Multi-objective power scheduling problem in smart homes using grey wolf optimiser

  • Sharif Naser MakhadmehEmail author
  • Ahamad Tajudin Khader
  • Mohammed Azmi Al-Betar
  • Syibrah Naim
Original Research


In this paper, the multi-objective grey wolf optimiser is utilised for the power scheduling problem (PSP). The grey wolf optimiser (GWO) is a recent swarm-based optimisation algorithm tailored for various optimisation problems. PSP is addressed by scheduling home appliances to a certain time horizon to minimise the electricity bill and peak-to-average ratio (PAR) and increase the comfort level of users. The multi-objective function is formalised and utilised in GWO to obtain an optimal schedule. Seven consumption profiles and seven real-time electricity prices with various characteristics are considered to evaluate the proposed multi-objective GWO. The performance of the proposed algorithm is tested against three factors, namely, electricity bill, PAR and user comfort level. The obtained schedule shows that all evaluation factors are optimally timetabled. For a comparative evaluation, the proposed method is firstly compared with the genetic algorithm. The proposed method exhibits and yield better performance than GA under the same consumption profiles. Secondly, the proposed method is compared with 19 state-of-the-art methods by using the recommended consumption profiles of these methods and their evaluation criteria. The proposed method nearly outperforms the compared methods in terms of minimisation of electricity bill and PAR. User comfort level is a criterion proposed in this study and has not been considered previously. It exerts a significant impact on the final schedule.


Smart grid Optimisation Grey wolf optimiser Power scheduling problem Multi-objective optimisation 



  1. Ali W, Rehman AU, Junaid M, Shaukat SAA, Faiz Z, Javaid N (2017) Home energy management using social spider and bacterial foraging algorithm. In: International conference on network-based information systems. Springer, pp 245–256Google Scholar
  2. Aman S, Simmhan Y, Prasanna VK (2013) Energy management systems: state of the art and emerging trends. IEEE Commun Mag 51(1):114–119CrossRefGoogle Scholar
  3. Asif S, Ambreen K, Iftikhar H, Khan H. N, Maroof R, Javaid N (2017) Energy management in residential area using genetic and strawberry algorithm. In: International conference on network-based information systems. Springer, pp 165–176Google Scholar
  4. Azevedo JPC (2013) Effective scheduling of energy consumption in smart grids. FEUP—DissertaçãoGoogle Scholar
  5. Batool S, Khalid A, Amjad Z, Arshad H, Aimal S, Farooqi M, Javaid N (2017) Pigeon inspired optimization and bacterial foraging optimization for home energy management. In: International conference on broadband and wireless computing, communication and applications. Springer, pp 14–24Google Scholar
  6. BCU Commission (2017) Bcuc issues report to bc government on residential inclining block rates.
  7. CE Company (2017) Real-time pricing for residential customers. Retrieved from
  8. Faiz Z, Bilal T, Awais M, Gull S, Javaid N, et al., (2017) Demand side management using chicken swarm optimization, in: International conference on intelligent networking and collaborative systems. Springer, pp 155–165Google Scholar
  9. Gao C, Chen S, Li X, Huang J, Zhang Z (2017) A physarum-inspired optimization algorithm for load-shedding problem. Appl Soft Comput 61:239–255CrossRefGoogle Scholar
  10. Gelazanskas L, Gamage KA (2014) Demand side management in smart grid: a review and proposals for future direction. Sustain Cities Soc 11:22–30CrossRefGoogle Scholar
  11. Gellings CW (1985) The concept of demand-side management for electric utilities. Proc IEEE 73(10):1468–1470CrossRefGoogle Scholar
  12. Hafeez G, Javaid N, Iqbal S, Khan FA (2018) Optimal residential load scheduling under utility and rooftop photovoltaic units. Energies 11(3):611CrossRefGoogle Scholar
  13. Iftikhar H, Asif S, Maroof R, Ambreen K, Khan H. N, Javaid N (2017) Biogeography based optimization for home energy management in smart grid. In: International conference on network-based information systems. Springer, pp 177–190Google Scholar
  14. Javaid N, Javaid S, Abdul W, Ahmed I, Almogren A, Alamri A, Niaz IA (2017) A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3):319CrossRefGoogle Scholar
  15. Khan MA, Javaid N, Mahmood A, Khan ZA, Alrajeh N (2015) A generic demand-side management model for smart grid. Int J Energy Res 39(7):954–964CrossRefGoogle Scholar
  16. Logenthiran T, Srinivasan D, Shun TZ (2012) Demand side management in smart grid using heuristic optimization. IEEE Trans Smart Grid 3(3):1244–1252CrossRefGoogle Scholar
  17. Ma K, Yao T, Yang J, Guan X (2016) Residential power scheduling for demand response in smart grid. Int J Electr Power Energy Syst 78:320–325CrossRefGoogle Scholar
  18. Mechanics C (2014) Heating and air conditioning blog. Retrieved from
  19. Miao H, Huang X, Chen G (2012) A genetic evolutionary task scheduling method for energy efficiency in smart homes. Int Rev Electri Eng (IREE) 7(5):5897–5904Google Scholar
  20. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  21. Mohsenian-Rad A-H, Wong VW, Jatskevich J, Schober R (2010) Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid. In: Innovative smart grid technologies (ISGT), 2010. IEEE, pp 1–6Google Scholar
  22. Mohsenian-Rad A-H, Wong VW, Jatskevich J, Schober R, Leon-Garcia A (2010) Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans Smart Grid 1(3):320–331CrossRefGoogle Scholar
  23. Muralitharan K, Sakthivel R, Shi Y (2016) Multiobjective optimization technique for demand side management with load balancing approach in smart grid. Neurocomputing 177:110–119CrossRefGoogle Scholar
  24. Peretto L (2010) The role of measurements in the smart grid era. IEEE Instrum Meas Mag 13(3):22–25CrossRefGoogle Scholar
  25. Pipattanasomporn M, Kuzlu M, Rahman S (2012) An algorithm for intelligent home energy management and demand response analysis. IEEE Trans Smart Grid 3(4):2166–2173CrossRefGoogle Scholar
  26. Qayyum F, Naeem M, Khwaja AS, Anpalagan A, Guan L, Venkatesh B (2015) Appliance scheduling optimization in smart home networks. IEEE Access 3:2176–2190CrossRefGoogle Scholar
  27. Rahim MH, Khalid A, Javaid N, Alhussein M, Aurangzeb K, Khan ZA (2018) Energy efficient smart buildings using coordination among appliances generating large data. IEEE AccessGoogle Scholar
  28. Rahim S, Javaid N, Ahmad A, Khan SA, Khan ZA, Alrajeh N, Qasim U (2016) Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build 129:452–470CrossRefGoogle Scholar
  29. Rasheed MB, Javaid N, Ahmad A, Khan ZA, Qasim U, Alrajeh N (2015) An efficient power scheduling scheme for residential load management in smart homes. Appl Sci 5(4):1134–1163CrossRefGoogle Scholar
  30. Rehman A. U, Aslam S, Abideen Z. U, Zahra A, Ali W, Junaid M, Javaid J (2017) Efficient energy management system using firefly and harmony search algorithm. In: International conference on broadband and wireless computing, communication and applications. Springer, pp 37–49Google Scholar
  31. Sales G (2017) Home appliances power consumption. Retrieved from
  32. Samadi P, Mohsenian-Rad H, Schober R, Wong VW (2012) Advanced demand side management for the future smart grid using mechanism design. IEEE Trans Smart Grid 3(3):1170–1180CrossRefGoogle Scholar
  33. Space B (2017) Home dehumidifiers. Retrieved from
  34. Yearbook GES (2017) Statistical analysis for global energy. Retrieved from
  35. Yi W, Dong W (2015) Modeling and simulation of discharging characteristics of external melt ice-on coil storage system. Int J Smart Home 9(2):179–192CrossRefGoogle Scholar
  36. Zhao Z, Lee WC, Shin Y, Song K-B (2013) An optimal power scheduling method for demand response in home energy management system. IEEE Trans Smart Grid 4(3):1391–1400CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Sharif Naser Makhadmeh
    • 1
    Email author
  • Ahamad Tajudin Khader
    • 1
  • Mohammed Azmi Al-Betar
    • 2
  • Syibrah Naim
    • 1
  1. 1.School of Computer SciencesUniversiti Sains MalaysiaGelugorMalaysia
  2. 2.Department of Information Technology, Al-Huson University CollegeAl-Balqa Applied UniversityIrbidJordan

Personalised recommendations