Dynamic Coalition Formation in Energy Micro-Grids

  • Muhammad Yasir
  • Martin Purvis
  • Maryam Purvis
  • Bastin Tony Roy Savarimuthu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9387)


In recent years the notion of electrical energy micro-grids, in which communities share their locally-generated power, has gained increasing interest. Typically the energy generated comes from renewable resources, which means that its availability is variable-sometimes there may be energy surpluses and at other times energy deficits. This energy variability can be ameliorted by trading energy with a connected main electricity utility grid. But since main electricity grids are subject to faults or other outages, it can be advantageous for energy micro-grids to form coalitions and share their energy among themselves. In this work we present our model for the dynamic formation of such micro-grid coalitions. Our agent-based model, which is scalable and affords autonomy among the micro-grids participating in the coalition (agents can join and depart from coalitions at any time), features methods to reduce overall discomfort, so that even when all participating micro-grids in a coalition experience deficits; they can share energy so that overall discomfort is minimized. We demonstrate the efficacy of our model by showing empirical studies conducted with real energy production and consumption data.


Renewable energy Multi-agent systems Coalition formation Micro-grids 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Carlsson, F., Martinsson, P.: Does it matter when a power outage occurs? A choice experiment study on the willingness to pay to avoid power outages. Energy Economics 30(3), 1232–1245 (2008)CrossRefGoogle Scholar
  2. 2.
    Carlsson, F., Martinsson, P., Akay, A.: The effect of power outages and cheap talk on willingness to pay to reduce outages. Energy Economics 33(5), 790–798 (2011)CrossRefGoogle Scholar
  3. 3.
    Chakraborty, S., Nakamura, S., Okabe, T.: Scalable and optimal coalition formation of microgrids in a distribution system. In: IEEE PES Innovative Smart Grid Technologies, pp. 1–6, Europe, October 2014Google Scholar
  4. 4.
    de O Ramos, G., Burguillo, J.C., Bazzan, A.L.: Dynamic constrained coalition formation among electric vehicles. Journal of the Brazilian Computer Society 20(1), 8 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Decker, K.S., Kamboj, S., Kempton, W.: Deploying power grid-integrated electric vehicles as a multi-agent system. In: 10th International Conference on Autonomous Agents and Multi-agent Systems, AAMAS, pp. 13–20, Taipei (2011)Google Scholar
  6. 6.
    Fadlullah, Z.M., Nozaki, Y., Takeuch, A., Kato, N.: A survey of game theoretic approaches in smart grid. In: International Conference on Wireless Communications and Signal Processing, WCSP, Nanjing (2011)Google Scholar
  7. 7.
    Ganguli, S., Singh, J., Engineering, I., Engineering, E., Sangrur, T.: Estimating the Solar Photovoltaic generation potential and possible plant capacity in Patiala. International journal of Applied Engineering Research 1(2), 253–260 (2010). DindigulGoogle Scholar
  8. 8.
    Jacobson, M.Z., Delucchi, M.A.: Providing all Global Energy with Wind, Water, and Solar Power, Part I: Technologies, Energy Resources, Quantities and Areas of Infrastructure, and Materials. Energy Policy 39(3), 1154–1169 (2011)CrossRefGoogle Scholar
  9. 9.
    Labortay, N.R.E.: Solar and Wind Forecasting. [Accessed on 10 April 2015]
  10. 10.
    Labortay, N.R.E.: Solar Radiation Research. [Accessed on 13 May 2015]
  11. 11.
    McArthur, S., Davidson, E., Catterson, V., Dimeas, A.: Multi-Agent Systems for Power Engineering ApplicationsPart I: Concepts, Approaches, and Technical Challenges. Power Systems 22(4), 1743–1752 (2007)CrossRefGoogle Scholar
  12. 12.
    Miller, A., Muljadi, E., Zinger, D.S.: A Variable Speed Wind Turbine Power Control. Energy Convers. 12(2), 181–186 (1997)CrossRefGoogle Scholar
  13. 13.
    Mondal, A., Misra, S.: Dynamic coalition formation in a smart grid: a game theoretic approach. In: 2013 IEEE International Conference on Communications Workshops (ICC), pp. 1067–1071. IEEE, Budapest (2013)Google Scholar
  14. 14.
    Oliveira, P., Pinto, T., Morais, H., Vale, Z.A., Praça, I.: MASCEM an electricity market simulator providing coalition support for virtual power players. In: 15th International Conference on Intelligent System Applications to Power Systems, 2009, ISAP 2009, pp. 1–6. IEEE, Curitiba (2009)Google Scholar
  15. 15.
    Pietsch, H.: Property service Division. [Accessed on 25 September 2014]
  16. 16.
    Ramchurn, S.D., Vytelingum, P., Rogers, A., Jennings, N.R.: Putting the ’Smarts’ Into the Smart Grid: A Grand Challenge for Artificial Intelligence. Communications of the ACM, 86–97 (2012)Google Scholar
  17. 17.
    Saad, W., Han, Z., Poor, H.: Coalitional game theory for cooperative micro-grid distribution networks. In: 2011 IEEE International Conference on Communications Workshops (ICC), pp. 1–5. IEEE, Kyoto (2011)Google Scholar
  18. 18.
    Saad, W., Han, Z., Poor, H.V., Bas, T.: Game Theoretic Methods for the Smart Grid. Sig. Process. Mag. IEEE 29(5), 86–105 (2012)CrossRefGoogle Scholar
  19. 19.
    Sandholm, T., Larson, K., Andersson, M., Shehory, O., Tohmé, F.: Coalition structure generation with worst case. Artif. Intell. 111(1–2), 209–238 (1999)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Shaalan, A.M.: Outages Cost Estimation for Residential Sector. Journal of King Abdulaziz University 12(2), 69–79 (2000)CrossRefGoogle Scholar
  21. 21.
    The National Climate Database. NIWA, The National Institute of Water and Atmospheric Research (2014)Google Scholar
  22. 22.
    Yasir, M., Purvis, M.K., Purvis, M., Savarimuthu, B.T.R.: Agent-based community coordination of local energy distribution. Ai & Society, December 2013Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Muhammad Yasir
    • 1
  • Martin Purvis
    • 1
  • Maryam Purvis
    • 1
  • Bastin Tony Roy Savarimuthu
    • 1
  1. 1.Department of Information ScienceUniversity of OtagoDunedinNew Zealand

Personalised recommendations