Comparison of BFA and EWA in Home Energy Management System Using RTP

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 7)


With the usage of demand side management (DSM) techniques, consumers such as residential, commercial and industrial are more flexible to use electricity according to their need. Many techniques are proposed to manage electricity cost, load, peak to average ratio (PAR) and user comfort of consumer appliances. In this paper we proposed a technique Earthworm Optimization Algorithm (EWA) that is developed for residential area in SG and compare with the Bacterial Foraging Algorithm (BFA). These algorithms are used for the scheduling the appliance load in real time pricing. Both algorithms are used to shifting the load from on-peak hours to off-peak hours in RTP and reduced the electric cost and PAR. We compare both algorithms in terms of electricity cost, PAR and used comfort. Our simulation results show that the EWA outperformed the BFA in terms of electricity cost however, BFA reduced the PAR as compared to EWA.


Bacterial Foraging Algorithm (BFA) Electricity Consumption Cost Maximize User Comfort Demand Side Management (DSM) Energy Management Control System (EMCS) 
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.
    Mesarica, P., Krajcar, S.: Home demand side management integrated with electric vehicles and renewable energy sources. Energy Build. 108, 1–9 (2015)CrossRefGoogle Scholar
  2. 2.
    Javaid, N., Javaid, S., Wadood, A., Ahmed, I., Almogren, A., Alamri, A., Niaz, I.A.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energy 10(3), 319 (2017)Google Scholar
  3. 3.
    Srinivasan, D., Rajgarhia, S., Radhakrishnan, B.M., Sharma, A., Khincha, H.: Game-Theory based dynamic pricing strategies for demand side management in smart grids. Energy 126, 132–143 (2017)CrossRefGoogle Scholar
  4. 4.
    Rahim, S., Javaid, N., Ahmad, A., Khan, S.A., Khanb, Z.A., Alrajehc, N., Qasim, U.: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build. 129, 452–470 (2016)CrossRefGoogle Scholar
  5. 5.
    Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H.: An integer linear programming based optimization for home demand-side management in smart grid. In: IEEE PES Conference on Innovative Smart Grid Technologies (ISGT) (2012)Google Scholar
  6. 6.
    Werminski, S., Jarnut, M., Benysek, G., Bojarski, J.: Demand side management using DADR automation in the peak load reduction. Renew. Sustain. Energy Rev. 67, 998–1007 (2017)CrossRefGoogle Scholar
  7. 7.
    He, X., Huang, T., Li, C., Che, H., Dong, Z.: A recurrent neural network for optimal real-time price in smart grid. Neurocomputing 149(B), 608–612 (2015)Google Scholar
  8. 8.
    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
  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, 1391–1400 (2013)Google Scholar
  10. 10.
    Ma, J., Chen, H., Song, L., Li, Y.: Residential load scheduling in smart grid: a cost efficiency perspective. IEEE Trans. Smart Grid, 771–784 (2016)Google Scholar
  11. 11.
    Hamed, S.G., Kazemi, A.: Multi-objective cost-load optimization for demand side management of a residential area in smart grids. Sustain. Cities Soc. (2017)Google Scholar
  12. 12.
    Li, C., Yu, X., Yu, W., Chen, G., Wang, J.: Efficient computation for sparse load shifting in demand side management. Trans. Smart Grid, 250–261 (2017)Google Scholar
  13. 13.
    Wang, G.G., Deb, S., Coelho, L.S.: Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems (2015)Google Scholar
  14. 14.
    Ipakchi, A., Albuyeh, F.: Grid of the future. IEEE Power Energ. Mag. 7(2), 52–62 (2009)CrossRefGoogle Scholar
  15. 15.
    Farhangi, H.: The path of the smart grid. IEEE Power Energy Mag. 8(1) (2009)Google Scholar
  16. 16.
    Fang, X., Misra, S., Xue, G., Yang, D.: Smart grid the new and improved power grid: a survey. IEEE Commun. Surv. Tutorials 14(4), 944–980 (2011)CrossRefGoogle Scholar
  17. 17.
    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. Industr. Inf. 7(4), 529–539 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

Personalised recommendations