Pigeon Inspired Optimization and Enhanced Differential Evolution Using Time of Use Tariff in Smart Grid

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


In this paper, a scheduler for Home Energy Management (HEM) is proposed using Pigeon Inspired Optimization (PIO) and Enhanced Differential Evolution (EDE). Performance of these two optimization algorithms is evaluated in this study. Performance is determined by the amount of energy consumed by the appliances in on-peak hours and off-peak hours. Time Of Use (TOU) tariff is used for bill calculation of the consumed energy. Evaluation is performed in terms of Peak to Average Ratio (PAR) and electricity cost. Simulation results show that PIO outperforms EDE in terms of cost, PAR reduction and waiting time.


Smart grid Home energy management Time of use tariff User comfort Pigeon inspired optimization Enhanced differential evolution 


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Rottondi, C., Barbato, A., Chen, L., Verticale, G.: Enabling privacy in a distributed game-theoretical scheduling system for domestic appliances. IEEE Trans. Smart Grid 8(3), 1220–1230 (2017)CrossRefGoogle Scholar
  3. 3.
    Alam, M.R., Reaz, M.B.I., Ali, M.A.M.: A review of smart homes past, present, and future. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(6), 1190–1203 (2012)CrossRefGoogle Scholar
  4. 4.
    Meng, F.-L., Zeng, X.-J.: An optimal real-time pricing algorithm for the smart grid: a bi-level programming approach. In: OASIcs-OpenAccess Series in Informatics, vol. 35. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2013)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Samadi, P., Wong, V.W.S.: 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
  7. 7.
    Liu, Y., Yuen, C., Rong, Y., 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
  8. 8.
    Erol-Kantarci, M., Mouftah, H.T.: Energy-efficient information and communication infrastructures in the smart grid: a survey on interactions and open issues. IEEE Commun. Surv. Tutorials 17(1), 179–197 (2015)CrossRefGoogle Scholar
  9. 9.
    Khan, M.A., Javaid, N., Mahmood, A., Khan, Z.A., Alrajeh, N.: A generic demand side management model for Smart Grid. Int. J. Energy Res. 39(7), 954–964 (2015)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Ma, J., Chen, H.H., Song, L., Li, Y.: Residential load scheduling in Smart Grid: a cost efficiency perspective. IEEE Trans. Smart Grid 7(2), 771–784 (2016)Google Scholar
  14. 14.
    Duan, H., Qiao, P.: Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int. J. Intell. Comput. Cybern. 7(1), 24–37 (2014)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Zhang, B., Duan, H.: Predator-prey pigeon-inspired optimization for UAV three-dimensional path planning. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds.) ICSI 2014. LNCS, vol. 8795, pp. 96–105. Springer, Cham (2014). doi: 10.1007/978-3-319-11897-0_12 Google Scholar
  16. 16.
    Zafar, A., Shah, S., Khalid, R., Hussain, S.M., Rahim, H., Javaid, N.: A meta-heuristic home energy management system, January 2017Google Scholar
  17. 17.
    Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M.: Optimal residential load management in smart grids: a decentralized framework. IEEE Trans. Smart Grid 7(4), 1836–1845 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

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