Smart-Grid Modelling and Simulation

  • Dimitris Ziouzios
  • Argiris Sideris
  • Dimitris Tsiktsiris
  • Minas DasygenisEmail author
Part of the Power Systems book series (POWSYS)


This chapter discusses a modeling and simulation algorithm for system behavior analysis and energy consumption in smart-grid environment. Since system behavior and energy consumption constitute very important information for designing an algorithm, the discussed solution models various smart-grids scenarios in an efficient and realistic way.


  1. 1.
    Al-Rubaye, S., Choi, B.J.: Energy load management for residential consumers in smart grid networks. In: 2016 IEEE International Conference on Consumer Electronics (ICCE), pp. 579–582 (2016).
  2. 2.
    Alaeddine Mokri Mona Aal Ali, M.E.: Solar energy in the United Arab Emirates: A review. Elsevier, Amsterdam (2013)Google Scholar
  3. 3.
    Asif, M.: Growth and sustainability trends in the buildings sector in the GCC region with particular reference to the KSA and UAE. Elsevier, Amsterdam (2016)CrossRefGoogle Scholar
  4. 4.
    Behar, O., Khellaf, A., Mohammedi, K.: A review of studies on central receiver solar thermal power plants. Renew. Sustain. Energy Rev. 23, 12–39 (2013)., Scholar
  5. 5.
    Behnam Zakeri, S.S.: Electrical energy storage systems: A comparative life cycle cost analysis, pp. 569–596. Elsevier, Amsterdam (2015)CrossRefGoogle Scholar
  6. 6.
    Hatzi, V.: Energy management and consumer modeling in smart grid systems. Ph.D. thesis, University of Thesaly, Department of Electrical and Computer Engineering (2017)Google Scholar
  7. 7.
    Hossain, M., Madlool, N., Rahim, N., Selvaraj, J., Pandey, A., Khan, A.F.: Role of smart grid in renewable energy: An overview. Renew. Sustain. Energy Rev. 60, 1168–1184 (2016)., Scholar
  8. 8.
    Huang, Y., Mao, S., Nelms, R.M.: Adaptive electricity scheduling in microgrids. IEEE Trans. Smart Grid 5(1), 270–281 (2014). Scholar
  9. 9.
    Loni, A., Parand, F.A.: A survey of game theory approach in smart grid with emphasis on cooperative games. In: 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC), pp. 237–242 (2017).
  10. 10.
    Lydia, M., Selvakumar, A.I., Kumar, S.S., Kumar, G.E.P.: Advanced algorithms for wind turbine power curve modeling. IEEE Trans. Sustain. Energy 4(3), 827–835 (2013). Scholar
  11. 11.
    Malik, M.M., Abdallah, S., Hussain, M.: Assessing supplier environmental performance: Applying Analytical Hierarchical Process in the United Arab Emirates healthcare chain. Elsevier, Amsterdam (2016)CrossRefGoogle Scholar
  12. 12.
    Mets, K., Strobbe, M., Verschueren, T., Roelens, T., Turck, F.D., Develder, C.: Distributed multi-agent algorithm for residential energy management in smart grids. In: 2012 IEEE Network Operations and Management Symposium, pp. 435–443 (2012).
  13. 13.
    Mohsenian-Rad, A.H., Wong, V.W.S., Jatskevich, J., Schober, R., Leon-Garcia, A.: Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans. Smart Grid 1(3), 320–331 (2010). Scholar
  14. 14.
    Parisio, A., Glielmo, L.: A mixed integer linear formulation for microgrid economic scheduling. In: 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 505–510 (2011).
  15. 15.
    Reeves, B., Cummings, J.J., Scarborough, J.K., Flora, J., Anderson, D.: Leveraging the engagement of games to change energy behavior. In: 2012 International Conference on Collaboration Technologies and Systems (CTS), pp. 354–358 (2012).
  16. 16.
    Reka, S.S., Ramesh, V.: A demand response modeling for residential consumers in smart grid environment using game theory based energy scheduling algorithm. Ain Shams Eng. J. 7(2), 835–845 (2016)., Scholar
  17. 17.
    Sabrina Barbosa, K.I.: Perspectives of double skin faades for naturally ventilated buildings: A review. Elsevier, Amsterdam (2014)CrossRefGoogle Scholar
  18. 18.
    Vardakas, J.S., Zorba, N., Verikoukis, C.V.: A survey on demand response programs in smart grids: pricing methods and optimization algorithms. IEEE Commun. Surv. Tutor. 17(1), 152–178 (2015). Scholar
  19. 19.
    van de Ven, P.M., Hegde, N., Massouli, L., Salonidis, T.: Optimal control of end-user energy storage. IEEE Trans. Smart Grid 4(2), 789–797 (2013). Scholar
  20. 20.
    Villalva, M.G., Gazoli, J.R., Filho, E.R.: Comprehensive approach to modelin and simulation of photovoltaic arrays. IEEE Trans. Power Electron. 24(5), 1198–1208 (2009). Scholar
  21. 21.
    Zhang, Y., Gatsis, N., Giannakis, G.B.: Robust distributed energy management for microgrids with renewables. In: 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm), pp. 510–515 (2012).
  22. 22.
    Zhang, Y., Wang, H., Xie, Y.: An intelligent hybrid model for power flow optimization in the cloud-iot electrical distribution network. Cluster Computing, pp. 1–10 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dimitris Ziouzios
    • 1
  • Argiris Sideris
    • 1
  • Dimitris Tsiktsiris
    • 1
  • Minas Dasygenis
    • 1
    Email author
  1. 1.Department of Informatics and Telecommunications EngineeringUniversity of Western MacedoniaKozaniGreece

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