Using generalized networks to forecast natural gas distribution and allocation during periods of shortage

Brooks, Robert

doi:10.1007/bfb0120936

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…The ABM approach used an existing model of the NG system short-term dynamics, called Gas Allocation Model (GAM) developed at Sandia National Laboratories as our starting point in terms of the software implementation and the GPCM model created by RBAC as fundamental motivation and a source of the data (see Brooks (1981) for more on the GPCM approach). More information about GAM can be found in Mitchell et al (2010).…”

Section: Simulation Model Implementationmentioning

confidence: 99%

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Natural Gas Value-Chain and Network Assessments

Kobos

Outkin

Beyeler

et al. 2015

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The current expansion of natural gas (NG) development in the United States requires an understanding of how this change will affect the natural gas industry, downstream consumers, and economic growth in order to promote effective planning and policy development. The impact of this expansion may propagate through the NG system and US economy via changes in manufacturing, electric power generation, transportation, commerce, and increased exports of liquefied natural gas. We conceptualize this problem as supply shock propagation that pushes the NG system and the economy away from its current state of infrastructure development and level of natural gas use. To illustrate this, the project developed two core modeling approaches. The first is an Agent-Based Modeling (ABM) approach which addresses shock propagation throughout the existing natural gas distribution system. The second approach uses a System Dynamics-based model to illustrate the feedback mechanisms related to finding new supplies of natural gas -notably shale gas -and how those mechanisms affect exploration investments in the natural gas market with respect to proven reserves. The ABM illustrates several stylized scenarios of large liquefied natural gas (LNG) exports from the U.S. The ABM preliminary results demonstrate that such scenario is likely to have substantial effects on NG prices and on pipeline capacity utilization. Our preliminary results indicate that the price of natural gas in the U.S. may rise by about 50% when the LNG exports represent 15% of the system-wide demand. The main findings of the System Dynamics model indicate that proven reserves for coalbed methane, conventional gas and now shale gas can be adequately modeled based on a combination of geologic, economic and technology-based variables. A base case scenario matches historical proven reserves data for these three types of natural gas. An environmental scenario, based on implementing a $50/tonne CO 2 tax results in less proven reserves being developed in the coming years while demand may decrease in the absence of acceptable substitutes, incentives or changes in consumer behavior. An increase in demand of 25% increases proven reserves being developed by a very small amount by the end of the forecast period of 2025. 5

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Section: Simulation Model Implementationmentioning

confidence: 99%

“…GPCM is a tool for the natural gas market analysis and forecasting, created by RBAC, Inc. See www.rbac.com andBrooks (1975Brooks ( , 1981 for more information.…”

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confidence: 99%

Natural Gas Value-Chain and Network Assessments

Kobos

Outkin

Beyeler

et al. 2015

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“…In the literature [5], [10], [11] and [16] stochastic approaches in the gas market deals mainly with the scheduling of development of gas fields, the use of gas storage and the gas delivery problem. The stochasticity is introduced through a stochastic data process ω = ( ω 1 , ω 2 ) where the first component ∆ represents the temperature and the second component I ψ represents the oil index price along the year.…”

Section: The Stochastic Omogas-2sv Modelmentioning

confidence: 99%

A stochastic optimization model for gas retail with temperature scenarios and oil price parameters

Maggioni

Bertocchi

Giacometti

et al. 2009

IMA Journal of Management Mathematics

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The paper deals with a new stochastic optimization model, named OMoGaS-2SV (Optimization Modelling for Gas Seller-Second Stochastic version), to assist companies dealing with gas retail commercialisation. We consider as source of stochasticity temperature, but we take into account also information on energetic indices. Temperature influences gas consumption of small consumers and is modelled by a mean reverting process. Oil prices and exchange rates influence the energetic indices to which sell and purchase prices are related. Forward curves of energetic indices are analyzed by a vector-autoregressive model while exhange rates are modelled by GARCH model. The profit function depends on the number of contracts with the final consumers, the tipology of such consumers, the cost supported to meet the final demand and penalties for daily maximum consumption exceeding daily maximum capacity. Linear constraints related to a maximum daily gas consumption and binary constraints on indexation formulas are included.

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“…Levary and Dean28 present a model for gas procurement by a natural gas utility. A LP framework to evaluate supply scenarios for planners on a statewide or national level is presented in Brooks 29. A chance‐constrained approach to making purchasing and storage decisions for a utility is presented in Guldmann 30.…”

Section: Literature Reviewmentioning

confidence: 99%

A short‐term operational planning model for natural gas production systems

Selot¹,

Kuok²,

Robinson³

et al. 2007

AIChE Journal

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in Wiley InterScience (www.interscience.wiley.com).A short-term operational planning model for natural gas production systems can help identify a consistent operational policy that satisfies contractual rules and customer specifications. Formulating and solving such a model poses challenges due to nonlinear pressure-flowrate relations, a multicommodity network and complex production-sharing contracts (PSC). A production allocation model is presented that can be viewed as a contractual model superimposed on an infrastructure model. The infrastructure model incorporates nonlinear pressure-flowrate relationships for wells and pipelines, multiple qualities of gas in the trunkline network and models of facilities. The contractual model is a mathematical representation of the PSC and associated operational rules. The model features are inspired by the Sarawak Gas Production System (SGPS) in East Malaysia. A case study similar to the Sarawak Gas Production System (SGPS) is presented. The final formulation is a nonconvex mixed-integer nonlinear program and is solved with GAMS/BARON to global optimality. A hierarchical multiobjective operational study is also presented.

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Using generalized networks to forecast natural gas distribution and allocation during periods of shortage

Cited by 4 publications

References 3 publications

Natural Gas Value-Chain and Network Assessments

Natural Gas Value-Chain and Network Assessments

A stochastic optimization model for gas retail with temperature scenarios and oil price parameters

A short‐term operational planning model for natural gas production systems

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