A Stochastic Negotiation Approach to Power Restoration Problems in a Smart Grid

  • Von-Wun Soo
  • Yen-Bo Peng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7047)


In this paper we propose a negotiation protocol for multi-feeder agents who must resolve the conflicts in order to find an optimal allocation of power in order for the power restoration after a blackout in a power grid. Since the power distribution domain constraints and cost, the optimal power distribution criteria involves multi-objectives such as number of changes of switches, number of power zones restored, and etc. It is not usually easy to come up with an optimal solution in a scaled-up complicated power grid topology within a limited time. We implemented two stochastic decision functions PDF and ZDF for feeder agents to decide whether to accept a proposal of other feeder agent who requests a restoration zone and whether to request a candidate target zone to deliver the power respectively in the negotiation. We show that with ZDF the feeder agents can negotiate faster to come up with the optimal solution than without ZDF. Finally we also show that our negotiation is a real time algorithm and show the performance curve of negotiation in terms of the restoration rate.


Stochastic negotiation power restoration smart grid 


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  1. 1.
    Anders Levander, L.N.: A stochastic negotiation model. Gnosie, Stockholm (2007)Google Scholar
  2. 2.
    Levy, H., Markowitz, H.M.: Approximating expected utility by a function of mean and variance. American Economic Review 69(3), 308–317 (1979)Google Scholar
  3. 3.
    Sandholm, T.W.: Contract types for satisficing task allocation: I theoretical results. AAAI (1998)Google Scholar
  4. 4.
    Andersson, M.R., Sandholm, T.W.: Contract types for satisficing task allocation: II experimental results. AAAI (1998)Google Scholar
  5. 5.
    Weiss, G.: Multiagent systems: a modern approach to distributed artificial intelligence. MIT press (1999)Google Scholar
  6. 6.
    Sankaran, S., Bui, T.: A stochastic negotiation model for organizational choice. In: Proceedings of the 37th Annual Hawaii International Conference on System Sciences (2004)Google Scholar
  7. 7.
    Li, X.D., Xu, Y.Q., Zhang, L.: Distribution service restoration with DGs based on multi-agent immune algorithm. In: 2nd International Conference on Power Electronics and Intelligent Transportation System, PEITS (2009)Google Scholar
  8. 8.
    Lambert-Torres, G., et al.: Particle Swarm Optimization Applied to System Restoration. IEEE Bucharest Powertech, Vols. 1-5, 1992–1997 (2009)Google Scholar
  9. 9.
    Stuart Russell, P.N.: Artificial Intelligence: A modern approach, 2nd edn (2003)Google Scholar
  10. 10.
    Wooldridge, M.: An Introduction to Multi Agent System, 2nd edn (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Von-Wun Soo
    • 1
    • 2
  • Yen-Bo Peng
    • 2
  1. 1.Institute of Information Systems and ApplicationsNational Tsing Hua UniversityHsinchuTaiwan
  2. 2.Dept. of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan

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