Data center industries have been facing huge energy challenges due to escalating power consumption and associated carbon emissions.In the context of carbon neutrality,the integration of data centers with renewable ene...Data center industries have been facing huge energy challenges due to escalating power consumption and associated carbon emissions.In the context of carbon neutrality,the integration of data centers with renewable energy has become a prevailing trend.To advance the renewable energy integration in data centers,it is imperative to thoroughly explore the data centers’operational flexibility.Computing workloads and refrigeration systems are recognized as two promising flexible resources for power regulationwithin data centermicro-grids.This paper identifies and categorizes delay-tolerant computing workloads into three types(long-running non-interruptible,long-running interruptible,and short-running)and develops mathematical time-shifting models for each.Additionally,this paper examines the thermal dynamics of the computer room and derives a time-varying temperature model coupled to refrigeration power.Building on these models,this paper proposes a two-stage,multi-time scale optimization scheduling framework that jointly coordinates computing workloads time-shift in day-ahead scheduling and refrigeration power control in intra-day dispatch to mitigate renewable variability.A case study demonstrates that the framework effectively enhances the renewable-energy utilization,improves the operational economy of the data center microgrid,and mitigates the impact of renewable power uncertainty.The results highlight the potential of coordinated computing workloads and thermal system flexibility to support greener,more cost-effective data center operation.展开更多
The net primary productivity(NPP)of forest ecosystems plays a crucial role in regulating the terrestrial carbon cycle under global climate change.While the temporal effect driven by ecosystem processes on NPP variatio...The net primary productivity(NPP)of forest ecosystems plays a crucial role in regulating the terrestrial carbon cycle under global climate change.While the temporal effect driven by ecosystem processes on NPP variations is well-documented,spatial variations(from local to regional scales)remain inadequately understood.To evaluate the scale-dependent effects of productivity,predictions from the Biome-BGC model were compared with moderate-resolution imaging spectroradiometer(MODIS)and biometric NPP data in a large temperate forest region at both local and regional levels.Linear mixed-effect models and variance partitioning analysis were used to quantify the effects of environmental heterogeneity and trait variation on simulated NPP at varying spatial scales.Results show that NPP had considerable predictability at the local scale,with a coefficient of determination(R^(2))of 0.37,but this predictability declined significantly to 0.02 at the regional scale.Environmental heterogeneity and photosynthetic traits collectively explained 94.8%of the local variation in NPP,which decreased to 86.7%regionally due to the reduced common effects among these variables.Locally,the leaf area index(LAI)predominated(34.6%),while at regional scales,the stomatal conductance and maximum carboxylation rate were more influential(41.1%).Our study suggests that environmental heterogeneity drives the photosynthetic processes that mediate NPP variations across spatial scales.Incorporating heterogeneous local conditions and trait variations into analyses could enhance future research on the relationship between climate and carbon cycles at larger scales.展开更多
Morlet wavelet transformation is used in this paper to analyze the multi time scale characteristics of pre cipitation data series from 1957 to 2005 in Guyuan region.The results showed that(1) the annual precipitation ...Morlet wavelet transformation is used in this paper to analyze the multi time scale characteristics of pre cipitation data series from 1957 to 2005 in Guyuan region.The results showed that(1) the annual precipitation evo lution process had obvious multi time scale variation characteristics of 15 25 years,7 12 years and 3 6 years,and different time scales had different oscillation energy densities;(2) the periods at smaller time scales changed more frequently,which often nested in a biggish quasi periodic oscillations,so the concrete time domain should be ana lyzed if necessary;(3) the precipitation had three main periods(22 year,9 year and 4 year) and the 22 year period was especially outstanding,and the analysis of this main period reveals that the precipitation would be in a relative high water period until about 2012.展开更多
The variability of the sea surface temperature（SST） in the China seas has been studied in seasonal,interannual and interdecadal scales based on the monthly data of HadISST spanning from 1870 to 2007. The main result...The variability of the sea surface temperature（SST） in the China seas has been studied in seasonal,interannual and interdecadal scales based on the monthly data of HadISST spanning from 1870 to 2007. The main results obtained are SST in the China offshore changes most actively at the seasonal scale with the intensity diminishing from north to south,as the temperature differences between summer and winter reaching 17 and 4 C in the northern and southern areas,respectively. Moreover,seasonal variation near the coastal regions seems relatively stronger than that far from the coastline;significant interannual variations are detected,with the largest positive anomaly occurring in 1998 in the overall area. But as far as different domains are concerned,there exists great diversity,and the difference is also found between winter and summer. Differed from the seasonal variations,where the strongest interannual variability takes place,resides to the south of that of the seasonal ones in the northern section,nevertheless in the South China Sea,the most significant interannual variability is found in the deep basin;interdecadal changes of summer,winter and annual mean SST in different domains likewise present various features. In addition,a common dominant warming in recent 20 a are found in the overall China offshore with the strongest center located in the vicinity of the Changjiang Estuary in the East China Sea,which intensifies as high as 1.3 C during the past 130 a.展开更多
With the development of carbon electricity,achieving a low-carbon economy has become a prevailing and inevitable trend.Improving low-carbon expansion generation planning is critical for carbon emission mitigation and ...With the development of carbon electricity,achieving a low-carbon economy has become a prevailing and inevitable trend.Improving low-carbon expansion generation planning is critical for carbon emission mitigation and a lowcarbon economy.In this paper,a two-layer low-carbon expansion generation planning approach considering the uncertainty of renewable energy at multiple time scales is proposed.First,renewable energy sequences considering the uncertainty in multiple time scales are generated based on the Copula function and the probability distribution of renewable energy.Second,a two-layer generation planning model considering carbon trading and carbon capture technology is established.Specifically,the upper layer model optimizes the investment decision considering the uncertainty at a monthly scale,and the lower layer one optimizes the scheduling considering the peak shaving at an hourly scale and the flexibility at a 15-minute scale.Finally,the results of different influence factors on low-carbon generation expansion planning are compared in a provincial power grid,which demonstrate the effectiveness of the proposed model.展开更多
Bio-inspired computer modelling brings solutions fromthe living phenomena or biological systems to engineering domains.To overcome the obstruction problem of large-scale wind power consumption in Northwest China,this ...Bio-inspired computer modelling brings solutions fromthe living phenomena or biological systems to engineering domains.To overcome the obstruction problem of large-scale wind power consumption in Northwest China,this paper constructs a bio-inspired computer model.It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load.First,the principle of wind power obstruction with the involvement of a high-energy load is examined in this work.In this step,highenergy load model with different regulation characteristics is established.Then,considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed,a multi-time scale model of coordination optimization is built.An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs,as well as to find the most optimal energy configurationwithin the system.Lastly,we take an example of regional power grid in Gansu Province for simulation analysis.Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.展开更多
Based on the annual frequency data of tropical cyclones from 1960 to 2005 and by the polynomial fit and statistical analysis, this work has discovered that TC activity in the 46a exhibits significant decadal-scale var...Based on the annual frequency data of tropical cyclones from 1960 to 2005 and by the polynomial fit and statistical analysis, this work has discovered that TC activity in the 46a exhibits significant decadal-scale variability. It has two high frequency periods (HFP) and two low frequency periods (LFP). Significant differences in the number of TCs between HFP and LFP are found in active TC seasons from July to October. Differences of large-scale circulation during HFP and LFP have been investigated with NCEP/NOAA data for the season. In HFP, the condition includes not only higher sea surface temperature, lower sea level pressure, larger divergence of upper air, larger relative vorticity at low levels and smaller vertical shear, but also 500-hPa wind vector being more available for TC activity and moving to western North Pacific, the position of the subtropical anticyclone over the western Pacific shifting more northward, and South Asian Anticyclone at 100-hPa being much smaller than that in LFP. The precipitation of western North Pacific has no clear influence on TC activity.展开更多
The Medieval Climate Anomaly(MCA,AD950-1250)is the most recent warm period lasting for several hundred years and is regarded as a reference scenario when studying the impact of and adaptation to global and regional wa...The Medieval Climate Anomaly(MCA,AD950-1250)is the most recent warm period lasting for several hundred years and is regarded as a reference scenario when studying the impact of and adaptation to global and regional warming.In this study,we investigated the characteristics of temperature variations on decadal-centennial scales during the MCA for four regions(Northeast,Northwest,Central-east,and Tibetan Plateau)in China,based on high-resolution temperature reconstructions and related warm-cold records from historical documents.The ensemble empirical mode decomposition method is used to analyze the time series.The results showed that for China as a whole,the longest warm period during the last 2000 years occurred in the 10th-13th centuries,although there were multi-decadal cold intervals in the middle to late 12th century.However,in the beginning and ending decades,warm peaks and phases on the decadal scale of the MCA for different regions were not consistent with each other.On the inter-decadal scale,regional temperature variations were similar from 950 to 1130;moreover,their amplitudes became smaller,and the phases did not agree well from 1130 to 1250.On the multi-decadal to centennial scale,all four regions began to warm in the early 10th century and experienced two cold intervals during the MCA.However,the Northwest and Central-east China were in step with each other while the warm periods in the Northeast China and Tibetan Plateau ended about 40-50 years earlier.On the multi-centennial scale,the mean temperature difference between the MCA and Little Ice Age was significant in Northeast and Central-east China but not in the Northwest China and Tibetan Plateau.Compared to the mean temperature of the 20th century,a comparable warmth in the MCA was found in the Central-east China,but there was a little cooling in Northeast China;meanwhile,there were significantly lower temperatures in Northwest China and Tibetan Plateau.展开更多
Phase delays between two Nino indices-sea surface temperatures in Nino regions 1+2 and 3.4 (1950-2001)-at different time scales are detected by wavelet analysis. Analysis results show that there are two types of perio...Phase delays between two Nino indices-sea surface temperatures in Nino regions 1+2 and 3.4 (1950-2001)-at different time scales are detected by wavelet analysis. Analysis results show that there are two types of period bifurcations in the Nino indices and that period bifurcation points exist only in the region where the wavelet power is small. Interdecadal variation features of phase delays between the two indices vary with different time scales. In the periods of 40-72 months, the phase delay changes its sign in 1977: Nino 1+2 indices are 2-4 months earlier than Nino 3.4 indices before 1977, but 3-6 months later afterwards. In the periods of 20-40 months, however, the phase delay changes its sign in another way: Nino 1+2 indices are 1-4 months earlier before 1980 and during 1986-90, but 1-4 months later during 1980-83 and 1993-2001.展开更多
As the proportion of renewable energy increases, the interaction between renewable energy devices and the grid continues to enhance. Therefore, the renewable energy dynamic test in a power system has become more and m...As the proportion of renewable energy increases, the interaction between renewable energy devices and the grid continues to enhance. Therefore, the renewable energy dynamic test in a power system has become more and more important. Traditional dynamic simulation systems and digital-analog hybrid simulation systems are difficult to compromise on the economy, flexibility and accuracy. A multi-time scale test system of doubly fed induction generator based on FPGA+ CPU heterogeneous calculation is proposed in this paper. The proposed test system is based on the ADPSS simulation platform. The power circuit part of the test system is setup up using the EMT(electromagnetic transient simulation) simulation, and the control part uses the actual physical devices. In order to realize the close-loop testing for the physical devices, the power circuit must be simulated in real-time. This paper proposes a multi-time scale simulation algorithm, in which the decoupling component divides the power circuit into a large time scale system and a small time scale system in order to reduce computing effort. This paper also proposes the FPGA+CPU heterogeneous computing architecture for implementing this multitime scale simulation. In FPGA, there is a complete small time-scale EMT engine, which support the flexibly circuit modeling with any topology. Finally, the test system is connected to an DFIG controller based on Labview to verify the feasibility of the test system.展开更多
Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a ...Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.展开更多
Recent advances in Global Positioning System （GPS） remote sensing technology allow for a direct estimation of the precipitable water vapor （PWV） from delayed signals transmitted by GPS satellites, which can be ass...Recent advances in Global Positioning System （GPS） remote sensing technology allow for a direct estimation of the precipitable water vapor （PWV） from delayed signals transmitted by GPS satellites, which can be assimilated into numerical models with four-dimensional variational （4DVAR） data assimilation. A mesoscale model and its 4DVAR system are used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-β-scale convective systems （MCS） along the mei-yu front in China. The experiments with GPS-PWV assimilation cluster and also eliminated the erroneous rainfall successfully simulated the evolution of the observed MCS systems found in the experiment without 4DVAR assimilation. Experiments with hourly rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems, however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation weightings and the control of spurious gravity waves.展开更多
The relationship between the ENSO and abnormal variation of precipitation and temperature in China is investigated based on the monthly data. Firstly, interannual variability of precipitation and temperature are discu...The relationship between the ENSO and abnormal variation of precipitation and temperature in China is investigated based on the monthly data. Firstly, interannual variability of precipitation and temperature are discussed in different sub-areas using Rotational EOF (REOF). Then, the variation of precipitation and temperature in different phases of ENSO cycle is each investigated with Complex Singular Value Decomposition (CSVD). Results show that, during the period of El Nio, precipitation in the eastern China, especially in the northeastern China and Yangtze River valley, is much more than normal and is apt to flood. Precipitation in northern China and Huanghe River valley, especially in the middle reach of Huanghe River, is less than normal and is apt to be less. Precipitation in the Yangtze River valley is closely related to the SSTA in the central and eastern tropical Pacific on the QFO scale, and the precipitation variation lags behind SSTA by about 3 months. For the variation of surface temperature, during the period of El Nio, it is usually colder than normal in northeastern China, and in other regions, especially in the region of Great Bend of the Yellow River and southwestern China, is warmer than normal. The temperature in northeast China is closely associated with SSTA in eastern Pacific on the QFO scale and the surface temperature variation in the northeast China lags behind that of SSTA about 2 months.展开更多
This paper focuses on the problem of traffic flow forecasting,with the aim of forecasting future traffic conditions based on historical traffic data.This problem is typically tackled by utilizing spatio-temporal graph...This paper focuses on the problem of traffic flow forecasting,with the aim of forecasting future traffic conditions based on historical traffic data.This problem is typically tackled by utilizing spatio-temporal graph neural networks to model the intricate spatio-temporal correlations among traffic data.Although these methods have achieved performance improvements,they often suffer from the following limitations:These methods face challenges in modeling high-order correlations between nodes.These methods overlook the interactions between nodes at different scales.To tackle these issues,in this paper,we propose a novel model named multi-scale dynamic hypergraph convolutional network(MSDHGCN)for traffic flow forecasting.Our MSDHGCN can effectively model the dynamic higher-order relationships between nodes at multiple time scales,thereby enhancing the capability for traffic forecasting.Experiments on two real-world datasets demonstrate the effectiveness of the proposed method.展开更多
In order to decrease the sensitivity of the constant scale parameter, adaptively optimize the scale parameter in the iteration regularization model （IRM） and attain a desirable level of applicability for image denoi...In order to decrease the sensitivity of the constant scale parameter, adaptively optimize the scale parameter in the iteration regularization model （IRM） and attain a desirable level of applicability for image denoising, a novel IRM with the adaptive scale parameter is proposed. First, the classic regularization item is modified and the equation of the adaptive scale parameter is deduced. Then, the initial value of the varying scale parameter is obtained by the trend of the number of iterations and the scale parameter sequence vectors. Finally, the novel iterative regularization method is used for image denoising. Numerical experiments show that compared with the IRM with the constant scale parameter, the proposed method with the varying scale parameter can not only reduce the number of iterations when the scale parameter becomes smaller, but also efficiently remove noise when the scale parameter becomes bigger and well preserve the details of images.展开更多
基金supported by Science and Technology Standard Project of Guangdong Electric Power Design Institute(ER11301W,ER11811W).
文摘Data center industries have been facing huge energy challenges due to escalating power consumption and associated carbon emissions.In the context of carbon neutrality,the integration of data centers with renewable energy has become a prevailing trend.To advance the renewable energy integration in data centers,it is imperative to thoroughly explore the data centers’operational flexibility.Computing workloads and refrigeration systems are recognized as two promising flexible resources for power regulationwithin data centermicro-grids.This paper identifies and categorizes delay-tolerant computing workloads into three types(long-running non-interruptible,long-running interruptible,and short-running)and develops mathematical time-shifting models for each.Additionally,this paper examines the thermal dynamics of the computer room and derives a time-varying temperature model coupled to refrigeration power.Building on these models,this paper proposes a two-stage,multi-time scale optimization scheduling framework that jointly coordinates computing workloads time-shift in day-ahead scheduling and refrigeration power control in intra-day dispatch to mitigate renewable variability.A case study demonstrates that the framework effectively enhances the renewable-energy utilization,improves the operational economy of the data center microgrid,and mitigates the impact of renewable power uncertainty.The results highlight the potential of coordinated computing workloads and thermal system flexibility to support greener,more cost-effective data center operation.
基金supported by the National Key R&D Program of China(No.2023YFF1304001-01)the Science and Technology Project of the Department of Transportation of Heilongjiang Province(No.HJK2023B024-3)the Program of National Natural Science Foundation of China(No.32371870).
文摘The net primary productivity(NPP)of forest ecosystems plays a crucial role in regulating the terrestrial carbon cycle under global climate change.While the temporal effect driven by ecosystem processes on NPP variations is well-documented,spatial variations(from local to regional scales)remain inadequately understood.To evaluate the scale-dependent effects of productivity,predictions from the Biome-BGC model were compared with moderate-resolution imaging spectroradiometer(MODIS)and biometric NPP data in a large temperate forest region at both local and regional levels.Linear mixed-effect models and variance partitioning analysis were used to quantify the effects of environmental heterogeneity and trait variation on simulated NPP at varying spatial scales.Results show that NPP had considerable predictability at the local scale,with a coefficient of determination(R^(2))of 0.37,but this predictability declined significantly to 0.02 at the regional scale.Environmental heterogeneity and photosynthetic traits collectively explained 94.8%of the local variation in NPP,which decreased to 86.7%regionally due to the reduced common effects among these variables.Locally,the leaf area index(LAI)predominated(34.6%),while at regional scales,the stomatal conductance and maximum carboxylation rate were more influential(41.1%).Our study suggests that environmental heterogeneity drives the photosynthetic processes that mediate NPP variations across spatial scales.Incorporating heterogeneous local conditions and trait variations into analyses could enhance future research on the relationship between climate and carbon cycles at larger scales.
基金National Key Project of ScientificTechnical Supporting Programs Funded by Ministry of Science & Technology of China during the 11th Five-Year Plan Period (Grant No. 2006BCA01A07-2).
文摘Morlet wavelet transformation is used in this paper to analyze the multi time scale characteristics of pre cipitation data series from 1957 to 2005 in Guyuan region.The results showed that(1) the annual precipitation evo lution process had obvious multi time scale variation characteristics of 15 25 years,7 12 years and 3 6 years,and different time scales had different oscillation energy densities;(2) the periods at smaller time scales changed more frequently,which often nested in a biggish quasi periodic oscillations,so the concrete time domain should be ana lyzed if necessary;(3) the precipitation had three main periods(22 year,9 year and 4 year) and the 22 year period was especially outstanding,and the analysis of this main period reveals that the precipitation would be in a relative high water period until about 2012.
基金The National Natural Science Foundation of China under contract No. 40805035China COPES Program under contract Nos GYHY-200706005 and NSF 90711003
文摘The variability of the sea surface temperature（SST） in the China seas has been studied in seasonal,interannual and interdecadal scales based on the monthly data of HadISST spanning from 1870 to 2007. The main results obtained are SST in the China offshore changes most actively at the seasonal scale with the intensity diminishing from north to south,as the temperature differences between summer and winter reaching 17 and 4 C in the northern and southern areas,respectively. Moreover,seasonal variation near the coastal regions seems relatively stronger than that far from the coastline;significant interannual variations are detected,with the largest positive anomaly occurring in 1998 in the overall area. But as far as different domains are concerned,there exists great diversity,and the difference is also found between winter and summer. Differed from the seasonal variations,where the strongest interannual variability takes place,resides to the south of that of the seasonal ones in the northern section,nevertheless in the South China Sea,the most significant interannual variability is found in the deep basin;interdecadal changes of summer,winter and annual mean SST in different domains likewise present various features. In addition,a common dominant warming in recent 20 a are found in the overall China offshore with the strongest center located in the vicinity of the Changjiang Estuary in the East China Sea,which intensifies as high as 1.3 C during the past 130 a.
基金supported partly by the National Key R&D Program of China(2018YFA0702200)the Science and Technology Project of State Grid Shandong Electric Power Company(520604190002)。
文摘With the development of carbon electricity,achieving a low-carbon economy has become a prevailing and inevitable trend.Improving low-carbon expansion generation planning is critical for carbon emission mitigation and a lowcarbon economy.In this paper,a two-layer low-carbon expansion generation planning approach considering the uncertainty of renewable energy at multiple time scales is proposed.First,renewable energy sequences considering the uncertainty in multiple time scales are generated based on the Copula function and the probability distribution of renewable energy.Second,a two-layer generation planning model considering carbon trading and carbon capture technology is established.Specifically,the upper layer model optimizes the investment decision considering the uncertainty at a monthly scale,and the lower layer one optimizes the scheduling considering the peak shaving at an hourly scale and the flexibility at a 15-minute scale.Finally,the results of different influence factors on low-carbon generation expansion planning are compared in a provincial power grid,which demonstrate the effectiveness of the proposed model.
基金supported by the Program for Innovative Research Team(in Science and Technology)in University of Henan Province(No.22IRTSTHN016)the Hubei Natural Science Foundation(No.2021CFB156)the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(KAKENHI)(No.JP21K17737).
文摘Bio-inspired computer modelling brings solutions fromthe living phenomena or biological systems to engineering domains.To overcome the obstruction problem of large-scale wind power consumption in Northwest China,this paper constructs a bio-inspired computer model.It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load.First,the principle of wind power obstruction with the involvement of a high-energy load is examined in this work.In this step,highenergy load model with different regulation characteristics is established.Then,considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed,a multi-time scale model of coordination optimization is built.An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs,as well as to find the most optimal energy configurationwithin the system.Lastly,we take an example of regional power grid in Gansu Province for simulation analysis.Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.
基金National Key Fundamental Research and Development Plan of China (2004CB418303)Natural Science Foundation of China (40425009 40233028)
文摘Based on the annual frequency data of tropical cyclones from 1960 to 2005 and by the polynomial fit and statistical analysis, this work has discovered that TC activity in the 46a exhibits significant decadal-scale variability. It has two high frequency periods (HFP) and two low frequency periods (LFP). Significant differences in the number of TCs between HFP and LFP are found in active TC seasons from July to October. Differences of large-scale circulation during HFP and LFP have been investigated with NCEP/NOAA data for the season. In HFP, the condition includes not only higher sea surface temperature, lower sea level pressure, larger divergence of upper air, larger relative vorticity at low levels and smaller vertical shear, but also 500-hPa wind vector being more available for TC activity and moving to western North Pacific, the position of the subtropical anticyclone over the western Pacific shifting more northward, and South Asian Anticyclone at 100-hPa being much smaller than that in LFP. The precipitation of western North Pacific has no clear influence on TC activity.
基金National Key R&D Program of China,No.2017YFA0603300National Natural Science Foundation of China,No.41671036,No.41831174The Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA 19040101。
文摘The Medieval Climate Anomaly(MCA,AD950-1250)is the most recent warm period lasting for several hundred years and is regarded as a reference scenario when studying the impact of and adaptation to global and regional warming.In this study,we investigated the characteristics of temperature variations on decadal-centennial scales during the MCA for four regions(Northeast,Northwest,Central-east,and Tibetan Plateau)in China,based on high-resolution temperature reconstructions and related warm-cold records from historical documents.The ensemble empirical mode decomposition method is used to analyze the time series.The results showed that for China as a whole,the longest warm period during the last 2000 years occurred in the 10th-13th centuries,although there were multi-decadal cold intervals in the middle to late 12th century.However,in the beginning and ending decades,warm peaks and phases on the decadal scale of the MCA for different regions were not consistent with each other.On the inter-decadal scale,regional temperature variations were similar from 950 to 1130;moreover,their amplitudes became smaller,and the phases did not agree well from 1130 to 1250.On the multi-decadal to centennial scale,all four regions began to warm in the early 10th century and experienced two cold intervals during the MCA.However,the Northwest and Central-east China were in step with each other while the warm periods in the Northeast China and Tibetan Plateau ended about 40-50 years earlier.On the multi-centennial scale,the mean temperature difference between the MCA and Little Ice Age was significant in Northeast and Central-east China but not in the Northwest China and Tibetan Plateau.Compared to the mean temperature of the 20th century,a comparable warmth in the MCA was found in the Central-east China,but there was a little cooling in Northeast China;meanwhile,there were significantly lower temperatures in Northwest China and Tibetan Plateau.
基金This work was supported by the National Natural Science Foundation of China under Grant No．40035010
文摘Phase delays between two Nino indices-sea surface temperatures in Nino regions 1+2 and 3.4 (1950-2001)-at different time scales are detected by wavelet analysis. Analysis results show that there are two types of period bifurcations in the Nino indices and that period bifurcation points exist only in the region where the wavelet power is small. Interdecadal variation features of phase delays between the two indices vary with different time scales. In the periods of 40-72 months, the phase delay changes its sign in 1977: Nino 1+2 indices are 2-4 months earlier than Nino 3.4 indices before 1977, but 3-6 months later afterwards. In the periods of 20-40 months, however, the phase delay changes its sign in another way: Nino 1+2 indices are 1-4 months earlier before 1980 and during 1986-90, but 1-4 months later during 1980-83 and 1993-2001.
基金supported by the State Grid Science and Technology Project (Title: Technology Research On Large Scale EMT Real-time simulation customized platform, FX71-17-001)
文摘As the proportion of renewable energy increases, the interaction between renewable energy devices and the grid continues to enhance. Therefore, the renewable energy dynamic test in a power system has become more and more important. Traditional dynamic simulation systems and digital-analog hybrid simulation systems are difficult to compromise on the economy, flexibility and accuracy. A multi-time scale test system of doubly fed induction generator based on FPGA+ CPU heterogeneous calculation is proposed in this paper. The proposed test system is based on the ADPSS simulation platform. The power circuit part of the test system is setup up using the EMT(electromagnetic transient simulation) simulation, and the control part uses the actual physical devices. In order to realize the close-loop testing for the physical devices, the power circuit must be simulated in real-time. This paper proposes a multi-time scale simulation algorithm, in which the decoupling component divides the power circuit into a large time scale system and a small time scale system in order to reduce computing effort. This paper also proposes the FPGA+CPU heterogeneous computing architecture for implementing this multitime scale simulation. In FPGA, there is a complete small time-scale EMT engine, which support the flexibly circuit modeling with any topology. Finally, the test system is connected to an DFIG controller based on Labview to verify the feasibility of the test system.
文摘Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.
文摘Recent advances in Global Positioning System （GPS） remote sensing technology allow for a direct estimation of the precipitable water vapor （PWV） from delayed signals transmitted by GPS satellites, which can be assimilated into numerical models with four-dimensional variational （4DVAR） data assimilation. A mesoscale model and its 4DVAR system are used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-β-scale convective systems （MCS） along the mei-yu front in China. The experiments with GPS-PWV assimilation cluster and also eliminated the erroneous rainfall successfully simulated the evolution of the observed MCS systems found in the experiment without 4DVAR assimilation. Experiments with hourly rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems, however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation weightings and the control of spurious gravity waves.
基金“Effect of sea-land-air interactions in the Asian monsoon region on the climate change in China”——a project of the Knowledge Innovation Project by the Chinese Academy of Sciences(ZKCX2-SW-210)National Natural Science Foundation of China(40023001,49775270)
文摘The relationship between the ENSO and abnormal variation of precipitation and temperature in China is investigated based on the monthly data. Firstly, interannual variability of precipitation and temperature are discussed in different sub-areas using Rotational EOF (REOF). Then, the variation of precipitation and temperature in different phases of ENSO cycle is each investigated with Complex Singular Value Decomposition (CSVD). Results show that, during the period of El Nio, precipitation in the eastern China, especially in the northeastern China and Yangtze River valley, is much more than normal and is apt to flood. Precipitation in northern China and Huanghe River valley, especially in the middle reach of Huanghe River, is less than normal and is apt to be less. Precipitation in the Yangtze River valley is closely related to the SSTA in the central and eastern tropical Pacific on the QFO scale, and the precipitation variation lags behind SSTA by about 3 months. For the variation of surface temperature, during the period of El Nio, it is usually colder than normal in northeastern China, and in other regions, especially in the region of Great Bend of the Yellow River and southwestern China, is warmer than normal. The temperature in northeast China is closely associated with SSTA in eastern Pacific on the QFO scale and the surface temperature variation in the northeast China lags behind that of SSTA about 2 months.
基金the National Key Research and Development Program of China(No.2021ZD0112400)。
文摘This paper focuses on the problem of traffic flow forecasting,with the aim of forecasting future traffic conditions based on historical traffic data.This problem is typically tackled by utilizing spatio-temporal graph neural networks to model the intricate spatio-temporal correlations among traffic data.Although these methods have achieved performance improvements,they often suffer from the following limitations:These methods face challenges in modeling high-order correlations between nodes.These methods overlook the interactions between nodes at different scales.To tackle these issues,in this paper,we propose a novel model named multi-scale dynamic hypergraph convolutional network(MSDHGCN)for traffic flow forecasting.Our MSDHGCN can effectively model the dynamic higher-order relationships between nodes at multiple time scales,thereby enhancing the capability for traffic forecasting.Experiments on two real-world datasets demonstrate the effectiveness of the proposed method.
基金The National Natural Science Foundation of China(No.60702069)the Research Project of Department of Education of Zhe-jiang Province (No.20060601)+1 种基金the Natural Science Foundation of Zhe-jiang Province (No.Y1080851)Shanghai International Cooperation onRegion of France (No.06SR07109)
文摘In order to decrease the sensitivity of the constant scale parameter, adaptively optimize the scale parameter in the iteration regularization model （IRM） and attain a desirable level of applicability for image denoising, a novel IRM with the adaptive scale parameter is proposed. First, the classic regularization item is modified and the equation of the adaptive scale parameter is deduced. Then, the initial value of the varying scale parameter is obtained by the trend of the number of iterations and the scale parameter sequence vectors. Finally, the novel iterative regularization method is used for image denoising. Numerical experiments show that compared with the IRM with the constant scale parameter, the proposed method with the varying scale parameter can not only reduce the number of iterations when the scale parameter becomes smaller, but also efficiently remove noise when the scale parameter becomes bigger and well preserve the details of images.
