Load Scheduling in Home Energy Management System Using Meta-Heuristic Techniques and Critical Peak Pricing Tariff

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 13)


In this modern world, the demand of energy rises exponentially, that makes it a valuable resource. New techniques and methods are being developed to solve the problem of energy crisis in residential areas. The strategy to handle this problem is by integrating the demand side management (DSM) with smart grid (SG). DSM enables the consumer to schedule their load profile effectively in order to reduce electricity cost and power peak creation, referred as peak-to-average ratio (PAR). This paper evaluates the performance of home energy management system (HEMS) using meta-heuristic techniques; harmony search algorithm (HSA) and flower pollination algorithm (FPA). In this regard, a single home is considered with smart appliances classified as automatically operated appliances (AOAs) and manually operated appliances (MOAs). Moreover, critical peak pricing (CPP) is used as a price signal. In this paper, emphasis is placed on the cost minimization and load scheduling by shifting the load between off-peak and on-peak hours, while considering the user comfort. Simulation results shows that the performance of FPA is better in terms of cost and PAR reduction, whereas there exists a trade-offs between electricity cost and user comfort level.


Demand response Optimization Smart grid User comfort Load scheduling Demand side management 


  1. 1.
    Gelazanskas, L., Gamage, K.A.A.: Demand side management in smart grid: a review and proposals for future direction. Sustain. Cities Soc. 11, 22–30 (2014)CrossRefGoogle Scholar
  2. 2.
    Siano, P.: Demand response and smart grids a survey. Renew. Sustain. Energy Rev. 30, 461–478 (2014)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Cherukuri, S.H.C., Saravanan, B.: A novel energy management algorithm for reduction of main grid dependence in future smart grids using electric springs. Sustain. Energy Technol. Assess. 21, 1–12 (2017)Google Scholar
  6. 6.
    Soares, J., et al.: A stochastic model for energy resources management considering demand response in smart grids. Electr. Power Syst. Res. 143, 599–610 (2017). 73CrossRefGoogle Scholar
  7. 7.
    Shi, W., et al.: Real-time energy management in microgrids. IEEE Trans. Smart Grid 8(1), 228–238 (2017). K. Elissa, Title of paper if known, unpublishedCrossRefGoogle Scholar
  8. 8.
    Yorozu, Y., Hirano, M., Oka, K., Tagawa, Y.: Electron spectroscopy studies on magneto-optical media and plastic substrate interface. IEEE Transl. J. Magn. Jpn. 2, 740–741 (1987). (Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982)CrossRefGoogle Scholar
  9. 9.
    Yu, C.-N., Mirowski, P., Ho, T.K.: A sparse coding approach to household electricity demand forecasting in smart grids. IEEE Trans. Smart Grid 8(2), 738–748 (2017)Google Scholar
  10. 10.
    Hazra, J., Das, K., Seetharam, D.P.: Smart grid congestion management through demand response. In: 2012 IEEE Third International Conference on Smart Grid Communications (Smart Grid Comm). IEEE (2012)Google Scholar
  11. 11.
    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
  12. 12.
    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
  13. 13.
    Zhu, Z., et al.: An integer linear programming based optimization for home demand-side management in smart grid. In: Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES. IEEE (2012)Google Scholar
  14. 14.
    Gem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  15. 15.
    Yang, X.-S.: Flower pollination algorithm for global optimization. In: Durand-Lose, J., Jonoska, N. (eds.) Unconventional Computation and Natural Computation. LNCS, vol. 7445, pp. 240–249. Springer, Berlin (2012)Google Scholar
  16. 16.
    Yang, X.S.: Harmony search as a metaheuristic algorithm. In: Music-Inspired Harmony Search Algorithm, pp. 1–14. Springer, Berlin (2009)Google Scholar

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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

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