A Stochastic Equilibrium Model for the North American Natural Gas Market

This dissertation is an endeavor in the field of energy modeling for the North American natural gas market using a mixed complementarity formulation combined with the stochastic programming. The genesis of the stochastic equilibrium model presented in this dissertation is the deterministic market equilibrium model developed in [Gabriel, Kiet and Zhuang, 2005]. Based on some improvements that we made to this model including proving new existence and uniqueness results, we present a multistage stochastic equilibrium model with uncertain demand for the deregulated North American natural gas market using the recourse method of the stochastic programming. The market participants considered by the model are pipeline operators, producers, storage operators, peak gas operators, marketers and consumers. Pipeline operators are described with regulated tariffs but also involve “congestion pricing” as a mechanism to allocate scarce pipeline capacity. Marketers are modeled as Nash-Cournot players…

Author: Zhuang, Jifang

Source: University of Maryland

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1 Introduction 1
1.1 Overview of the Industry
1.2 Game Theory
1.3 NCP/VI and Stochastic Programming
1.4 Energy Modeling Activities
1.5 Contributions and Organization of Dissertation
2 A Simpliļ¬ed Market Structure
2.1 Demand Seasonality
2.2 Market Participants
2.3 Natural Gas Network
2.4 Contracts and Market
2.5 Conclusions
3 A Deterministic Model D-NGEM
3.1 Model D-NGEM
3.1.1 Pipeline Operator
3.1.2 Producer
3.1.3 Storage Operator
3.1.4 Peak Gas Operator
3.1.5 Marketer
3.1.6 NCP/VI Formulation of Model D-NGEM
3.2 Existence Results
3.3 Uniqueness Results
3.4 Conclusions
4 A Stochastic Model S-NGEM with Recourse Method 84
4.1 Stochastic Programming
4.1.1 Recourse Method
4.1.2 Chance-Constraint Method
4.1.3 The Value of Information and Stochastic Solution
4.2 A Scenario Tree
4.3 Model S-NGEM
4.3.1 Pipeline Operator
4.3.2 Producer
4.3.3 Storage Operator
4.3.4 Peak Gas Operator
4.3.5 Marketer
4.3.6 NCP/VI formulation of Model S-NGEM
4.4 Conclusions
5 Example Application of Model S-NGEM
5.1 Data Set
5.1.1 Sample Network
5.1.2 Deterministic Parameters
5.1.3 Stochastic Parameters
5.2 Numerical Results
5.2.1 Base Case
5.2.2 Case 1
v5.2.3 Case 2
5.2.4 Case 3
5.3 V SS and EV PI
5.4 Conclusions
6 Summary and Future Work
A Notation
B Numerical Results for Base Case

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