A Model for Optimizing Cost of Energy and Dissatisfaction for Household Consumers in Smart Home

  • Nilima R. DasEmail author
  • Satyananda C. Rai
  • Ajit Nayak
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)


A lot of research is being done to implement demand-side energy management for the consumers in order to reduce their peak hour power consumption which may result in a stable grid system and reduced daily electricity bill. Reduction in peak hour usage reduces the pressure on the power grid leading to an efficient and robust grid system which ensures the availability of electricity even during critical hours. However, in the process of minimizing the peak hour consumption, the consumer may experience some dissatisfaction. In this work, an effective demand-side energy management technique has been designed which not only finds an optimal time schedule for the user’s appliances to lower the peak hour consumption and daily electricity bill but also tries to minimize the user’s dissatisfaction that occurs as a result of cost minimization when an appliance is not operated at the user’s preferred time.


DSM Smart grid Time-varying prices 


  1. 1.
    Mohsenian-Rad H, Leon-Garcia A (2010) Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans Smart Grid 1(2):120–133CrossRefGoogle Scholar
  2. 2.
    Zhu Z, Tang J, Lambotharan S, Chin WH, Fan Z (2011) An integer linear programming and game theory based optimization for demand-side management in smart grid. In: IEEE international workshop on smart grid communications and networksGoogle Scholar
  3. 3.
    Zhu Z, Tang J, Lambotharan S, Chin WH, Fan Z (2012) An integer linear programming based optimization for home demand-side management in smart grid. IEEE PES Innov Smart Grid Technol (ISGT) (2012)Google Scholar
  4. 4.
    Nguyen HK,Songl JB, Hanl Z (2012) Demand side management to reduce peak-to-average ratio using game theory in smart grid. In: Proceedings of IEEE INFOCOM (2012)Google Scholar
  5. 5.
    Liu Y, Yuen C, Huang S, Hassan NU, Wang X, Xie S (2014) Peak-to-average ratio constrained demand-side management with consumer’s preference in residential smart grid. IEEE PES Innov Smart Grid Technol 8(6):1084–1097 CrossRefGoogle Scholar
  6. 6.
    Soliman HM, Leon-Garcia A (2014) Game-theoretic demand-side management with storage devices for the future smart grid. IEEE Trans Smart Grid 5:1475–1485CrossRefGoogle Scholar
  7. 7.
    Fadlullah ZMd, Quan DM, Kato N, Stojmenovic I (2014) GTES: an optimized game-theoretic demand-side management scheme for smart grid. IEEE Syst J 8:588–597CrossRefGoogle Scholar
  8. 8.
    Ye F, Qian Y, Hu RQ (2016) A real-time information based demand-side management system in smart grid. IEEE Trans Parallel Distrib Syst 27(2):329–339CrossRefGoogle Scholar
  9. 9.
    Labeeuw W, Deconinck G (2013) Residential electrical load model based on mixture model clustering and markov models. IEEE Trans Industr Inf 9(3):1561–1568CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Nilima R. Das
    • 1
    Email author
  • Satyananda C. Rai
    • 2
  • Ajit Nayak
    • 1
  1. 1.Faculty of Engineering & TechnologySiksha ‘O’ Anusandhan Deemed to Be UniversityBhubaneswarIndia
  2. 2.Department of ITSilicon Institute of TechnologyBhubaneswarIndia

Personalised recommendations