Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/87367
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dc.titleOptimizing natural gas supply and energy portfolios of a generation company
dc.contributor.authorKittithreerapronchai, O.
dc.contributor.authorJirutitijaroen, P.
dc.contributor.authorKim, S.
dc.contributor.authorPrina, J.
dc.date.accessioned2014-10-07T10:27:19Z
dc.date.available2014-10-07T10:27:19Z
dc.date.issued2010
dc.identifier.citationKittithreerapronchai, O., Jirutitijaroen, P., Kim, S., Prina, J. (2010). Optimizing natural gas supply and energy portfolios of a generation company. 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2010 : 231-237. ScholarBank@NUS Repository.
dc.identifier.isbn9781424457236
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/87367
dc.description.abstractIn a deregulated electricity market environment, a natural gas-fired generation company must manage its natural gas supply and construct energy portfolios by engaging in contracts to buy natural gas and generate electricity. The contracts protect the company from fluctuations in prices and demands, but provide minimal profits. The company may gain larger profits - and possible loses- by accessing natural gas spot and electricity pool markets. To capture such hedging decisions and the interactions between the natural gas and electricity markets, we formulate a Stochastic Programming model and study its benefits over an expected value problem. The stochastic model enables the company to optimize the electricity generation schedule and the natural gas consumption as well as to develop managerial insights. © 2010 IEEE.
dc.sourceScopus
dc.subjectEnergy portfolio
dc.subjectNatural gas supply portfolio
dc.subjectStochastic programming
dc.subjectValue of the stochastic solution
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.contributor.departmentINDUSTRIAL & SYSTEMS ENGINEERING
dc.description.sourcetitle2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2010
dc.description.page231-237
dc.identifier.isiutNOT_IN_WOS
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