An Efficient Home Energy Management and Power Trading in Smart Grid

  • Sheraz Aslam
  • Sakeena Javaid
  • Nadeem JavaidEmail author
  • Syed Muhammad Mohsin
  • Saad Sulman Khan
  • Mariam Akbar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 773)


In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization.


Smart grid Heuristic algorithms Energy management Power trading 


  1. 1.
    Benzi, F., Anglani, N., Bassi, E., Frosini, L.: Electricity smart meters interfacing the households. IEEE Trans. Ind. Electron. 58, 4487–4494 (2011)CrossRefGoogle Scholar
  2. 2.
    Evangelisti, S., Lettieri, P., Clift, R., Borello, D.: Distributed generation by energy from waste technology: a life cycle perspective. Process Saf. Environ. Prot. 93, 161–172 (2015)CrossRefGoogle Scholar
  3. 3.
    Khalid, A., Javaid, N., Guizani, M., Alhussein, M., Aurangzeb, K., Ilahi, M.: Towards dynamic coordination among home appliances using multi-objective energy optimization for demand side management in smart buildings. IEEE Access (2018).
  4. 4.
    Albadi, M.H., El-Saadany, E.F.: A summary of demand response in electricity markets. Electr. Power Syst. Res. 78, 1989–1996 (2008)CrossRefGoogle Scholar
  5. 5.
    Avci, M., Erkoc, M., Rahmani, A., Asfour, S.: Model predictive HVAC load control in buildings using real-time electricity pricing. Energy Build. 60, 199–209 (2013)CrossRefGoogle Scholar
  6. 6.
    Yang, J., Zhang, G., Ma, K.: Matching supply with demand: a power control and real time pricing approach. Int. J. Electr. Power Energy Syst. 61, 111–117 (2014)CrossRefGoogle Scholar
  7. 7.
    Ahmad, A., Khan, A., Javaid, N., Hussain, H.M., Abdul, W., Almogren, A., Alamri, A., Azim Niaz, I.: An optimized home energy management system with integrated renewable energy and storage resources. Energies 10, 549 (2017)CrossRefGoogle Scholar
  8. 8.
    Aslam, S., Iqbal, Z., Javaid, N., Khan, Z.A., Aurangzeb, K., Haider, S.I.: Towards efficient energy management of smart buildings exploiting heuristic optimization with real time and critical peak pricing schemes. Energies 10, 2065 (2017)CrossRefGoogle Scholar
  9. 9.
    Van der Stelt, S., AlSkaif, T., van Sark, W.: Techno-economic analysis of household and community energy storage for residential prosumers with smart appliances. Appl. Energy 209, 266–276 (2018)CrossRefGoogle Scholar
  10. 10.
    Liu, R.S., Hsu, Y.F.: A scalable and robust approach to demand side management for smart grids with uncertain renewable power generation and bi-directional energy trading. Int. J. Electr. Power Energy Syst. 97, 396–407 (2018)CrossRefGoogle Scholar
  11. 11.
    Bradac, Z., Kaczmarczyk, V., Fiedler, P.: Optimal scheduling of domestic appliances via MILP. Energies 8, 217–232 (2014)CrossRefGoogle Scholar
  12. 12.
    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: Proceedings of the 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA, 16–20 January 2012, pp. 1–5 (2012)Google Scholar
  13. 13.
    Zhang, D., Evangelisti, S., Lettieri, P., Papageorgiou, L.G.: Economic and environmental scheduling of smart homes with microgrid: DER operation and electrical tasks. Energy Convers. Manag. 110, 113–124 (2016)CrossRefGoogle Scholar
  14. 14.
    Mohamed, F.A., Koivo, H.N.: Online management genetic algorithms of microgrid for residential application. Energy Convers. Manag. 64, 562–568 (2012)CrossRefGoogle Scholar
  15. 15.
    Hafeez, G., Javaid, N., Iqbal, S., Ali Khan, F.: Optimal residential load scheduling under utility and rooftop photovoltaic units. Energies 11(3), 611 (2018)CrossRefGoogle Scholar
  16. 16.
    Samadi, P., Wong, V.W., 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
  17. 17.
    Qayyum, F.A., Naeem, M., Khwaja, A.S., Anpalagan, A., Guan, L., Venkatesh, B.: Appliance scheduling optimization in smart home networks. IEEE Access 3, 2176–2190 (2015)CrossRefGoogle Scholar
  18. 18.
    Agnetis, A., de Pascale, G., Detti, P., Vicino, A.: Load scheduling for household energy consumption optimization. IEEE Trans. Smart Grid 4, 2364–2373 (2013)CrossRefGoogle Scholar
  19. 19.
    Tushar, M.H.K., Assi, C., Maier, M., Uddin, M.F.: Smart microgrids: optimal joint scheduling for electric vehicles and home appliances. IEEE Trans. Smart Grid 5, 239–250 (2014)CrossRefGoogle Scholar
  20. 20.
    Erdinc, O.: Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households. Appl. Energy 126, 142–150 (2014)CrossRefGoogle Scholar
  21. 21.
    Mary, G.A., Rajarajeswari, R.: Smart grid cost optimization using genetic algorithm. Int. J. Res. Eng. Technol. 3, 282–287 (2014)Google Scholar
  22. 22.
    Javaid, N., Ullah, I., Akbar, M., Iqbal, Z., Khan, F.A., Alrajeh, N., Alabed, M.S.: An intelligent load management system with renewable energy integration for smart homes. IEEE Access 5, 13587–13600 (2017)CrossRefGoogle Scholar
  23. 23.
    Wang, Y., Saad, W., Han, Z., Poor, H.V., Baar, T.: A game-theoretic approach to energy trading in the smart grid. IEEE Trans. Smart Grid 5, 1439–1450 (2014). 3, 282–287 (2014)CrossRefGoogle Scholar
  24. 24.
    Ding, Y.M., Hong, S.H., Li, X.H.: A demand response energy management scheme for industrial facilities in smart grid. IEEE Trans. Ind. Inform. 10, 2257–2269 (2014)CrossRefGoogle Scholar
  25. 25.
    Aslam, S., Javaid, N., Ali Khan, F., Alamri, A., Almogren, A., Abdul, W.: Towards efficient energy management and power trading in a residential area via integrating grid-connected microgrid. Sustainability 10(4), 1245 (2018). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Sheraz Aslam
    • 1
  • Sakeena Javaid
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Syed Muhammad Mohsin
    • 1
  • Saad Sulman Khan
    • 2
  • Mariam Akbar
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.COMSATS Institute of Information TechnologyWah CanttPakistan

Personalised recommendations