Sustainability metrics for coal power generation in Australia

The basis of this work was to investigate the relative environmental impacts of various power generators knowing that all plants are located in totally different environments and that different receptors will experience different impacts. Based on IChemE sustainability metrics paradigm, we calculated potential environmental indicators (P-EI) that represent the environmental burden of masses of potential pollutants discharged into different receiving media. However, a P-EI may not be of significance, as it may not be expressed at all in different conditions, so to try and include some receiver significance we developed a methodology to take into account some specific environmental indicators (S-EI) that refer to the environmental attributes of a specific site. In this context, we acquired site specific environmental data related to the airsheds and water catchment areas in different locations for a limited number of environmental indicators such as human health (carcinogenic) effects, atmospheric acidification, photochemical (ozone) smog and eutrophication. The S-EI results from this particular analysis show that atmospheric acidification has highest impact value while health risks due to fly ash emissions are considered not to be as significant. This is due to the fact that many coal power plants in Australia are located in low population density air sheds. The contribution of coal power plants to photochemical (ozone) smog and eutrophication were not significant. In this study, we have considered emission related data trends to reflect technology performance (e.g., P-EI indicators) while a real sustainability metric can be associated only with the specific environmental conditions of the relevant sites (e.g., S-EI indicators).

A novel grid computing approach for probabilistic small signal analysis

Grid computing is an advanced technique for collaboratively solving complicated scientific problems using geographically and organisational dispersed computational, data storage and other recourses. Application of grid computing could provide significant benefits to all aspects of power system that involves using computers. Based on our previous research, this paper presents a novel grid computing approach for probabilistic small signal stability (PSSS) analysis in electric power systems with uncertainties. A prototype computing grid is successfully implemented in our research lab to carry out PSSS analysis on two benchmark systems. Comparing to traditional computing techniques, the gird computing has given better performances for PSSS analysis in terms of computing capacity, speed, accuracy and stability. In addition, a computing grid framework for power system analysis has been proposed based on the recent study.