Typhoons can cause large-area blackouts or partial outages of distribution networks.We define a partial outage state in the distribution network as a gray state and propose a gray-start strategy and two-stage distribu...Typhoons can cause large-area blackouts or partial outages of distribution networks.We define a partial outage state in the distribution network as a gray state and propose a gray-start strategy and two-stage distribution network emergency recovery framework.A phase-space reconstruction and stacked integrated model for predicting wind and photovoltaic generation during typhoon disasters is proposed in the first stage.This provides guidance for second-stage post-disaster emergency recovery scheduling.The emergency recovery scheduling model is established in the second stage,and this model is supported by a thermal power-generating unit,mobile emergency generators,and distributed generators.Distributed generation includes wind power generation,photovoltaics,fuel cells,etc.Simultaneously,we con-sider the gray-start based on the pumped storage unit to be an important first step in the emergency recovery strategy.This model is val-idated on the improved IEEE 33 node system,which utilizes data from the 2022 super typhoon“Muifa”in Zhoushan,Zhejiang,China.Simulations indicate the superiority of a gray start with a pumped storage unit and the proposed emergency recovery strategy.展开更多
To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) s...To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.展开更多
The increasing proportion of distributed photovoltaics(DPVs)and electric vehicle charging stations in low-voltage distribution networks(LVDNs)has resulted in challenges such as distribution transformer overloads and v...The increasing proportion of distributed photovoltaics(DPVs)and electric vehicle charging stations in low-voltage distribution networks(LVDNs)has resulted in challenges such as distribution transformer overloads and voltage violations.To address these problems,we propose a coordinated planning method for flexible interconnections and energy storage systems(ESSs)to improve the accommodation capacity of DPVs.First,the power-transfer characteristics of flexible interconnection and ESSs are analyzed.The equipment costs of the voltage source converters(VSCs)and ESSs are also analyzed comprehensively,considering the differences in installation and maintenance costs for different installation locations.Second,a bilevel programming model is established to minimize the annual comprehensive cost and yearly total PV curtailment capacity.Within this framework,the upper-level model optimizes the installation locations and capacities of the VSCs and ESSs,whereas the lower-level model optimizes the operating power of the VSCs and ESSs.The proposed model is solved using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-II).The effectiveness of the proposed planning method is validated through an actual LVDN scenario,which demonstrates its advantages in enhancing PV accommodation capacity.In addition,the economic benefits of various planning schemes with different flexible interconnection topologies and different PV grid-connected forms are quantitatively analyzed,demonstrating the adaptability of the proposed coordinated planning method.展开更多
The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,an...The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.展开更多
The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications.The emergence of these application session requests and system daemon service...The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications.The emergence of these application session requests and system daemon services has created large persistent flows with diverse performance requirements that need to coexist with other types of traffic.Current routing methods such as equal-cost multipath(ECMP)and Hedera do not take into consideration specific traffic characteristics nor performance requirements,which make these methods difficult to meet the quality of service(QoS)for high-priority flows.In this paper,we tailored the best routing for different kinds of cloud storage flows as an integer programming problem and utilized grey relational analysis(GRA)to solve this optimization problem.The resulting method is a GRAbased service-aware flow scheduling(GRSA)framework that considers requested flow types and network status to select appropriate routing paths for flows in cloud storage datacenter networks.The results from experiments carried out on a real traffic trace show that the proposed GRSA method can better balance traffic loads,conserve table space and reduce the average transmission delay for high-priority flows compared to ECMP and Hedera.展开更多
The ternary-element storage and flow concept for shale oil reservoirs in Jiyang Depression of Bohai Bay Basin,East China,was proposed based on the data of more than 10000 m cores and the production of more than 60 hor...The ternary-element storage and flow concept for shale oil reservoirs in Jiyang Depression of Bohai Bay Basin,East China,was proposed based on the data of more than 10000 m cores and the production of more than 60 horizontal wells.The synergy of three elements(storage,fracture and pressure)contributes to the enrichment and high production of shale oil in Jiyang Depression.The storage element controls the enrichment of shale oil;specifically,the presence of inorganic pores and fractures,as well as laminae of lime-mud rocks,in the saline lake basin,is conducive to the storage of shale oil,and the high hydrocarbon generating capacity and free hydrocarbon content are the material basis for high production.The fracture element controls the shale oil flow;specifically,natural fractures act as flow channels for shale oil to migrate and accumulate,and induced fractures communicate natural fractures to form complex fracture network,which is fundamental to high production.The pressure element controls the high and stable production of shale oil;specifically,the high formation pressure provides the drive force for the migration and accumulation of hydrocarbons,and fracturing stimulation significantly increases the elastic energy of rock and fluid,improves the imbibition replacement of oil in the pores/fractures,and reduces the stress sensitivity,guaranteeing the stable production of shale oil for a long time.Based on the ternary-element storage and flow concept,a 3D development technology was formed,with the core techniques of 3D well pattern optimization,3D balanced fracturing,and full-cycle optimization of adjustment and control.This technology effectively guides the production and provides a support to the large-scale beneficial development of shale oil in Jiyang Depression.展开更多
A multi-user view file system (MUVFS) and a security scheme are developed to improve the security of the united storage network (USN) that integrates a network attached storage (NAS) and a storage area network (SAN). ...A multi-user view file system (MUVFS) and a security scheme are developed to improve the security of the united storage network (USN) that integrates a network attached storage (NAS) and a storage area network (SAN). The MUVFS offers a storage volume view for each authorized user who can access only the data in his own storage volume, the security scheme enables all users to encrypt and decrypt the data of their own storage view at client-side, and the USN server needs only to check the users’ identities and the data’s integrity. Experiments were performed to compare the sequential read, write and read/write rates of NFS+MUVFS+secure_module with those of NFS. The results indicate that the security of the USN is improved greatly with little influence on the system performance when the MUVFS and the security scheme are integrated into it.展开更多
As part of the ongoing information revolution,smart power grid technology has become a key focus area for research into power systems.Intelligent electrical appliances are now an important component of power systems,p...As part of the ongoing information revolution,smart power grid technology has become a key focus area for research into power systems.Intelligent electrical appliances are now an important component of power systems,providing a smart power grid with increased control,stability,and safety.Based on the secure communication requirements of cloud energy storage systems,this paper presents the design and development of a node controller for a cloud energy storage network.The function division and system deployment processes were carried out to ensure the security of the communication network used for the cloud energy storage system.Safety protection measures were proposed according to the demands of the communication network,allowing the system to run safely and stably.Finally,the effectiveness of the system was verified through a client-side distributed energy storage demonstration project in Suzhou,China.The system was observed to operate safely and stably,demonstrating good peak-clipping and valley filling effects,and improving the system load characteristics.展开更多
Battery energy storage systems(ESS) have been widely used in mobile base stations(BS) as the main backup power source. Due to the large number of base stations, massive distributed ESSs have largely stayed in idle and...Battery energy storage systems(ESS) have been widely used in mobile base stations(BS) as the main backup power source. Due to the large number of base stations, massive distributed ESSs have largely stayed in idle and very difficult to achieve high asset utilization. In recent years, the fast-paced development of digital energy storage(DES) technology has revolutionized the traditional operation and maintenance of ESSs by transforming them into digital assets, further enabling battery energy storage services, raising up a new way to achieve a much higher utilization of such kind of largely idle ESS resources. In this paper, the disruptive DES technology will be introduced and its application under the context of mobile BSs will be studied, and then a cloud-based energy storage(CES) platform is proposed based on a large scale distributed DESs to provide a new cyber-enabled energy storage service to the local utility company. A real-world case study shows the effectiveness and efficiency of the CES platform.展开更多
Purpose： In order to explain and predict the adoption of personal cloud storage, this study explores the critical factors involved in the adoption of personal cloud storage and empirically validates their relationshi...Purpose： In order to explain and predict the adoption of personal cloud storage, this study explores the critical factors involved in the adoption of personal cloud storage and empirically validates their relationships to a user＇s intentions.Design/methodology/approach： Based on technology acceptance model（TAM）, network externality, trust, and an interview survey, this study proposes a personal cloud storage adoption model. We conducted an empirical analysis by structural equation modeling based on survey data obtained with a questionnaire.Findings： Among the adoption factors we identified, network externality has the salient influence on a user＇s adoption intention, followed by perceived usefulness, individual innovation, perceived trust, perceived ease of use, and subjective norms. Cloud storage characteristics are the most important indirect factors, followed by awareness to personal cloud storage and perceived risk. However, although perceived risk is regarded as an important factor by other cloud computing researchers, we found that it has no significant influence. Also, subjective norms have no significant influence on perceived usefulness. This indicates that users are rational when they choose whether to adopt personal cloud storage.Research limitations： This study ignores time and cost factors that might affect a user＇s intention to adopt personal cloud storage.Practical implications： Our findings might be helpful in designing and developing personal cloud storage products, and helpful to regulators crafting policies.Originality/value： This study is one of the first research efforts that discuss Chinese users＇ personal cloud storage adoption, which should help to further the understanding of personal cloud adoption behavior among Chinese users.展开更多
This paper presents a solution for optimal business continuity, with storage architecture for enterprise applications, which will ensure zero data loss and quick recovery. The solution makes use of Internet protocol s...This paper presents a solution for optimal business continuity, with storage architecture for enterprise applications, which will ensure zero data loss and quick recovery. The solution makes use of Internet protocol storage area network （IPSAN）, which is used for data management without burdening the application server, as well as mix of synchronous and semi-synchronous replication techniques to replicate data to remote disaster recovery site. We have presented the detailed design of both synchronous and semi-synchronous with case study of using open source database postgres to prove our point for optimal business continuity. The theoretical presentation is also given for the same.展开更多
Beams typically do not travel through the magnet centers because of errors in storage rings.The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down.Beam-b...Beams typically do not travel through the magnet centers because of errors in storage rings.The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down.Beam-based alignment(BBA)is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes.For storage rings with many quadrupoles,the conventional BBA procedure is time-consuming,particularly in the commissioning phase,because of the necessary iterative process.In addition,the conventional BBA method can be affected by strong coupling and the nonlinearity of the storage ring optics.In this study,a novel method based on a neural network was proposed to determine the golden orbit in a much shorter time with reasonable accuracy.This golden orbit can be used directly for operation or adopted as a starting point for conventional BBA.The method was demonstrated in the HLS-II storage ring for the first time through simulations and online experiments.The results of the experiments showed that the golden orbit obtained using this new method was consistent with that obtained using the conventional BBA.The development of this new method and the corresponding experiments are reported in this paper.展开更多
基金supported in part by the National Nat-ural Science Foundation of China(52177110)Key Pro-gram of the National Natural Science Foundation of China(U22B20106,U2142206)+2 种基金Shenzhen Science and Technology Program(JCYJ20210324131409026)the Science and Technology Project of the State Grid Corpo-ration of China(5200-202319382A-2-3-XG)State Grid Zhejiang Elctric Power Co.,Ltd.Science and Tech-nology Project(B311DS24001A).
文摘Typhoons can cause large-area blackouts or partial outages of distribution networks.We define a partial outage state in the distribution network as a gray state and propose a gray-start strategy and two-stage distribution network emergency recovery framework.A phase-space reconstruction and stacked integrated model for predicting wind and photovoltaic generation during typhoon disasters is proposed in the first stage.This provides guidance for second-stage post-disaster emergency recovery scheduling.The emergency recovery scheduling model is established in the second stage,and this model is supported by a thermal power-generating unit,mobile emergency generators,and distributed generators.Distributed generation includes wind power generation,photovoltaics,fuel cells,etc.Simultaneously,we con-sider the gray-start based on the pumped storage unit to be an important first step in the emergency recovery strategy.This model is val-idated on the improved IEEE 33 node system,which utilizes data from the 2022 super typhoon“Muifa”in Zhoushan,Zhejiang,China.Simulations indicate the superiority of a gray start with a pumped storage unit and the proposed emergency recovery strategy.
基金This work is funded by National Natural Science Foundation of China(Nos.42202292,42141011)the Program for Jilin University(JLU)Science and Technology Innovative Research Team(No.2019TD-35).The authors would also like to thank the reviewers and editors whose critical comments are very helpful in preparing this article.
文摘To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.
基金supported by the Science and Technology Support Program of Guizhou Province([2022]General 012)the Key Science and Technology Project of China Southern Power Grid Corporation(GZKJXM20220043)。
文摘The increasing proportion of distributed photovoltaics(DPVs)and electric vehicle charging stations in low-voltage distribution networks(LVDNs)has resulted in challenges such as distribution transformer overloads and voltage violations.To address these problems,we propose a coordinated planning method for flexible interconnections and energy storage systems(ESSs)to improve the accommodation capacity of DPVs.First,the power-transfer characteristics of flexible interconnection and ESSs are analyzed.The equipment costs of the voltage source converters(VSCs)and ESSs are also analyzed comprehensively,considering the differences in installation and maintenance costs for different installation locations.Second,a bilevel programming model is established to minimize the annual comprehensive cost and yearly total PV curtailment capacity.Within this framework,the upper-level model optimizes the installation locations and capacities of the VSCs and ESSs,whereas the lower-level model optimizes the operating power of the VSCs and ESSs.The proposed model is solved using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-II).The effectiveness of the proposed planning method is validated through an actual LVDN scenario,which demonstrates its advantages in enhancing PV accommodation capacity.In addition,the economic benefits of various planning schemes with different flexible interconnection topologies and different PV grid-connected forms are quantitatively analyzed,demonstrating the adaptability of the proposed coordinated planning method.
基金funded by the National Key Research and Development Program of China(No.2022YFD2200503-02)。
文摘The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.
基金supported by National Natural Science Foundation of China(Nos.61861013,61662018)Science and Technology Major Project of Guangxi(No.AA18118031)+2 种基金Guangxi Natural Science Foundation of China(No.2018 GXNSFAA050028)the Doctoral Research Foundation of Guilin University of Electronic Science and Technology(No.UF19033Y)Director Fund project of Key Laboratory of Cognitive Radio and Information Processing of Ministry of Education(No.CRKL190102)。
文摘The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications.The emergence of these application session requests and system daemon services has created large persistent flows with diverse performance requirements that need to coexist with other types of traffic.Current routing methods such as equal-cost multipath(ECMP)and Hedera do not take into consideration specific traffic characteristics nor performance requirements,which make these methods difficult to meet the quality of service(QoS)for high-priority flows.In this paper,we tailored the best routing for different kinds of cloud storage flows as an integer programming problem and utilized grey relational analysis(GRA)to solve this optimization problem.The resulting method is a GRAbased service-aware flow scheduling(GRSA)framework that considers requested flow types and network status to select appropriate routing paths for flows in cloud storage datacenter networks.The results from experiments carried out on a real traffic trace show that the proposed GRSA method can better balance traffic loads,conserve table space and reduce the average transmission delay for high-priority flows compared to ECMP and Hedera.
基金Supported by Sinopec Key Science and Technology Research Project(P21060)。
文摘The ternary-element storage and flow concept for shale oil reservoirs in Jiyang Depression of Bohai Bay Basin,East China,was proposed based on the data of more than 10000 m cores and the production of more than 60 horizontal wells.The synergy of three elements(storage,fracture and pressure)contributes to the enrichment and high production of shale oil in Jiyang Depression.The storage element controls the enrichment of shale oil;specifically,the presence of inorganic pores and fractures,as well as laminae of lime-mud rocks,in the saline lake basin,is conducive to the storage of shale oil,and the high hydrocarbon generating capacity and free hydrocarbon content are the material basis for high production.The fracture element controls the shale oil flow;specifically,natural fractures act as flow channels for shale oil to migrate and accumulate,and induced fractures communicate natural fractures to form complex fracture network,which is fundamental to high production.The pressure element controls the high and stable production of shale oil;specifically,the high formation pressure provides the drive force for the migration and accumulation of hydrocarbons,and fracturing stimulation significantly increases the elastic energy of rock and fluid,improves the imbibition replacement of oil in the pores/fractures,and reduces the stress sensitivity,guaranteeing the stable production of shale oil for a long time.Based on the ternary-element storage and flow concept,a 3D development technology was formed,with the core techniques of 3D well pattern optimization,3D balanced fracturing,and full-cycle optimization of adjustment and control.This technology effectively guides the production and provides a support to the large-scale beneficial development of shale oil in Jiyang Depression.
文摘A multi-user view file system (MUVFS) and a security scheme are developed to improve the security of the united storage network (USN) that integrates a network attached storage (NAS) and a storage area network (SAN). The MUVFS offers a storage volume view for each authorized user who can access only the data in his own storage volume, the security scheme enables all users to encrypt and decrypt the data of their own storage view at client-side, and the USN server needs only to check the users’ identities and the data’s integrity. Experiments were performed to compare the sequential read, write and read/write rates of NFS+MUVFS+secure_module with those of NFS. The results indicate that the security of the USN is improved greatly with little influence on the system performance when the MUVFS and the security scheme are integrated into it.
基金supported by the Technical Project of the State Grid Corporation of China(research and demonstration application of key technology of energy storage cloud for mobile energy storage application of electric vehicles 5419-201971217a-0-0-00)。
文摘As part of the ongoing information revolution,smart power grid technology has become a key focus area for research into power systems.Intelligent electrical appliances are now an important component of power systems,providing a smart power grid with increased control,stability,and safety.Based on the secure communication requirements of cloud energy storage systems,this paper presents the design and development of a node controller for a cloud energy storage network.The function division and system deployment processes were carried out to ensure the security of the communication network used for the cloud energy storage system.Safety protection measures were proposed according to the demands of the communication network,allowing the system to run safely and stably.Finally,the effectiveness of the system was verified through a client-side distributed energy storage demonstration project in Suzhou,China.The system was observed to operate safely and stably,demonstrating good peak-clipping and valley filling effects,and improving the system load characteristics.
基金partly supported by the National Key R&D Program of China under the granted No. 2018YFC1902202.
文摘Battery energy storage systems(ESS) have been widely used in mobile base stations(BS) as the main backup power source. Due to the large number of base stations, massive distributed ESSs have largely stayed in idle and very difficult to achieve high asset utilization. In recent years, the fast-paced development of digital energy storage(DES) technology has revolutionized the traditional operation and maintenance of ESSs by transforming them into digital assets, further enabling battery energy storage services, raising up a new way to achieve a much higher utilization of such kind of largely idle ESS resources. In this paper, the disruptive DES technology will be introduced and its application under the context of mobile BSs will be studied, and then a cloud-based energy storage(CES) platform is proposed based on a large scale distributed DESs to provide a new cyber-enabled energy storage service to the local utility company. A real-world case study shows the effectiveness and efficiency of the CES platform.
基金supported by Social Science Fund of Hebei Province (Grant No.:HB15TQ019)
文摘Purpose： In order to explain and predict the adoption of personal cloud storage, this study explores the critical factors involved in the adoption of personal cloud storage and empirically validates their relationships to a user＇s intentions.Design/methodology/approach： Based on technology acceptance model（TAM）, network externality, trust, and an interview survey, this study proposes a personal cloud storage adoption model. We conducted an empirical analysis by structural equation modeling based on survey data obtained with a questionnaire.Findings： Among the adoption factors we identified, network externality has the salient influence on a user＇s adoption intention, followed by perceived usefulness, individual innovation, perceived trust, perceived ease of use, and subjective norms. Cloud storage characteristics are the most important indirect factors, followed by awareness to personal cloud storage and perceived risk. However, although perceived risk is regarded as an important factor by other cloud computing researchers, we found that it has no significant influence. Also, subjective norms have no significant influence on perceived usefulness. This indicates that users are rational when they choose whether to adopt personal cloud storage.Research limitations： This study ignores time and cost factors that might affect a user＇s intention to adopt personal cloud storage.Practical implications： Our findings might be helpful in designing and developing personal cloud storage products, and helpful to regulators crafting policies.Originality/value： This study is one of the first research efforts that discuss Chinese users＇ personal cloud storage adoption, which should help to further the understanding of personal cloud adoption behavior among Chinese users.
文摘This paper presents a solution for optimal business continuity, with storage architecture for enterprise applications, which will ensure zero data loss and quick recovery. The solution makes use of Internet protocol storage area network （IPSAN）, which is used for data management without burdening the application server, as well as mix of synchronous and semi-synchronous replication techniques to replicate data to remote disaster recovery site. We have presented the detailed design of both synchronous and semi-synchronous with case study of using open source database postgres to prove our point for optimal business continuity. The theoretical presentation is also given for the same.
基金supported by the National Natural Science Foundation of China(No.11975227)。
文摘Beams typically do not travel through the magnet centers because of errors in storage rings.The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down.Beam-based alignment(BBA)is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes.For storage rings with many quadrupoles,the conventional BBA procedure is time-consuming,particularly in the commissioning phase,because of the necessary iterative process.In addition,the conventional BBA method can be affected by strong coupling and the nonlinearity of the storage ring optics.In this study,a novel method based on a neural network was proposed to determine the golden orbit in a much shorter time with reasonable accuracy.This golden orbit can be used directly for operation or adopted as a starting point for conventional BBA.The method was demonstrated in the HLS-II storage ring for the first time through simulations and online experiments.The results of the experiments showed that the golden orbit obtained using this new method was consistent with that obtained using the conventional BBA.The development of this new method and the corresponding experiments are reported in this paper.
