GreyWolf Optimization Technique for HEMS Using Day Ahead Pricing Scheme

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


With the emergence of Smart Grid, users adopt different scheduling methods to reduce their energy consumption with different objectives. In this paper, we implemented a Meta heuristic techniques named as Grey Wolf Optimizer (GWO) and Bacterial Foraging Algorithm (BFA) for Home Energy Management System (HEMS). We implemented these techniques due to the inspiration from the working behavior of GW and B. In GW, wolves are categorized into four forms namely Alpha, Beta, Delta and Omega. There are three major steps in GW for getting their prey which are first searching the prey then encircling the prey and finally attacking the prey. We proposed a generic architecture of Demand Side management (DSM) that incorporates residential area domain. We use Day Ahead Pricing (DAP) to calculate the cost of energy consumption. Results are compared with the BFA. Results show that GWO has better performance as compared to the other Meta heuristic technique BFA. GWO effectively reduces the cost of energy consumption as compared to BFA. Therefore implementation of this technique is useful for both users and utility.


Home Energy Management System (HEMS) Bacterial Foraging Algorithm (BFA) Grey Wolf Optimizer (GWO) Demand Side Management (DSM) Binary Particle Swarm Optimization (BPSO) 
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  1. 1.
    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: 2012 IEEE PES in Innovative Smart Grid Technologies (ISGT), pp. 1–5 (2012)Google Scholar
  2. 2.
    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
  3. 3.
    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. on Smart Grid 4(3), 1391–1400 (2013)CrossRefGoogle Scholar
  4. 4.
    Javaid, N., Abdul, S., Ahmed, W., Almogren, I., 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.
    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 (2012)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Rasheed, M.B., Javaid, N., Awais, M., Khan, Z.A., Qasim, U., Alrajeh, N., Iqbal, Z., Javaid, Q.: Real time information based energy management using customer preferences and dynamic pricing in smart homes. Energies 9(7), 542 (2016)CrossRefGoogle Scholar
  8. 8.
    Ullah, I., Javaid, N., Khan, Z.A., Qasim, U., Mehmood, S.A.: An incentive-based optimal energy consumption scheduling algorithm for residential user. Procedia Comput. Sci. 52, 851–857 (2015)CrossRefGoogle Scholar
  9. 9.
    Rasheed, M.B., Javaid, N., Ahmad, A., Khan, Z.A., Qasim, U., Alrajeh, N.: An efficient power scheduling scheme for residential load management in smart homes. Appl. Sci. 5, 1134–1163 (2015)CrossRefGoogle Scholar
  10. 10.
    Shirazi, E., Jadid, S.: Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS. Energy Build. 93, 40–49 (2015)CrossRefGoogle 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, 1836–1845 (2016)CrossRefGoogle Scholar
  12. 12.
    Ma, J., Chen, H.H., Song, L., Li, Y.: Residential load scheduling in smart grid: a cost efficiency perspective. IEEE Trans. Smart Grid 7, 771–784 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

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