A comprehensive approach to electricity investment planning for multiple objectives and uncertainty

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dc.contributor.authorHeinrich, Glen Seanen_ZA
dc.date.accessioned2014-07-31T11:09:31Z
dc.date.available2014-07-31T11:09:31Z
dc.date.issued2008en_ZA
dc.descriptionIncludes abstract.
dc.descriptionIncludes bibliographical references.
dc.description.abstractAppropriate Energy-Environment-Economic (E3) modelling provides key information for policy makers in the Electricity Supply Industry (ESI) faced with navigating a sustainable development path. Key challenges include engaging with stakeholder values and preferences, and exploring trade-offs between competing objectives in the face of underlying uncertainty. As such, a comprehensive framework is needed that integrates multiple objectives and uncertainty into a transparent methodology that policy makers and planners can use to analyse and plan for investment in the ESI, in a way which shapes decision outcomes, and enables confident choices to be made. This thesis is aimed at developing such a framework. As a case study the South African ESI was represented using a partial equilibrium (Energy-Economic-Environment) E3 modelling approach. This approach was extended to include multiple objectives under selected future uncertainties. This extension was achieved by assigning cost penalties (PGPs – Pareto Generation Parameters) to non-cost attributes to force the model’s least-cost objective function to better satisfy non-cost criteria. It was shown that using PGPs is an efficient method for extending the analysis to multiple objectives as the solutions generated are non-dominated and are generated from ranges of performances in the various criteria rather than from arbitrarily forcing the selection of particular technologies. Extensive sections of the non-dominated solution space can be generated and later screened to allow further, more detailed exploration of areas of the solution space. Aspects of flexibility to demand growth uncertainty were incorporated into each future expansion alternative (FEA) by introducing stochastic programming with recourse into the model. Technology lead times were taken into account by the inclusion of a decision node along the time horizon where aspects of real options theory were considered within the planning process by splitting power station investments into their Owner’s Development Cost (ODC) and Equipment and Procurement Cost (EPC) components. Hedging in the recourse programming was automatically translated from being purely financial, to include the other attributes that the cost penalties represented. The hedged solutions improved on the naïve solutions under the multiple criteria considered as well as better satisfying the non-cost objectives relative to the base case (least cost solution). From a retrospective analysis of the cost penalties, the correct market signals could be derived to meet policy goal, with due regard to demand uncertainty.en_ZA
dc.identifier.apacitationHeinrich, G. S. (2008). <i>A comprehensive approach to electricity investment planning for multiple objectives and uncertainty</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Chemical Engineering. Retrieved from http://hdl.handle.net/11427/5319en_ZA
dc.identifier.chicagocitationHeinrich, Glen Sean. <i>"A comprehensive approach to electricity investment planning for multiple objectives and uncertainty."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Chemical Engineering, 2008. http://hdl.handle.net/11427/5319en_ZA
dc.identifier.citationHeinrich, G. 2008. A comprehensive approach to electricity investment planning for multiple objectives and uncertainty. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Heinrich, Glen Sean AB - Appropriate Energy-Environment-Economic (E3) modelling provides key information for policy makers in the Electricity Supply Industry (ESI) faced with navigating a sustainable development path. Key challenges include engaging with stakeholder values and preferences, and exploring trade-offs between competing objectives in the face of underlying uncertainty. As such, a comprehensive framework is needed that integrates multiple objectives and uncertainty into a transparent methodology that policy makers and planners can use to analyse and plan for investment in the ESI, in a way which shapes decision outcomes, and enables confident choices to be made. This thesis is aimed at developing such a framework. As a case study the South African ESI was represented using a partial equilibrium (Energy-Economic-Environment) E3 modelling approach. This approach was extended to include multiple objectives under selected future uncertainties. This extension was achieved by assigning cost penalties (PGPs – Pareto Generation Parameters) to non-cost attributes to force the model’s least-cost objective function to better satisfy non-cost criteria. It was shown that using PGPs is an efficient method for extending the analysis to multiple objectives as the solutions generated are non-dominated and are generated from ranges of performances in the various criteria rather than from arbitrarily forcing the selection of particular technologies. Extensive sections of the non-dominated solution space can be generated and later screened to allow further, more detailed exploration of areas of the solution space. Aspects of flexibility to demand growth uncertainty were incorporated into each future expansion alternative (FEA) by introducing stochastic programming with recourse into the model. Technology lead times were taken into account by the inclusion of a decision node along the time horizon where aspects of real options theory were considered within the planning process by splitting power station investments into their Owner’s Development Cost (ODC) and Equipment and Procurement Cost (EPC) components. Hedging in the recourse programming was automatically translated from being purely financial, to include the other attributes that the cost penalties represented. The hedged solutions improved on the naïve solutions under the multiple criteria considered as well as better satisfying the non-cost objectives relative to the base case (least cost solution). From a retrospective analysis of the cost penalties, the correct market signals could be derived to meet policy goal, with due regard to demand uncertainty. DA - 2008 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2008 T1 - A comprehensive approach to electricity investment planning for multiple objectives and uncertainty TI - A comprehensive approach to electricity investment planning for multiple objectives and uncertainty UR - http://hdl.handle.net/11427/5319 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/5319
dc.identifier.vancouvercitationHeinrich GS. A comprehensive approach to electricity investment planning for multiple objectives and uncertainty. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Chemical Engineering, 2008 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/5319en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Chemical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherChemical Engineeringen_ZA
dc.titleA comprehensive approach to electricity investment planning for multiple objectives and uncertaintyen_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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