The computational grid provides a promising platform for the deployment of various high-performance computing applications. A grid system consists of heterogeneous resource domains, while the computational tasks of fi...The computational grid provides a promising platform for the deployment of various high-performance computing applications. A grid system consists of heterogeneous resource domains, while the computational tasks of finite element analysis may differ in demand of computing power. The cost-effective utilization of resources in the grid can be obtained through scheduling tasks to optimal resource domains. Firstly, a cost-effective scheduling strategy is presented for finite element applications. Secondly, aiming at the conjugate gradient solver stemming from finite element analysis, a performance evaluation formula is presented for determining optimal resouree domains, which is derived from phase parallel model and takes the heterogeneous characteristic of resource domains into account. Finally, experimental results show that the presented formula delivers a good estimation of the actual execution time, and indicate that the presented formula can be used to determine optimal resource domains in the grid environment.展开更多
Decarbonization of electricity industry for the goal of sustainability success has resulted in large investment in alternative energy sources such as wind, solar, biomass. Although these energy resources are sustainab...Decarbonization of electricity industry for the goal of sustainability success has resulted in large investment in alternative energy sources such as wind, solar, biomass. Although these energy resources are sustainable and have the potential of reducing the world carbon foot print, there are costs associated with its utilization. In recent time, electricity from alternative energy sources like wind and solar are not cost competitive with electricity from the conventional power plant. This paper is aimed at investigating the optimum investment in a typical wind farm project using a TSA （time series analysis） alongside simple economic tool, AAP （annual annuity payment） model. This study involves a year round analysis of （8,760h） at different wind farm capacity connected to a 132/33kV DS （distribution system）. It also focused on digressing from the technical and environmental benefits to financial assessment of increasing wind generation capacity in the DS. Indeed, this development presents a risk of investment to the stakeholders which necessitates proper scrutiny and to ensure profitability of the venture. The level of capital cost along with operation and maintenance （OM） costs are either financed by private or public sectors on wind farm with the sole aim of achieving the ROI （return-on-investment）. The results obtained from this study shows the possible ROI is not proportional to the wind capacity invested. Also, a sensitivity analysis conducted revealed the profit derived from wind farm is more responsive to the investment/capital cost and the price at which the electricity is being sold.展开更多
Long-term meteorological observation series are fundamental for reflecting climate changes.However,almost all meteorological stations inevitably undergo relocation or changes in observation instruments,rules,and metho...Long-term meteorological observation series are fundamental for reflecting climate changes.However,almost all meteorological stations inevitably undergo relocation or changes in observation instruments,rules,and methods,which can result in systematic biases in the observation series for corresponding periods.Homogenization is a technique for adjusting these biases in order to assess the true trends in the time series.In recent years,homogenization has shifted its focus from the adjustments to climate mean status to the adjustments to information about climate extremes or extreme weather.Using case analyses of ideal and actual climate series,here we demonstrate the basic idea of homogenization,introduce new understanding obtained from recent studies of homogenization of climate series in China,and raise issues for further studies in this field,especially with regards to climate extremes,uncertainty of the statistical adjustments,and biased physical relationships among different climate variables due to adjustments in single variable series.展开更多
文摘The computational grid provides a promising platform for the deployment of various high-performance computing applications. A grid system consists of heterogeneous resource domains, while the computational tasks of finite element analysis may differ in demand of computing power. The cost-effective utilization of resources in the grid can be obtained through scheduling tasks to optimal resource domains. Firstly, a cost-effective scheduling strategy is presented for finite element applications. Secondly, aiming at the conjugate gradient solver stemming from finite element analysis, a performance evaluation formula is presented for determining optimal resouree domains, which is derived from phase parallel model and takes the heterogeneous characteristic of resource domains into account. Finally, experimental results show that the presented formula delivers a good estimation of the actual execution time, and indicate that the presented formula can be used to determine optimal resource domains in the grid environment.
文摘Decarbonization of electricity industry for the goal of sustainability success has resulted in large investment in alternative energy sources such as wind, solar, biomass. Although these energy resources are sustainable and have the potential of reducing the world carbon foot print, there are costs associated with its utilization. In recent time, electricity from alternative energy sources like wind and solar are not cost competitive with electricity from the conventional power plant. This paper is aimed at investigating the optimum investment in a typical wind farm project using a TSA （time series analysis） alongside simple economic tool, AAP （annual annuity payment） model. This study involves a year round analysis of （8,760h） at different wind farm capacity connected to a 132/33kV DS （distribution system）. It also focused on digressing from the technical and environmental benefits to financial assessment of increasing wind generation capacity in the DS. Indeed, this development presents a risk of investment to the stakeholders which necessitates proper scrutiny and to ensure profitability of the venture. The level of capital cost along with operation and maintenance （OM） costs are either financed by private or public sectors on wind farm with the sole aim of achieving the ROI （return-on-investment）. The results obtained from this study shows the possible ROI is not proportional to the wind capacity invested. Also, a sensitivity analysis conducted revealed the profit derived from wind farm is more responsive to the investment/capital cost and the price at which the electricity is being sold.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA05090105)the R&D Special Fund for Public Welfare Industry(Meteorology)(Grant No.GYHY201206013)the National Key Technology R&D program(Grant No.2012BAC22B04)
文摘Long-term meteorological observation series are fundamental for reflecting climate changes.However,almost all meteorological stations inevitably undergo relocation or changes in observation instruments,rules,and methods,which can result in systematic biases in the observation series for corresponding periods.Homogenization is a technique for adjusting these biases in order to assess the true trends in the time series.In recent years,homogenization has shifted its focus from the adjustments to climate mean status to the adjustments to information about climate extremes or extreme weather.Using case analyses of ideal and actual climate series,here we demonstrate the basic idea of homogenization,introduce new understanding obtained from recent studies of homogenization of climate series in China,and raise issues for further studies in this field,especially with regards to climate extremes,uncertainty of the statistical adjustments,and biased physical relationships among different climate variables due to adjustments in single variable series.
