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dc.contributor.advisorDyer, James S.
dc.contributor.advisorButler, John C.(Clinical associate professor)
dc.creatorCuevas, Pedro Pablo
dc.date.accessioned2016-09-20T16:43:11Z
dc.date.available2016-09-20T16:43:11Z
dc.date.issued2016-05
dc.date.submittedMay 2016
dc.identifierdoi:10.15781/T2N00ZV36
dc.identifier.urihttp://hdl.handle.net/2152/40929
dc.description.abstractThe effects of changing regulatory and fuel-cost environments have far reaching implications on the ability of electric markets to plan and provide cheap, clean, and reliable electric grids. The current state of the art tools for modeling the regulations and fuel prices requires days to process and access to these tools is also held by a small number licensed users that must also have the training and technical ability to run the model, which limits the study of planning and electricity market design.. This thesis presents an Excel model that simulates the operations of ERCOT over the next fifteen years. Tradeoffs between accuracy, run time, cost, and model complexity will be discussed. The advantages of this model are speed and accessibility, which will allow more users to understand the major implications of policy discussions and scenarios without needing a commercial tool. The model predicts the fuel mix and average market price for 2014 with less than a 1% and 2% error respectively. For 2015, the model predicts the fuel mix with less than a 5% error. Using the current trends assumptions, the model predicts that by 2030 the energy mix will undergo significant changes. Coal generation will drop from 28% to 21%, while gas generation will decline from 48% to 46%. Renewable generation will increase with wind going from 12% to 17% and solar from 0% to 7%. The model also predicts that a carbon tax between $20 and $60 per short ton of CO2, could rise the operational and capital costs of ERCOT in present value terms until 2030 from $75 billion to $218 billion. Finally the model forecasts that the reserve margin in ERCOT will not reach the target of 13.75% in 2020 and that renewable energy addition does not affect this indicator. Even more, the reserve margin is increased when solar energy enters the market.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectElectric grids
dc.subjectTexas
dc.subjectSpreadsheet dispatch model
dc.subjectForecasting
dc.subjectEnergy market
dc.subjectMathematical model
dc.subjectIndependent system operator
dc.subjectERCOT
dc.subjectLACE
dc.subjectLCOE
dc.subjectCO2
dc.titleExcel model for electric markets : ERCOT
dc.title.alternativeExcel model for electric markets : Electric Reliability Council of Texas
dc.typeThesis
dc.date.updated2016-09-20T16:43:11Z
dc.contributor.committeeMemberHahn, Joe
dc.description.departmentEnergy and Earth Resources
thesis.degree.departmentGeological Sciences
thesis.degree.disciplineEnergy and earth resources
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Energy and Earth Resources
dc.creator.orcid0000-0003-3472-2012
dc.type.materialtext


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