Enhanced Differential Evolution and Crow Search Algorithm Based Home Energy Management in Smart Grid

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


In this paper, we used two techniques: Enhanced Differential Evolution (EDE) and Crow Search Algorithm (CSA), in order to evaluate the performance of Home Energy Management System (HEMS). The total load is categorized into three groups based on their energy consumption pattern, and time of use of appliances. Critical Peak Pricing (CPP) scheme is used to calculate electricity bill. Our goals are electricity cost reduction, energy consumption minimization, Peak to Average Ratio (PAR) minimization, and user comfort maximization. However, there is trade-off between multiple objectives (goals). The simulation results show that, there is trade-off between PAR and total cost, and there is trade-off as well between PAR and waiting time. The simulation results also show that CSA performs better in terms of total cost and user comfort than EDE and unscheduled.


Smart Grid Demand Side Management Meta heuristic techniques Enhanced Differential Evolution Crow Search Algorithm 


  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Anees, A., Chen, Y.-P.P.: True real time pricing and combined power scheduling of electric appliances in residential energy management system. Appl. Energy 165, 592–600 (2016)CrossRefGoogle Scholar
  5. 5.
    Shirazi, E., Jadid, S.: Cost reduction and peak shaving through domestic load shifting and DERs. Energy 124, 146–159 (2017)CrossRefGoogle Scholar
  6. 6.
    Rasheed, M.B., Javaid, N., Awais, M., Khan, Z.A., Qasim, U., Alrajeh, N., Iqbal, Z., Javaid, Q.: Real time information based energy management using customer preferences and dynamic pricing in smart homes. Energies 9(7), 542 (2016)CrossRefGoogle Scholar
  7. 7.
    Ogwumike, C., Short, M., Denai, M.: Near-optimal scheduling of residential smart home appliances using heuristic approach. In: 2015 IEEE International Conference on Industrial Technology (ICIT), pp. 3128–3133. IEEE (2015)Google 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.
    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
  10. 10.
    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 on Innovative Smart Grid Technologies (ISGT), pp. 1–5. IEEE (2012)Google Scholar
  11. 11.
    Ma, K., Yao, T., Yang, J., Guan, X.: Residential power scheduling for demand response in smart grid. International Journal of Electrical Power and Energy Systems 78, 320–325 (2016)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

Personalised recommendations