Combined hybrid energy storage system and transmission grid model for peak shaving based on time series operation simulation

This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.

Energy Storage Modeling of Inverter Air Conditioning for Output Optimizing of Wind Generation in the Electricity Market

In order to achieve the compatibility of the air conditioning(AC)loads with the current dispatch models,this pa-per utilizes demand response(DR)technology as energy storage resources to optimize the aggregator’s behaviors in the real-time market for less economic loss caused by the fluctuations of wind power.The inverter AC,as a typical demand response resource,is constructed as a power type battery model(PTBM)and a capacity type battery model(CTBM)according to the different control methods,which are expressed through a circuit model and mathematical model to describe the energy storage characteristics of ACs.Moreover,the comparisons between the PTBM and CTBM are given analytically by their response speed,power&energy capacity and the cost of control,which will be helpful to guide the associated operators to choose the appropriate models to take part in demand response.Considering that the wind generation fluctuates frequently and greatly,the PTBM is chosen to take part of the demand response for output optimizing of the wind generation.The simulation results demonstrate that PTBMs can work in the way of conventional batteries(CBs)to optimize wind generation in the real-time market.

A Novel Big Data Storage Reduction Model for Drill Down Search

Multi-level searching is called Drill down search.Right now,no drill down search feature is available in the existing search engines like Google,Yahoo,Bing and Baidu.Drill down search is very much useful for the end user tofind the exact search results among the huge paginated search results.Higher level of drill down search with category based search feature leads to get the most accurate search results but it increases the number and size of thefile system.The purpose of this manuscript is to implement a big data storage reduction binaryfile system model for category based drill down search engine that offers fast multi-levelfiltering capability.The basic methodology of the proposed model stores the search engine data in the binaryfile system model.To verify the effectiveness of the proposedfile system model,5 million unique keyword data are stored into a binaryfile,thereby analysing the proposedfile system with efficiency.Some experimental results are also provided based on real data that show our storage model speed and superiority.Experiments demonstrated that ourfile system expansion ratio is constant and it reduces the disk storage space up to 30%with conventional database/file system and it also increases the search performance for any levels of search.To discuss deeply,the paper starts with the short introduction of drill down search followed by the discussion of important technologies used to implement big data storage reduction system in detail.

A review of CO2 storage in geological formations emphasizing modeling,monitoring and capacity estimation approaches

The merits of CO2 capture and storage to the environmental stability of our world should not be underestimated as emissions of greenhouse gases cause serious problems.It represents the only technology that might rid our atmosphere of the main anthropogenic gas while allowing for the continuous use of the fossil fuels which still power today’s world.Underground storage of CO2 involves the injection of CO2 into suitable geological formations and the monitoring of the injected plume over time,to ensure containment.Over the last two or three decades,attention has been paid to technology developments of carbon capture and sequestration.Therefore,it is high time to look at the research done so far.In this regard,a high-level review article is required to provide an overview of the status of carbon capture and sequestration research.This article presents a review of CO2 storage technologies which includes a background of essential concepts in storage,the physical processes involved,modeling procedures and simulators used,capacity estimation,measuring monitoring and verification techniques,risks and challenges involved and field-/pilot-scale projects.It is expected that the present review paper will help the researchers to gain a quick knowledge of CO2 sequestration for future research in this field.

Influences of crustal thickening in the Tibetan Plateau on loading modeling and inversion associated with water storage variation

We use the average crustal structure of the CRUST1.0 model for the Tibetan Plateau to establish a realistic earth model termed as TC1 P, and data from the Global Land Data Assimilation System(GLDAS) hydrology model and Gravity Recovery and Climate Experiment(GRACE) data, to generate the hydrology signals assumed in this study. Modeling of surface radial displacements and gravity variation is performed using both TC1 P and the global Preliminary Reference Earth Model(PREM). Furthermore, inversions of the hydrology signals based on simulated Global Positioning System(GPS) and GRACE data are performed using PREM. Results show that crust in TC1 P is harder and softer than that in PREM above and below a depth of 15 km, respectively, causing larger differences in the computed load Love numbers and loading Green’s functions. When annual hydrology signals are assumed,the differences of the radial displacements are found to be as large as approximately0.6 mm for the truncated degree of 180; while for hydrology-trend signals the differences are very small. When annual hydrology signals and the trends are assumed, the differences in the surface gravity variation are very small. It is considered that TC1 P can be used to efficiently remove the hydrological effects on the monitoring of crustal movement. It was also found that when PREM is used inappropriately, the inversion of the hydrology signals from simulated annual GPS signals can only recover approximately 88.0% of the annual hydrology signals for the truncated degree of 180, and the inversion of hydrology signals from the simulated trend GPS signals can recover approximately 92.5% for the truncated degree of 90. However, when using the simulated GRACE data, it is possible to recover almost 100%. Therefore, in future, the TC1 P model can be used in the inversions ofhydrology signals based on GPS network data. PREM is also valid for use with inversions of hydrology signals from GRACE data at resolutions of approximately 220 km and larger.

A Scalable Double-Chain Storage Module for Blockchain

With the growing maturity of blockchain technology,its peer-topeer model and fully duplicated data storage pattern enable blockchain to act as a distributed ledger in untrustworthy environments.Blockchain storage has also become a research hotspot in industry,finance,and academia due to its security,and its unique data storage management model is gradually becoming a key technology to play its value in various fields’applications.However,with the increasing amount of data written into the blockchain,the blockchain system faces many problems in its actual implementation of the application,such as high storage space occupation,low data flexibility and availability,low retrieval efficiency,poor scalability,etc.To improve the above problems,this paper combines off-chain storage technology and deduplication technology to optimize the blockchain storage model.Firstly,this paper adopts the double-chain model to reduce the data storage of the major chain system,which stores a small amount of primary data and supervises the vice chain through an Application Programming Interface(API).The vice chain stores a large number of copies of data as well as non-transactional data.Our model divides the vice chain storage system into two layers,including a storage layer and a processing layer.In the processing layer,deduplication technology is applied to reduce the redundancy of vice chain data.Our doublechain storage model with high scalability enhances data flexibility,is more suitable as a distributed storage system,and performs well in data retrieval.

Optimized XML Storage in NXD Based on Tree-Structure Disassemble

Independent XML storage based on XSD （XML Schema Document） is adopted in NXD（Native XML Data base）, XMI. storage structure based on tree-structure disassemble and the algorithm used in dynamically updating XML document are provided in this paper. The main idea is that in term of data model of XML document, XML document is parsed to Document Structure-Tree with Hierarchical Model and Leaf-Data with Relation Model for storage. Simultaneously Proxy node is imported in order to solve the problem that XML data store in cross-blocks. And with XSD model information, sparse index is constructed to save storage space. It is proved that this storage structure could improve efficiency of XML document operation.

A four-dimensional model for the information storage/output of life

A large amount of progress has achieved in neuroscience,however,there is still a lack of reasonable model for the storage/output(S/O)of life information.The cyclical motion of cardio-and pulmonary-myocyte is a typical process of the life information S/O,while the opening and closing sites of Ca^(2+)ion channels during the motion can form a genetically programmed time-dependent three-dimensional(3D)pattern.Those phenomena indicate a strong correlation of the information S/O model of these myocytes with the time-sequence 3D patterns.Therefore,based on the time-dependent Ca^(2+)fluorescence imaging during the motion of cardio-and pulmonary-myocyte,here we suggest a four-dimensional(4D)code of information S/O model in cell and nervous system.Further from the fact of pulmonary myocyte motion able to be controlled by brain,it is deduced that the 4D code in brain has a role of controlling muscles through a pathway of the central nervous system,peripheral nervous system,neuromuscular junction,and muscle cells.In addition,we also suggested the 4D code of non-innate skill that can be programmed by the learning/training of a long time(~3 years),such as walking,writing,painting,sports,speech,singing,and dancing.Noticeably,this 4D S/O model is reasonable for the ultralow energy consumption of life information transmission.

Storage capacity model for cloud download

With the rise and world wide deployment of cloud utilities, the principle of the cloud download is proposed to provide high-quality file content distribution by using dedicated servers as cloud cache to guarantee the data availability and enhance the data transfer rate. As the system scales up to a large population, how to design appropriate storage capacity of cloud cache is a key challenge for cloud download. In this paper, primary elements impacting on storage capacity are explored through deliberating in large-scale commercial cloud download system, i.e. general user usage pattern and available period. And based on statistical analysis of real-world traces, we formulate storage capacity related to these two elements, which is the original contribution different from all previous works. This model gives guidance of potential system policy design. Finally, the effectivity of this model is demonstrated by simulation results compared with empirical data of practical system.

Low-frequency variability of terrestrial water budget in China using GRACE satellite measurements from 2003 to 2010

Mass variations in terrestrial water storage（TWS） obtained from eight years of satellite data from the Gravity Recovery and Climate Experiment（GRACE） are used to describe low frequency TWS through Empirical Orthogonal Function（EOF） analysis. Results of the second seasonal EOF mode show the influence of the Meiyu season. Annual variability is clearly shown in the precipitation distribution over China, and two new patterns of interannual variability are presented for the first time from observations, where two periods of abrupt acceleration are seen in 2004 and 2008. GRACE successfully measures drought events in southern China, and in this respect, an association with the Arctic Oscillation and El Nino-Southern Oscillation is discussed. This study demonstrates the unique potential of satellite gravity measurements in monitoring TWS variations and large-scale severe drought in China.

Resilience-oriented Valuation for Energy Storage Amidst Extreme Events

In power grids,the frequency is increasing of extreme accidents which have a low probability but high risk such as natural disasters and deliberate attacks.This has sparked discussions on the resilience of power grids.Energy-storage systems(ESSs)are critical for enhancing the resilience of power grids.ESSs,with their mechanism of flexible charging and discharging,adjust energy usage as needed during disasters,thereby mitigating the impact on the grid and enhancing security and resilience.This,in turn,ensures the power system’s stable operation.Currently,there is limited systematic research quantifying the economic value of energy storage in resilience scenarios.Therefore,a model and methodology were proposed to quantify the value of energy storage systems for enhancing grid resilience during extreme events.A two-stage stochastic optimization mathematical model was developed.The first stage involves pre-deployment based on day-ahead expectations,and the second stage involves simulating potential failure scenarios through real-time scheduling.Considering the temporal dimension,the energy storage systems with flexible regulation capabilities was used as emergency power sources to reduce occurrences of load-shedding.Here,a novel index was proposed that quantifies the resilience value of energy storage as the economic value of energy storage per unit of capacity,as reflected in the emergency dispatch model.This index helps determine the balance between the energy storage investment cost and resilience value.Finally,an IEEE-30 node transmission system was used to verify the feasibility and effectiveness of the proposed method.The findings revealed a significant improvement in the resilience value,with a 23.49%increase observed when energy storage systems were implemented compared to the scenario without energy storage systems.The optimal capacity configurations for the flywheel,lithium-ion batteries,and pumped hydro storage were 10 MW,11 MW,and 12 MW,respectively,highlight their potential to maximize value in experimental system.

Charging-rate-based Battery Energy Storage System in Wind Farm and Battery Storage Cooperation Bidding Problem

Wind power has been proven to have the ability to participate in the frequency modulation(FM)market.Using batteries to improve wind power stability can better aid wind farms participating in the FM market.Battery energy storage system(BESS)has a promising future in applying regulation and load management in the power grid.For regulation services,normally,the regulation power prediction is estimated based on the required maximum regulation capacity;the power needed for the specific regulation service is unknown to the BESS owner.However,this information is needed in the regulation model when formulating the linearised BESS model with a constraint on the state of charge(SoC).This compromises the accuracy of the model greatly when it is applied for regulation service.Moreover,different control strategies can be employed by BESS.However,the current depth of discharge(DoD)based models have difficulties in being used in a linearization problem.Due to the consideration of the control strategy,the model becomes highly nonlinear and cannot be solved.In this paper,a charging rate(C-rate)based model is introduced,which can consider different control strategies of a BESS for cooperation with wind farms to participate in wind farm estimation error compensation,load management,energy bid,and regulation bid.First,the limitation of conventional BESS models are listed,and a new C-rate-based model is introduced.Then the C-rate-based BESS model is adopted in a wind farm and BESS cooperation scheme.Finally,experimental studies are carried out,and the DoD model and C-rate model optimization results are compared to prove the rationality of the C-rate model.