A Metaheuristic Scheduling of Home Energy Management System

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 17)


Smart grid (SG) provides a prodigious opportunity to turn traditional energy infrastructure into a new era of reliability, sustainability and robustness. The outcome of new infrastructure contributes to technology improvements, environmental health, grid stability, energy saving programs and optimal economy as well. One of the most significant aspects of SG is home energy management system (HEMS). It encourages utilities to participate in demand side management programs to enhance efficiency of power generation system and residential consumers to execute demand response programs in reducing electricity cost. This paper presents HEMS on consumer side and formulates an optimization problem to reduce energy consumption, electricity payment, peak load demand, and maximize user comfort. For efficient scheduling of household appliances, we classify appliances into two types on the basis of their energy consumption pattern. In this paper, a meta-heuristic firefly algorithm is deployed to solve our optimization problem under real time pricing environment. Simulation results signify the proposed system in reducing electricity cost and alleviating peak to average ratio.


Smart grid Firefly algorithm Renewable energy sources Real time pricing signal Demand side management Demand response 


  1. 1.
    Tushar, W., Chai, B., Yuen, C., Smith, D.B., Wood, K.L., Yang, Z., Vincent Poor, H.: Three-party energy management with distributed energy resources in smart grid. IEEE Trans. Ind. Electron. 62(4), 2487–2498 (2015)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    Hakimi, S.M., Moghaddas-Tafreshi, S.M.: Optimal planning of a smart microgrid including demand response and intermittent renewable energy resources. IEEE Trans. Smart Grid 5(6), 2889–2900 (2014)CrossRefGoogle Scholar
  4. 4.
    Garcia, J.A.M., Mena, A.J.G.: Optimal distributed generation location and size using a modified teaching learning based optimization algorithm. Int. J. Electr. Power Energy Syst. 50, 65–75 (2013)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Shakeri, M., Shayestegan, M., Abunima, H., Reza, S.M.S., Akhtaruzzaman, M., Alamoud, A.R.M., Sopian, K., Amin, N.: An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid. Energy Build. 138, 154–164 (2017)CrossRefGoogle Scholar
  7. 7.
    Rajalingam, S., Malathi, V.: HEM algorithm based smart controller for home power management system. Energy Build. 131, 184–192 (2016)CrossRefGoogle Scholar
  8. 8.
    Barbato, A., Capone, A., Chen, L., Martignon, F., Paris, S.: A distributed demand-side management framework for the smart grid. Comput. Commun. 57, 13–24 (2015)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    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
  11. 11.
    Ahmad, A., Khan, A., Javaid, N., Hussain, H.M., Abdul, W., Almogren, A., Alamri, A., Niaz, I.A.: An optimized home energy management system with integrated renewable energy and storage resources. Energies 10(4), 549 (2017)CrossRefGoogle Scholar
  12. 12.
    Zhang, D., Shah, N., Papageorgiou, L.G.: Efficient energy consumption and operation management in a smart building with microgrid. Energy Convers. Manage. 74, 209–222 (2013)CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Yang, X.-S.: Firefly algorithms for multimodal optimization. In: International Symposium on Stochastic Algorithms, pp. 169–178. Springer, Heidelberg (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.National University of Sicence and TechnologyIslamabadPakistan
  3. 3.CIS, Higher Colleges of TechnologyFujairahUAE

Personalised recommendations