Advertisement

Cooperative Energy Management Using Coalitional Game Theory for Reducing Power Losses in Microgrids

  • Muhammad Usman Khalid
  • Nadeem Javaid
  • Muhammad Nadeem Iqbal
  • Ali Abdur Rehman
  • Muhammad Umair Khalid
  • Mian Ahmer Sarwar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 772)

Abstract

Smart Grid (SG) has attained the great attention of the research community. SG integrates Distributed Energy Generators (DG) to produce electricity. Micro Grids (MG) exploit many Renewable Energy Sources (RES) such as wind turbine and solar panels. Due to intermittent nature of RES, the power output cannot be controlled and MGs often have a surplus or deficient energy to exchange with Utility Grid (UG). However, power line losses and energy sharing cost between UG and each MG are higher than among the MGs. In contrast, energy sharing among MGs is a promising solution to alleviate power line losses and minimize energy sharing cost. Authors proposed a cooperative model in which MGs make coalitions using coalitional game theory depending upon the distance among MGs. MGs exchange energy with other MGs as well as with UG in such a manner to optimize the objective function. Simulation results demonstrated that cooperative model alleviates power line losses by 42% and minimize energy sharing cost as compared to the non-cooperative model.

Keywords

Supply side management Smart Grid Micro Grid Coalitions game theory 

References

  1. 1.
    Rheinisch-Westfalische Elektrizitatswerke (RWE): Typical Daily Consumption of Electrical Power in Germany (2005)Google Scholar
  2. 2.
    National Energy Technology Laboratory, United States Department of Energy: A vision for the modern grid (2007). https://www.smartgrid.gov/files/VisionforModernGrid200701.pdf
  3. 3.
    NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 3.0. http://www.nist.gov/smartgrid/upload/NISTDraftFrameworkOct2013.pdf
  4. 4.
    Liu, H., Xu, X., Ding, M.: The energy management system of the smartgrid characterized by multi-parts interactions. In: 2014 China International Conference on Electricity Distribution (CICED), Shenzhen, pp. 1056–1063 (2014)Google Scholar
  5. 5.
    Kersting, W.H.: Distribution System Modeling and Analysis, 3rd edn. CRC Press, Boca Raton (2012)zbMATHGoogle Scholar
  6. 6.
    Paterakis, N.G., Erdin, O., Pappi, I.N., Bakirtzis, A.G., Catalo, J.P.S.: Coordinated operation of a neighborhood of smart households comprising electric vehicles, energy storage and distributed generation. IEEE Trans. Smart Grid 7(6), 2736–2747 (2016)CrossRefGoogle Scholar
  7. 7.
    Mangiatordi, F., Pallotti, E., Panzieri, D., Capodiferro, L.: Multi agent system for cooperative energy management in microgrids. 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, pp. 1–5 (2016)Google Scholar
  8. 8.
    Rahbar, K., Chai, C.C., Zhang, R.: Energy cooperation optimization in microgrids with renewable energy integration. IEEE Trans. Smart Grid 9(2), 1482–1493 (2018)CrossRefGoogle Scholar
  9. 9.
    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(3), 1439–1450 (2014)CrossRefGoogle Scholar
  10. 10.
    Chakraborty, S., Nakamura, S., Okabe, T.: Real-time energy exchange strategy of optimally cooperative microgrids for scale-flexible distribution system. Expert Syst. Appl. 42(10), 4643–4652 (2015)CrossRefGoogle Scholar
  11. 11.
    Wei, C., Fadlullah, Z.M., Kato, N., Takeuchi, A.: GT-CFS: a game theoretic coalition formulation strategy for reducing power loss in micro grids. IEEE Trans. Parallel Distrib. Syst. 25(9), 2307–2317 (2014)CrossRefGoogle Scholar
  12. 12.
    Wang, Z., Zhu, Q., Huang, M., Yang, B.: Optimization of economic/environmental operation management for micro-grids by using hybrid fireworks algorithm. Int. Trans. Electr. Energy Syst. 27(12) (2017)CrossRefGoogle Scholar
  13. 13.
    Hafeez, G., Javaid, N., Iqbal, S., Khan, F.A.: Optimal residential load scheduling under utility and rooftop photovoltaic units. Energies 11(3), 611–637 (2018)CrossRefGoogle Scholar
  14. 14.
    Ni, J., Ai, Q.: Economic power transaction using coalitional game strategy in micro-grids. IET Gener. Transm. Distrib. 10(1), 10–18 (2016)CrossRefGoogle Scholar
  15. 15.
    Lee, W.P., Choi, J.Y., Won, D.J.: Coordination strategy for optimalscheduling of multiple microgrids based on hierarchical system. Energies 10(9), 1336–1353 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Muhammad Usman Khalid
    • 1
  • Nadeem Javaid
    • 1
  • Muhammad Nadeem Iqbal
    • 2
  • Ali Abdur Rehman
    • 1
  • Muhammad Umair Khalid
    • 2
  • Mian Ahmer Sarwar
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.COMSATS Institute of Information TechnologyWah CanttPakistan

Personalised recommendations