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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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    1. Egging, Ruud, 2013. "Benders Decomposition for multi-stage stochastic mixed complementarity problems – Applied to a global natural gas market model," European Journal of Operational Research, Elsevier, vol. 226(2), pages 341-353.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. repec:eee:eneeco:v:64:y:2017:i:c:p:520-529 is not listed on IDEAS
    5. 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.
    6. Holz, Franziska & Brauers, Hanna & Richter, Philipp M. & Roobeek, Thorsten, 2017. "Shaking Dutch grounds won’t shatter the European gas market," Energy Economics, Elsevier, vol. 64(C), pages 520-529.
    7. 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.
    8. Lorenczik, Stefan & Malischek, Raimund & Trüby, Johannes, 2017. "Modeling strategic investment decisions in spatial markets," European Journal of Operational Research, Elsevier, vol. 256(2), pages 605-618.
    9. repec:aen:journl:ej37-si3-holz is not listed on IDEAS
    10. repec:eee:ejores:v:268:y:2018:i:1:p:25-36 is not listed on IDEAS
    11. repec:eee:ejores:v:267:y:2018:i:2:p:643-658 is not listed on IDEAS
    12. repec:eee:ejores:v:266:y:2018:i:3:p:1086-1099 is not listed on IDEAS

    More about this item

    Keywords

    Natural gas; equilibrium model; endogenous investment; capacity expansion; logarithmic cost function;

    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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