Energy Optimization in Smart Grid Using Grey Wolf Optimization Algorithm and Bacterial Foraging Algorithm

  • C. H. Anwar ul Hassan
  • Muhammad Sufyan Khan
  • Asad Ghafar
  • Syeda Aimal
  • Sikandar Asif
  • Nadeem Javaid
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 8)

Abstract

Nowadays, energy is the most valuable resource, new techniques and methods are discovered to fulfill the energy demand. These techniques and methods are very useful for Home Energy Management System (HEMS) in terms of electricity cost reduction, load balancing and power consumption. We evaluated the performance of HEMS using Grey Wolf Optimization (GWO) and Bacterial Foraging Algorithm (BFA) techniques inspired by the nature of grey wolf and bacterium respectively. For this purpose we categorize the home appliances into two classes on the bases of their power consumption pattern. Critical Peak Pricing (CPP) scheme is used to calculate the electricity bill. The load is balanced by scheduling the appliances in Peak Hours (PHs) and Off Peak Hours (OPHs) in order to reduce the cost and Peak to Average Ratio (PAR) and manage the power consumption.

References

  1. 1.
    Zhu, Z., et al.: An integer linear programming based optimization for home demand-side management in Smart Grid. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT). IEEE (2012)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Zhao, Z., et al.: An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 4(3), 1391–1400 (2013)CrossRefGoogle Scholar
  4. 4.
    Javaid, N., et al.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)CrossRefGoogle Scholar
  5. 5.
    Ma, K., et al.: Residential power scheduling for demand response in Smart Grid. Int. J. Electr. Power Energy Syst. 78, 320–325 (2016)CrossRefGoogle Scholar
  6. 6.
    Rahim, S., et al.: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build. 129, 452–470 (2016)CrossRefGoogle Scholar
  7. 7.
    Zhou, Y., et al.: Home energy management with PSO in Smart Grid. In: 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE). IEEE (2014)Google Scholar
  8. 8.
    Liu, Y., et al.: Queuing-based energy consumption management for heterogeneous residential demands in Smart Grid. IEEE Trans. Smart Grid 7(3), 1650–1659 (2016)CrossRefGoogle Scholar
  9. 9.
    Mahmood, D., et al.: Realistic scheduling mechanism for smart homes. Energies 9(3), 202 (2016)CrossRefGoogle Scholar
  10. 10.
    Ma, J., et al.: Residential load scheduling in Smart Grid: a cost efficiency perspective. IEEE Trans. Smart Grid 7(2), 771–784 (2016)Google Scholar
  11. 11.
    Ranjini, A., Zoraida, B.S.E.: Intelligent residential energy management in Smart Grid. Indian J. Sci. Technol. 9(45) (2016)Google Scholar
  12. 12.
    Allouhi, A., et al.: Energy consumption and efficiency in buildings: current status and future trends. J. Cleaner Prod. 109, 118–130 (2015)CrossRefGoogle Scholar
  13. 13.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • C. H. Anwar ul Hassan
    • 1
  • Muhammad Sufyan Khan
    • 1
  • Asad Ghafar
    • 1
  • Syeda Aimal
    • 1
  • Sikandar Asif
    • 1
  • Nadeem Javaid
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

Personalised recommendations