Demand Side Management Scheduling of Appliances Using Meta Heuristic Algorithms

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 25)


Energy is the most needed commodity of the current era. Recently, the demand of energy is far higher than the available energy. By the incorporation of Demand Side Management (DSM) with the Smart Grid (SG) results in the solution of this problem. Different techniques are utilized in SG to minimize the electricity cost and manage load in industrial, residential areas, and commercial to minimize the Peak to Average Ratio (PAR) and decrease in the waiting time of appliances which leads to maximize user comfort. In this article, we propose six Meta heuristic techniques in Home Energy Management System (HEMS); Firefly Algorithm (FA), Bacterial foraging Algorithm (BFA), Earth Worm Optimization Algorithm (EWA), Genetic Algorithm (GA), Hybrid of Genetic and Bacterial foraging (HBG), and Harmony Search Algorithm (HSA). We have achieved minimization in PAR, electric cost, upturn user comfort through appliances scheduling using the optimization techniques.


Demand Side Management (DSM) Home Energy Management System (HEMS) Electric Cost Bacterial Foraging Algorithm (BFA) User Comfort 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  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 Innovative Smart Grid Technologies (ISGT), pp. 1–5. IEEE (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. Smart Grid 4(3), 1391–1400 (2013)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.
    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
  6. 6.
    Ullah, I., Javaid, N., Khan, Z.A., Qasim, U., Khan, Z.A., Mehmood, S.A.: An incentive-based optimal energy consumption scheduling algorithm for residential user. Procedia Comput. Sci. 52, 851–857 (2015)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Ahmad, A., Alrajeh, N., Javaid, N., Khan, Z.A., Qasim, U., Rasheed, M.B.: An efficient power scheduling scheme for residential load management in smart homes (2015)Google Scholar
  9. 9.
    Razzaq, S., Zafar, R., Khan, N.A., Butt, A.R., Mahmood, A.: A novel prosumer-based energy sharing and management (PESM) approach for cooperative demand side management (DSM) in smart grid. Appl. Sci. 6, 275 (2016)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Wang, G.-G., Deb, S., Coelho, L.D.S.: Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Int. J. Bio-Inspired Comput. 33, 477–496 (2015)Google Scholar
  12. 12.
    Energy Information Administration, December 2015.
  13. 13.
    Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in SG using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)CrossRefGoogle Scholar
  14. 14.
    Palensky, P., Dietrich, D.: Demand side management: demand response intelligent energy systems and smart loads. IEEE Trans. Ind. Inform. 7(3), 381–388 (2011)CrossRefGoogle Scholar
  15. 15.
    Gellings, C.W., Chamberlin, J.H.: Demand-side management, pp. 1–5. EPRI, Palo Alto (1988)Google Scholar
  16. 16.
    Ayub, N., et al.: An efficient scheduling of power and appliances using metaheuristic optimization technique. In: International Conference on Intelligent Networking and Collaborative Systems. Springer, Cham (2017)Google Scholar
  17. 17.
    Ishaq, A., et al.: An efficient scheduling using meta heuristic algorithms for home demand-side management in smart grid. In: International Conference on Intelligent Networking and Collaborative Systems. Springer, Cham (2017)Google Scholar
  18. 18.
    Zahoor, S.: Cloud-fog-based smart grid model for efficient resource management. Sustainability (2071-1050) 10(6), 2079 (2018)CrossRefGoogle Scholar
  19. 19.
    Rasheed, M.B., et al.: Real time information based energy management using customer preferences and dynamic pricing in smart homes. Energies 9(7), 542 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.COMSATS UniversityIslamabadPakistan
  2. 2.Federal Urdu UniversityIslamabadPakistan

Personalised recommendations