Browsing by Author "Basson, L"

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    Electricity supply industry modeling for multiple objectives under demand growth uncertainty
    (Elsevier, 2007) Heinrich, G; Howells, M I; Basson, L; Petrie, J G
    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.
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    Electricity supply industry modeling for multiple objectives under demand growth uncertainty
    (Elsevier, 2007) Heinrich, G; Basson, L; Howells, M; Petrie, J
    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.
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    Ranking power expansion alternatives for multiple objectives under certainty
    (Elsevier, 2007) Heinrich, G; Basson, L; Cohen, B; Petrie, J; Howells, M
    Strategic planning in the electricity supply industry is a complex task due to the multiple and often conflicting objectives of the decision makers, as well as the inherent technical and valuation uncertainties involved. As such, a transparent decision support framework is needed, for guiding information management throughout the decision process, in a way which shapes decision outcomes, and enables confident choices to be made. This paper outlines a methodology for the ranking of power expansion alternatives given multiple objectives and uncertainty, and demonstrates this using the South African electricity supply industry. This methodology uses a value function MCDA approach that is augmented with scenario analysis to yield information relating to both the relative performance and credibility of power expansion alternatives. A portfolio of preferred alternatives is then identified based on performance and confidence criteria. Finally a more detailed analysis of the reduced solution set examines short-term technology investment details alongside attribute performance information, so as to gain insight into the decision problem and relate it back to real life actions.