Skip to main content
SpringerLink
Log in

Comparison of Binary Optimization Techniques for Real-Time Management of Sustainable Autonomous Microgrid

  • Conference paper
  • First Online:
Book cover Intelligent Data Engineering and Automated Learning – IDEAL 2016 (IDEAL 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9937))

Abstract

Sustainable autonomous microgrid is an integrated power ecosystem consisting of Distributed Generators (DGs), storage devices and loads. Such microgrids are expected to become an integral part of the future power system. Existence of intermittent renewable based sources, loads with different priorities and limited generation capacity makes power balancing in an autonomous microgrid a challenging task. During real-time implementation, desired reliability and stability is achieved in such an infrastructure by utilizing a fast acting algorithm for priority based load management and network reconfiguration. Primary task of the algorithm is to identify ON/OFF status of the load breakers and the tie/sectionalizing breakers in the system. As the breaker status is represented by ‘1’ or ‘0’, binary version of optimization techniques need to be used to find the optimum solution. In this paper, the Binary coded Genetic Algorithm (BGA) and Binary Particle Swarm Optimization (BPSO) is used in the algorithm for real-time management of a sustainable autonomous microgrid and their performances are compared. The results show that BPSO has outperformed BGA in obtaining the solution.

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. Hu, Y., Hua, N., Wang, C., Gong, J., Li, X.: Research on distribution network reconfiguration. In: Proceedings of the International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), vol. 1, pp. 176–180, August 2010

    Google Scholar 

  2. Zhan, X., Xiang, T., Chen, H., Zhou, B., Yang, Z.: Vulnerability assessment and reconfiguration of microgrid through search vector artificial physics optimization algorithm. Int. J. Electr. Power Energy Syst. 62, 679–688 (2014)

    Article  Google Scholar 

  3. do Nascimento, V.C., Lambert-Torres, G., de Almeida Costa, C.I., da Silva, L.E.B.: Control model for distributed generation and network automation for microgrids operation. Electr. Power Syst. Res. 127, 151–159 (2015)

    Article  Google Scholar 

  4. Nafisi, H., Farahani, V., Askarian Abyaneh, H., Abedi, M.: Optimal daily scheduling of reconfiguration based on minimisation of the cost of energy losses and switching operations in microgrids. IET Gener. Transm. Distrib. 9(6), 513–522 (2015)

    Article  Google Scholar 

  5. Mokari-Bolhasan, A., Seyedi, H., Mohammadi-ivatloo, B., Abapour, S., Ghasemzadeh, S.: Modified centralized ROCOF based load shedding scheme in an islanded distribution network. Int. J. Electr. Power Energy Syst. 62, 806–815 (2014)

    Article  Google Scholar 

  6. Ketabi, A., Fini, M.H.: An underfrequency load shedding scheme for islanded microgrids. Int. J. Electr. Power Energy Syst. 62, 599–607 (2014)

    Article  Google Scholar 

  7. Shariatzadeh, F., Vellaithurai, C., Biswas, S., Zamora, R., Srivastava, A.: Real-time implementation of intelligent reconfiguration algorithm for microgrid. IEEE Trans. Sustain. Energy 5(2), 598–607 (2014)

    Article  Google Scholar 

  8. Mitra, P., Venayagamoorthy, G.: Implementation of an intelligent reconfiguration algorithm for an electric ship’s power system. IEEE Trans. Indus. Appl. 47(5), 2292–2300 (2011)

    Article  Google Scholar 

  9. Vuppalapati, S., Srivastava, A.: Application of ant colony optimisation for reconfiguration of shipboard power system. Int. J. Eng. Sci. Technol. 2(3), 119–131 (2010)

    Article  Google Scholar 

  10. Kumar, R.H., Ushakumari, S.: Optimal management of islanded microgrid using binary particle swarm optimization. In: Proceedings of International Conference on Advances in Green Energy (ICAGE), pp. 251–257 (2014)

    Google Scholar 

  11. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)

    MATH  Google Scholar 

  12. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, WA, Vol. 4, pp. 1942–1948, November 1995

    Google Scholar 

  13. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: BGSA: binary gravitational search algorithm. Nat. Comput. 9(3), 727–745 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  14. Mirjalili, S., Lewis, A.: S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evol. Comput. 9, 1–14 (2013)

    Article  Google Scholar 

  15. Kirthiga, M.V., Daniel, S.A., Gurunathan, S.: A methodology for transforming an existing distribution network into a sustainable autonomous micro-grid. IEEE Trans. Sustain. Energy 4(1), 31–41 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Engineering Trivandrum, Thiruvananthapuram, Kerala, India

    R. Hari Kumar & S. Ushakumari

Authors
  1. R. Hari Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Ushakumari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Hari Kumar .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. Nanjing University, Nanjing, China

    Yang Gao

  3. Yangzhou University, Yangzhou, Jiangsu, China

    Bin Li

  4. Aeronautics and Astronautics, Nanjing University Aeronautics and Astronautics, Nanjing, China

    Daoqiang Zhang

  5. Nanjing Normal University, Nanjing, China

    Ming Yang

  6. Yangzhou University, Yangzhou, Jiangsu, China

    Yun Li

  7. Ostfalia University of Applied Sciences, Wolfenbüttel, Germany

    Frank Klawonn

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Hari Kumar, R., Ushakumari, S. (2016). Comparison of Binary Optimization Techniques for Real-Time Management of Sustainable Autonomous Microgrid. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46257-8_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46256-1

  • Online ISBN: 978-3-319-46257-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation