Home Energy Management by Differential Evolution and Enhanced Differential Evolution in Smart Grid Environment

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 13)


This paper introduces Home Energy Management System (HEMS) which is the most revolutionary application of Smart Grid (SG) technology. It allows consumers to schedule the appliances according to their desires and requirements without effecting their living comfort along with the advantage of reducing electricity expenses. As a result, Peak to Average Ratio (PAR) is reduced for the benefit of utility. This paper focuses on optimizing power consumption in residential sector using Demand Side Management (DSM) strategy. Authors estimate the performance of Home Energy Management System (HEMS) by using optimization techniques: Differential Evolution (DE) and Enhanced Differential Evolution (EDE) and Real Time Pricing (RTP) signal is used for the calculation of electricity bills. As there is always a tradeoff between two parameters, so in our approach there exists a tradeoff between User Comfort (UC) and electricity cost. Simulation results show that in terms of waiting time DE performs better than EDE, however EDE performs better in terms of cost.


  1. 1.
    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, 319 (2017)CrossRefGoogle Scholar
  2. 2.
    Ziming, Z., Tang, J., Lambotharan, S., Chin, W.H., Fan, Z.: An integer linear programming based optimization for home demand-side management in smart grid. IEEE (2012)Google Scholar
  3. 3.
    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
  4. 4.
    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 Buildings 129, 452–470 (2016)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Ma, K., Yao, T., Yang, J., Guan, X.: Residential power scheduling for demand response in smart grid. Electr. Power Energy Syst. 78, 320–325 (2016)CrossRefGoogle Scholar
  7. 7.
    Mahmood, A., Baig, F., Alrajeh, N., Qasim, U., Khan, Z.A., Javaid, N.: An enhanced system architecture for optimized demand side management in smart grid. Appl. Sci. 6, 122 (2016). CrossRefGoogle Scholar
  8. 8.
    Rasheed, M.B., Javaid, N., Ahmad, A., Khan, Z.A., Qasim, M., Alrajeh, N.: An efficient power scheduling scheme for residential load management in smart homes. Appl. Sci. 5, 1134–1163 (2015). CrossRefGoogle Scholar
  9. 9.
    Anvari-Moghaddam, A., Monsef, H., Rahimi-Kian, A.: Optimal smart home energy management considering energy saving and a comfortable lifestyle. IEEE Trans. Smart Grid 6(1), 324–332 (2015)CrossRefGoogle Scholar
  10. 10.
    Basit, A., Sidhu, G.A.S., Mahmood, A., Gao, F.: Efficient and autonomous energy management techniques for the future smart homes. IEEE Trans. Smart Grid 8(2), 917–926 (2017)Google Scholar
  11. 11.
    Shi, W., Xie, X., Chu, C.-C., Gadh, R.: Distributed optimal energy management in micro grids. IEEE Trans. Smart Grid 6(3), 1137–1146 (2015)CrossRefGoogle Scholar
  12. 12.
    Shi, W., Li, N., Chu, C.-C., Gadh, R.: Real-time energy management in micro grids. IEEE Trans. Smart Grid 8(1), 228–238 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

Personalised recommendations