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A stochastic generalized Nash-Cournot model for the northwestern European natural gas markets: The S-GaMMES model

Author

Listed:
  • Ibrahim Abada
  • Pierre-André Jouvet

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 captures the possible fuel substitution that can be made between oil, coal, and natural gas in the overall fossil energy consumption. The demand is made random because of the oil price fluctuations and we take into account long-term contracts in an endogenous way. The model is applied to represent the European natural gas market and to forecast, until 2035, after a calibration process, patterns of consumption, prices, production, and long-term contract prices and volumes. In terms of policy implications, we show how the perception of the oil price’s uncertainty modifies the gas long-term contract volumes in Europe between the producers and the midstreamers. Finally, we define the value, gain and loss of the stochastic solution adapted to our model and calculate them for each market actor.

Suggested Citation

  • 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.
  • Handle: RePEc:cec:wpaper:1308
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    File URL: http://www.chaireeconomieduclimat.org/RePEc/cec/wpaper/13-09-Cahier-R-2013-08-Abada-and-Jouvet.pdf
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    References listed on IDEAS

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    4. Stevie Lochran, 2021. "GNOME: A Dynamic Dispatch and Investment Optimisation Model of the European Natural Gas Network and Its Suppliers," SN Operations Research Forum, Springer, vol. 2(4), pages 1-44, December.
    5. Chen, Yue & Wei, Wei & Liu, Feng & Wu, Qiuwei & Mei, Shengwei, 2018. "Analyzing and validating the economic efficiency of managing a cluster of energy hubs in multi-carrier energy systems," Applied Energy, Elsevier, vol. 230(C), pages 403-416.
    6. repec:aen:journl:ej37-si3-kiss is not listed on IDEAS
    7. Istemi Berk & Simon Schulte, 2017. "Turkey's Role in Natural Gas - Becoming a Transit Country?," EWI Working Papers 2017-1, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    8. Guo, Yingjian & Hawkes, Adam, 2019. "The impact of demand uncertainties and China-US natural gas tariff on global gas trade," Energy, Elsevier, vol. 175(C), pages 205-217.
    9. Ibrahim Abada & Andreas Ehrenmann, 2016. "The prisoner's dilemma in Cournot models: when endogenizing the level of competition leads to competitive behaviors," Working Papers EPRG 1619, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    10. Sziklai, Balázs R. & Kóczy, László Á. & Csercsik, Dávid, 2020. "The impact of Nord Stream 2 on the European gas market bargaining positions," Energy Policy, Elsevier, vol. 144(C).
    11. Arnaud Z. Dragicevic, 2019. "Market Coordination Under Non-Equilibrium Dynamics," Networks and Spatial Economics, Springer, vol. 19(3), pages 697-715, September.
    12. Olivier Massol & Albert Banal-Estanol, 2018. "Market Power and Spatial Arbitrage between Interconnected Gas Hubs," The Energy Journal, , vol. 39(2_suppl), pages 67-96, December.
    13. Veronika Grimm & Lars Schewe & Martin Schmidt & Gregor Zöttl, 2019. "A multilevel model of the European entry-exit gas market," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 89(2), pages 223-255, April.
    14. Avraam, Charalampos & Chu, Daniel & Siddiqui, Sauleh, 2020. "Natural gas infrastructure development in North America under integrated markets," Energy Policy, Elsevier, vol. 147(C).
    15. Balazs Sziklai & Laszlo A. Koczy & David Csercsik, 2018. "The geopolitical impact of Nord Stream 2," KRTK-KTI WORKING PAPERS 1821, Institute of Economics, Centre for Economic and Regional Studies.
    16. Elisabetta Allevi & Adriana Gnudi & Igor V. Konnov & Giorgia Oggioni, 2017. "Dynamic Spatial Auction Market Models with General Cost Mappings," Networks and Spatial Economics, Springer, vol. 17(2), pages 367-403, June.
    17. Rodica Ioana Lung & Noémi Gaskó & Mihai Alexandru Suciu, 2020. "Pareto-based evolutionary multiobjective approaches and the generalized Nash equilibrium problem," Journal of Heuristics, Springer, vol. 26(4), pages 561-584, August.

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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