The Baker Institute World Gas Trade Model

Hartley, Peter; Medlock, Kenneth B.

doi:10.1017/cbo9780511493492.012

Cited by 33 publications

(23 citation statements)

References 3 publications

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“…In this respect, the model we present is approaching the level of detail of the Rice University World Gas Trade model [36], or the Gas Systems Analysis Model (GSAM) for North America ( [28], [27]). This level of detail with a representation of the global LNG market and combined with the strategic behavior of the producers (via their transmitters) is unique and represents the main contribution of this work.…”

Section: Diw-discussion Paper 732mentioning

confidence: 99%

A complementarity model for the European natural gas market

et al. 2008

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Abstract:In this paper, we present a detailed and comprehensive complementarity model for computing market equilibrium values in the European natural gas system. Market players include producers and their marketing arms which we call "transmitters", pipeline and storage operators, marketers, LNG lique…ers, regasi…ers, tankers, and three end-use consumption sectors. The economic behavior of producers, transmitters, pipeline and storage operators, lique…ers and regasi…ers is modeled via optimization problems whose Karush-Kuhn-Tucker (KKT) optimality conditions in combination with market-clearing conditions form the complementarity system. The LNG tankers, marketers and consumption sectors are modeled implicitly via appropriate cost functions, aggregate demand curves, and ex-post calculations, respectively. The model is run on several case studies that highlight its capabilities, including a simulation of a disruption of Russian supplies via Ukraine.

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Section: Diw-discussion Paper 732mentioning

confidence: 99%

A complementarity model for the European natural gas market

et al. 2008

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“…The model details and underlying gas demand algorithms are included in an Appendix of Hartley and Medlock ( [28], pp. 389e395).…”

Section: Specific Energy Market Equilibrium Modelsmentioning

confidence: 99%

Review of models and actors in energy mix optimization – can leader visions and decisions align with optimum model strategies for our future energy systems?

Weijermars

Taylor

Bahn

et al. 2012

Energy Strategy Reviews

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A B S T R A C TOrganizational behavior and stakeholder processes continually influence energy strategy choices and decisions. Although theoretical optimizations can provide guidance for energy mix decisions from a pure physical systems engineering point of view, these solutions might not be optimal from a political or social perspective. Improving the transparency of our vision sharing and strategy making processes in a systematic way is therefore as important as the actual systems engineering solutions proposed by the modeling tools. Energy trend forecasting and back-casting, scenarios and system analysis have matured into powerful modeling tools for providing advice on optimizing our future energy solutions. The integrated use and iterative improvement of all these approaches can result in energy systems that become better optimized. Such an integrated approach is particularly important to those who have decision-making power over our future energy direction. Some of the challenges and opportunities for energy strategists that strive to promote optimal decisions on our future energy solutions are highlighted in this state-ofthe-art review.

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“…Hence, in these natural gas market models the cost curves do not vary over time. One exception can be found in Hartley and Medlock (2006) where the long-run production cost curves shift in the future according to an assumed rate of technological innovation in exploration and development costs. However, these changes are exogenous as they are not dependent on the change of other model variables.…”

Section: Equilibrium Modeling Of Energy Resource Marketsmentioning

confidence: 99%