A Survey of Optimization Techniques for Scheduling in Home Energy Management Systems in Smart Grid

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


This survey paper is based on comprehensive study of optimization techniques used in smart grid and reviews one of the most popular evolutionary optimization technique i.e., differential evolution (DE) optimization. In addition, different types of DE algorithm currently used in literature are also discussed. These include enhanced DE, modified DE and hybrid DE algorithm. Furthermore, the role of these techniques in solving optimization tasks and scheduling is also discussed.


Home Energy Management System Solving Optimization Tasks Phosphoric Acid Fuel Cell (PAFC) PEVs Battery Plug-in Electric Vehicles (PEVs) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Lo, C.H., Ansari, N.: The progressive smart grid system from both power and communications aspects. IEEE Commun. Surv. Tutorials 14(3), 799–821 (2012)Google Scholar
  2. 2.
    Karaboa, D., Kdem, S.: A simple and global optimization algorithm for engineering problems: differential evolution algorithm. Turk. J. Electr. Eng. Comput. Sci. 12(1), 53–60 (2004)Google Scholar
  3. 3.
    Tiwari, N., Srivastava, L.: Generation scheduling and micro-grid energy management using differential evolution algorithm. In: 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1–7. IEEE, March 2016Google Scholar
  4. 4.
    Galvn-Lpez, E., Schoenauer, M., Patsakis, C., Trujillo, L.: Demand-side management: optimising through differential evolution plug-in electric vehicles to partially fulfil load demand. In: Computational Intelligence, pp. 155–174. Springer (2015)Google Scholar
  5. 5.
    Moradi, M.H., Abedini, M., Tousi, S.R., Hosseinian, S.M.: Optimal siting and sizing of renewable energy sources and charging stations simultaneously based on differential evolution algorithm. Int. J. Electr. Power Energy Syst. 73, 1015–1024 (2015)CrossRefGoogle Scholar
  6. 6.
    Arafa, M., Sallam, E.A., Fahmy, M.M.: An enhanced differential evolution optimization algorithm. In: 2014 Fourth International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP), pp. 216–225. IEEE, May 2014Google Scholar
  7. 7.
    Huang, C.M., Chen, S.J., Huang, Y.C., Yang, S.P.: Optimal active-reactive power dispatch using an enhanced differential evolution algorithm. In: 2011 6th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1869–1874. IEEE, June 2011Google Scholar
  8. 8.
    Sum-Im, T.: An enhanced differential evolution algorithm application to economic dispatch with valve-point effects and system losses considerations. In: 2015 50th International Universities Power Engineering Conference (UPEC), pp. 1–6. IEEE, September 2015Google Scholar
  9. 9.
    Ahmad, A., Javaid, N., Guizani, M., Alrajeh, N., Khan, Z.A.: An accurate and fast converging short-term load forecasting model for industrial applications in a smart grid. IEEE Trans. Industr. Inf. (2016)Google Scholar
  10. 10.
    Ali, M., Pant, M., Abraham, A.: A modified differential evolution algorithm and its application to engineering problems. In: SoCPaR, pp. 196–201, December 2009Google Scholar
  11. 11.
    Nayak, M.R., Krishnanand, K.R., Rout, P.K.: Modified differential evolution optimization algorithm for multi-constraint optimal power flow. In: 2011 International Conference on Energy, Automation, and Signal (ICEAS), pp. 1–7. IEEE, December 2011Google Scholar
  12. 12.
    Yu, M., Wang, Y., Li, Y.G.: Energy management of wind turbine-based DC microgrid utilizing modified differential evolution algorithm (2015)Google Scholar
  13. 13.
    Zhang, J., Wu, Y., Guo, Y., Wang, B., Wang, H., Liu, H.: A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints. Appl. Energy 183, 791–804 (2016)CrossRefGoogle Scholar
  14. 14.
    Carreiro, A.M., Oliveira, C., Antunes, C.H., Jorge, H.M.: An energy management system aggregator based on an integrated evolutionary and differential evolution approach. In: European Conference on the Applications of Evolutionary Computation, pp. 252–264. Springer, April 2015Google Scholar
  15. 15.
    Fan, G.M., Huang, H.J.: A hybrid discrete differential evolution algorithm for stochastic resource allocation. In: 2016 35th Chinese Control Conference (CCC), pp. 2756–2759. IEEE, July 2016Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.Mirpur University of Science and TechnologyMirpur Azad KashmirPakistan

Personalised recommendations