Skip to main content

Advertisement

SpringerLink
Log in

A new hybrid algorithm with genetic-teaching learning optimization (G-TLBO) technique for optimizing of power flow in wind-thermal power systems

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

Abstract

In this study, a new hybrid genetic teaching learning- based optimization algorithm is proposed for wind-thermal power systems. The proposed algorithm is applied to a 19 bus 7336 MW Turkish-wind-thermal power system under power flow and wind energy generation constraints and three different loading conditions. Also, a conventional genetic algorithm and teaching learning-based (TLBO) algorithms were used to analyse the same power system for the performance comparison. Two performance criteria which are fuel cost and algorithm run time were utilized for comparison. The proposed algorithm combines the specialties of conventional genetic and TLBO algorithms to reach the global and local minimum points effectively. The simulation results show that the proposed algorithm developed in this study performs better than the conventional optimization algorithms with respect to the fuel cost and algorithm run time for wind-thermal power systems.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. Phan D, Kalagnanam J (2014) Some efficient optimization methods for solving the security-constrained optimal power flow problem. IEEE Trans Power Syst 29:863–972. doi:10.1109/TPWRS.2013.2283175

    Article  Google Scholar 

  2. Sun Y, Lam AYS, et al (2012) Chemical reaction optimization for the optimal power flow problem. IEEE World Congr Comput Intell 1–8. doi:10.1109/CEC.2012.6253003

  3. International Energy Agency (2014) The report of World Energy Outlook 2014: an executive summary. http://www.iea.org/Textbase/npsum/WEO2014SUM.pdf. Accessed date 23 Feb 2015

  4. Tinney WF, Hart CE (1967) Power flow solution by Newton’s method. IEEE Trans Power Appar Syst 86:1449–1460. doi:10.1109/TPAS.1967.291823

    Article  Google Scholar 

  5. Chandrasekharan E, Potti MSN, Sreeramakumar R, Mohandas KP (2002) Improved general purpose fast decoupled load flow. Electr Power Energy Syst 24:481–488. doi:10.1016/S0142-0615(01)00061-8

    Article  Google Scholar 

  6. Lai LL, Ma JT, Yokoyama R, Zhao M (1997) Improved genetic algorithms for optimal power flow under both normal and contingent operation states. Int. J. of Elect. Power Energy Syst 19:287–292. doi:10.1016/S0142-0615(96)00051-8

    Article  Google Scholar 

  7. Kahourzade Z, Mahmoudi A, Mokhlis BH (2015) A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm. Electr Eng 97:1–12. doi:10.1007/s00202-014-0307-0

    Article  Google Scholar 

  8. Edward BJ, Rajasekar N, Sathiyasekar K, Senthilnathan N, Sarjila R (2013) An enhanced bacterial foraging algorithm approach for optimal power flow problem including FACTS devices considering system loadability. ISA Trans 52:622–628. doi:10.1016/j.isatra.2013.04.002

    Article  Google Scholar 

  9. Soares J, Sousa T, Vale ZA et al (2011) Ant colony search algorithm for the optimal power flow problem. IEEE Power Energy Soc Gen Meet 1–8 doi:10.1109/PES.2011.6039840

  10. Duman S, Güvenç U, Sönmez Y, Yörükeren N (2012) Optimal power flow using gravitational search algorithm. Energy Convers Manag 59:86–95. doi:10.1016/j.enconman.2012.02.024

    Article  Google Scholar 

  11. Sivasubramani S, Swarup SK (2010) Multi-objective harmony search algorithm for optimal power flow problem. Int J Electr Power Energy Syst 33:745–752. doi:10.1016/j.ijepes.2010.12.031

    Article  Google Scholar 

  12. Kılıç U (2014) Backtracking search algorithm-based optimal power flow with valve point effect and prohibited zones. Electr Eng. doi:10.1007/s00202-014-0315-0

    Google Scholar 

  13. Shengsong L, Min W, Zhijian H (2003) Hybrid algorithm of chaos optimisation and SLP for optimal power flow problems with multimodal characteristic. IEEE Proc Gen Trans Distrib 150:543–547. doi:10.1049/ip-gtd:20030561

    Article  Google Scholar 

  14. Li Y, Wang Y, Li B (2013) A hybrid artificial bee colony assisted differential evolution algorithm for optimal reactive power flow. Int. J. of Elect. Power Energy Syst 52:25–33. doi:10.1016/j.ijepes.2013.03.016

    Article  Google Scholar 

  15. Joorabian M, Afzalan E (2014) Optimal power flow under both normal and contingent operation conditions using the hybrid fuzzy particle swarm optimisation and Nelder–Mead algorithm (HFPSO-NM). Appl Soft Comput 14:623–633. doi:10.1016/j.asoc.2013.09.015

    Article  Google Scholar 

  16. Liang HR, Tsai RS, Chen TY, Tseng TW (2011) Optimal power flow by a fuzzy based hybrid particle swarm optimization approach. Electr Power Syst Res 81:1466–1474. doi:10.1016/j.epsr.2011.02.011

    Article  Google Scholar 

  17. Sedighizadeh M, Esmaili M, Esmaeili M (2014) Application of the hybrid big bang-big crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems. Energy 76:920–930. doi:10.1016/j.energy.2014.09.004

    Article  Google Scholar 

  18. Tolabi BH, Ali HM, Ayob MBS, Rizwan M (2014) Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation. Energy 71:507–515. doi:10.1016/j.energy.2014.04.099

  19. Elattar EE (2015) A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem. Int. J. of Elect. Power Energy Syst 69:18–26. doi:10.1016/j.ijepes.2014.12.091

    Article  Google Scholar 

  20. Holland JH (1992) Genetic algorithms. Sci Am J 267:66–72

    Google Scholar 

  21. Rao VR, Patel V (2012) An elitist teaching–learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput 3:535–560. doi:10.5267/j.ijiec.2012.03.007

  22. Grefenstette J, Schultz A (1994) An evolutionary approach to learning in robots. In: Proceedings of the machine learning workshop on robot learning

  23. Turkey Wind Energy Association (2014) Wind energy statistics report. http://www.tureb.com.tr/attachments/article/169/Turkiye_Ruzgar_Enerjisi_istatistikk_Raporu_Ocak_2014.pdf. Acessed date 24 Feb 2015

  24. Saadat H (1999) Power system analysis. Mc Graw Hill, New York

    Google Scholar 

  25. Nayak RM, Nayak KC, Rout KP (2012) Application of multi-objective teaching learning based optimization algorithm to optimal power flow problem. Proc Technol 6:255–264. doi:10.1016/j.protcy.2012.10.031

    Article  Google Scholar 

  26. Haghighi SA, Seifi RA, Niknam T (2012) A modified teaching–learning based optimization for multi-objective optimal power flow problem. Energy Convers Manag 77:597–607. doi:10.1016/j.protcy.2012.10.031

    Article  Google Scholar 

  27. He Z, Jianyuan X (2009) Active power output calculation of wind farms connected to power grids based on fuzzy chance-constrained programming. In: The 4th IEEE conference on industrial electronics and applications 2141–2144. doi:10.1109/ICIEA.2009.5138575

  28. Sources of General Directorate of Electric Power Resources Survey and Development Administration (2014) http://www.eie.gov.tr/YEKrepa/REPA-duyuru_01.html Accessed 24 Feb 2015

  29. Hetzer J, Yu CD, Bhattarai K (2008) An economic dispatch model incorporating wind power. IEEE Trans Energy Convers 23(2008):603–611. doi:10.1109/TEC.2007.914171

    Article  Google Scholar 

  30. Başaran Ü (2004) Türkiye’deki 380 kV’luk enterkonnekte güç sisteminde çeşitli güç akışı ve ekonomik dağıtım analizleri. Master’s thesis, Anadolu University

  31. Vestas Wind Turbines (2014) http://www.vestas.com/Files/Filer/EN/Brochures/Vestas_V_90-3MW-11-2009-EN.pdf. Available 24 Feb 2014

  32. Nordex Wind Turbines (2014) http://www.nordex-online.com/fileadmin/MEDIA/Gamma/Nordex_Gamma_N100_en.pdf. Available 24 Feb 2014

  33. Farahmand H, Warland L, Hernando HD (2014) The impact of active power losses on the wind energy exploitation of the North Sea. Energy Proc 53:70–85. doi:10.1016/j.egypro.2014.07.216

    Article  Google Scholar 

  34. Huang MC, Huang CY (2012) Combined differential evolution algorithm and ant system for optimal reactive power dispatch. Energy Proc 14:1238–1243. doi:10.1016/j.egypro.2011.12.1082

    Article  Google Scholar 

  35. Bouchekara HERH, Abido AM, Boucherma M (2014) Optimal power flow using teaching–learning-based optimization technique. Electr Power Syst Res 114:49–59. doi:10.1016/j.epsr.2014.03.032

    Article  Google Scholar 

  36. Melanie M (1998) An introduction to genetic algorithms. Genetic algorithms in problem solving. First paperback edn MIT Press, London, pp 128–130

Download references

Author information

Authors and Affiliations

  1. Department of Electricity, Ahi Evran Üniversity, Kırşehir, Turkey

    Mehmet Güçyetmez & Ertuğrul Çam

  2. Department of Electrical Electronic Engineering, Faculty of Engineering, Kırıkkale University, Kırıkkale, Turkey

    Mehmet Güçyetmez & Ertuğrul Çam

Authors
  1. Mehmet Güçyetmez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ertuğrul Çam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehmet Güçyetmez.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Güçyetmez, M., Çam, E. A new hybrid algorithm with genetic-teaching learning optimization (G-TLBO) technique for optimizing of power flow in wind-thermal power systems. Electr Eng 98, 145–157 (2016). https://doi.org/10.1007/s00202-015-0357-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-015-0357-y

Keywords

Search

Navigation