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
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
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
Three types of spatial function zoning is an effective measure for regional environmental protection and orderly development.For ecological and economic coordinated development, spatial function zones should be divide...Three types of spatial function zoning is an effective measure for regional environmental protection and orderly development.For ecological and economic coordinated development, spatial function zones should be divided scientifically to clear its direction of development and protection. Therefore, based on ecological constraints, a beneficial discussion would be about the key ecological function areas adopting the concept of ecological protection restriction and supporting socioeconomic development for spatial function zoning. In this paper, the researchers, taking Tacheng Basin, Xinjiang of China as an example, choose township as basic research unit and set up an evaluation index system from three aspects, namely, ecological protection suitability, agricultural production suitability, and urban development suitability, which are analyzed by using spatial analysis functions and exclusive matrix method. The results showed that: 1) This paper formed a set of multilevel evaluation index systems for three types of spatial function zoning of the key ecological function areas based on a novel perspective by scientifically dividing Tacheng Basin into ecological space, agricultural space, and urban space,which realized the integration and scientific orientation for spatial function at the township scale. 2) Under the guidance of three types of spatial pattern, the functional orientation and suggestions of development and protection was clearly defined for ecological protection zones,ecological economic zones, agricultural production zones, and urban development zones. 3) A new idea of space governance is provided to promote the coordinated and sustainable development between ecology and economy, which can break the traditional mode of thinking about regional economic development, and offers a scientific basis and reference for macro decision-making.展开更多
Due to the phenomenon of abandoning wind power and photo voltage(PV)power in the“Three Northern Areas”in China,this paper presents an optimal strategy for coordinating and dispatching“source-load”in power system b...Due to the phenomenon of abandoning wind power and photo voltage(PV)power in the“Three Northern Areas”in China,this paper presents an optimal strategy for coordinating and dispatching“source-load”in power system based on multiple time scales.On the basis of the analysis of the uncertainty of wind power and PV power as well as the characteristics of load side resource dispatching,the optimal model of coordinating and dispatching“source-load”in power system based on multiple time scales is established.It can simultaneously and effectively dispatch conventional generators,wind plant,PV power station,pumped-storage power station and load side resources by optimally using three time scales:day-ahead,intra-day and real-time.According to the latest predicted information of wind power,PV power and load,the original generation schedule can be rolled and amended by using the corresponding time scale.The effectiveness of the model can be verified by a real system.The simulation results show that the proposed model can make full use of“source-load”resources to improve the ability to consume wind power and PV power of the grid-connected system.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
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
Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation...Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize trafficmanagement and enhance urban mobility and sustainability.However,traditional predictivemodels struggle to capture long-term temporal dependencies and are computationally intensive,limiting their practicality in real-time.Moreover,many approaches overlook the periodic characteristics inherent in traffic data,further impacting performance.To address these challenges,we introduce ST-MambaGCN,a State-Space-Based Spatio-Temporal Graph Convolution Network.Unlike conventionalmodels,ST-MambaGCN replaces the temporal attention layer withMamba,a state-space model that efficiently captures long-term dependencies with near-linear computational complexity.The model combines Chebyshev polynomial-based graph convolutional networks(GCN)to explore spatial correlations.Additionally,we incorporate a multi-temporal feature capture mechanism,where the final integrated features are generated through the Hadamard product based on learnable parameters.This mechanism explicitly models shortterm,daily,and weekly traffic patterns to enhance the network’s awareness of traffic periodicity.Extensive experiments on the PeMS04 and PeMS08 datasets demonstrate that ST-MambaGCN significantly outperforms existing benchmarks,offering substantial improvements in both prediction accuracy and computational efficiency for long-term traffic flow prediction.展开更多
To overcome the drawbacks such as irregular circuit construction and low system throughput that exist in conventional methods, a new factor correction scheme for coordinate rotation digital computer（ CORDIC） algorit...To overcome the drawbacks such as irregular circuit construction and low system throughput that exist in conventional methods, a new factor correction scheme for coordinate rotation digital computer（ CORDIC） algorithm is proposed. Based on the relationship between the iteration formulae, a new iteration formula is introduced, which leads the correction operation to be several simple shifting and adding operations. As one key part, the effects caused by rounding error are analyzed mathematically and it is concluded that the effects can be degraded by an appropriate selection of coefficients in the iteration formula. The model is then set up in Matlab and coded in Verilog HDL language. The proposed algorithm is also synthesized and verified in field-programmable gate array （FPGA）. The results show that this new scheme requires only one additional clock cycle and there is no change in the elementary iteration for the same precision compared with the conventional algorithm. In addition, the circuit realization is regular and the change in system throughput is very minimal.展开更多
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 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.
基金Under the auspices of Grant Program of Clean Development Mechanism Fund of China(CDMF)(No.2014092)Task 2 of Key Service Project 5 for the Characteristic Institute of Chinese Academy of Sciences(No.TSS-2015-014-FW-5-2)+2 种基金National Natural Science Foundation of China(No.41571159)The Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA20010301)Jiangsu Natural Science Foundation(No.BK20181105)
文摘Three types of spatial function zoning is an effective measure for regional environmental protection and orderly development.For ecological and economic coordinated development, spatial function zones should be divided scientifically to clear its direction of development and protection. Therefore, based on ecological constraints, a beneficial discussion would be about the key ecological function areas adopting the concept of ecological protection restriction and supporting socioeconomic development for spatial function zoning. In this paper, the researchers, taking Tacheng Basin, Xinjiang of China as an example, choose township as basic research unit and set up an evaluation index system from three aspects, namely, ecological protection suitability, agricultural production suitability, and urban development suitability, which are analyzed by using spatial analysis functions and exclusive matrix method. The results showed that: 1) This paper formed a set of multilevel evaluation index systems for three types of spatial function zoning of the key ecological function areas based on a novel perspective by scientifically dividing Tacheng Basin into ecological space, agricultural space, and urban space,which realized the integration and scientific orientation for spatial function at the township scale. 2) Under the guidance of three types of spatial pattern, the functional orientation and suggestions of development and protection was clearly defined for ecological protection zones,ecological economic zones, agricultural production zones, and urban development zones. 3) A new idea of space governance is provided to promote the coordinated and sustainable development between ecology and economy, which can break the traditional mode of thinking about regional economic development, and offers a scientific basis and reference for macro decision-making.
基金Major Projects of Gansu Province(No.17ZD2GA010)Power Company Technology Projects of State Grid Corporation in Gansu Province(No.52272716000K)
文摘Due to the phenomenon of abandoning wind power and photo voltage(PV)power in the“Three Northern Areas”in China,this paper presents an optimal strategy for coordinating and dispatching“source-load”in power system based on multiple time scales.On the basis of the analysis of the uncertainty of wind power and PV power as well as the characteristics of load side resource dispatching,the optimal model of coordinating and dispatching“source-load”in power system based on multiple time scales is established.It can simultaneously and effectively dispatch conventional generators,wind plant,PV power station,pumped-storage power station and load side resources by optimally using three time scales:day-ahead,intra-day and real-time.According to the latest predicted information of wind power,PV power and load,the original generation schedule can be rolled and amended by using the corresponding time scale.The effectiveness of the model can be verified by a real system.The simulation results show that the proposed model can make full use of“source-load”resources to improve the ability to consume wind power and PV power of the grid-connected system.
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 byNationalNatural Science Foundation of China,GrantNo.62402046the Beijing Forestry University Science and Technology Innovation Project under Grant No.BLX202358.
文摘Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize trafficmanagement and enhance urban mobility and sustainability.However,traditional predictivemodels struggle to capture long-term temporal dependencies and are computationally intensive,limiting their practicality in real-time.Moreover,many approaches overlook the periodic characteristics inherent in traffic data,further impacting performance.To address these challenges,we introduce ST-MambaGCN,a State-Space-Based Spatio-Temporal Graph Convolution Network.Unlike conventionalmodels,ST-MambaGCN replaces the temporal attention layer withMamba,a state-space model that efficiently captures long-term dependencies with near-linear computational complexity.The model combines Chebyshev polynomial-based graph convolutional networks(GCN)to explore spatial correlations.Additionally,we incorporate a multi-temporal feature capture mechanism,where the final integrated features are generated through the Hadamard product based on learnable parameters.This mechanism explicitly models shortterm,daily,and weekly traffic patterns to enhance the network’s awareness of traffic periodicity.Extensive experiments on the PeMS04 and PeMS08 datasets demonstrate that ST-MambaGCN significantly outperforms existing benchmarks,offering substantial improvements in both prediction accuracy and computational efficiency for long-term traffic flow prediction.
基金The National High Technology Research and Development Program of China (863 Program)(No.2007AA01Z280)
文摘To overcome the drawbacks such as irregular circuit construction and low system throughput that exist in conventional methods, a new factor correction scheme for coordinate rotation digital computer（ CORDIC） algorithm is proposed. Based on the relationship between the iteration formulae, a new iteration formula is introduced, which leads the correction operation to be several simple shifting and adding operations. As one key part, the effects caused by rounding error are analyzed mathematically and it is concluded that the effects can be degraded by an appropriate selection of coefficients in the iteration formula. The model is then set up in Matlab and coded in Verilog HDL language. The proposed algorithm is also synthesized and verified in field-programmable gate array （FPGA）. The results show that this new scheme requires only one additional clock cycle and there is no change in the elementary iteration for the same precision compared with the conventional algorithm. In addition, the circuit realization is regular and the change in system throughput is very minimal.
