Demand Side Management Using Meta-Heuristic Optimization Techniques

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 12)


In this paper, we present a Home Energy Management System (HEMS) using two meta-heuristic optimization techniques: Bacterial Foraging Optimization Algorithm (BFOA) and Bat Algorithm (BA). HEMS will provide different services to end user to manage and control their energy usage with time of use. The proposed model used for load scheduling between peak hour and off-peak hour. In this regard, we perform appliances scheduling to manage the frequent demand from the consumer. The aim of the proposed scheduling is to minimize peak to average ratio and the cost while having some trade-off in user comfort to achieve an optimal management of load. Simulation results show that the BA outperform than BFOA in selected performance parameters.


  1. 1.
    Ahmad, A., Khan, A., Javaid, N., Hussain, H.M., Abdul, W., Almogren, A., Alamri, A., Niaz, I.A.: An optimized home energy management system with integrated renewable energy and storage resources. Energies 10(4), 549 (2017)CrossRefGoogle Scholar
  2. 2.
    Bahmani-Firouzi, B., Azizipanah-Abarghooee, R.: Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm. Int. J. Electr. Power Ener. Syst. 56, 4254 (2014)Google Scholar
  3. 3.
    Barbato, A., Capone, A., Chen, L., Martignon, F., Paris, S.: A distributed demand-side management framework for the smart grid. Comput. Commun. 57, 1324 (2015)CrossRefGoogle Scholar
  4. 4.
    Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A., Niaz, I.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)CrossRefGoogle Scholar
  5. 5.
    Muratori, M., Rizzoni, G.: Residential demand response: dynamic energy management and time-varying electricity pricing. IEEE Trans. Power Syst. 31(2), 1108–1117 (2016)CrossRefGoogle Scholar
  6. 6.
    Ma, K., Yao, T., Yang, J., Guan, X.: Residential power scheduling for demand response in smart grid. Int. J. Electr. Power Ener. Syst. 78, 320–325 (2016)CrossRefGoogle Scholar
  7. 7.
    Seyedali, M., Mirjalili, S.M., Yang, X.-S.: Binary bat algorithm. Neural Comput. Appl. 25, 663–681 (2014)CrossRefGoogle Scholar
  8. 8.
    Bin, N., Nawi, M., Atika, N., Razali, B., Rehman, M.Z., Khan, A.: Echo-location in bat algorithm. ARPN J. Eng. Appl. Sci. 11(22), 13252–13258 (2016)Google Scholar
  9. 9.
    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. Ener. Buildings 129, 452–470 (2016)CrossRefGoogle Scholar
  10. 10.
    Rehman, N., Rahim, H., Ahmad, A., Khan, Z.A., Qasim, U., Javaid, N.: Heuristic algorithm based energy management system in smart grid (2016)Google Scholar
  11. 11.
    Abushnaf, J., Rassau, A., Gornisiewicz, W.: Impact on electricity use of introducing time-of-use pricing to a multi-user home energy management system. Int. Trans. Electr. Ener. Syst. 26(5), 993–1005 (2016)CrossRefGoogle Scholar
  12. 12.
    Shariatzadeh, F., Mandal, P., Srivastava, A.K.: Demand response for sus-tainable energy systems: a review, application and implementation strategy. Renew. Sustain. Ener. Rev. 45, 343–350 (2015)CrossRefGoogle Scholar
  13. 13.
    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, 1391–1400 (2013). CrossRefGoogle Scholar
  14. 14.
    Ullah, I., Javaid, N., Khan, Z.A., Qasim, U., Khan, Z.A., Mehmood, S.A.: An incentive-based optimal energy consumption scheduling algorithm for residential users. Proc. Comput. Sci. 52(Seit), 851–857 (2015)CrossRefGoogle Scholar
  15. 15.
    Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H., Fan, Z.: An integer linear pro-gramming based optimization for home demand-side management in smart grid. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), p. 15 (2012)Google Scholar
  16. 16.
    Priya Esther, B., Shivarama Krishna, K., Sathish Kumar, K., Ravi, K.: Demand side management using bacterial foraging optimization algorithm. In: Information Systems Design and Intelligent Applications, pp. 657–666, Springer India (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.National University of Sciences and TechnologyIslamabadPakistan

Personalised recommendations