Advertisement

An Energy Efficient Scheduling of a Smart Home Based on Optimization Techniques

  • Aqib Jamil
  • Nadeem Javaid
  • Muhammad Usman Khalid
  • Muhammad Nadeem Iqbal
  • Saad Rashid
  • Naveed Anwar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 773)

Abstract

After the introduction of smart grid in power system, two-way communication is now possible which helps in optimizing the energy consumption of consumers. To optimize the energy consumption on the consumer side, demand side management is used. In this paper, we focused on the optimization of smart home appliances with the help of optimization techniques. Cuckoo search algorithm, earthworm optimization and a hybrid technique cuckoo-earthworm optimization are used for scheduling the smart home appliances. Home appliances are classified into three groups and real-time pricing scheme is used. Techniques are evaluated and a performance comparison is performed. Results show that the proposed hybrid technique has decreased the electricity cost by 49% as compared to unscheduled cost and a trade-off exists between electricity cost and user comfort.

Keywords

Cuckoo search algorithm Earthworm optimization Smart grid Demand side management Heuristic techniques 

References

  1. 1.
    Evangelisti, S., Lettieri, P., Clift, R., Borello, D.: Distributed generation by energy from waste technology: a life cycle perspective. Process Safety Environ. Protect. 93, 161–172 (2015)CrossRefGoogle Scholar
  2. 2.
    Vardakas, J.S., Zorba, N., Verikoukis, C.V.: A survey on demand response programs in smart grids: pricing methods and optimization algorithms. IEEE Commun. Surv. Tutor. 17(1), 152–178 (2015)CrossRefGoogle Scholar
  3. 3.
    Yi, P., Dong, X., Iwayemi, A., Zhou, C., Li, S.: Real-time opportunistic scheduling for residential demand response. IEEE Trans. Smart Grid 4(1), 227–234 (2013)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Aslam, S., Iqbal, Z., Javaid, N., Khan, Z.A., Aurangzeb, K., Haider, S.I.: Towards efficient energy management of smart buildings exploiting heuristic optimization with real time and critical peak pricing schemes. Energies 10(12), 2065 (2017)CrossRefGoogle Scholar
  6. 6.
    Mahmood, A., Javaid, N., Khan, N.A., Razzaq, S.: An optimized approach for home appliances scheduling in smart grid. In: 19th International Multi-topic Conference (INMIC), Islamabad, pp. 1–5 (2016)Google Scholar
  7. 7.
    Motevasel, M., Seifi, A.R.: Expert energy management of a micro-grid considering wind energy uncertainty. Energy Convers. Manage. 83, 58–72 (2014)CrossRefGoogle Scholar
  8. 8.
    Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)CrossRefGoogle Scholar
  9. 9.
    Muralitharan, K., Sakthivel, R., Shi, Y.: Multiobjective optimization technique for demand side management with load balancing approach in smart grid. Neurocomputing 177, 110–119 (2016)CrossRefGoogle Scholar
  10. 10.
    Liu, Y., Yuen, C., Yu, R., Zhang, Y., Xie, S.: Queuing-based energy consumption management for heterogeneous residential demands in smart grid. IEEE Trans. Smart Grid 7(3), 1650–1659 (2016)CrossRefGoogle Scholar
  11. 11.
    Wang, C., Zhou, Y., Wu, J., Wang, J., Zhang, Y., Wang, D.: Robust-index method for household load scheduling considering uncertainties of customer behavior. IEEE Trans. Smart Grid 6(4), 1806–1818 (2015)CrossRefGoogle Scholar
  12. 12.
    Setlhaolo, D., Xia, X., Zhang, J.: Optimal scheduling of household appliances for demand response. Electr. Power Syst. Res. 116, 24–28 (2014)CrossRefGoogle Scholar
  13. 13.
    Melhem, F.Y., Grunder, O., Hammoudan, Z., Moubayed, N.: Optimization and energy management in smart home considering photovoltaic, wind, and battery storage system with integration of electric vehicles. Canad. J. Electr. Comput. Eng. 40(2), 128–138 (2017)Google Scholar
  14. 14.
    Yang, X.S., Deb, S.: Cuckoo search via levy flights. In: World Congress on Nature and Biologically Inspired Computing (NaBIC), Coimbatore, pp. 210–214 (2009)Google Scholar
  15. 15.
    Wang, G.G., Deb, S., Coelho, L.D.S.: Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Int. J. Bio Inspired Comput. 7, 1–23 (2015)CrossRefGoogle Scholar
  16. 16.
    Xhafa, F., Gonzalez, J.A., Dahal, K.P., Abraham, A.: A GA (TS) hybrid algorithm for scheduling in computational grids. In: International Conference on Hybrid Artificial Intelligence Systems (HAIS), Berlin, pp. 285–292 (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Aqib Jamil
    • 1
  • Nadeem Javaid
    • 1
  • Muhammad Usman Khalid
    • 1
  • Muhammad Nadeem Iqbal
    • 2
  • Saad Rashid
    • 2
  • Naveed Anwar
    • 3
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.COMSATS Institute of Information TechnologyWah CanttPakistan
  3. 3.University of WahWah CanttPakistan

Personalised recommendations