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

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

Abstract

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.

Keywords

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

Notes

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Copyright information

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

Authors and Affiliations

  • Sharif Naser Makhadmeh
    • 1
  • 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

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