment using particle swarm optimization combined with Lagrange relaxation. In: Proceedings of IEEE power engineering society general meeting, San Francisco, vol 3, pp 2752–2759

  6. Xiong W, Li MJ, Cheng YL (2008) An improved particle swarm optimization algorithm for unit commitment. In: Proceedings of international conference on intelligent computation technology and automation (ICICTA-2008), vol 2, Changsha, Hunan, pp 21–25

  7. Jeong YW, Park JB, Jang SH, Lee KY (2009) A new quantum-inspired binary PSO for thermal unit commitment problems. In: Proceedings of 15th international conference on intelligent system applications to power systems, Curitiba, pp 1–6

  8. Ge W (2010) Ramp rate constrained unit commitment by improved priority list and enhanced particle swarm optimization. In: Proceedings of 2010 international conference on computational intelligence and software engineering (CiSE 2010), Wuhan pp 1–8

  9. Borghetti A, Frangioni A, Lacalandra F, Lodi A, Martello S, Nucci CA, Trebbi A (2001) Lagrangian relaxation and tabu search approaches for the unit commitment problem. In: Proceedings of IEEE power tech conference, Porto, vol 3, pp 1–7

  10. Gaing ZL (2003) Discrete particle swarm optimization algorithm for unit commitment. In: Proceedings of IEEE power engineering society general meeting, Toronto vol 1, pp 418–424

  11. Rajan CCA, Mohan MR, Manivannan K (2003) Neural based tabu search method for solving unit commitment problem. IEEE Proc Gener Transm Distrib 150(4):469–474

    Article  Google Scholar 

  12. Gaing ZL (2003) Discrete particle swarm optimization algorithm for unit commitment. In: IEEE power engineering society general meeting, 2003, vol 1, pp 418–424, 13–17 July 2003

  13. Zhao B, Guo CX, Bai BR, Cao YJ (2006) An improved particle swarm optimization algorithm for unit commitment. Int J Electr Power Energy Syst 28:482–490

    Article  Google Scholar 

  14. Lee TY, Chen CL (2007) Unit commitment with probabilistic reserve: an IPSO approach. Energy Convers Manag 48(2):486–493

    Article  Google Scholar 

  15. Samudi C, Das GP, Ojha PC, Sreeni TS, Cherian S (2008) Hydro-thermal scheduling using particle swarm optimization. In: IEEE/PES transmission and distribution conference and exhibition, pp 1–5, April 2008

  16. Yuan X, Nie H, Su A, Wang L, Yuan Y (2009) An improved binary particle swarm optimization for unit commitment problem. Expert Syst Appl 36(4):8049–8055

    Article  Google Scholar 

  17. Mirjalili Seyedali, Lewis Andrew (2014) Adaptive gbest-guided gravitational search algorithm. Neural Comput Appl 25(7–8):1569–1584

    Article  Google Scholar 

  18. Dhillon JS, Kothari DP (2010) Power system optimization, 2nd edn. PHI, New Delhi

    Google Scholar 

  19. Mirjalili S, Mirjalili v, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  20. Anita JM, Raglend IJ, Kothari DP (2012) Solution of unit commitment problem using shuffled frog leaping algorithm. IOSR J Electr Electron Eng (IOSRJEEE) 1(4):9–26. ISSN: 2278-1676

  21. Anita JM, Raglend IJ, Kothari DP (2012) Solution of unit commitment problem using shuffled frog leaping algorithm. IOSR J Electr Electron Eng (IOSRJEEE) 1(4):9–26

    Article  Google Scholar 

  22. Marifeld TT, Sheble GB (1996) Genetic based unit commitment algorithm. IEEE Trans Power Syst 11(3):1359–1370

    Article  Google Scholar 

  23. Tokoro K, Masuda Y, Nishino H (2008) Solving unit commitment problem by combining of continuous relaxation method and genetic algorithm. In: SICE annual conference 2008, The University Electro-Communications, Japan, August 20–22, 2008

  24. Tokoro KI, Masuda Y, Nishina H (2008) Solving unit commitment problem by combining of continuous relaxation method and genetic algorithm. In: SICE annual conference, Japan: The University Electro-Communications, pp 3474–3478

  25. Damousis IG, Bakirtzis AG, Dokopoulos PS (2004) A solution to the unit commitment problem using integer-coded genetic algorithm. IEEE Trans Power Syst 19(2):1165–1172

    Article  Google Scholar 

  26. Sheble GB et al (1997) Unit commitment by genetic algorithm with penalty method and a comparison of lagrangian search and genetic algorithm economic dispatch example. Int J Electr Power Energy Syst 9(1):45–55

    Google Scholar 

  27. Yuan X, Nie H, Su A, Wang L, Yuan Y (2009) An improved binary particle swarm optimization for unit commitment problem. Expert Syst Appl 36(4):8049–8055

    Article  Google Scholar 

  28. Ganguly D, Sarkar V, Pal J (2004) A new genetic approach for solving the unit commitment problem. In: International conference on power system technology-POWERCON 2004, Singapore, pp 542–547, 21–24 November, 2004

  29. Grefensttete JJ (1986) Optimization of control parameters for genetic algorithm. IEEE Trans Syst Man Cybern 16:122–128

    Article  Google Scholar 

  30. Lee S, Park H, Jeon M (2007) Binary particle swarm optimization with bit change mutation. IEICE Trans Fundam Electron Commun Comput Sci E-90A(10):2253–2256

    Article  Google Scholar 

  31. 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 

  32. Wang B, Li Y, Watada J (2011) Re-scheduling the unit commitment problem in fuzzy environment. In: 2011 IEEE international conference on fuzzy systems, 27–30 June, 2011, Taipei

  33. Eldin AS, El-sayed MAH, Youssef HKM (2008) A two-stage genetic based technique for the unit commitment optimization problem. In: 12th international middle east power system conference, MEPCO, Aswan, 2008, p 425e30

  34. Victoire TAA, Jeyakumar AE (2004) Hybrid PSO-SQP for economic dispatch with valve-point effect. Electr Power Syst Res 71(1):51–59

    Article  Google Scholar 

  35. Simopoulos DN, Kavatza SD, Vournas CD (2009) Unit commitment by an enhanced simulated annealing algorithm. In: Power systems conference and exposition, 2006. PSCE ‘06. 2006 EEE PES October 29 2006-Nov. 1 2006, pp 193–201

  36. Kazarlis SA, Bakirtzis AG, Petridis V (1996) A genetic algorithm solution to the unit commitment problem. IEEE Trans Power Syst 11(1):83–92

    Article  Google Scholar 

  37. Dimitris N, Simopoulos SD, Kavatza, Vournas CD (2006) Unit commitment by an enhanced simulated annealing algorithm. IEEE Trans Power Syst 21(1):68–76

    Article  Google Scholar 

  38. Sriyanyong P, Song YH (2005) Unit commitment using particle swarm optimization combined with lagrange relaxation. In: IEEE power engineering society general meeting. San Francisco, USA, pp 1522–1529

Download references

Acknowledgments

The authors wish to thank Dr. J. S. Dhillon, Professor, Sant Longowal Institute of Engineering and Technology, Punjab (India), for their guidance, continuous support and encouragement and Punjab Technical University, Jalandhar, for providing contemporary research facilities for research work.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Punjab Technical University, Jalandhar, India

    Vikram Kumar Kamboj

  2. Department of Electrical Engineering, DAV University, Jalandhar, India

    Vikram Kumar Kamboj

Authors
  1. Vikram Kumar Kamboj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vikram Kumar Kamboj.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kamboj, V.K. A novel hybrid PSO–GWO approach for unit commitment problem. Neural Comput & Applic 27, 1643–1655 (2016). https://doi.org/10.1007/s00521-015-1962-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-015-1962-4

Keywords

  • Grey Wolf Optimizer (GWO)
  • Particle swarm optimization (PSO)
  • Single-area unit commitment problem (SAUCP)