Browsing by Author "Howells, M I"

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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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    A model of household energy services in a low-income rural African village
    (Elsevier, 2005) Howells, M I; Alfstada T; Victor, D G; Goldsteinc, G; Remmed, U
    Energy use is closely linked to quality of life in rural Africa. The gathering of fuel-wood and other traditional fuels is a strenuous and time consuming task mainly performed by women; indoor exposure to particulate matter, mainly from cooking and heating with traditional fuels, causes about 2.5 million deaths each year in developing countries (Bruce et al., Bull World Org. 78 (2000) 1078). Modern fuels and appliances allow households to reduce their exposure to smoke from biomass cookers and heaters. Yet modern fuels are costly for income-poor households and often carry their own external costs. For example, numerous children are poisoned from ingesting paraffin, and whole villages have burned from fires triggered by paraffin stoves and lamps.
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    Open Access
    Targeting of industrial audits for DSM planning
    (University of Cape Town, 2006) Howells, M I
    The scope of this section of the study is to establish which industries to target for energy audits and demand side management (DSM) projects. As only a limited number of audits will be conducted, it is important to establish how to maximise the return on the invested efforts and resources. The aim is thus, to develop a ranking of industries based on their potential for savings from DSM interventions. It considers the following criteria: 1. Electricity consumption and potential DSM savings from retrofits at existing plants; 2. Electricity consumption and potential DSM savings for new plants; 3. Potential DSM interventions by industry; 4. The costs of a suite of DSM interventions by industry; and 5. The technical ease with which DSM may be implemented by industry. The potential for DSM savings for different industrial sectors is evaluated based on these criteria, using aggregated values sourced from local and international studies. DSM measures are applied to the various ‘end uses’ of electricity within each industry. From these we suggest a shortlist of 10 industries to target for energy audits and data gathering. We consider both industry and mining, and refer to the group collectively as industry. The data gathered in the energy audits will be used to refine estimates of the potential for DSM savings in each sector. Data loggers will be installed to measure electricity consumption and demand profiles (kW load as a function of time), which will be used to estimate the impact of DSM interventions on national demand for energy and power. This can provide valuable input to power system planning and analysis in the future