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Endogenous Investment Decisions in Natural Gas Equilibrium Models with Logarithmic Cost Functions

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  • Daniel Huppmann

Abstract

The liberalisation of the natural gas markets and the importance of natural gas as a transition fuel to a low-carbon economy have led to the development of several large-scale equilibrium models in the last decade. These models combine long-term market equilibria and investments in infrastructure while accounting for market power by certain suppliers. They are widely used to simulate market outcomes given different scenarios of demand and supply development, environmental regulations and investment options. In order to capture the specific characteristics of natural gas production, most of these models apply a logarithmic production cost function. However, no model has so far combined this cost function type with endogenous investment decisions in production capacity. Given the importance of capacity constraints in the determination of the natural gas supply, this is a serious shortcoming of the current literature. This paper provides a proof that combining endogenous investment decisions and a logarithmic cost function yields indeed a convex minimization problem, paving the way for an important extension of current state-of-the-art equilibrium models.

Suggested Citation

  • Daniel Huppmann, 2012. "Endogenous Investment Decisions in Natural Gas Equilibrium Models with Logarithmic Cost Functions," Discussion Papers of DIW Berlin 1253, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1253
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    References listed on IDEAS

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    Cited by:

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    5. Bertsch, Valentin & Devine, Mel & Sweeney, Conor & Parnell, Andrew C., 2018. "Analysing long-term interactions between demand response and different electricity markets using a stochastic market equilibrium model," Papers WP585, Economic and Social Research Institute (ESRI).
    6. Mel T. Devine & Valentin Bertsch, 2023. "The role of demand response in mitigating market power: a quantitative analysis using a stochastic market equilibrium model," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(2), pages 555-597, June.
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    8. Yang, Dong & Zhang, Lingge & Luo, Meifeng & Li, Feng, 2020. "Does shipping market affect international iron ore trade?– An equilibrium analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    9. Huppmann, Daniel & Egging, Ruud, 2014. "Market power, fuel substitution and infrastructure – A large-scale equilibrium model of global energy markets," Energy, Elsevier, vol. 75(C), pages 483-500.
    10. Franziska Holz, Philipp M. Richter, and Ruud Egging, 2016. "The Role of Natural Gas in a Low-Carbon Europe: Infrastructure and Supply Security," The Energy Journal, International Association for Energy Economics, vol. 0(Sustainab).
    11. Bertsch, Valentin & Devine, Mel, 2019. "The Role of Demand Response in Mitigating Market Power — A Quantitative Analysis Using a Stochastic Market Equilibrium Model," Papers WP635, Economic and Social Research Institute (ESRI).
    12. 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.
    13. Böttger, T. & Grimm, V. & Kleinert, T. & Schmidt, M., 2022. "The cost of decoupling trade and transport in the European entry-exit gas market with linear physics modeling," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1095-1111.
    14. Wang, Xiaolin & Qiu, Yangyang & Chen, Jun & Hu, Xiangping, 2022. "Evaluating natural gas supply security in China: An exhaustible resource market equilibrium model," Resources Policy, Elsevier, vol. 76(C).
    15. Sankaranarayanan, Sriram & Feijoo, Felipe & Siddiqui, Sauleh, 2018. "Sensitivity and covariance in stochastic complementarity problems with an application to North American natural gas markets," European Journal of Operational Research, Elsevier, vol. 268(1), pages 25-36.
    16. 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.
    17. Feijoo, Felipe & Huppmann, Daniel & Sakiyama, Larissa & Siddiqui, Sauleh, 2016. "North American natural gas model: Impact of cross-border trade with Mexico," Energy, Elsevier, vol. 112(C), pages 1084-1095.
    18. Delalibera, Bruno R. & Serrano-Quintero, Rafael & Zimmermann, Guilherme G., 2023. "Reforms in the natural gas sector and economic development," Economic Modelling, Elsevier, vol. 125(C).
    19. Baltensperger, Tobias & Füchslin, Rudolf M. & Krütli, Pius & Lygeros, John, 2016. "Multiplicity of equilibria in conjectural variations models of natural gas markets," European Journal of Operational Research, Elsevier, vol. 252(2), pages 646-656.
    20. Stähr, Fabian & Madlener, Reinhard & Hilgers, Christoph & Holz, Franziska, 2015. "Modeling the Geopolitics of Natural Gas: LNG Exports from the US to Eastern Europe," FCN Working Papers 15/2015, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    21. Bruno R. Delaribera & Rafael SerranoQuintero & Guilherme G. Zimmermann, 2023. "Reforms in the Natural Gas Sector and Economic Development," UB School of Economics Working Papers 2023/446, University of Barcelona School of Economics.
    22. Pineda, Salvador & Boomsma, Trine K. & Wogrin, Sonja, 2018. "Renewable generation expansion under different support schemes: A stochastic equilibrium approach," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1086-1099.

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    More about this item

    Keywords

    Natural gas; equilibrium model; endogenous investment; capacity expansion; logarithmic cost function;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • L71 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Mining, Extraction, and Refining: Hydrocarbon Fuels

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