Development of a comprehensive decision making framework for power projects in New South Wales (NSW)
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In the backdrop of emerging concerns about sustainability, the contribution of electricity generation to sustainability, the complexity of power projects, and the narrowness of existing decision approaches for selecting power projects – this research develops a comprehensive decision-making framework that can be applied to select power projects for meeting future electricity needs in NSW. This framework is based on Multi-Attribute Decision Analysis (Analytical Hierarchy Process). This framework will assist with complex decisions regarding projects typified by multiple objectives, multiple decision makers, multiple attributes, conflicts, and socio-economic concerns. The appropriateness of this framework is established in this research in terms of its ability to assist with project choices (from among several alternatives), to meet medium (2035) and long term (2050) electricity needs of NSW, in a sustainable manner. The backdrop for the application of this approach is provided by five scenarios, representing alternative technological pathways, energy & environmental and socio-political settings. Fourteen attributes, reflecting major areas of concern relating to economic, environmental, technical, and socio-political issues are considered, guided by literature review and expert opinion. The overall ranking for each alternative is developed on the basis of, first, assessing the economic, environmental, and social impacts of the alternatives; second, incorporating decision makers’ (expert) preferences for selected attributes, through a pair-wise comparison of various attributes; next, developing a weighted average across all attributes. These individual scenario rankings are then used to compare alternatives represented by various scenarios. The analysis suggest that, overall, the BAU scenario, representing a continuation of existing trends in generation-mix is likely to be the most detrimental scenario for achieving sustainable outcomes in NSW, as it will result in highest levels of levelized cost, GHG emissions, total waste, air pollution, visual impact, water use, resource (fuel) use, severe accidents; and lowest levels of new jobs, and political and social acceptance. The best option in the medium term (2035) for NSW will be the HR-1 scenario (40% share of renewables, with nuclear), as it will result in the highest levels of new jobs, political and social acceptance; and lowest levels of levelized cost, GHG emissions, total waste, air pollution, resource (fuel) use. The best option in the long term (2050) for NSW will be the HR-2 scenario (80% share of renewables, without nuclear), as it will result in the highest levels of new jobs, political and social acceptance; lowest levels of total waste, water use, severe accidents; and moderate levels of levelized cost, air pollution, water and resource use. It is interesting to note the change of preference from HR-1 scenario in the medium term, to HR-2 scenario in the long term, as informed by trade-offs between various attributes. The above insights clearly demonstrate the usefulness of the proposed framework for making complex decisions about power projects.
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