Efficient Scheduling of Smart Home Appliances for Energy Management by Cost and PAR Optimization Algorithm in Smart Grid

  • Sahibzada Muhammad Shuja
  • Nadeem JavaidEmail author
  • Sajjad Khan
  • Hina Akmal
  • Murtaza Hanif
  • Qazi Fazalullah
  • Zain Ahmad Khan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)


As the energy demand for consumption is comparably higher than the generation of energy, which produce the shortage of energy. Many new schemes are being developed to fulfill the energy consumer demand. In this paper, we proposed our meta-heuristic algorithm Runner Updation Optimization Algorithm (RUOA) to schedule the consumption pattern of residential appliances. We compared the results of our scheme with other meta-heuristic algorithm Strawberry Algorithm (SBA) and Firefly Algorithm (FA). Critical Peak Price (CPP) and Real Time Price (RTP) are the two electricity pricing scheme that we used in this paper for calculation of electricity cost. The main objective of this paper is to minimize the electricity cost and Peak to Average Ratio (PAR). However, consumer comfort is not satisfied.


SA FA RUOA Meta-heuristic techniques Home Energy Management System Smart Grid 


  1. 1.
    Gungor, V.C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., et al.: Smart grid technologies: communication technologies and standards. IEEE Trans. Ind. Inform. 7(4), 529–539 (2011)CrossRefGoogle Scholar
  2. 2.
    Esther, B.P., Kumar, K.S.: A survey on residential demand side management architecture, approaches, optimization models and methods. Renew. Sustain. Energy Rev. 59, 342–351 (2016)CrossRefGoogle Scholar
  3. 3.
    Wu, Z., Tazvinga, H., Xia, X.: Demand side management of photovoltaic-battery hybrid system. Appl. Energy 148, 294–304 (2015)CrossRefGoogle Scholar
  4. 4.
    Strbac, G.: Demand side management: benefits and challenges. Energy Policy 36, 4419–4426 (2008). VancouverCrossRefGoogle Scholar
  5. 5.
    Zhu, Z., Jie, T., 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
  6. 6.
    Agnetis, A., de Pascale, G., Detti, P., Vicino, A.: Load scheduling for household energy consumption optimization. Smart Grid IEEE Trans. 4, 2364–2373 (2013)CrossRefGoogle Scholar
  7. 7.
    Colmenar-Santos, A., de Lober, L.N.T., Borge-Diez, D., Castro-Gil, M.: Solutions to reduce energy consumption in the management of large buildings. Energy Build. 56, 66–77 (2013)CrossRefGoogle Scholar
  8. 8.
    Marzband, M., Ghazimirsaeid, S.S., Uppal, H., Fernando, T.: A real-time evaluation of energy management systems for smart hybrid home Microgrids. Electr. Power Syst. Res. 143, 624–633 (2017)CrossRefGoogle Scholar
  9. 9.
    Moon, S., Lee, J.-W.: Multi-residential demand response scheduling with multi-class appliances in smart grid. IEEE Trans. Smart Grid 9(4), 2518–2528 (2018)CrossRefGoogle Scholar
  10. 10.
    Bahrami, S., Wong, V.W.S., Huang, J.: An online learning algorithm for demand response in smart grid. IEEE Trans. Smart Grid 9(5), 4712–4725 (2018)CrossRefGoogle Scholar
  11. 11.
    Bera, S., Misra, S., Chatterjee, D.: C2C: community-based cooperative energy consumption in smart grid. IEEE Trans. Smart Grid 9(5), 4262–4269 (2018)CrossRefGoogle Scholar
  12. 12.
    Behrens, D., Schoormann, T., Knackstedt, R.: Developing an algorithm to consider mutliple demand response objectives. Eng. Technol. Appl. Sci. Res. 8(1), 2621–2626 (2018)Google Scholar
  13. 13.
    Javaid, N., Ullah, I., Akbar, M., Iqbal, Z., Khan, F.A., Alrajeh, N., Alabed, M.S.: An intelligent load management system with renewable energy integration for smart homes. IEEE Access 5, 13587–13600 (2017)CrossRefGoogle Scholar
  14. 14.
    Aslam, S., Iqbal, Z., Javaid, N., Khan, Z.A., Aurangzeb, K., Haider, S.I.: Towards efficient energy management of smart buildings exploiting heuristic optimization with real time and critical peak pricing schemes. Energies 10(12), 2065 (2017)CrossRefGoogle Scholar
  15. 15.
    Khan, I.U., Ma, X., Taylor, C.J., Javaid, N., Gamage, K.: Heuristic algorithm based dynamic scheduling model of home appliances in smart grid (2018)Google Scholar
  16. 16.
    do Prado, J.C., Qiao, W.: A stochastic decision-making model for an electricity retailer with intermittent renewable energy and short-term demand response. IEEE Trans. Smart Grid (2018)Google Scholar
  17. 17.
    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). 129, 452–470CrossRefGoogle Scholar
  18. 18.
    Marzband, M., Yousefnejad, E., Sumper, A., Domnguez-Garca, J.L.: Real time experimental implementation of optimum energy management system in standalone microgrid by using multi-layer ant colony optimization. Int. J. Electr. Power Energy Syst. 75, 265–274 (2016)CrossRefGoogle Scholar
  19. 19.
    Merrikh-Bayat, F.: A Numerical Optimization Algorithm Inspired by the Strawberry Plant. arXiv 2014, arXiv:1407.7399 (2014)
  20. 20.
    Yang, X.-S.: Firefly Algorithms for multimodal optimization. In: International Symposium on Stochastic Algorithms. Springer, Berlin (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sahibzada Muhammad Shuja
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Sajjad Khan
    • 1
  • Hina Akmal
    • 2
  • Murtaza Hanif
    • 3
  • Qazi Fazalullah
    • 1
  • Zain Ahmad Khan
    • 4
  1. 1.COMSATS University IslamabadIslamabadPakistan
  2. 2.University of Lahore (Islamabad Campus)IslamabadPakistan
  3. 3.Central South UniversityChangshaChina
  4. 4.COMSATS University IslamabadAbbottabadPakistan

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