Skip to main content
SpringerLink
Log in

Biogeography Based Optimization for Home Energy Management in Smart Grid

  • Conference paper
  • First Online:
Advances in Network-Based Information Systems (NBiS 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 7))

Included in the following conference series:

Abstract

In this paper, we use meta-heuristic algorithm: Genetic Algorithm (GA) and Biogeography based Optimization (BBO) integrated in Energy Management Controller (EMC) to evaluate the performance of home energy management in residential area. EMC is introduced with the objective of cost reduction and to manage high peak demand problem. Time of use tariff model is used for electricity bill calculation. Simulation results show the effectiveness and efficiency of proposed scheme by load management and cost reduction. BBO based EMC performs better than GA based EMC. We also perform comparison both GA based EMC and BBO based EMC with unscheduled scheme and results show both outperform than unscheduled. BBO based EMC is more efficient in electricity cost minimization and peak to average ratio minimization as compared to GA based EMC.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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. Rahim, S., Javaid, N., Ahmad, A., Khan, S.A., Khan, Z.A., Alrajeh, N., Qasim, U.: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build. 129, 452–470 (2016)

    Article  Google Scholar 

  2. Ma, K., Yao, T., Yang, J., Guan, X.: Residential power scheduling for demand response in smart grid. Int. J. Electr. Power Energy Syst. 78, 320–325 (2016)

    Article  Google Scholar 

  3. Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A., Niaz, I.A.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)

    Article  Google Scholar 

  4. Rajalingam, S., Malathi, V.: HEM algorithm based smart controller for home power management system. Energy Build. 131, 184–192 (2016)

    Article  Google Scholar 

  5. Keshtkar, A., Arzanpour, S., Keshtkar, F.: Adaptive residential demand-side management using rule-based techniques in smart grid environments. Energy Build. 133, 281–294 (2016)

    Article  Google Scholar 

  6. Barbato, A., Capone, A., Chen, L., Martignon, F., Paris, S.: A distributed demand-side management framework for the smart grid. Comput. Commun. 57, 13–24 (2015)

    Article  Google Scholar 

  7. Zhao, Z., Lee, W.C., Shin, Y., Song, K.-B.: An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 4(3), 1391–1400 (2013)

    Article  Google Scholar 

  8. Samadi, P., Wong, V.W.S., Schober, R.: Load scheduling and power trading in systems with high penetration of renewable energy resources. IEEE Trans. Smart Grid 7(4), 1802–1812 (2016)

    Article  Google Scholar 

  9. Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H., Fan, Z.: An integer linear programming based optimization for home demand-side management in smart grid. In: 2012 IEEE PES, Innovative Smart Grid Technologies (ISGT), pp. 1–5. IEEE (2012)

    Google Scholar 

  10. Mik, S., Stuchl, J., Plato, J., Krmer, P.: A heuristic approach to active demand side management in off-grid systems operated in a smart-grid environment. Energy Build. 96, 272–284 (2015)

    Article  Google Scholar 

  11. Jalali, M.M., Kazemi, A.: Demand side management in a smart grid with multiple electricity suppliers. Energy 81, 766–776 (2015)

    Article  Google Scholar 

  12. Shakeri, M., Shayestegan, M., Abunima, H., Reza, S.M.S., Akhtaruzzaman, M., Alamoud, A.R.M., Sopian, K., Amin, N.: An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid. Energy Build. 138, 154–164 (2017)

    Article  Google Scholar 

  13. Mardle, S., Pascoe, S.: An overview of genetic algorithms for the solution of optimisation problems. Comput. Higher Educ. Econ. Rev. 13(1), 16–20 (1999)

    Google Scholar 

  14. Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)

    Article  Google Scholar 

  15. Ammu, P.K., Sivakumar, K.C., Rejimoan, R.: Biogeography-based optimizationa survey. Int. J. Electron. Comput. Sci. Eng. 2(1), 154–160 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. COMSATS Institute of Information Technology, Islamabad, 44000, Pakistan

    Hina Iftikhar, Salma Asif, Rubab Maroof, Khadija Ambreen, Hasan Nasir Khan & Nadeem Javaid

Authors
  1. Hina Iftikhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salma Asif
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rubab Maroof
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Khadija Ambreen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hasan Nasir Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nadeem Javaid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nadeem Javaid .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Rissho University, Tokyo, Japan

    Tomoya Enokido

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Iftikhar, H., Asif, S., Maroof, R., Ambreen, K., Khan, H.N., Javaid, N. (2018). Biogeography Based Optimization for Home Energy Management in Smart Grid. In: Barolli, L., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-65521-5_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-65521-5_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65520-8

  • Online ISBN: 978-3-319-65521-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation