Demand Side Management Using Meta-Heuristic Techniques and ToU in Smart Grid

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 7)


In this paper, we perform performance evaluation of home energy management system (HEMS) for demand side management (DSM) in smart grid. In this work, smart home is equipped with HEMS, smart meter, and smart appliances for two-way communication between utility and consumer. HEMS performs scheduling of smart appliances based on meta-heuristic techniques to balance load for whole day to avoid peak creation in any hour. Smart meter performs electricity cost calculation for consumed energy based on time of use (ToU) pricing signal provided by utility. Our focus is to efficiently handle user demand, reduction in peak-to-average ratio (PAR) and electricity cost minimization. The implemented meta-heuristic techniques in this work are: Enhanced differential evolution (EDE), harmony search algorithm (HSA), bacterial foraging algorithm (BFA), and genetic algorithm (GA). The simulation results show the performance of HEMS based on optimization techniques using ToU.


  1. 1.
    Mhanna, S., Verbi, G., Chapman, A.C.: A faithful distributed mechanism for demand response aggregation. IEEE Trans. Smart Grid 7(3), 1743–1753 (2016)CrossRefGoogle Scholar
  2. 2.
    Ye, F., Qian, Y., Hu, R.Q.: A real-time information based demand-side management system in smart grid. IEEE Trans. Parallel Distrib. Syst. 27(2), 329–339 (2016)CrossRefGoogle Scholar
  3. 3.
    Nguyen, H.K., Song, J.B., Han, Z.: Distributed demand side management with energy storage in smart grid. IEEE Trans. Parallel Distrib. Syst. 26(12), 3346–3357 (2015)CrossRefGoogle Scholar
  4. 4.
    Basit, A., Sardar Sidhu, G.A., Mahmood, A., Gao, F.: Efficient and autonomous energy management techniques for the future smart homes. IEEE Trans. Smart Grid (2015)Google Scholar
  5. 5.
    Li, C., Xinghuo, Y., Wenwu, Y., Chen, G., Wang, J.: Efficient computation for sparse load shifting in demand side management. IEEE Trans. Smart Grid 8(1), 250–261 (2017)CrossRefGoogle Scholar
  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.
    Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M.: Optimal residential load management in smart grids: a decentralized framework. IEEE Transactions on Smart Grid 7(4), 1836–1845 (2016)CrossRefGoogle Scholar
  8. 8.
    Ma, J., Chen, H.H., Song, L., Li, Y.: Residential load scheduling in smart grid: a cost efficiency perspective. IEEE Trans. Smart Grid 7(2), 771–784 (2016)Google Scholar
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    Khalid, A., Javaid, N., Mateen, A., Khalid, B., Khan, Z.A., Qasim, U.: Demand side management using hybrid bacterial foraging and genetic algorithm optimization techniques. In: 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), pp. 494–502. IEEE (2016)Google Scholar
  14. 14.
    Zafar, A., Shah, S., Khalid, R., Mehboob Hussain, S., Rahim, H., Javaid, N.: A meta-heuristic home energy management system. In: 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 244–250. IEEE (2017)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.National University of Sciences and TechnologyIslamabadPakistan
  3. 3.Roots Millennium SchoolIslamabadPakistan

Personalised recommendations