A chance-constrained programming approach to natural gas curtailment decisions

Guldmann, Jean‐Michel

doi:10.1016/0165-0572(81)90020-7

Cited by 2 publications

(2 citation statements)

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“…Probabilistic uncertainties were represented as random variables with presumed probability distribution functions. The related methods included (Guldman, 1981(Guldman, , 1986Fronza and Melli, 1984;Fuessle et al, 1987;Ellis et al, 1985;Ellis, 1990;Altman et al, 1996;Lu et al, 2008). Imprecise and vague constraints and goals resulting from human subjectivities were presented as fuzzy sets with known membership functions in (Rommelfanger et al, 1989;Tong, 1994;Liu et al, 2003;Li et al, 2006;Lin et al, 2009).…”

Section: Review Of Existing Related Studiesmentioning

confidence: 99%

Synchronic interval Gaussian mixed-integer programming for air quality management

Cheng

Huang

Dong

2015

Science of The Total Environment

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Section: Review Of Existing Related Studiesmentioning

confidence: 99%

Synchronic interval Gaussian mixed-integer programming for air quality management

Cheng

Huang

Dong

2015

Science of The Total Environment

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“…Em 1981, o modelo apresentado em [46] propôs considerar risco nas restrições de forma a levar em conta movimentos de demanda decorrentes de variações climáticas. A formulação tinha abrangência temporal de múltiplos períodos discretizados em meses.…”

Section: Lista De Figurasunclassified

Dimensionamento De Uma Estocagem De Gás Natural Sob Incerteza De Demanda E Preço De GNL

MARTINS¹

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À minha família, meu porto seguro, por todas as palavras e gestos de amor, carinho e incentivo. Pelos abraços, beijos e momentos de alegria. Agradeço em especial à Juana, Leila e Olívia. Percorrer este caminho -e muitos outros -com vocês é um privilégio. À minha família do trabalho, amigos e mentores, pelo exemplo precioso, permanente e incansável de profissionalismo, ética, isenção e competência. Pelo companheirismo e as boas risadas. Pela compreensão nos momentos difíceis. Um agradecimento especial a Abramant, Álvaro, Denilton, Diórgenes, Edgard e Victor. A ordem é alfabética, no coração estão todos juntos.Ao meu orientador, Alexandre Street, por ter me acolhido e orientado de forma objetiva, clara, detalhada, entusiasmada, lúcida e serena, tornando leve esta jornada de intenso aprendizado.Aos colegas do labMAD, em particular Bruno e Alexandre, pela ajuda preciosa que rendeu conhecimento e poupou tempo.Por fim, agradeço à Petrobras pelo tempo e recursos cedidos para que eu pudesse ter participado deste mestrado. AbstractMartins, Lilian Alves; Aguiar, Alexandre Street (Advisor). Sizing of a natural gas storage under demand and price uncertainty. Rio de Janeiro 2012, 140p. MSc Dissertation -Departamento de Engenharia Industrial, Pontifícia Universidade Católica do Rio de Janeiro.In Brazil, natural gas demand has stochastic behavior since gas-fired power plants operate in conjunction with the hydroelectric system. Natural gas supply to these plants relies upon Liquefied Natural Gas (LNG), imported through cryogenic ships. LNG acquisitions must occur before the natural gas demand is known because of the time of displacement of the ships. This lack of synchronism stimulates the use of harmonizing mechanisms between the electric sector and the natural gas sector. In this context, natural gas storage could be used to introduce flexibility into the system and increase synergy between natural gas supply and demand dynamics. However, the economic performance of the storage will depend on actual gas prices and demand behavior during the period of analysis.This study aims to construct a linear programming model to determine the size of a natural gas storage under demand and LNG price uncertainty. The model is a hybrid of a stochastic optimization algorithm -developed to consider gas demand uncertainty -and a robust optimization algorithm -built to take into account LNG price uncertainty. A convex combination between Conditional Value-at-Risk (CVaR) and expected value is also used to indicate the supplier risk profile as well as a security criterion, introduced to represent a deficit-averse supply process. At the end, a hypothetic case is presented to evaluate the implementation of a natural gas storage. The case presented uses public data from the Brazilian electric and gas natural sectors and considers 2.000 demand scenarios and various levels of robustness to LNG price variation.

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A chance-constrained programming approach to natural gas curtailment decisions

Cited by 2 publications

References 13 publications

Synchronic interval Gaussian mixed-integer programming for air quality management

Synchronic interval Gaussian mixed-integer programming for air quality management

Dimensionamento De Uma Estocagem De Gás Natural Sob Incerteza De Demanda E Preço De GNL

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