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Rolling optimisation, stochastic demand modelling and scenario reduction applied to the UK gas market

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dc.contributor.advisor Gleeson, James P.
dc.contributor.advisor Kinsella, John
dc.contributor.advisor Ramsey, David
dc.contributor.author Devine, Mel T.
dc.date.accessioned 2013-01-29T15:59:33Z
dc.date.available 2013-01-29T15:59:33Z
dc.date.issued 2012
dc.identifier.uri http://hdl.handle.net/10344/2836
dc.description peer-reviewed en_US
dc.description.abstract In recent years the daily gas demand in the UK and Ireland has become increasingly uncertain. This due to the changing nature of electricity markets, where intermittent wind energy levels lead to variations in the demand for gas needed to produce electricity. As a result, there is an increasing need for models of natural gas markets that include stochastic demand. In this thesis, a Rolling Optimisation Model (ROM) of the UK natural gas market is introduced. It takes as an input demand scenarios simulated from a stochastic process of UK gas demand which is developed as a part of this work. The model is informed by an analysis of the two main types of natural gas market models: complementarity-based equilibrium models and cost minimisation models. This analysis shows that when market power (i.e. Nash-Cournot competition) is removed from complementarity-based equilibrium models the outputs are equivalent to those from a corresponding cost minimisation model. The outputs of the Rolling Optimisation Model are the ows of gas in the UK, i.e., how the different sources of supply meet demand, as well as how gas ows in to and out of gas storage facilities, and the daily System Average Price of gas in the UK. The model was found to t reasonably well to historic data (from the UK National Grid) for the years starting on the 1st of April for both 2010 and 2011. This work also investigates the bene t of using scenario reduction techniques on the set of demand scenarios used in ROM. These techniques allow the effects of large sets of stochastically-generated scenarios to be captured in ROM, whilst maintaining a relatively low computational cost for solving the model. In the nal chapter of this thesis, ROM is used to predict future ows and prices of gas in the UK and investigate various `What-if' scenarios in the UK natural gas market. en_US
dc.language.iso eng en_US
dc.publisher University of Limerick en_US
dc.subject UK natural gas market en_US
dc.subject ROM en_US
dc.subject Ireland en_US
dc.title Rolling optimisation, stochastic demand modelling and scenario reduction applied to the UK gas market en_US
dc.type info:eu-repo/semantics/doctoralThesis en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.type.supercollection ul_theses_dissertations en_US
dc.contributor.sponsor SFI en_US
dc.relation.projectid 06/MI/005 en_US
dc.relation.projectid 06/IN.1/I366 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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