Demand Side Management Using Strawberry Algorithm and Bacterial Foraging Optimization Algorithm in Smart Grid

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

Abstract

In Home Energy Management System (HEMs), consumer has the opportunity to schedule home appliances. In this paper, we have purposed an efficient HEMs by using two heuristic techniques: Bacterial Foraging Optimization Algorithm (BFA) and Strawberry Algorithm (SBA). We divide the appliances into three categories. The primary objective of this work is electricity cost and Peak to Average Ratio (PAR) minimization. Simulation results show that our optimization schemes reduce the total electricity cost and peak to average ratio by shifting the load from on peak hours to off peak hours. Results show that BFA performs better than SBA regarding electricity cost minimization. However, a trade-off always exists between cost and user comfort.

References

  1. 1.
    Muralitharan, K., Sakthivel, R., Shi, Y.: Multiobjective optimization technique for demand side management with load balancing approach in smart grid. Neurocomputing 177, 110–119 (2016)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    Zafar, A., Shah, S., Khalid, R., Hussain, S.M., Rahim, H., Javaid, N.: A meta-heuristic home energy management system. In: 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 244–250. IEEE, March 2017Google Scholar
  4. 4.
    Derakhshan, G., Shayanfar, H.A., Kazemi, A.: The optimization of demand response programs in smart grids. Energy Policy 94, 295–306 (2016)CrossRefGoogle Scholar
  5. 5.
    Yang, J., Zhang, G., Ma, K.: A nonlinear control method for price-based demand response program in smart grid. Int. J. Electr. Power Energy Syst. 74, 322–328 (2016)CrossRefGoogle Scholar
  6. 6.
    Samadi, P., Wong, V.W., 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.
    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, January 2012Google Scholar
  8. 8.
    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
  9. 9.
    Lujano-Rojas, J.M., Dufo-Lpez, R., Bernal-Agustn, J.L., Catalo, J.P.: Optimizing daily operation of battery energy storage systems under real-time pricing schemes. IEEE Trans. Smart Grid 8(1), 316–330 (2017)CrossRefGoogle Scholar
  10. 10.
    Meng, F.L., Zeng, X.J.: A profit maximization approach to demand response management with customers behavior learning in smart grid. IEEE Trans. Smart Grid 7(3), 1516–1529 (2016)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Samadi, P., Mohsenian-Rad, A.H., Schober, R., Wong, V.W., Jatskevich, J.: Optimal real-time pricing algorithm based on utility maximization for smart grid. In: 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 415–420. IEEE, October 2010Google Scholar
  13. 13.
    Nguyen, D.B., Scherpen, J.M., Bliek, F.: Distributed optimal control of smart electricity grids with congestion management. IEEE Trans. Autom. Sci. Eng. 14(2), 494–504 (2017)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Merrikh-Bayat, F.: A Numerical Optimization Algorithm Inspired by the Strawberry Plant (2014). arXiv preprint arXiv:1407.7399
  16. 16.
    Khalid, A., Javaid, N., Mateen, A., Khalid, B., Khan, Z.A., Qasim, U.: Demand side management using hybrid bacterial foraging and genetic algorithm optimization techniques. In: 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), pp. 494–502. IEEE, July 2016Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

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

Personalised recommendations