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Energy Management in Residential Area using Genetic and Strawberry Algorithm

  • Salma Asif
  • Khadija Ambreen
  • Hina Iftikhar
  • Hasan Nasir Khan
  • Rubab Maroof
  • Nadeem Javaid
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 7)

Abstract

In our work, we consider the problem of load management in residential area. We adopt Genetic Algorithm (GA) and Strawberry Algorithm (SBA) for load scheduling. These algorithms are used to manage residential load between shoulder, on-peak and off-peak hours. Time of Use (ToU) pricing scheme has been used for bill calculation. Simulation results show that GA based energy optimization controller perform good than SBA based energy optimization controller in term of Peak to Average Ratio (PAR), electricity bill reduction and waiting time.

References

  1. 1.
    Gelazanskas, L., Gamage, K.A.A.: Demand side management in smart grid: a review and proposals for future direction. Sustain. Cities Soc. 11, 22–30 (2014)CrossRefGoogle Scholar
  2. 2.
    Siano, P.: Demand response and smart grids a survey. Renew. Sustain. Energy Rev. 30, 461–478 (2014)CrossRefGoogle Scholar
  3. 3.
    Tsui, K.M., Chan, S.-C.: Demand response optimization for smart home scheduling under real-time pricing. IEEE Trans. Smart Grid 3(4), 1812–1821 (2012)CrossRefGoogle Scholar
  4. 4.
    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: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), pp. 1–5. IEEE (2012)Google Scholar
  5. 5.
    Kriett, P.O., Salani, M.: Optimal control of a residential microgrid. Energy 42(1), 321–330 (2012)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Khan, M.A., Javaid, N., Mahmood, A., Khan, Z.A., Alrajeh, N.: A generic demand side management model for smart grid. Int. J. Energy Res. 39(7), 954–964 (2015)CrossRefGoogle Scholar
  8. 8.
    Zhou, Y., Chen, Y., Xu, G., Zhang, Q., Krundel, L.: Home energy management with PSO in smart grid. In: 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), pp. 1666–1670. IEEE (2014)Google Scholar
  9. 9.
    AlSkaif, T., Luna, A.C., Zapata, M.G., Guerrero, J.M., Bellalta, B.: Reputation-based joint scheduling of households appliances and storage in a microgrid with a shared battery. Energy Build. 138, 228–239 (2017)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    Mahmood, D., Javaid, N., Alrajeh, N., Khan, Z.A., Qasim, U., Ahmed, I., Ilahi, M.: Realistic scheduling mechanism for smart homes. Energies 9(3), 202 (2016)CrossRefGoogle Scholar
  14. 14.
    Jalali, M.M., Kazemi, A.: Demand side management in a smart grid with multiple electricity suppliers. Energy 81, 766–776 (2015)CrossRefGoogle Scholar
  15. 15.
    Ma, K., Yao, T., Yang, J., Guan, X.: Residential power scheduling for demand response in smart grid. Int. J. Electr. Power Energy Syst. 78, 320–325 (2016)CrossRefGoogle Scholar
  16. 16.
    Hu, R.L., Skorupski, R., Entriken, R., Ye, Y.: A mathematical programming formulation for optimal load shifting of electricity demand for the smart Grid. IEEE Trans. Big Data (2016)Google Scholar
  17. 17.
    Ahmed, M.S., Mohamed, A., Khatib, T., Shareef, H., Homod, R.Z., Ali, J.A.: Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm. Energy Build. 138, 215–227 (2017)CrossRefGoogle Scholar
  18. 18.
    Shakeri, M., Shayestegan, M., Abunima, H., Reza, S.M.S., Akhtaruzzaman, M., Alamoud, A.R.M., Sopian, K., Amin, N.: An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid. Energy Build. 138, 154–164 (2017)CrossRefGoogle Scholar
  19. 19.
    Merrikh-Bayat, F.: A numerical optimization algorithm inspired by the strawberry plant. arXiv preprint arXiv:1407.7399 (2014)

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Salma Asif
    • 1
  • Khadija Ambreen
    • 1
  • Hina Iftikhar
    • 1
  • Hasan Nasir Khan
    • 1
  • Rubab Maroof
    • 1
  • Nadeem Javaid
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

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