Electricity supply industry modeling for multiple objectives under demand growth uncertainty

 

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dc.contributor.author Heinrich, G
dc.contributor.author Howells, M I
dc.contributor.author Basson, L
dc.contributor.author Petrie, J G
dc.date.accessioned 2016-01-27T11:19:34Z
dc.date.available 2016-01-27T11:19:34Z
dc.date.issued 2007
dc.identifier http://dx.doi.org/10.1016/j.energy.2007.05.007
dc.identifier.citation Heinrich, G., Howells, M., Basson, L., & Petrie, J. (2007). Electricity supply industry modelling for multiple objectives under demand growth uncertainty. Energy, 32(11), 2210-2229. en_ZA
dc.identifier.issn 0360-5442 en_ZA
dc.identifier.uri http://hdl.handle.net/11427/16582
dc.description.abstract 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 a case study we represent the South African ESI using a partial equilibrium E3 modelling approach, and extend the approach to include multiple objectives under selected future uncertainties. This extension is achieved by assigning cost penalties to non-cost attributes to force the model's least-cost objective function to better satisfy non-cost criteria. This paper incorporates aspects of flexibility to demand growth uncertainty into each future expansion alternative by introducing stochastic programming with recourse into the model. Technology lead times are taken into account by the inclusion of a decision node along the time horizon where aspects of real options theory are considered within the planning process. Hedging in the recourse programming is automatically translated from being purely financial, to include the other attributes that the cost penalties represent. From a retrospective analysis of the cost penalties, the correct market signals, can be derived to meet policy goal, with due regard to demand uncertainty. en_ZA
dc.language eng en_ZA
dc.publisher Elsevier en_ZA
dc.source Energy en_ZA
dc.source.uri http://www.sciencedirect.com/science/journal/03605442
dc.subject.other Electricity supply industry modelling
dc.subject.other Sustainability
dc.subject.other Uncertainty
dc.subject.other Multi-objective optimisation
dc.title Electricity supply industry modeling for multiple objectives under demand growth uncertainty en_ZA
dc.type Journal Article en_ZA
dc.date.updated 2016-01-27T11:11:53Z
uct.type.publication Research en_ZA
uct.type.resource Article en_ZA
uct.subject.keywords Electricity supply industry modelling en_ZA
uct.subject.keywords Sustainability en_ZA
uct.subject.keywords Uncertainty en_ZA
uct.subject.keywords Multi-objective optimisation en_ZA
uct.subject.keywords Stochastic programming en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Engineering and the Built Environment
dc.publisher.department Energy Research Centre en_ZA
uct.type.filetype
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Heinrich, G., Howells, M. I., Basson, L., & Petrie, J. G. (2007). Electricity supply industry modeling for multiple objectives under demand growth uncertainty. <i>Energy</i>, http://hdl.handle.net/11427/16582 en_ZA
dc.identifier.chicagocitation Heinrich, G, M I Howells, L Basson, and J G Petrie "Electricity supply industry modeling for multiple objectives under demand growth uncertainty." <i>Energy</i> (2007) http://hdl.handle.net/11427/16582 en_ZA
dc.identifier.vancouvercitation Heinrich G, Howells MI, Basson L, Petrie JG. Electricity supply industry modeling for multiple objectives under demand growth uncertainty. Energy. 2007; http://hdl.handle.net/11427/16582. en_ZA
dc.identifier.ris TY - Journal Article AU - Heinrich, G AU - Howells, M I AU - Basson, L AU - Petrie, J G 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 a case study we represent the South African ESI using a partial equilibrium E3 modelling approach, and extend the approach to include multiple objectives under selected future uncertainties. This extension is achieved by assigning cost penalties to non-cost attributes to force the model's least-cost objective function to better satisfy non-cost criteria. This paper incorporates aspects of flexibility to demand growth uncertainty into each future expansion alternative by introducing stochastic programming with recourse into the model. Technology lead times are taken into account by the inclusion of a decision node along the time horizon where aspects of real options theory are considered within the planning process. Hedging in the recourse programming is automatically translated from being purely financial, to include the other attributes that the cost penalties represent. From a retrospective analysis of the cost penalties, the correct market signals, can be derived to meet policy goal, with due regard to demand uncertainty. DA - 2007 DB - OpenUCT DP - University of Cape Town J1 - Energy LK - https://open.uct.ac.za PB - University of Cape Town PY - 2007 SM - 0360-5442 T1 - Electricity supply industry modeling for multiple objectives under demand growth uncertainty TI - Electricity supply industry modeling for multiple objectives under demand growth uncertainty UR - http://hdl.handle.net/11427/16582 ER - en_ZA


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