Skip to main content
SpringerLink
Log in

Solution for Multi-area Unit Commitment Problem Using PSO-Based Modified Firefly Algorithm

  • Chapter
  • First Online:
Advances in Systems, Control and Automation

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 442))

Abstract

A new approach applying particle swarm optimization (PSO) and firefly algorithm (FA) has been proposed for solving economic dispatch and multi-area unit commitment problems, in this paper. In FA, the flashing behavior depends on the random movement factor, which gets fixed and causes the solution to vary uncertainly. Hence, PSO algorithm is used to optimize the random movement factor of FA. Using the proposed PSO-based FA, the ON/OFF status of generating units of multi-area system is determined. The effective equality and inequality constraints are considered to solve the multi-area system. The proposed algorithm is implemented in MATLAB working platform and is applied to four areas with 26 generating unit systems for a 24-h schedule. The performance of proposed method has been compared with other recent reported results. The implication of proposed method is clarified and verified by numerical results.

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Chowdary, N., Billinton, R.: Unit commitment in interconnected generating systems using a probabilistic technique. IEEE Trans. Power Syst. 5, 1231–1238 (1990)

    Google Scholar 

  2. Wang, Q., Watson, J-P., Guan, Y.: Two-stage robust optimization for contingency-constrained unit commitment. IEEE Trans. Power Syst. 28, 2366–2375 (2013)

    Google Scholar 

  3. Wu, Z., Chow, T.W.S.: Binary neighbourhood field optimization for unit commitment problems. IET Gener. Transm. Distrib. 7, 298–308 (2013)

    Google Scholar 

  4. Damousis, I.G., Bakirtzis, A.G., Dokopoulos, P.S.: A solution to the unit-commitment problem using integer-coded genetic algorithm. IEEE Trans. Power Syst. 19, 1165–1172 (2004)

    Google Scholar 

  5. Petridis, V., Kazarlis, S., Bakirtzis, A.: Varying fitness functions in genetic algorithm constrained optimization: the cutting stock and unit commitment problems. IEEE Trans. Syst. Man Cybern. B Cybern. 28(5), 629–640 (1998)

    Article  Google Scholar 

  6. Victoire, T.A.A. Jeyakumar, A.E.: Unit commitment by a tabu-search-based hybrid-optimization technique. IEE Proc. Gener. Transm. Distrib. 152, 563–574 (2005)

    Google Scholar 

  7. Xiong, P., Jirutitijaroen, P.: Two-stage adjustable robust optimization for unit commitment under uncertainty. IET Gener. Transm. Distrib. 8, 573–582 (2014)

    Google Scholar 

  8. Xiong, P., Jirutitijaroen, P.: A stochastic optimization formulation of unit commitment with reliability constraints. IEEE Trans. Smart Grid 4, 2200–2208 (2013)

    Google Scholar 

  9. Maifeld, T.T., Sheble, G.B.: Genetic-based unit commitment algorithm. IEEE Trans. Power Syst. 11, 1359–1370 (1996)

    Google Scholar 

  10. Wang, B., Li, Y., Watada J.: Supply reliability and generation cost analysis due to load forecast uncertainty in unit commitment problems. IEEE Trans. Power Syst. 28, 2242–2252 (2013)

    Google Scholar 

  11. Saber, A.Y., Senjyu, T., Yona, A., Funabashi, T.: Unit commitment computation by fuzzy adaptive particle swarm optimization. IET Gener. Transm. Distrib. 1, 456–465 (2007)

    Google Scholar 

  12. Chung, C.Y., Yu, H., Wong, K.P.: An advanced quantum-inspired evolutionary algorithm for unit commitment. IEEE Trans. Power Syst. 26, 847–854 (2011)

    Google Scholar 

  13. Li, Y-F., Pedroni, N., Zio, E.: A memetic evolutionary multi-objective optimization method for environmental power unit commitment. IEEE Trans. Power Syst. 28, 2660–2669 (2013)

    Google Scholar 

  14. Chen, C-L.: Key issues of wind capacity integration in congested areas of the Taiwan power system. IET Renew Power Gener. 8(1), 10–21 (2014)

    Google Scholar 

  15. Ting, T.O., Rao, M.V.C., Loo, C.K.: A novel approach for unit commitment problem via an effective hybrid particle swarm optimization. IEEE Trans. Power Syst. 21(1), 411–418 (2006)

    Google Scholar 

  16. Pappala V.S., Erlich,I : IEEE Trans. Power Syst. 25(3), 1696–1704

    Google Scholar 

  17. Ouyang Z, Shahidehpour, S.M.: Heuristic multi-area unit commitment with economic dispatch. IEE Proc C-Gener. Transm and Distrib. 138(3), 242 (1991)

    Google Scholar 

  18. Wang C., Shahidehpour S.M.: A decomposition approach to nonlinear multi-area generation scheduling with tie-line constraints using expert systems. IEEE Trans. Power Syst. 7(4), 1409–1418

    Google Scholar 

  19. Ahmadi-Khatir, A., Bozorg, M., Cherkaoui, R.: IEEE Trans. Power Syst. 28(3), 2819–2829

    Google Scholar 

  20. Yingvivatanapong C., Lee W-J., Liu E.: IEEE Trans. Power Syst. 23(1), 196–203

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, SCSVMV University, Kanchipuram, Tamil Nadu, India

    Samikkannu Prabakaran

  2. Department of Electrical and Electronics Engineering, SSN College of Engineering, Chennai, India

    Selvaraj Tamilselvi

  3. Department of Electrical & Electronic Engineering, Pondicherry Engineering College, Pondicherry University, Pillaichavadi, Puducherry, India

    Perianayagam Ajay-D-Vimal Raj & Mahalingam Sudhakaran

  4. Researcher of NEC Laboratories, NEC Asia Pacific Pte. Ltd., Singapore, Singapore

    Selvamuthukumaran Rajasekar

Authors
  1. Samikkannu Prabakaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Selvaraj Tamilselvi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Perianayagam Ajay-D-Vimal Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mahalingam Sudhakaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Selvamuthukumaran Rajasekar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Perianayagam Ajay-D-Vimal Raj .

Editor information

Editors and Affiliations

  1. Clinical Engineering, University of Virginia Health System, Charlottesville, Virginia, USA

    Avinash Konkani

  2. Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology(SMIT), Rangpo, Sikkim, India

    Rabindranath Bera

  3. Department of Energy Engineering, North-Eastern Hill University, Shillong, Megalaya, India

    Samrat Paul

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Prabakaran, S., Tamilselvi, S., Ajay-D-Vimal Raj, P., Sudhakaran, M., Rajasekar, S. (2018). Solution for Multi-area Unit Commitment Problem Using PSO-Based Modified Firefly Algorithm. In: Konkani, A., Bera, R., Paul, S. (eds) Advances in Systems, Control and Automation. Lecture Notes in Electrical Engineering, vol 442. Springer, Singapore. https://doi.org/10.1007/978-981-10-4762-6_60

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-4762-6_60

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4761-9

  • Online ISBN: 978-981-10-4762-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation