Earth Worm Optimization for Home Energy Management System in Smart Grid

  • Mudabbir Ali
  • Samia Abid
  • Asad Ghafar
  • Nasir Ayub
  • Hafsa Arshad
  • Sajawal Khan
  • Nadeem Javaid
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 12)

Abstract

Smart grid based energy management system promises an efficient consumption of electricity. For optimized energy consumption, a bio inspired meta-heuristic algorithms: Earth Worm Algorithm (EWA) and Bacterial Foraging Algorithm (BFA) are presented in this paper. In this work, we targeted residential area. Our aim is to reduce the electricity cost and Peak to Average Ratio (PAR). We have used the Critical Peak Pricing (CPP) scheme for calculating electricity bill. Through simulations, we have compared the results of EWA, BFA and unscheduled appliances. After implementing our techniques, EWA based energy management controller gives more efficient results than BFA in term of cost, while for PAR reduction, BFA performs better than EWA.

Keywords

Smart grid Meta heuristic techniques EWA algorithm BFA algorithm Critical peak point Home Energy Management System PAR 

References

  1. 1.
    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
  2. 2.
    Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A., Niaz, I.A.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Wang, G.G., Deb, S., Coelho, L.D.S.: Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Int. J. Bio-Inspired Comput. (2015)Google Scholar
  5. 5.
    Khalid, A., Javaid, N., Mateen, A., Khan, Z.A., Qasim, U.: Demand side management using hybrid bacterial foraging and genetic algorithm optimization techniques. IEEE Transactions on Smart Grid, Conference Paper, Japan, 6–8 July 2016Google Scholar
  6. 6.
    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
  7. 7.
    Liu, Y., Yuen, C., Yu, R., Zhang, Y., Xie, S.: Queuing-based energy consumption management for heterogeneous residential demands in smart grid. IEEE Trans. Smart Grid 7(3), 1650–1659 (2016)CrossRefGoogle Scholar
  8. 8.
    Huang, Q., Li, X., Zhao, J., Wu, D., Li, X.-Y.: Social networking reduces peak power consumption in smart grid. IEEE Trans. Smart Grid 6(3), 1403–1413 (2015)CrossRefGoogle Scholar
  9. 9.
    Yaagoubi, N., Mouftah, H.T.: User-aware game theoretic approach for demand management. IEEE Trans. Smart Grid 6(2), 716–725 (2014)CrossRefGoogle Scholar
  10. 10.
    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. In: Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, pp. 1–5. IEEE (2012)Google Scholar
  11. 11.
    Ma, K., Yao, T., Yang, J., Guan, X.: Residential power scheduling for demand response in smart grid. Int. J. Electric. Power Energy Syst. 78, 320–325 (2016)CrossRefGoogle Scholar
  12. 12.
    Yoon, J.H., Baldick, R., Novoselac, A.: Dynamic demand response controller based on real-time retail price for residential buildings. IEEE Trans. Smart Grid 5(1), 121–129 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Mudabbir Ali
    • 1
  • Samia Abid
    • 1
  • Asad Ghafar
    • 1
  • Nasir Ayub
    • 1
  • Hafsa Arshad
    • 1
  • Sajawal Khan
    • 1
  • Nadeem Javaid
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

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