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A stochastic generalized Nash-Cournot model for the northwestern European natural gas markets with a fuel substitution demand function: The S-GaMMES model

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  • Ibrahim Abada

    (EDF Research and Development, IFP Energies nouvelles and EconomiX-CNRS, University of Paris 10, France)

Abstract

This article presents a stochastic dynamic Generalized Nash-Cournot model to describe the evolution of the natural gas markets. The major gas chain players are depicted including: producers, consumers, storage, and pipeline operators, as well as intermediate local traders. Our economic structure description takes into account market power and the demand representation tries to capture the possible fuel substitution that can be made between the consumption of oil, coal, and natural gas in the overall fossil energy consumption. The demand is made random because of the oil price fluctuations. We take into account the long-term aspects inherent to some markets, in an endogenous way. This particularity of our description makes the model a Generalized Nash Equilibrium problem that needs to be solved using specialized mathematical techniques. The model has been applied to represent the European natural gas market and to forecast, until 2035, after a calibration process, consumption, prices, production, and long-term contract prices and volumes. Finally, we defined and calculated the value of stochastic solution adapted to our model.

Suggested Citation

  • Ibrahim Abada, 2012. "A stochastic generalized Nash-Cournot model for the northwestern European natural gas markets with a fuel substitution demand function: The S-GaMMES model," Working Papers 1202, Chaire Economie du climat.
  • Handle: RePEc:cec:wpaper:1202
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    References listed on IDEAS

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    Cited by:

    1. Abada, Ibrahim & Briat, Vincent & Massol, Olivier, 2013. "Construction of a fuel demand function portraying interfuel substitution, a system dynamics approach," Energy, Elsevier, vol. 49(C), pages 240-251.
    2. Steven Gabriel & Sauleh Siddiqui & Antonio Conejo & Carlos Ruiz, 2013. "Solving Discretely-Constrained Nash–Cournot Games with an Application to Power Markets," Networks and Spatial Economics, Springer, vol. 13(3), pages 307-326, September.
    3. Egging, Ruud & Holz, Franziska, 2016. "Risks in global natural gas markets: Investment, hedging and trade," Energy Policy, Elsevier, vol. 94(C), pages 468-479.
    4. Egging, Ruud & Pichler, Alois & Kalvø, Øyvind Iversen & Walle–Hansen, Thomas Meyer, 2017. "Risk aversion in imperfect natural gas markets," European Journal of Operational Research, Elsevier, vol. 259(1), pages 367-383.
    5. Roman Mendelevitch, 2013. "The Role of CO2-EOR for the Development of a CCTS Infrastructure in the North Sea Region: A Techno-Economic Model and Application," Discussion Papers of DIW Berlin 1308, DIW Berlin, German Institute for Economic Research.
    6. Huppmann, Daniel, 2013. "Endogenous production capacity investment in natural gas market equilibrium models," European Journal of Operational Research, Elsevier, vol. 231(2), pages 503-506.
    7. Mel Devine & James Gleeson & John Kinsella & David Ramsey, 2014. "A Rolling Optimisation Model of the UK Natural Gas Market," Networks and Spatial Economics, Springer, vol. 14(2), pages 209-244, June.
    8. Fanchao Liao & Eric Molin & Bert van Wee, 2017. "Consumer preferences for electric vehicles: a literature review," Transport Reviews, Taylor & Francis Journals, vol. 37(3), pages 252-275, May.
    9. Ibrahim Abada & Pierre-André Jouvet, 2013. "A stochastic generalized Nash-Cournot model for the northwestern European natural gas markets: The S-GaMMES model," Working Papers 1308, Chaire Economie du climat.
    10. Brekke, Kjell Arne & Golombek, Rolf & Kaut, Michal & Kittelsen, Sverre A.C. & Wallace, Stein W., 2017. "Stochastic energy market equilibrium modeling with multiple agents," Energy, Elsevier, vol. 134(C), pages 984-990.
    11. Riepin, I. & Müsgens, F., 2019. "Seasonal Flexibility in the European Natural Gas Market," Cambridge Working Papers in Economics 1976, Faculty of Economics, University of Cambridge.

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