Power Management in Smart Grid for Residential Consumers

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


In this paper we have studied the power management for residential area. A proper load management brought fruitful results in term of Peak to Average Ratio (PAR) reduction and electricity cost. In order to achieve these objectives, we provide an energy management structure to perform scheduling on the basis of Genetic Algorithm (GA) and Fish Swarm Optimization (FSO). Time Of Use (TOU) pricing scheme has been used to calculate electricity cost. After experiments a noticeable difference has been found in the performance of our proposed algorithms GA and FSO. GA provides us better results in term of energy consumption and PAR reduction as compared to FSO. However, FSO performs more efficiently than GA in term of electricity cost reduction.


  1. 1.
    Molderink, A., Bakker, V., Bosman, M.G., Hurink, J.L., Smith, G.J.: Domestic energy management methodology for optimizing efficiency in smart grids. In: PowerTech (2009)Google Scholar
  2. 2.
    Sousa, T., Morais, H., Vale, Z., Faria, P., Soares, J.: Intelligent energy resource management considering vehicle-to-grid: a simulated annealing approach. IEEE Trans. Smart Grid 3, 535–542 (2012)CrossRefGoogle Scholar
  3. 3.
    Soares, J., Sousa, T., Morais, H., Vale, Z., Faria, P.: An optimal scheduling problem in distribution networks considering V2G. In: 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) (2011)Google Scholar
  4. 4.
    Tsui, K.M., Chan, S.C.: Demand response optimization for smart home scheduling under real-time pricing. IEEE Trans. Smart Grid 3, 1812–1821 (2012)CrossRefGoogle Scholar
  5. 5.
    Rahim, S., Javaid, N., Khan, S.A., Khan, Z.A., Aljareh, N., Qasim, U.: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build. 129, 452–470 (2016)CrossRefGoogle Scholar
  6. 6.
    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
  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 (2012)Google Scholar
  8. 8.
    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
  9. 9.
    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, 1802–1812 (2016)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, 917–926 (2017)Google Scholar
  11. 11.
    Moghaddam, A.A., Monsef, H., Kian, A.R.: Optimal smart home energy management considering energy saving and a comfortable lifestyle. IEEE Trans. Smart Grid 6, 324–332 (2015)CrossRefGoogle Scholar
  12. 12.
    Ma, J., Chen, H., Song, L., Li, Y.: Residential load scheduling in smart grid: a cost efficiency perspective. IEEE Trans. Smart Grid 7, 771–784 (2016)Google Scholar
  13. 13.
    Roh, H.T., Lee, J.W.: Residential demand response scheduling with multiclass appliances in the smart grid. IEEE Trans. Smart Grid 7, 94–104 (2016)CrossRefGoogle Scholar
  14. 14.
    Hamed, S.G., Kazemi, A.: Multi-objective cost-load optimization for demand side management of a residential area in smart grids. Sustain. Cities Soc. 32, 171–180 (2017)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Yuce, B., Rezgui, Y., Mourshed, M.: ANNGA smart appliance scheduling for optimized energy management in the domestic sector. Energy Build. 111, 311–325 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.National University of Science and TechnologyIslamabadPakistan
  3. 3.University of Engineering and TechnologyLahorePakistan

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