Towards Efficient Scheduling of Smart Appliances for Energy Management by Candidate Solution Updation Algorithm in Smart Grid

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 926)


The energy demand is increasing day by day due to the huge amount of residential appliance’s energy consumption, which creates more shortage of electricity. Industrial and commercial areas are also consuming large amount of energy, but residential energy demand is more flexible as compared to other two. Nowadays, many of the techniques are presented for scheduling of Smart Appliances to reduce the peak to average ratio (PAR) and consumer delay time. However, they didn’t consider the total electricity cost and consumer waiting time. In this paper, we reduce the cost through load shifting techniques. In order to consider above objective, we employed some feature of the Jaya algorithm (JA) on a bat algorithm (BA) to develop a candidate solution updation algorithm (CSUA). Simulation was conducted to compare the result of existing BA and Jaya for single smart home with 15 smart appliances. We used time of use (ToU) and critical peak price (CPP). The result depicts that successful achievement of load shifting from higher price time slot to lower price time slot, which basically bring out the reduction in electricity bills.


BA JA CSUA Metaheuristic techniques Appliances scheduling Home Energy Management Demand Side Management 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. Industr. Inf. 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)CrossRefGoogle Scholar
  5. 5.
    Zhao, J., Guo, Z., Su, Z., Zhao, Z., Xiao, X., Liu, F.: An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed. Appl. Energy 162, 808–826 (2016)CrossRefGoogle Scholar
  6. 6.
    Heng, J., Wang, J., Xiao, L., Lu, H.: Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting. Appl. Energy 208, 845–866 (2017)CrossRefGoogle Scholar
  7. 7.
    Medina, J., Muller, N., Roytelman, I.: Demand response and distribution grid operations: opportunities and challenges. IEEE Trans. Smart Grid 1(2), 193–198 (2010)CrossRefGoogle Scholar
  8. 8.
    Roh, H.-T., Lee, J.-W.: Residential demand response scheduling with multiclass appliances in the smart grid. IEEE Trans. Smart Grid 7(1), 94–104 (2016)CrossRefGoogle Scholar
  9. 9.
    Baharlouei, Z., Hashemi, M., Narimani, H., Mohsenian-Rad, H.: Achieving optimality and fairness in autonomous demand response: benchmarks and billing mechanisms. IEEE Trans. Smart Grid 4(2), 968–975 (2013)CrossRefGoogle Scholar
  10. 10.
    Agnetis, A., de Pascale, G., Detti, P., Vicino, A.: Load scheduling for household energy consumption optimization. IEEE Trans. Smart Grid 4, 2364–2373 (2013)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Marzband, M., Ghazimirsaeid, S.S., Uppal, H., Fernando, T.: A real-time evaluation of energy management systems for smart hybrid home Microgrids. Elec. Power Syst. Res. 143, 624–633 (2017)CrossRefGoogle Scholar
  13. 13.
    Zhang, D., Evangelisti, S., Lettieri, P., Papageorgiou, L.G.: Economic and environmental scheduling of smart homes with microgrid: DER operation and electrical tasks. Energy Convers. Manag. 110, 113–124 (2016)CrossRefGoogle Scholar
  14. 14.
    Zhang, Y., Zhang, T., Wang, R., Liu, Y., Guo, B.: Optimal operation of a smart residential microgrid based on model predictive control by considering uncertainties and storage impacts. Sol. Energy 122, 1052–1065 (2015)CrossRefGoogle Scholar
  15. 15.
    Liu, G., Xu, Y., Tomsovic, K.: Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization. IEEE Trans. Smart Grid 7, 227–237 (2016)CrossRefGoogle Scholar
  16. 16.
    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: Proceedings of the 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA, 16–20 January 2012, pp. 1-5 (2012)Google Scholar
  17. 17.
    Mohamed, F.A., Koivo, H.N.: Online management genetic algorithms of microgrid for residential application. Energy Convers. Manag. 64, 562–568 (2012)CrossRefGoogle Scholar
  18. 18.
    Mahmood, A., Javaid, N., Khan, N.A., Razzaq, S.: An optimized approach for home appliances scheduling in smart grid. In: Proceedings of the 2016 19th International Multi-Topic Conference (INMIC), Islamabad, Pakistan, 5–6 December 2016, pp. 1–5 (2016)Google Scholar
  19. 19.
    Mary, G.A., Rajarajeswari, R.: Smart grid cost optimization using genetic algorithm. Int. J. Res. Eng. Technol. 3, 282–287 (2014)Google Scholar
  20. 20.
    Bharathi, C., Rekha, D., Vijayakumar, V.: Genetic algorithm based demand side management for smart grid. Wirel. Pers. Commun. 93, 481–502 (2017)CrossRefGoogle Scholar
  21. 21.
    Huang, Y., Wang, L., Guo, W., Kang, Q., Wu, Q.: Chance constrained optimization in a home energy management system. IEEE Trans. Smart Grid (2016). Scholar
  22. 22.
    Setlhaolo, D., Xia, X., Zhang, J.: Optimal scheduling of household appliances for demand response. Electr. Power Syst. Res. 116, 24–28 (2014)CrossRefGoogle Scholar
  23. 23.
    Samadi, P., Wong, V.W., Schober, R.: Load scheduling and power trading in systems with high penetration of renewable energy resources. IEEE Trans. Smart Grid 7, 1802–1812 (2016)CrossRefGoogle Scholar
  24. 24.
    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 users. Procedia Comput. Sci. 52, 851–857 (2015)CrossRefGoogle Scholar
  25. 25.
    Shirazi, E., Jadid, S.: Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS. Energy Build. 93, 40–49 (2015)CrossRefGoogle Scholar
  26. 26.
    Erdinc, O.: Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households. Appl. Energy 126, 142–150 (2014)CrossRefGoogle Scholar
  27. 27.
    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
  28. 28.
    Cakmak, R., Altas, I.H.: Scheduling of domestic shiftable loads via Cuckoo Search optimization algorithm. In: 2016 4th International Istanbul Smart Grid Congress and Fair (ICSG), pp. 1–4. IEEE, April 2016Google Scholar
  29. 29.
    Li, C., Yu, X., Yu, W., 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
  30. 30.
    Bharathi, C., Rekha, D., Vijayakumar, V.: Genetic algorithm based demand side management for smart grid. Wireless Pers. Commun. 93(2), 481–502 (2017)CrossRefGoogle Scholar
  31. 31.
    Khalid, A., Javaid, N., Guizani, M., Alhussein, M., Aurangzeb, K., Ilahi, M.: Towards dynamic coordination among home appliances using multi-objective energy optimization for demand side management in smart buildings. IEEE Access 6, 19509–19529 (2018). Scholar
  32. 32.
    Marzband, M., Yousefnejad, E., Sumper, A., Domínguez-García, 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
  33. 33.
    Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65–74. Springer, Heidelberg (2010)Google Scholar
  34. 34.
    Radosavljevi’c, J., Klimenta, D., Jevti’c, M., Arsi’c, N.: Optimal power flow using a hybrid optimization algorithm of particle swarm optimization and gravitational search algorithm. Electr. Power Compon. Syst. 43, 1958–1970 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.COMSATS University IslamabadIslamabadPakistan
  2. 2.University of Lahore (Islamabad Campus)IslamabadPakistan
  3. 3.Cameron LibraryUniversity of AlbertaEdmontonCanada
  4. 4.Central South UniversityChangshaChina

Personalised recommendations