Risk aversion in imperfect natural gas markets

Egging, Ruud; Pichler, Alois; Kalvø, Oyvind Iversen; Walle-Hansen, Thomas Meyer

doi:10.1016/j.ejor.2016.10.020

Cited by 22 publications

(5 citation statements)

References 42 publications

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“…A natural gas market equilibrium model considering risk aversion related to uncertainty in shale gas reserve exploration is proposed by Egging et al. (). Three types of agents are considered: suppliers (who take risks), a transmission operator, and consumers.…”

Section: Energymentioning

confidence: 99%

Conditional value‐at‐risk beyond finance: a survey

Filippi

Guastaroba

Speranza

2019

Int Trans Operational Res

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A large number of problems involve making decisions in an uncertain environment and, hence, with unknown outcomes. Optimization models aimed at controlling the trade‐off between risk and return in finance have been widely studied since the seminal work by Markowitz in 1952. In financial applications, shortfall or quantile risk measures are receiving ever‐increasing attention. Conditional value‐at‐risk (CVaR) is arguably the most popular of such measures. In the last decades, optimization models aimed at controlling risk have been applied to several application domains different from financial optimization. This survey provides an overview of the main contributions where CVaR is incorporated into an optimization approach and applied to a context different from financial engineering. The literature is classified following an application‐oriented perspective. The applications cover classical areas studied in operational research—such as supply chain management, scheduling, and networks—and less classical areas such as energy and medicine. For each area, concise paper excerpts are provided that convey the main ideas of the problems studied, and analyze how the CVaR has been used to cope with different sources of uncertainty. Finally, some open research directions are outlined.

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Section: Energymentioning

confidence: 99%

Conditional value‐at‐risk beyond finance: a survey

Filippi

Guastaroba

Speranza

2019

Int Trans Operational Res

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“…Nwaoha et al [40] indicated a decline in the production of oil, but growth in the utilization of natural gas globally. However, production dropped by 1.7% in Europe and Eurasia [41]. Though domestic natural-gas production is decreasing in Europe, consumption has increased with a higher number of imports, making it 15.3% of the total global consumption in 2011 [42].…”

Section: Countrymentioning

confidence: 99%

Natural Gas Engine Technologies: Challenges and Energy Sustainability Issue

2018

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Energy sustainability and environmental issues pose greater challenges on different primary energy sectors as the global energy demand increases and it is projected to further increase with an upsurge in population. On the other hand, energy sources from conventional fossil-based fuels are depleting, forcing explorations in challenging and difficult locations. As a result, the use of alternative fuels received dramatic consideration to substitute these conventional fuels, of which natural gas took the significant share. However, the share of natural-gas vehicles in the current vehicle market is quite small, and it is estimated to be below 5%. This paper reviews the current resource scenarios including proven and potential reserves, current production, and consumption, along with the fueling infrastructure, distribution, and storage. It also provides summary of the development of fuel-injection technologies aimed to enhance the performance of gas engines. More attention was also given to natural-gas engines and their limitations. Parameters affecting the performance and combustion of compressed natural gas (CNG) in spark-ignition (SI) engines are thoroughly assessed, among which compression ratio and injection timing play major roles in the optimization of CNG-fueled engines. Furthermore, different technologies that help close the performance gap between conventional liquid-fuel and natural-gas engines and future directions of the research are presented.

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“…Let also say that P n ⊆ P, C n ⊆ C are located in node n ∈ N. Let A n be the pipelines connected to node n. The symbols used here are explained in Table 1, 2 and 3. Most of the analysis closely follow [18] and [14]. Random parameters are denoted by an (ω) beside them.…”

Section: Natural Gas Market -Complementarity Formulationmentioning

confidence: 99%

“…The behavior of a solution to a complementarity problem with random parameters was first addressed in [25], where such problems were referred to as stochastic complementarity problems (SCP). Authors in [9,14,23,30,42] define various formulations of SCP for different applications and have devised algorithms to solve the problem. Authors in [35] compute confidence intervals for solution of the expected value formulation of the problem, however they do not have efficient methods to find the second-order statistics for large-scale complementarity problems.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity and covariance in stochastic complementarity problems with an application to North American natural gas markets

Sankaranarayanan

Feijoo

Siddiqui

2018

European Journal of Operational Research

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We provide an efficient method to approximate the covariance between decision variables and uncertain parameters in solutions to a general class of stochastic nonlinear complementarity problems. We also develop a sensitivity metric to quantify uncertainty propagation by determining the change in the variance of the output due to a change in the variance of an input parameter. The covariance matrix of the solution variables quantifies the uncertainty in the output and pairs correlated variables and parameters. The sensitivity metric helps in identifying the parameters that cause maximum fluctuations in the output. The method developed in this paper optimizes the use of gradients and matrix multiplications which makes it particularly useful for large-scale problems. Having developed this method, we extend the deterministic version of the North American Natural Gas Model (NANGAM), to incorporate effects due to uncertainty in the parameters of the demand function, supply function, infrastructure costs, and investment costs. We then use the sensitivity metrics to identify the parameters that impact the equilibrium the most.

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Risk aversion in imperfect natural gas markets

Cited by 22 publications

References 42 publications

Conditional value‐at‐risk beyond finance: a survey

Conditional value‐at‐risk beyond finance: a survey

Natural Gas Engine Technologies: Challenges and Energy Sustainability Issue

Sensitivity and covariance in stochastic complementarity problems with an application to North American natural gas markets

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