Home Energy Managment System Using Meta-heuristic Techniques

  • Tamour Bilal
  • Muhammad Awais
  • Muhammad Junaid
  • Zafar Faiz
  • Mujeeb Ur Rehman
  • Nadeem Javaid
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 7)

Abstract

In this paper, we have evaluated the performance of Home Energy Management (HEM) using two meta-heuristic techniques: Chicken Swarm Optimization (CSO) and Bacterial Foraging Algorithm (BFA). We have classified the appliances in two catagories: fixed and shiftable/elastic appliances. Time of Use (ToU) pricing scheme is used for the calculation of electricity bill. The main objective of this paper is the minimization of electricity cost, reduction of Peak to Average (PAR) and balancing of load between peak and off peak hours while taking User Comfort (UC) under consideration. These algrithms performs efficiently in achieving multiple objectives. However results and simulations shows that CSO performs better than BFA in terms of PAR reduction, while BFA performs better in reducing electrcity cost.

References

  1. 1.
    Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart grid using heuristic optimization techniques. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)CrossRefGoogle Scholar
  2. 2.
    Peter, P., Dietmar, D.D.: Demand side management: demand response, intelligent energy 408 systems, and smart loads. IEEE Trans. Ind. Inform. 7(3), 381–388 (2011)CrossRefGoogle Scholar
  3. 3.
    Shahidehpour, M., Yamin, H., Li, Z., Shun, T.Z.: Market overview in electric power systems. Market operations in electric power systems: forecasting, scheduling, and risk management. IEEE Trans. Smart Grid, 1–20 (2002)Google Scholar
  4. 4.
    Javaid, N., Javed, S., et al.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. IEEE Trans. Evol. Comput. (2008)Google Scholar
  5. 5.
    Khalid, A., Javaid, N., Mateen, A., Khalid, B.: Demand side management using hybrid bacterial foraging and genetic algorithm optimization techniques. In: (CISIS) (2016)Google Scholar
  6. 6.
    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. IEEE (2011). 978-1-4577-2159-5/12/31.00Google Scholar
  7. 7.
    Roh, H.T., Lee, J.W.: Residential demand response scheduling with multiclass appliances in the smart grid. IEEE Trans. Smart Grid (2015)Google Scholar
  8. 8.
    Basit, A., Sidhu, G.A.S., Mahmood, A., Gao, F.: Efficient and autonomous energy management techniques for the future smart homes. IEEE Trans. Smart Grid (2015)Google Scholar
  9. 9.
    Hamed, S.G., Kazemi, A.: Multi-objective cost-load optimization for demand side management of a residential area in smart grids. Sustain. Cities Soc. http://dx.doi.org/10.1016/j.scs.2017.03.018
  10. 10.
    Moghaddam, A.A., Monsef, H., Kian, A.R.: Optimal smart home energy management considering energy saving and a comfortable lifestyle. IEEE Trans. Smart Grid 6(1), 324–332 (2014)CrossRefGoogle Scholar
  11. 11.
    Ma, J., Chen, H., Song, L., Li, Y.: Residential load scheduling in smart grid: a cost efficiency perspective. IEEE Trans. Smart Grid 7(2), 771–784 (2016)Google Scholar
  12. 12.
    Rahim, S., Javaid, N., Ahmad, A., Khan, S.A., Khan, Z.A., Alrajeh, N., Qasim, U.: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build. 129, 452–470 (2016)CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Zhao, Z., Lee, W.C., Shin, Y., Song, K.B.: An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 4(3), 1391–1400 (2013)CrossRefGoogle Scholar
  15. 15.
    Passino, K.M.: Biomimicry of BFA for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Tamour Bilal
    • 1
  • Muhammad Awais
    • 1
  • Muhammad Junaid
    • 1
  • Zafar Faiz
    • 1
  • Mujeeb Ur Rehman
    • 1
  • Nadeem Javaid
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

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