In recent years,with the rapid development of high-speed railways(HSRs),power interruptions or disturbances in traction power supply systems have become increasingly dangerous.However,it is often impossible to detect ...In recent years,with the rapid development of high-speed railways(HSRs),power interruptions or disturbances in traction power supply systems have become increasingly dangerous.However,it is often impossible to detect these faults immediately through single-point monitoring or collecting data after accidents.To coordinate the power quality data of both traction power supply systems(TPSSs)and high-speed trains(HSTs),a monitoring and assessing system is proposed to access the power quality issues on HSRs.By integrating train monitoring,traction substation monitoring and data center,this monitoring system not only realizes the real-time monitoring of operational behaviors for both TPSSs and HSTs,but also conducts a comprehensive assessment of operational quality for train-network systems.Based on a large number of monitoring data,the field measurements show that this real-time monitoring system is effective for monitoring and evaluating a traction-network system.展开更多
<span style="font-family:Verdana;">Develop</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;&qu...<span style="font-family:Verdana;">Develop</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ment</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> of renewable energy (RE) and mitigation of carbon dioxide, as the two largest climate action initiatives are the most challenging factors for new generation green data center (GDC). Reduction of conventional electricity consumption as well as cost of electricity (COE) with preferred quality</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of service (QoS) has been recognized as the interesting research topic in Information and Communication Technology (ICT) sector. Moreover, it becomes challenging to design a large-scale sustainable GDC with standalone RE supply. This paper gives spotlight on hybrid energy supply solution for the GDC to reduce grid electricity usage and minimum net system cost. The proposed framework includes RE source such as solar photovoltaic, wind turbine and non-renewable energy sources as Disel Generator (DG) and Battery. A hybrid optimization model is designed using HOMER software for cost assessment and energy evaluation to validate the effectiveness of the suggested scheme focusing on eco-friendly implication.</span></span></span>展开更多
With the promotion of“dual carbon”strategy,data center(DC)access to high-penetration renewable energy sources(RESs)has become a trend in the industry.However,the uncertainty of RES poses challenges to the safe and s...With the promotion of“dual carbon”strategy,data center(DC)access to high-penetration renewable energy sources(RESs)has become a trend in the industry.However,the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids.In this paper,a multi-timescale optimal scheduling model is established for interconnected data centers(IDCs)based on model predictive control(MPC),including day-ahead optimization,intraday rolling optimization,and intraday real-time correction.The day-ahead optimization stage aims at the lowest operating cost,the rolling optimization stage aims at the lowest intraday economic cost,and the real-time correction aims at the lowest power fluctuation,eliminating the impact of prediction errors through coordinated multi-timescale optimization.The simulation results show that the economic loss is reduced by 19.6%,and the power fluctuation is decreased by 15.23%.展开更多
Data centers,as the infrastructure of all information services,cost tremendous amount of energy.Reducing the hot spot temperature in the data center room is benefit to prevent overheating of devices,and to increase co...Data centers,as the infrastructure of all information services,cost tremendous amount of energy.Reducing the hot spot temperature in the data center room is benefit to prevent overheating of devices,and to increase cooling system efficiency.In this paper,we study the problem of optimal power distribution among racks for minimal hot spot temperature.The temperature rise matrix(TRM)model is used for the purpose of fast estimation of the thermal environment.The accuracy of the model is evaluated by conducting numerical simulations of computational fluid dynamics(CFD).Using the TRM model,optimal distributing of heating power is converted into a linear programming problem,which can be solved by highly efficient algorithms,such as Simplex.Furthermore,with realistic constraints including rack idle power and power upper limit,an iteration method is proposed to calculate the optimal power distribution along with the optimal on/off states of the racks.Obtained solutions are discussed and validated by comparing with CFD simulations.Results show that the TRM model is acceptable in evaluating temperature rises in the forced-convection-dominated scenarios,and the proposed method is able to obtain optimal power distributions under various levels of total power demand.展开更多
风电机组并网容量占比的不断增大为电力系统风电消纳带来了巨大挑战。数据中心作为高灵活性电负荷,具有电网风电消纳巨大潜力。因此,提出一种计及数据中心和风电不确定性的微电网经济调度模型。首先,根据数据中心的分层结构建立信息层...风电机组并网容量占比的不断增大为电力系统风电消纳带来了巨大挑战。数据中心作为高灵活性电负荷,具有电网风电消纳巨大潜力。因此,提出一种计及数据中心和风电不确定性的微电网经济调度模型。首先,根据数据中心的分层结构建立信息层和电力层之间的耦合模型;其次,针对风电出力不确定性,搭建计及数据中心和风电不确定性的微电网经济调度模型;最后,基于对偶理论和两阶段鲁棒优化算法,将调度模型转化为鲁棒优化模型并采用列和约束生成算法(column and constraint generation,C&CG)和对偶理论进行求解。算例结果表明:数据中心参与微电网经济调度可有效降低运行成本,同时系统运营商按需求可灵活调整风电出力不确定性。展开更多
Edge data centers(EDCs)have been widely developed recently to supply delay-sensitive computing services,which impose prohibitively increasing electricity costs for EDC operators.This paper presents a new spatiotempora...Edge data centers(EDCs)have been widely developed recently to supply delay-sensitive computing services,which impose prohibitively increasing electricity costs for EDC operators.This paper presents a new spatiotemporal reallocation(STR)method for energy management in EDCs.This method uses spare resources,including servers and energy storage systems(ESSs)within EDCs to reduce energy costs based on both spatial and temporal features of spare resources.This solution:1)reallocates flexible workload between EDCs within one cluster;and 2)coordinates the electricity load of data processing,ESSs and distributed energy resources(DERs)within one EDC cluster to gain benefits from flexible electricity tariffs.In addition,this paper for the first time develops a Bit-Watt transformation to simplify the STR method and represent the relationship between data workload and electricity consumption of EDCs.Case studies justifying the developed STR method delivers satisfying cost reductions with robustness.The STR method fully utilized both spatial and temporal features of spare resources in EDCs to gain benefits from 1)varying electricity tariffs,and 2)maximumly consuming DER generation.展开更多
文摘In recent years,with the rapid development of high-speed railways(HSRs),power interruptions or disturbances in traction power supply systems have become increasingly dangerous.However,it is often impossible to detect these faults immediately through single-point monitoring or collecting data after accidents.To coordinate the power quality data of both traction power supply systems(TPSSs)and high-speed trains(HSTs),a monitoring and assessing system is proposed to access the power quality issues on HSRs.By integrating train monitoring,traction substation monitoring and data center,this monitoring system not only realizes the real-time monitoring of operational behaviors for both TPSSs and HSTs,but also conducts a comprehensive assessment of operational quality for train-network systems.Based on a large number of monitoring data,the field measurements show that this real-time monitoring system is effective for monitoring and evaluating a traction-network system.
文摘<span style="font-family:Verdana;">Develop</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ment</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> of renewable energy (RE) and mitigation of carbon dioxide, as the two largest climate action initiatives are the most challenging factors for new generation green data center (GDC). Reduction of conventional electricity consumption as well as cost of electricity (COE) with preferred quality</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of service (QoS) has been recognized as the interesting research topic in Information and Communication Technology (ICT) sector. Moreover, it becomes challenging to design a large-scale sustainable GDC with standalone RE supply. This paper gives spotlight on hybrid energy supply solution for the GDC to reduce grid electricity usage and minimum net system cost. The proposed framework includes RE source such as solar photovoltaic, wind turbine and non-renewable energy sources as Disel Generator (DG) and Battery. A hybrid optimization model is designed using HOMER software for cost assessment and energy evaluation to validate the effectiveness of the suggested scheme focusing on eco-friendly implication.</span></span></span>
文摘With the promotion of“dual carbon”strategy,data center(DC)access to high-penetration renewable energy sources(RESs)has become a trend in the industry.However,the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids.In this paper,a multi-timescale optimal scheduling model is established for interconnected data centers(IDCs)based on model predictive control(MPC),including day-ahead optimization,intraday rolling optimization,and intraday real-time correction.The day-ahead optimization stage aims at the lowest operating cost,the rolling optimization stage aims at the lowest intraday economic cost,and the real-time correction aims at the lowest power fluctuation,eliminating the impact of prediction errors through coordinated multi-timescale optimization.The simulation results show that the economic loss is reduced by 19.6%,and the power fluctuation is decreased by 15.23%.
基金supported by the Project of Shanghai Municipal Science and Technology Commission (No.22DZ2291100)the National Natural Science Foundation of China (No.51976062)the Opening Project of the Key Laboratory of Heat Transfer Enhancement and Energy Conservation of Education Ministry (South China University of Technology,No.202000105).
文摘Data centers,as the infrastructure of all information services,cost tremendous amount of energy.Reducing the hot spot temperature in the data center room is benefit to prevent overheating of devices,and to increase cooling system efficiency.In this paper,we study the problem of optimal power distribution among racks for minimal hot spot temperature.The temperature rise matrix(TRM)model is used for the purpose of fast estimation of the thermal environment.The accuracy of the model is evaluated by conducting numerical simulations of computational fluid dynamics(CFD).Using the TRM model,optimal distributing of heating power is converted into a linear programming problem,which can be solved by highly efficient algorithms,such as Simplex.Furthermore,with realistic constraints including rack idle power and power upper limit,an iteration method is proposed to calculate the optimal power distribution along with the optimal on/off states of the racks.Obtained solutions are discussed and validated by comparing with CFD simulations.Results show that the TRM model is acceptable in evaluating temperature rises in the forced-convection-dominated scenarios,and the proposed method is able to obtain optimal power distributions under various levels of total power demand.
文摘风电机组并网容量占比的不断增大为电力系统风电消纳带来了巨大挑战。数据中心作为高灵活性电负荷,具有电网风电消纳巨大潜力。因此,提出一种计及数据中心和风电不确定性的微电网经济调度模型。首先,根据数据中心的分层结构建立信息层和电力层之间的耦合模型;其次,针对风电出力不确定性,搭建计及数据中心和风电不确定性的微电网经济调度模型;最后,基于对偶理论和两阶段鲁棒优化算法,将调度模型转化为鲁棒优化模型并采用列和约束生成算法(column and constraint generation,C&CG)和对偶理论进行求解。算例结果表明:数据中心参与微电网经济调度可有效降低运行成本,同时系统运营商按需求可灵活调整风电出力不确定性。
文摘Edge data centers(EDCs)have been widely developed recently to supply delay-sensitive computing services,which impose prohibitively increasing electricity costs for EDC operators.This paper presents a new spatiotemporal reallocation(STR)method for energy management in EDCs.This method uses spare resources,including servers and energy storage systems(ESSs)within EDCs to reduce energy costs based on both spatial and temporal features of spare resources.This solution:1)reallocates flexible workload between EDCs within one cluster;and 2)coordinates the electricity load of data processing,ESSs and distributed energy resources(DERs)within one EDC cluster to gain benefits from flexible electricity tariffs.In addition,this paper for the first time develops a Bit-Watt transformation to simplify the STR method and represent the relationship between data workload and electricity consumption of EDCs.Case studies justifying the developed STR method delivers satisfying cost reductions with robustness.The STR method fully utilized both spatial and temporal features of spare resources in EDCs to gain benefits from 1)varying electricity tariffs,and 2)maximumly consuming DER generation.