User Comfort Oriented Residential Power Scheduling in Smart Homes

  • Awais Manzoor
  • Fahim Ahmed
  • Malik Ali Judge
  • Adnan Ahmed
  • Mirza Amaad Ul Haq Tahir
  • Zahoor Ali Khan
  • Umar Qasim
  • Nadeem JavaidEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 612)


Smart grid is an emerging technology which is considered as an ultimate solution to meet the increasing power demand challenges. Modern communication technologies has enabled the successful implementation of smart grid, which aims at provision of demand side management mechanisms, such as demand response. In this paper, we propose residential load scheduling model for demand side management. It is assumed that electric prices are announced on day-ahead basis. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem, and an optimal schedule is achieved by solving the minimization problem. Simulation results validate that teacher learning based optimization performs better as compared to genetic algorithm, showing comparable results with linear programming with less computational efforts. TLBO is able to obtain the desired trade-off between consumer electric bill and user discomfort.


  1. 1.
    Wu, Z., Tazvinga, H., Xia, X.: Demand side management of photovoltaic-battery hybrid system. Appl. Energy 148, 294–304 (2015)CrossRefGoogle Scholar
  2. 2.
    Ogunjuyigbe, A., Ayodele, T., Oladimeji, O.: Management of loads in residential buildings installed with PV system under intermittent solar irradiation using mixed integer linear programming. Energy Build. 130, 253–271 (2016)CrossRefGoogle Scholar
  3. 3.
    El-Baz, W., Tzscheutschler, P.: Short-term smart learning electrical load prediction algorithm for home energy management systems. Appl. Energy 147, 10–19 (2015)CrossRefGoogle Scholar
  4. 4.
    Soares, A., Gomes, A., Antunes, C.H., Oliveira, C.: A customized evolutionary algorithm for multi-objective management of residential energy resources. IEEE Trans. Ind. Inform. 13(2), 492–501 (2016)Google Scholar
  5. 5.
    Rastegar, M., Fotuhi-Firuzabad, M., Zareipour, H.: Home energy management incorporating operational priority of appliances. Int. J. Electr. Power Energy Syst. 74, 286–292 (2016)CrossRefGoogle Scholar
  6. 6.
    Yi, P., Dong, X., Iwayemi, A., Zhou, C., Li, S.: Real-time opportunistic scheduling for residential demand response. IEEE Trans. Smart Grid 4(1), 227–234 (2013)CrossRefGoogle Scholar
  7. 7.
    Muralitharan, K., Sakthivel, R., Shi, Y.: Multiobjective optimization technique for demand side management with load balancing approach in smart grid. Neurocomputing 177, 110–119 (2016)CrossRefGoogle Scholar
  8. 8.
    Alham, M., Elshahed, M., Ibrahim, D.K., El Zahab, E.E.D.A.: A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management. Renew. Energy 96, 800–811 (2016)CrossRefGoogle Scholar
  9. 9.
    Vardakas, J.S., Zorba, N., Verikoukis, C.V.: Performance evaluation of power demand scheduling scenarios in a smart grid environment. Appl. Energy 142, 164–178 (2015)CrossRefGoogle Scholar
  10. 10.
    Vardakas, J.S., Zorba, N., Verikoukis, C.V.: Power demand control scenarios for smart grid applications with finite number of appliances. Appl. Energy 162, 83–98 (2016)CrossRefGoogle Scholar
  11. 11.
    Bharathi, C., Rekha, D., Vijayakumar, V.: Genetic algorithm based demand side management for smart grid. Wirel. Pers. Commun. 93(2), 481–502 (2017)CrossRefGoogle Scholar
  12. 12.
    Gupta, A., Singh, B.P., Kumar, R.: Optimal provision for enhanced consumer satisfaction and energy savings by an intelligent household energy management system. In: 2016 IEEE 6th International Conference on Power Systems (ICPS), pp. 1–6. IEEE (2016)Google Scholar
  13. 13.
    Ogunjuyigbe, A., Ayodele, T., Akinola, O.: User satisfaction-induced demand side load management in residential buildings with user budget constraint. Appl. Energy 187, 352–366 (2017)CrossRefGoogle Scholar
  14. 14.
    Erdinc, O., Paterakis, N.G., Mendes, T.D., Bakirtzis, A.G., Catalão, J.P.: Smart household operation considering bi-directional EV and ESS utilization by real-time pricing-based DR. IEEE Trans. Smart Grid 6(3), 1281–1291 (2015)CrossRefGoogle Scholar
  15. 15.
    Bradac, Z., Kaczmarczyk, V., Fiedler, P.: Optimal scheduling of domestic appliances via MILP. Energies 8(1), 217–232 (2014)CrossRefGoogle Scholar
  16. 16.
    Zhang, D., Shah, N., Papageorgiou, L.G.: Efficient energy consumption and operation management in a smart building with microgrid. Energy Convers. Manag. 74, 209–222 (2013)CrossRefGoogle Scholar
  17. 17.
    De Angelis, F., Boaro, M., Fuselli, D., Squartini, S., Piazza, F., Wei, Q.: Optimal home energy management under dynamic electrical and thermal constraints. IEEE Trans. Ind. Inform. 9(3), 1518–1527 (2013)CrossRefGoogle Scholar
  18. 18.
    Agnetis, A., de Pascale, G., Detti, P., Vicino, A.: Load scheduling for household energy consumption optimization. IEEE Trans. Smart Grid 4(4), 2364–2373 (2013)CrossRefGoogle Scholar
  19. 19.
    Yu, M., Hong, S.H.: A real-time demand-response algorithm for smart grids: a stackelberg game approach. IEEE Trans. Smart Grid 7(2), 879–888 (2016)Google Scholar
  20. 20.
    Zhang, D., Evangelisti, S., Lettieri, P., Papageorgiou, L.G.: Economic and environmental scheduling of smart homes with microgrid: der operation and electrical tasks. Energy Conv. Manag. 110, 113–124 (2016)CrossRefGoogle Scholar
  21. 21.
    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: Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, pp. 1–5. IEEE (2012)Google Scholar
  22. 22.
    Dupont, B., Tant, J., Belmans, R.: Automated residential demand response based on dynamic pricing. In: 2012 3rd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe), pp. 1–7. IEEE (2012)Google Scholar
  23. 23.
    Sou, K.C., Weimer, J., Sandberg, H., Johansson, K.H.: Scheduling smart home appliances using mixed integer linear programming. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), pp. 5144–5149. IEEE (2011)Google Scholar
  24. 24.
    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
  25. 25.
    Fadel, E., Gungor, V.C., Nassef, L., Akkari, N., Malik, M.A., Almasri, S., Akyildiz, I.F.: A survey on wireless sensor networks for smart grid. Comput. Commun. 71, 22–33 (2015)CrossRefGoogle Scholar
  26. 26.
    Depuru, S.S.S.R., Wang, L., Devabhaktuni, V.: Smart meters for power grid: challenges, issues, advantages and status. Renew. Sustain. Energy Rev. 15(6), 2736–2742 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Awais Manzoor
    • 1
  • Fahim Ahmed
    • 1
  • Malik Ali Judge
    • 1
  • Adnan Ahmed
    • 1
  • Mirza Amaad Ul Haq Tahir
    • 1
  • Zahoor Ali Khan
    • 2
  • Umar Qasim
    • 3
  • Nadeem Javaid
    • 1
    Email author
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.Computer Information Science, Higher Colleges of TechnologyFujairahUnited Arab Emirates
  3. 3.Cameron LibraryUniversity of AlbertaEdmontonCanada

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