A New Meta-heuristic Optimization Algorithm Inspired from Strawberry Plant for Demand Side Management in Smart Grid

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 8)


In recent years, different Demand Side Management (DSM) techniques have been proposed to involve users in decision making process of Smart Grid (SG). Power consumption pattern of shiftable home appliances is schedule to achieve desired benefits of high User Comfort (UC) and low energy consumption. In this paper, an Energy Management Controller (EMC) is designed by using two meta-heuristic algorithms: Strawberry Algorithm (SBA) and Enhanced Differential Evolution (EDE). The main objectives are electricity bill minimization, reduction in Peak to Average Ratio (PAR) and maximization of UC. However, there always exist a trade-off between cost minimization and UC maximization. Simulation results verify that, SBA perform better then EDE in terms of cost reduction while EDE perform far better than SBA in terms of UC maximization.


Demand side management Smart grid Meta-heuristic techniques Strawberry algorithm Enhanced differential evolution Real time pricing scheme 


  1. 1.
    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
  2. 2.
    Zhao, Z., et al.: An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 4(3), 1391–1400 (2013)CrossRefGoogle Scholar
  3. 3.
    Zhu, Z., et al.: An integer linear programming based optimization for home demand-side management in smart grid. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT). IEEE (2012)Google Scholar
  4. 4.
    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
  5. 5.
    Merrikh-Bayat, F.: A numerical optimization algorithm inspired by the strawberry plant. arXiv preprint arXiv:1407.7399 (2014)
  6. 6.
    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
  7. 7.
    Ma, J., et al.: Residential load scheduling in smart grid: a cost efficiency perspective. IEEE Trans. Smart Grid 7(2), 771–784 (2016)Google Scholar
  8. 8.
    Khan, M.A., et al.: A generic demand side management model for smart grid. Int. J. Energy Res. 39(7), 954–964 (2015)CrossRefGoogle Scholar
  9. 9.
    Jalali, M.M., Kazemi, A.: Demand side management in a smart grid with multiple electricity suppliers. Energy 81, 766–776 (2015)CrossRefGoogle Scholar
  10. 10.
    Ma, K., et al.: Residential power scheduling for demand response in smart grid. Int. J. Electr. Power Energy Syst. 78, 320–325 (2016)CrossRefGoogle Scholar
  11. 11.
    Mahmood, D., et al.: Realistic scheduling mechanism for smart homes. Energies 9(3), 202 (2016)CrossRefGoogle Scholar
  12. 12.
    Rasheed, M.B., et al.: An efficient power scheduling scheme for residential load management in smart homes. Appl. Sci. 5(4), 1134–1163 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Zafar, A., et al.: A meta-heuristic home energy management system. In: 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE (2017)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

Personalised recommendations