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Probabilistic game approaches for network cost allocation

Author

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  • bhakar, Rohit
  • sriram, V.s.
  • padhy, Narayana prasad
  • gupta, Hari om

Abstract

In a restructured power market, the network cost is to be allocated between multiple players utilizing the system in varying capacities. Cooperative game approaches based on Shapley value and Nucleolus provide stable models for embedded cost allocation of power networks. Varying network usage necessitates the introduction of probabilistic approaches to cooperative games. This paper proposes a variety of probabilistic cooperative game approaches. These have variably been modeled based upon the probability of existence of players, the probability of existence of coalitions, and the probability of players joining a particular coalition along with their joining in a particular sequence. Application of these approaches to power networks reflects the system usage in a more justified way. Consistent and stable results qualify the application of probabilistic cooperative game approaches for cost allocation of power networks.

Suggested Citation

  • bhakar, Rohit & sriram, V.s. & padhy, Narayana prasad & gupta, Hari om, 2010. "Probabilistic game approaches for network cost allocation," MPRA Paper 29003, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:29003
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    References listed on IDEAS

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    Cited by:

    1. Churkin, Andrey & Bialek, Janusz & Pozo, David & Sauma, Enzo & Korgin, Nikolay, 2021. "Review of Cooperative Game Theory applications in power system expansion planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    2. Farzaneh Pourahmadi & Payman Dehghanian, 2018. "A Game-Theoretic Loss Allocation Approach in Power Distribution Systems with High Penetration of Distributed Generations," Mathematics, MDPI, vol. 6(9), pages 1-14, September.
    3. Rahmani-Dabbagh, Saeed & Sheikh-El-Eslami, Mohammad Kazem, 2016. "A profit sharing scheme for distributed energy resources integrated into a virtual power plant," Applied Energy, Elsevier, vol. 184(C), pages 313-328.
    4. Gao, Evelyn & Sowlati, Taraneh & Akhtari, Shaghaygh, 2019. "Profit allocation in collaborative bioenergy and biofuel supply chains," Energy, Elsevier, vol. 188(C).
    5. Zhi Zhang & Jiaorong Ren & Kaichao Xiao & Zhenzhi Lin & Jiayu Xu & Wei Wang & Chuanxun Pei, 2019. "Cost Allocation Mechanism Design for Urban Utility Tunnel Construction Based on Cooperative Game and Resource Dependence Theory," Energies, MDPI, vol. 12(17), pages 1-16, August.
    6. Stoilov, Dimo & Stoilov, Luben, 2013. "Improving inter-transmission compensation in EU," Energy Policy, Elsevier, vol. 62(C), pages 282-291.
    7. Boyuan Wei & Geert Deconinck, 2019. "Distributed Optimization in Low Voltage Distribution Networks via Broadcast Signals †," Energies, MDPI, vol. 13(1), pages 1-18, December.
    8. M. Fiestras-Janeiro & Ignacio García-Jurado & Manuel Mosquera, 2011. "Cooperative games and cost allocation problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(1), pages 1-22, July.

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    More about this item

    Keywords

    Cooperative games; embedded cost allocation; probabilistic games; transmission pricing;
    All these keywords.

    JEL classification:

    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory

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