Optimized Energy Management Strategy for Home and Office

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 17)


In smart grid, Demand Side Management (DSM) plays a vital role in dealing with consumer’s demand and making communication efficient. DSM not only reduces electricity cost but also increases the stability of the grid. In this regard, we introduce an energy management system model for a home and office, then propose efficient scheduling techniques for power usage in both. This system schedule the appliances on the basis of four different optimization techniques to achieve objectives that are electricity cost minimization, reduction in Peak to Average Ratio and energy consumption management. Moreover, we use Real Time Pricing because it is highly flexible and provides an understanding to consumer about price signal variations. Simulation results show that the proposed model for energy management work efficiently to achieve the objectives and provide cost-effective solution to increase the stability of smart grid.


Energy management system Real time pricing Smart grid Demand response Appliances Optimization techniques 


  1. 1.
    Gungor, V.C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., Hancke, G.P.: Smart grid technologies: communication technologies and standards. IEEE Trans. Ind. Inf. 7(4), 529–539 (2011)CrossRefGoogle Scholar
  2. 2.
    Shi, W., Xie, X., Chu, C.-C., Gadh, R.: A distributed optimal energy management strategy for microgrids. In: 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 200–205. IEEE (2014)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.
    Ju, L., Li, H., Zhao, J., Chen, K., Tan, Q., Tan, Z.: Multi-objective stochastic scheduling optimization model for connecting a virtual power plant to wind-photovoltaic-electric vehicles considering uncertainties and demand response. Energy Convers. Manage. 128, 160–177 (2016)CrossRefGoogle Scholar
  5. 5.
    Mary, G.A., Rajarajeswari, R.: Smart grid cost optimization using genetic algorithm. Int. J. Res. Eng. Technol. 3(07), 282–287 (2014)Google Scholar
  6. 6.
    Soares, J., Silva, M., Sousa, T., Vale, Z., Morais, H.: Distributed energy resource short-term scheduling using Signaled Particle Swarm Optimization. Energy 42(1), 466–476 (2012)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Faria, P., Soares, J., Vale, Z., Morais, H., Sousa, T.: Modified particle swarm optimization applied to integrated demand response and DG resources scheduling. IEEE Trans. Smart Grid 4(1), 606–616 (2013)CrossRefGoogle Scholar
  9. 9.
    Rahim, S., Iqbal, Z., Shaheen, N., Khan, Z.A., Qasim, U., Khan, S.A., Javaid, N.: Ant colony optimization based energy management controller for smart grid. In: 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), pp. 1154–1159. IEEE (2016)Google Scholar
  10. 10.
    Mehrshad, M., Tafti, A.D., Effatnejad, R.: Demand-side management in the smart grid based on energy consumption scheduling by NSGA-II. Int. J. Eng. Pract. Res. 2, 197–200 (2013)Google Scholar
  11. 11.
    Motevasel, M., Seifi, A.R.: Expert energy management of a micro-grid considering wind energy uncertainty. Energy Convers. Manage. 83, 58–72 (2014)CrossRefGoogle Scholar
  12. 12.
    Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)CrossRefGoogle Scholar
  13. 13.
    Barbato, A., Capone, A., Chen, L., Martignon, F., Paris, S.: A distributed demand-side management framework for the smart grid. Comput. Commun. 57, 13–24 (2015)CrossRefGoogle Scholar
  14. 14.
    Yuce, B., Rezgui, Y., Mourshed, M.: ANN-GA smart appliance scheduling for optimised energy management in the domestic sector. Energy Build. 111, 311–325 (2016)CrossRefGoogle Scholar
  15. 15.
    Lu, X., Zhou, K., Chan, F.T.S., Yang, S.: Optimal scheduling of household appliances for smart home energy management considering demand response. Nat. Hazards 88, 1–15 (2017)CrossRefGoogle Scholar
  16. 16.
    Ahmed, M.S., Mohamed, A., Homod, R.Z., Shareef, H.: Hybrid LSA-ANN based home energy management scheduling controller for residential demand response strategy. Energies 9(9), 716 (2016)CrossRefGoogle Scholar
  17. 17.
    Awais, M., Javaid, N., Shaheen, N., Iqbal, Z., Rehman, G., Muhammad, K., Ahmad, I.: An efficient genetic algorithm based demand side management scheme for smart grid. In: 2015 18th International Conference on Network-Based Information Systems (NBiS), pp. 351–356. IEEE (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceCOMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.Abasyn University, Islamabad CampusIslamabadPakistan
  3. 3.CISHigher Colleges of TechnologyFujairahUAE

Personalised recommendations