Towards Efficient Energy Management in a Smart Home Using Updated Population

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


Energy management using demand side management (DSM) techniques plays a key role in smart grid (SG) domain. Smart meters and energy management controllers are the important components of the SG. A lot of research has been done on energy management system (EMS) for scheduling the appliances. The aim of current research is to organize the power of the residential units in an optimized way. Intelligent energy optimization techniques play a vital role in reduction of the electricity bill via scheduling home appliances. Through appliance’s scheduling, consumer gets feasible cost in response to the consumed electricity. The utility provides the facility for consumers to schedule their appliances for the reduction of electricity bill and peak demand reduction. The utility company is allowed to remotely shut down their appliances in emergency conditions through direct load control programs. A lot of research has been done on energy management system (EMS) for scheduling the appliances. In this work, an efficient EMS is proposed for controlling the load in residential units. Meta-heuristic algorithms have been used for the optimization of the user energy consumption schedules in an efficient way. Our proposed scheme is used to minimize the user waiting time. User waiting time is inversely proportional to the total cost and peak to average ratio (PAR). Simulation result shows the minimum user waiting time, however, the total cost is compromised due to the high demand of the load. In the end, our proposed scheme will be validated through simulations.


Smart grid Home energy management system Real time price 


  1. 1.
    Bradac, Z., Kaczmarczyk, V., Fiedler, P.: Optimal scheduling of domestic appliances via MILP. Energies 8(1), 217–232 (2014)CrossRefGoogle Scholar
  2. 2.
    Mary, G.A., Rajarajeswari, R.: Smart grid cost optimization using genetic algorithm. Int. J. Res. Eng. Technol. 3(07), 282–287 (2014)Google Scholar
  3. 3.
    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
  4. 4.
    Setlhaolo, D., Xia, X., Zhang, J.: Optimal scheduling of household appliances for demand response. Electr. Power Syst. Res. 116, 24–28 (2014)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    Ahmed, M.S., Mohamed, A., Khatib, T., Shareef, H., Homod, R.Z., Ali, J.A.: Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm. Eng. Build. 138, 215–227 (2017)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    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
  11. 11.
    Aslam, S., Javaid, N., Khalid, A.: An efficient home energy management scheme using cuckoo search. In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing 3PGCIC 2017: Advances on P2P, Parallel, Grid, Cloud and Internet Computing, pp 167–178 (2017)Google Scholar
  12. 12.
    Khan, A., Javaid, N., Khan, M.I.: Time and device based priority induced comfort management in smart home within the consumer budget limitationGoogle Scholar
  13. 13.
    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
  14. 14.
    Mohan, N., Sivaraj, R., Pyira, R.D.: A comprehensive reviews of bat algorithm and its application to various optimization problems. Asian J. Res. Soc. Sci. Humanti. 6, 676–690 (2016)Google Scholar
  15. 15.
    Al Hasib, A. Nikitin, N., & Natvig, L. (2015) Cost comfort balancing in a smart Residential building with bidirectional energy trading. Sustainable interent and ICT for sustainability ( SustainIT), 2015 (pp, 1-6)Google Scholar
  16. 16.
    Chen, Y., Lin, R.P., Wang, C., Groot, M.D., Zeng, Z.: Consumer operational comfort level based power demand management in the smart grid. In: Proceedings of the 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Berlin, Germany, 14–17 October 2012, pp. 1–6 (2012)Google Scholar
  17. 17.
    Hassan, N.U., Khalid, Y.I., Yuen, C., Huang, S., Pasha, M.A., Wood, K.L., Kerk, S.G.: Framework for minimum user participation rate determination to achieve specific demand response management objectives in the residential smart grids. Int. J. Electri. Power Energy Syst. 74, 91–103 (2016)CrossRefGoogle Scholar
  18. 18.
    Kuzlu, M.: Score-based intelligent home energy management (HEM) algorithm for demand response applications and impact of HEM operation on customer comfort. IET Gener. Transm. Distrib. 9, 627–635 (2015)CrossRefGoogle Scholar
  19. 19.
    Aslam, S., Javaid, N., Khan, F.A., Alamri, A., Almogren, A., Abdul, W.: Towards efficient energy management and power trading in a residential area via integrating a grid-connected microgridGoogle Scholar
  20. 20.
    Abhinav, S., Schizas, I.D., Ferrese, F., Davoudi, A.: Optimization-based AC microgrid synchronization. IEEE Trans. Ind. Inform. 13, 2339–2349 (2017)CrossRefGoogle Scholar
  21. 21.
    Dehghanpour, K., Nehrir, H.: Real-time multiobjective microgrid power management using distributed optimization in an agent-based bargaining framework. IEEE Trans. Smart Grid 2017. 2708686Google Scholar
  22. 22.
    Liu, J., Chen, H., Zhang, W., Yurkovich, B., Rizzoni, G.: Energy management problems under uncertainties for grid-connected microgrids: a chance constrained programming approach. IEEE Trans. Smart Grid 8, 2585–2596 (2017)CrossRefGoogle Scholar
  23. 23.
    Luo, Z., Wu, Z., Li, Z., Cai, H., Li, B., Gu, W.: A two-stage optimization and control for CCHP microgrid energy management. Appl. Therm. Eng. 125, 513–522 (2017)CrossRefGoogle Scholar
  24. 24.
    Moga, D., Petreus, D., Muresan, V., Stroia, N., Cosovici, G.: Optimal generation scheduling in Islanded microgrids. IFAC-PapersOnLine 49, 135–139 (2016)CrossRefGoogle Scholar
  25. 25.
    Chen, J., Chen, J.: Stability analysis and parameters optimization of islanded microgrid with both ideal and dynamic constant power loads. IEEE Trans. Ind. Electron. 65, 3263–3274 (2017)CrossRefGoogle Scholar
  26. 26.
    Su, W., Wang, J., Roh, J.: Stochastic energy scheduling in microgrids with intermittent renewable energy resources. IEEE Trans. Smart Grid 5, 1876–1883 (2014)CrossRefGoogle Scholar
  27. 27.
    Shuai, H., Fang, J., Ai, X., Tang, Y., Wen, J., He, H.: Stochastic optimization of economic dispatch for microgrid based on approximate dynamic programming. IEEE Trans. Smart Grid 2018 (2018). 2798039Google Scholar
  28. 28.
    Razmara, M., Bharati, G.R., Shahbakhti, M., Paudyal, S., Robinett III., R.D.: Bilevel optimization framework for smart building-to-grid systems. IEEE Trans. Smart Grid 9, 582–593 (2016)Google Scholar
  29. 29.
    Esfahani, M.M., Hariri, A., Mohammed, O.A.: A multiagent-based game-theoretic and optimization approach for market operation of multi-microgrid systems. IEEE Trans. Ind. Inform. (2018)Google Scholar
  30. 30.
    Li, D., Sun, H., Chiu, W.-Y., Vincent Poor, H.: Multiobjective Optimization for Demand Side Management in Smart GridGoogle Scholar
  31. 31.
    Macedo, M.N.Q., Galo, J.J.M., De Almeida, L.A.L., Lima, A.D.C.: Demand side management using artificial neural networks in a Smart Grid environment. Renew. Sustain. Energy Rev. 41, 128–133 (2015)CrossRefGoogle Scholar
  32. 32.
    Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)CrossRefGoogle Scholar
  33. 33.
    Elham, S., Shahram, J.: Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS. Energy Build. 93, 40–49 (2015)CrossRefGoogle Scholar
  34. 34.
    Adika, C.O., Wang, L.: Smart charging and appliance scheduling approaches to demand side management. Int. J. Elect. Power Energy Syst. 57, 232240 (2014)CrossRefGoogle Scholar
  35. 35.
    Jovanovic, R., Bousselham, A., Bayram, I.S.: Residential demand response scheduling with consideration of consumer preferences. Appl. Sci. 6(1), 1–16 (2016)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Shirazi, E., Zakariazadeh, A., Jadid, S.: Optimal joint scheduling of electrical and thermal appliances in a smart home environment. Energy Convers. Manage. 106, 181–193 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Comsats University IslamabadIslamabadPakistan
  2. 2.Computer Information ScienceHigher Colleges of TechnologyFujairahUAE
  3. 3.NCBA&EMultanPakistan

Personalised recommendations