Skip to main content

Demand Side Management Using Bacterial Foraging and Crow Search Algorithm Optimization Techniques

  • Conference paper
  • First Online:
  • 1276 Accesses

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 8))

Abstract

Energy is the most valuable resource in every day life. However, energy demand is going high day by day. The high consumption of energy causes series of energy crisis. This problem can be handled with many optimization techniques by integrating demand side management with traditional grid. The main purpose of demand side management is to reduce the peak load and smart grid targets reduce the electric cost and load management by shifting the load from on peak hours to off peak hours. In this work, I adopt the Bacterial Foraging Algorithm (BFA) and Crow Search Algorithm (CSA). Simulation results show that our propose techniques reduce the total cost and peak average ratio by scheduling the load for 24 h. Results show that BFA is perform better than CSA and archived the objectives.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
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

Learn about institutional subscriptions

References

  1. Gul, M.S., Patidar, S.: Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy Build. 87, 155–165 (2015)

    Article  Google Scholar 

  2. Palensky, P., Dietrich, D.: Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans. Ind. Inf. 7(3), 381–388 (2011)

    Article  Google Scholar 

  3. Rabiee, A., Sadeghi, M., Aghaeic, J., Heidari, A.: Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties. Renew. Sustain. Energy Rev. 57, 721–739 (2016)

    Article  Google Scholar 

  4. Chiu, W.-Y., Sun, H., Poor, H.V.: Energy imbalance management using a robust pricing scheme. IEEE Trans. Smart Grid 4(2), 896–904 (2013)

    Article  Google Scholar 

  5. Chavali, P., Yang, P., Nehorai, A.: A distributed algorithm of appliance scheduling for home energy management system. IEEE Trans. Smart Grid 5(1), 282–290 (2014)

    Article  Google Scholar 

  6. 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), p. 15. IEEE (2012)

    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. 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 

  9. 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 

  10. 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 

  11. 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)

    Google Scholar 

  12. Ozturk, Y., Senthilkumar, D., Kumar, S., Lee, G.: An intelligent home energy management system to improve demand response. IEEE Trans. Smart Grid 4(2), 694–701 (2012)

    Article  Google Scholar 

  13. Samadi, P., Mohsenian-Rad, H., Wong, V.W.S., Schober, R.: Tackling the load uncertainty challenges for energy consumption scheduling in smart grid. IEEE Trans. Smart Grid 4(2), 1007–1016 (2013)

    Article  Google Scholar 

  14. Joe-Wong, C., Sen, S., Ha, S., Chiang, M.: Optimized day-ahead pricing for smart grids with device-specific scheduling flexibility. IEEE J. Sel. Areas. Commun. 30(6), 1075–1085 (2012)

    Article  Google Scholar 

  15. Aghaei, J., Alizadeh, M.I.: Demand response in smart electricity grids equipped with renewable energy sources: a review. Renew. Sustain. Energy Rev. 18, 64–72 (2013)

    Article  Google Scholar 

  16. Pina, A., Silva, C., Ferrao, P.: The impact of demand side management strategies in the penetration of renewable electricity. Energy 41(1), 128–137 (2012)

    Article  Google Scholar 

  17. Alonso, M., Amaris, H., Alvarez-Ortega, C.: Integration of renewable energy sources in smart grids by means of evolutionary optimization algorithms. Expert Syst. Appl. 39(5), 5513–5522 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nadeem Javaid .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Tabssam, A., Pervaz, K., Saba, A., Abdeen, Z.u., Farooqi, M., Javaid, N. (2018). Demand Side Management Using Bacterial Foraging and Crow Search Algorithm Optimization Techniques. In: Barolli, L., Woungang, I., Hussain, O. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-65636-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-65636-6_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65635-9

  • Online ISBN: 978-3-319-65636-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics