Skip to main content
SpringerLink
Log in

Learning-Based Evolutionary Optimization for Optimal Power Flow

  • Conference paper
  • First Online:
Book cover Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

Included in the following conference series:

  • 1766 Accesses

Abstract

This paper proposes a learning-based evolutionary optimization (LBEO) for solving optimal power flow (OPF) problem. The LBEO is a simple and effective algorithm, which simplifies the structure of teaching-learning-based optimization (TLBO) and enhances the convergence speed. The performance of this method is implemented on IEEE 30-bus test system with the minimized fuel cost objective function, and the results show that LBEO is practicable for OPF problem compared with other methods in the literature.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Dommel, H.W., Tinney, T.F.: Optimal power flow solutions. IEEE Trans. Power Appar. Syst. 87(5), 1866–1876 (1968)

    Article  Google Scholar 

  2. Paranjothi, S.R., Anburaja, K.: Optimal power flow using refined genetic algorithm. Electric Power Compon. Syst. 30, 1055–1063 (2002)

    Article  Google Scholar 

  3. Liang, R.H., Tsai, S.R., Chen, Y.T., Wan-Tsun, T.: Optimal power flow by a fuzzybased hybrid particle swarm optimization approach. Electr. Power Syst. Res. 81(7), 1466–1474 (2011)

    Article  Google Scholar 

  4. Cai, H.R., Chung, C.Y., Wong, K.P.: Application of differential evolution algorithm for transient stability constrained optimal power flow. IEEE Trans. Power Syst. 23(2), 719–728 (2008)

    Article  Google Scholar 

  5. Niu, Q., Zhang, H.Y., Li, K.: An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models. Int. J. Hydrogen Energy 39(8), 3837–3854 (2014)

    Article  Google Scholar 

  6. Frank, S., Steponavice, I., Rebennack, S.: Optimal power flow: a bibliographic survey I. formulations and deterministic methods. Energy Syst. 3(3), 221–258 (2012)

    Article  Google Scholar 

  7. Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43(3), 303–315 (2011)

    Article  Google Scholar 

  8. Alsac, O., Stott, B.: Optimal load flow with steady-state security. IEEE Trans. Power Appar. Syst. PAS 93(3), 745–751 (1974)

    Article  Google Scholar 

  9. Lee, K.Y., Park, Y.M., Ortiz, J.L.: A united approach to optimal real and reactive power dispatch. IEEE Trans. Power Appar. Syst. 104(5), 1147–1153 (1985)

    Article  Google Scholar 

  10. Bouchekara, H.R.E.H., Abido, M.A., Boucherma, M.: Optimal power flow using teaching-learning-based optimization technique. Electr. Power Syst. Res. 114, 49–59 (2014)

    Article  MATH  Google Scholar 

  11. Bhattacharya, A., Chattopadhyay, P.K.: Application of biogeography-based optimisation to solve different optimal power flow problems. IET Gener. Transm. Distrib. 5, 70–80 (2011)

    Article  Google Scholar 

  12. Abou El Ela, A.A., Abido, M.A., Spea, S.R.: Optimal power flow using differential evolution algorithm. Electr. Power Syst. Res. 80, 878–885 (2010)

    Article  MATH  Google Scholar 

  13. Ghasemi, M., Ghavidel, S., Gitizadeh, M., Akbari, E.: An improved teaching-learning-based optimization algorithm using LÉVy mutation strategy for non-smooth optimal power flow. Electr. Power Energy Syst. 65, 375–384 (2015)

    Article  Google Scholar 

  14. Roa-Sepulveda, C.A., Pavez-Lazo, B.J.: A solution to the optimal power flow using simulated annealing. Electr. Power Energy Syst. 25, 47–57 (2003)

    Article  Google Scholar 

  15. Reddy, S.S., Bijwe, P.R., Abhyankar, A.R.: Faster evolutionary algorithm based optimal power flow using incremental variables. Electr. Power Energy Syst. 54, 198–210 (2014)

    Article  Google Scholar 

  16. Gitizadeh, M., Ghavidel, S., Aghaei, J.: Using SVC to economically improve transient stability in long transmission lines. IETE J. Res. 60, 319–327 (2014)

    Article  Google Scholar 

  17. Vaisakh, K., Srinivas, L.R.: Evolving ant direction differential evolution for opf with non-smooth cost functions. Eng. Appl. Artif. Intell. 24, 426–436 (2011)

    Article  Google Scholar 

  18. Abido, M.: Optimal Power Flow Using Particle Swarm Optimization. Int. J. Electr. Power Energy Syst. 24, 563–571 (2002)

    Article  Google Scholar 

  19. Adaryani, M.R., Karami, A.: Artificial bee colony algorithm for solving multi-objective optimal power flow problem. Int. J. Electr. Power Energy Syst. 53, 219–230 (2013)

    Article  Google Scholar 

  20. Selvakumar, A.I., Thanushkodi, K.: A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans. Power Syst. 22, 42–51 (2007)

    Article  Google Scholar 

  21. Hsiao, Y.-T., Chen, C.-H., Chien, C.-C.: Optimal capacitor placement in distribution systems using a combination fuzzy-GA method. Int. J. Electr. Power Energy Syst. 26, 501–508 (2004)

    Article  Google Scholar 

  22. Sayah, S., Zehar, K.: Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Convers. Manage. 49, 3036–3042 (2008)

    Article  Google Scholar 

  23. Ongsakul, W., Tantimaporn, T.: Optimal powers flow by improved evolutionary programming. Electr. Power Comput. Syst. 34, 79–95 (2006)

    Article  Google Scholar 

  24. Yuryevich, J., Wong, K.P.: Evolutionary programming based optimal power flow algorithm. IEEE Trans. Power Syst. 14(4), 1245–1250 (1999)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (61273040), and Shanghai Rising-Star Program (12QA1401100).

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Qun Niu, Wenjun Peng & Letian Zhang

Authors
  1. Qun Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjun Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Letian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qun Niu .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Niu, Q., Peng, W., Zhang, L. (2015). Learning-Based Evolutionary Optimization for Optimal Power Flow. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22180-9_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation