Advertisement

Energy Optimization Techniques for Demand-Side Management in Smart Homes

  • Syeda Aimal
  • Komal Parveez
  • Arje Saba
  • Sadia Batool
  • Hafsa Arshad
  • Nadeem Javaid
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 8)

Abstract

Due to increase in population, demand of energy is increasing. To make energy demand efficient and reliable many techniques are integrated in home areas. We implemented Grey Wolf Optimization (GWO) using Time of Use (TOU) pricing scheme, to achieve an optimal balanced load and to minimize user comfort, then we compared the results of GWO and Bacterial Foraging Algorithm (BFA). The scheduling mechanism is capable of achieving the optimal operational time. Simulation results are presented to demonstrate the effectiveness of optimization techniques.

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., et al.: 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. Autom. Auton. Syst. 8(6), 176–178 (2016)Google 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.
    Kai, M., 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.
    Shi, W., et al.: Real-time energy management in microgrids. IEEE Trans. Smart Grid 8(1), 228–238 (2017)CrossRefGoogle Scholar
  8. 8.
    Shi, W., et al.: Distributed optimal energy management in microgrids. IEEE Trans. Smart Grid 6(3), 1137–1146 (2015)CrossRefGoogle Scholar
  9. 9.
    Basit, A., et al.: Efficient and autonomous energy management techniques for the future smart homes. IEEE Trans. Smart Grid (2015)Google Scholar
  10. 10.
    Yu, C.-N., et al.: A sparse coding approach to household electricity demand forecasting in smart grids. IEEE Trans. Smart Grid 8(2), 738–748 (2017)Google Scholar
  11. 11.
    Bollen, M.H., et al.: Power quality concerns in implementing smart distribution-grid applications. IEEE Trans. Smart Grid 8(1), 391–399 (2017)CrossRefGoogle Scholar
  12. 12.
    Chen, J., et al.: A game-theoretic framework for resilient and distributed generation control of renewable energies in microgrids. IEEE Trans. Smart Grid 8(1), 285–295 (2017)CrossRefGoogle Scholar
  13. 13.
    Mirjalili, S., et al.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  14. 14.
    Li, F., et al.: Smart transmission grid: vision and framework. IEEE Trans. Smart Grid 1(2), 168–177 (2010)CrossRefGoogle Scholar
  15. 15.
    Zhangt, P., et al.: Next generation monitoring, analysis and control for the future smart control centre. IEEE Trans. Smart Grid 1(2), 186–192 (2010)CrossRefGoogle Scholar
  16. 16.
    Ayesha, Z., et al.: A meta-heuristic home energy management systemGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Syeda Aimal
    • 1
  • Komal Parveez
    • 1
  • Arje Saba
    • 1
  • Sadia Batool
    • 1
  • Hafsa Arshad
    • 1
  • Nadeem Javaid
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

Personalised recommendations