Pigeon Inspired Optimization and Bacterial Foraging Optimization for Home Energy Management

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 12)


In this paper, we are dealing with Home Energy Management System (HEMS) using Bacterial Foraging Optimization (BFO) and Pigeon Inspired Optimization (PIO) techniques in a single home. Performance of Both techniques is evaluated through simulations in term of reduction in electricity cost, Peak to Average Ratio (PAR) by scheduling smart appliances. We have used Critical Peak Pricing (CPP) as a pricing signal and we have gained electricity cost reduction upto 40%.


Smart grid Home energy management Pigeon inspired optimization Bacterial foraging optimization. 


  1. 1.
    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
  2. 2.
    Duan, H., Qiao, P.: Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int. J. Intelli. Comput. Cybern. 7(1), 24–37 (2014). CrossRefMathSciNetGoogle Scholar
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Yu, M., Hong, S.H.: Supply demand balancing for power management in smart grid: a Stackelberg game approach. Appl. Energy 164, 702–710 (2016). ISSN 0306–2619CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    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
  8. 8.
    Srinivasan, D., Rajgarhia, S., Radhakrishnan, B.M., Sharma, A., Khincha, H.P.: Game-theory based dynamic pricing strategies for demand side management in smart grids. Energy 126, 132–143 (2017). ISSN 0360–5442CrossRefGoogle Scholar
  9. 9.
    Mahmood, D., Javaid, N., Alrajeh, N., Khan, Z.A., Qasim, U., Ahmed, I., Ilahi, M.: Realistic scheduling mechanism for smart homes. Energies 9(3), 202 (2016)CrossRefGoogle Scholar
  10. 10.
    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: Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, pp. 1–5. IEEE (2012)Google Scholar
  11. 11.
    Ahmed, M.S., Mohamed, A., Khatib, T., Shareef, H., Homod, R.Z., Ali, J.A.: Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm. Energy Build. 1(138), 215–27 (2017)CrossRefGoogle Scholar
  12. 12.
    Rottondi, C., Barbato, A., Chen, L., Verticale, G.: Enabling privacy in a distributed game-theoretical scheduling system for domestic appliances. IEEE Trans. on Smart Grid 8(3), 1220–1230 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

Personalised recommendations