Purchase Cost Research Articles

Blockchain-Based Power Transaction Method for Active Distribution Network

A blockchain-based power transaction method is proposed for Active Distribution Network (ADN), considering the poor security and high cost of a centralized power trading system. Firstly, the decentralized blockchain structure of the ADN power transaction is built and the transaction information is kept in blocks. Secondly, considering the transaction needs between users and power suppliers in ADN, an energy request mechanism is proposed, and the optimization objective function is designed by integrating cost aware requests and storage aware requests. Finally, the particle swarm optimization algorithm is used for multi-objective optimal search to find the power trading scheme with the minimum power purchase cost of users and the maximum power sold by power suppliers. The experimental demonstration of the proposed method based on the experimental platform shows that when the number of participants is no more than 10, the transaction delay time is 0.2 s, and the transaction cost fluctuates at 200,000 yuan, which is better than other comparison methods.

Bi-Level Optimizing Model for Microgrids With Fast Lithium Battery Energy Storage Considering Degradation Effect

The nonlinear degradation effect of the fast lithium battery energy storage system (FLBESS) and the time-scale variability of peak shaving and frequency regulation both make the optimal operation of the microgrid more challenging. Therefore, this paper presents a bi-level optimization model to balance the degradation cost of lithium battery and the benefit of peak shaving and frequency regulation and improve the utilization of FLBESS while dealing with the multiple time scales optimization problems. The upper-level model mainly optimizes the day-ahead hourly power to minimize the power purchase cost and wind and solar power curtailment cost while calculating the internal parameters of the FLBESS to supply the lower-level model for the solution. The lower model optimizes the intra-hour economic cost and coordinates the frequency regulation benefit and lithium battery degradation cost. Finally, case studies are conducted using a regional microgrid to verify that the model solution satisfies the requirements for optimal system operation. In contrast, the model has the advantages of lower economic cost, more significant life extension effect, and higher energy storage utilization rate. The results show that the annual costs were reduced by 14.9%, and the battery life was increased by 19.0% than without considering degradation.

Energy Cooperation Among Sustainable Base Stations in Multi-Operator Cellular Networks

Energy Harvesting technology contributes significantly to green cellular networking by ensuring self-sustainability and extinguishing environmental hazards. Due to the imbalance between the harvested energy and traffic load of the base stations (BSs), energy cooperation has become a crucial requirement. However, the decision of optimal energy cooperation among the BSs in a multi-operator cellular network is a challenging task due to the consideration of various factors, such as cost, loss of energy, future information of traffic load, and harvested energy of the BSs, etc. The two conflicting objectives are minimizing the energy buying cost and the loss of energy while transferring through the power links. In this work, we present an optimal energy cooperation framework, formulated as a multi-objective linear programming (MOLP) problem which brings a trade-off between the two above-mentioned conflicting objectives considering the harvested energy and load of the BSs at future time slots. For the prediction of harvested energy of the BSs, we develop a Deep Q-Learning-based prediction method that intelligently increases measurement accuracy through continuous exploration and exploitation. The results of simulation experiments carried out in MATLAB depict that the proposed multi-operator energy cooperation framework outperforms state-of-the-art works in terms of cost, performance, and energy-loss reduction.

Economics of Smallholder Animal Husbandry in Lalitpur District of Nepal

Livestock farming which contributes 11 percent of the total GDP and 32 percent of agriculture GDP is mostly managed by farm household and small enterprise in Nepal. The study aims to analyze the economic rationale of livestock holding using the benefit-cost ratio of livestock farming by the household level and to assess the determinants of dairy cattle herd size in small farm households. The study was conducted in Dalchoki, a ward of Konjyosom rural municipality of Lalitpur district in the months of October and November, 2016. Structured questionnaires were used to collect data from 178 households using census method following the inclusion criteria as least five goat or a cow or a buffalo. Cost items included feeding cost, livestock purchasing cost, labour cost, insemination, veterinary cost, shed and equipment cost whereas benefit items consisted value of milk, live animal and meat and egg, draught power, value of manure among others. Linear regression was employed to examine the determinants of dairy cattle herd size. The results showed positive net benefit from animal husbandry with labor cost excluded and negative net benefit with labour cost included. The regression results showed that landholding size, remittance, net benefit, ethnicity and economically active family members have positive effect in determining size of the number of dairy cattle.

ЛОГІСТИЧНІ МОДЕЛІ ОПТИМІЗАЦІЇ ПРОЦЕСУ ЗАБЕЗПЕЧЕННЯ ПІДПРИЄМСТВА МАТЕРІАЛЬНИМИ РЕСУРСАМИ

The article examines the peculiarities of optimizing the process of providing enterprises with material resources based on the complex use of logistic models. The need to quickly respond to the needs of consumers, which are constantly changing, caused increased interest in this issue both in scientific circles and in the applied sense. The tasks of ensuring the efficiency of material flows can be conventionally divided into those related to the movement of production and technical products, and tasks related to the movement of goods. To solve these problems, the article proposes to use modeling methods that involve the definition of the objective function. Having this data and information about the input parameters of the material flow, it is possible to obtain a solution to the problem with the highest level of efficiency. The article systematizes a number of logistical problems related to material flows that are typical for most enterprises. The problem of choosing the optimal order size using the Harris (Wilson) formula is considered in detail on the example of real data. According to this model, total costs should be minimal, the occupied volume of the warehouse should not exceed the actual volume of the warehouse, and the costs of purchase and storage should not exceed the available budget of the enterprise. Also, the solution to the problem of choosing a "Make-or-Buy" raw material supply system was implemented: to buy or to manufacture independently. Solving the "Make-or-Buy" problem allows you to make quick decisions when changing the production capabilities of both the manufacturer and the supplier. At the same time, the main goal remains cost minimization. Both problems were implemented using MS Excel "Solution Search" tools and have appropriate graphic support. Thus, having the input parameters of the problem and the objective function, the considered models allow generating mathematically justified and effective management decisions. The use of information technologies significantly accelerates and facilitates the process of using the specified models and allows creating a system of constant monitoring of changes in the company's needs for material resources.

Using analytical hierarchy process to determine intra-field heterogeneity zones upon implementation of precision farming

The study aimed to develop a methodology for determining zones of intra-field heterogeneity for precision farming. In this study, we took into account the Belarussian national land use system which provides for the absence of private ownership of agricultural land. The spatial distribution of intra-field heterogeneity zones within the land use area of 7549.49 thousand hectares was identified using the analytical hierarchy process (AHP). The algorithm for determining zones of spatial heterogeneity provides for: (1) the selection of indicators and their ranking; (2) developing a pairwise comparison matrix, (3) estimating relative weights and (4) assessing matrix consistency. It is recommended to use data from agrochemical soil studies which are conducted centrally every 4 years for each agricultural enterprise as input parameters. These data include the humus content in the soil, the content of available phosphorus and potassium, soil pH, and the content of B, Cu, Zn, Ca, and Mg. The data should be carefully examined using spatial statistics tools to provide a more accurate delineation of the management-zones boundaries. The developed technique makes it possible to determine fertile and marginal areas within each field and differentiate the use of fertilizers, taking into account the presence of intra-field heterogeneity. This will reduce the total cost of purchasing and applying phosphorus fertilizers by 34 $·ha-1 and potash fertilizers by 9 $·ha-1 due to the redistribution of the fertilizer dose calculated for the planned yield, taking into account the identified site-specific management zones. At the same time, the level of chemical pressure per hectare of arable land will decrease by 6.7% without loss of crops productivity.

Teachers’ Views on Integrating Augmented Reality in Education: Needs, Opportunities, Challenges and Recommendations

The integration of augmented reality (AR) in education is promising since it enhances teaching and offers more engaging and appealing learning experiences. Teachers can have a catalytic role towards the adoption of AR in education; therefore, their perspectives with regard to AR in teaching and learning are very important. The current study explores teachers’ views on the integration of AR in education through an open-ended questionnaire that has been answered by 93 educators worldwide. A set of digital skills that can support student-centered pedagogies in an appropriate infrastructure are the main requirement for effective teaching with AR. Among the perceived benefits and opportunities are interactive teaching and learning, increased interest and engagement, better understanding of complex concepts. As barriers, participants reported the lack of AR educational applications, the cost of buying and maintaining AR equipment and resources, the lack of teachers’ and students’ digital skills, classroom management issues, and security and ethical issues. Moreover, survey participants highlighted the need for raising teachers’ awareness for the added value of AR in education and the need for teachers’ continuous professional development. Implications and future research recommendations on the integration of AR in education are discussed.

ON THE QUESTION OF DETERMINING THE EFFICIENCY OF THE MODERNIZATION OF ELECTRIC TRAINS

The article presents the results of the development of a methodological approach to determining the components of the efficiency of the modernization of rolling stock using the example of the exploitation of bushing units with new generation bearings. Factors affecting the amount of additional capital costs for the modernization of electric trains with improved bushing units are analyzed. It is shown that additional capital investments arise due to the costs of research and development works, which are associated with the modernization of electric trains, as well as the costs of purchasing and equipping an electric train with new-generation bushing units instead of basic ones. It is proposed to adapt the calculation of the annual operating costs for the operation of the electric train to the conditions of the specific route served by it and to the existing system of maintaining the rolling stock in a technically sound condition. At the same time, the calculation of operational indicators of the use of the electric train on the route was performed. The influence of electric train modernization on the change in the annual operating costs of the suburban rail transport economy is analyzed. The average annual savings in operating costs for the repair and maintenance of the new generation of bogie assemblies per one electric train of the EPL9t series and the discounted economic effect of modernization for the remaining period of operation of the electric train were determined. It was established that the payback period for additional capital investments does not exceed 5.5 years. The proposed scientific and practical procedure for determining the components of efficiency allows to scientifically justify the feasibility of modernizing electric trains with new-generation bogies, taking into account the remaining period of their operation.

Methodology for estimating energy consumption for an electric bus and traction battery parameters in operating conditions

Problem. An important task when replacing diesel and gas buses with electric buses is to determine the effectiveness of such replacement in terms of its impact on the environment and the cost of purchasing, owning and using electric vehicles during their life cycle, in particular, the task of optimizing the design of electric buses, an important component of which is determining the optimal parameters of the traction battery for the specific operating conditions of such a vehicle. Goal. The goal is determination of real electricity costs and the required capacity of the traction battery for an electric bus, which will ensure with a probability of at least 95% traffic on the route during the day, and the planned period of the traction battery operation. Methodology. The research methods are analytical and mathematical. Results. The method is proposed to determine the real specific consumption of electricity during the operation of an electric bus and substantiate the capacity of the traction battery to ensure the term of its use with a given probability for work on a route of a known length. Originality. It is shown that traction battery capacity must be sufficient to compensate for energy consumption for the movement, electricity constant consumption for powering auxiliary circuits (heating systems, air conditioning, etc.) and take into account with a probability of 0.95 or 0.99 the required additional capacity that will ensure not exceeding the maximum allowable discharge of the traction battery set by its manufacturer. Practical value. The method can be used to determine the real values of specific electricity consumption and traction battery capacity for arbitrary models of electric buses.

Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow

This document presents a master–slave methodology for solving the problem of optimal operation of photovoltaic (PV) distributed generators (DGs) in direct current (DC) networks. This problem was modeled using a nonlinear programming model (NLP) that considers the minimization of three different objective functions in a daily operation of the system. The first one corresponds to the minimization of the total operational cost of the system, including the energy purchasing cost to the conventional generators and maintenance costs of the PV sources; the second objective function corresponds to the reduction of the energy losses associated with the transport of energy in the network, and the third objective function is related to the minimization of the total emissions of CO2 by the conventional generators installed on the DC grid. The minimization of these objective functions is achieved by using a master–slave optimization approach through the application of the Vortex Search algorithm combined with a matrix hourly power flow. To evaluate the effectiveness and robustness of the proposed approach, two test scenarios were used, which correspond to a grid-connected and a standalone network located in two different regions of Colombia. The grid-connected system emulates the behavior of the solar resource and power demand of the city of Medellín-Antioquia, and the standalone network corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá-Choco. A numerical comparison was performed with four optimization methodologies reported in the literature: particle swarm optimization, multiverse optimizer, crow search algorithm, and salp swarm algorithm. The results obtained demonstrate that the proposed optimization approach achieved excellent solutions in terms of response quality, repeatability, and processing times.

Coordinated optimization scheduling operation of integrated energy system considering demand response and carbon trading mechanism

The low-carbon economy operation of the integrated energy system can be realized by introducing the demand response and carbon trading mechanism into the optimal scheduling of the integrated energy system. In this paper, an optimal scheduling model based on CCHP and carbon capture device is proposed, which takes into account the demand response of cooling, heating and electricity load and ladder-type carbon trading mechanism. Firstly, a multi-energy and multi-type demand response model based on time-of-use electricity price and incentive mechanism is established, and user satisfaction is used to evaluate it. Then, a carbon trading model of integrated energy system is established considering the actual carbon emissions of the system and the ladder-type carbon trading mechanism. Finally, taking the minimum sum of energy purchase cost, maintenance cost, carbon emission cost and compensation cost as the objective function, combined with the operation constraints of multi-energy flow of integrated energy system, an optimal scheduling model which takes into account both low-carbon and economy is constructed, and the problem is transformed into a mixed integer linear problem and solved by CPLEX. By setting up four scenarios for example analysis, the results show that the system total cost of the ladder-type carbon trading is decreased by 5.9% compared with the traditional carbon trading, and on the basis, the system total operating cost is decreased by 3.1% after considering the user-side DR. The simulation results further show that the introduction of DR and ladder-type carbon trading mechanism can flexibly transfer load, reduce gas purchase and reduce system carbon emissions, which has significant application value.

Economic optimization analysis of different electric powertrain technologies for vans applied to last mile delivery fleets

Freight transport companies are facing new challenges linked to sustainability, energy efficiency, and cost optimization issues in the urban distribution of goods. In this sense, battery electric vehicles (BEVs) will help in reaching sustainability targets due to their local cero emissions and lesser expensive operational costs than fossil fuel vans. However, shortly other electric powertrain technologies will be available, such as hydrogen fuel cell vehicles (FCEVs) and range extender hybrid electric/hydrogen fuel cell vehicles (FC-EREVs). Depending on the powertrain typology, electric vans present operational limitations related to range, recharging time, recharging infrastructure, and the lack of experience in the powertrain performance or economic feasibility. The variety of available powertrain options and the operational uncertainties urge the development of tools to assist fleet managers in vehicle replacement decisions. In this research work, it has been developed an optimization model to assess the influence of several operational and economic aspects of van fleets within the framework of the Spanish market, taking into account the powertrain technologies available and their related infrastructure to shape the cost-optimal van fleet composition in an established period and the associated cost breakdown. This research determines the key operational and economic variables from the cost per kilometer point of view where a certain van type becomes competitive compared to the other options. The results indicate that FC-EREV vans using purchased hydrogen delivered at the depot are the best option in a wide range of economic and operational conditions. Additionally, considering onsite hydrogen production via electrolysis using the grid, the BEV van is the preferred vehicle, but FC-EREV could be a suitable option for limited yearly distances (up to 25.000 km/year) and hydrogen purchase costs over 4,2 €/kg.

Energy Management Strategy for Seaport Integrated Energy System under Polymorphic Network

This paper studies the energy management problem of a seaport integrated energy system under the polymorphic network. Firstly, with the diversity of energy devices, a seaport integrated energy system based on the polymorphic network is established to ensure information exchange and energy interaction between heterogeneous devices, including the service layer, control layer, and data layer. Secondly, by analyzing the characteristics of different loads and the energy conversion hub, such as the power to gas (P2G) and combined cooling heating and power (CCHP), the energy management model for the seaport integrated energy system is constructed. Finally, we obtain the optimal solution by mixed integer linear programming, and the proposed strategy is used to a seaport integrated energy system including CCHP, P2G, clean energy and energy storage device. By comparing four different cases, the simulation results show a reduction in the cost of energy purchase and carbon emissions when applying our strategy with various device types and device failures. Moreover, considering the application of the proposed energy management strategy under seasonal variations, the optimal solution for the energy management problem of the seaport integrated energy system is obtained.

An optimal coordinated planning strategy for distributed energy station based on characteristics of electric vehicle charging behavior under carbon trading mechanism

Under the dual-carbon goal, the number of electric vehicle (EV) has surged, and the uncertainty of the temporal and spatial characteristics of the load has become prominent, which brings the challenge for higher requirements to the equipment composition of distributed energy station (DES) and performance of station-network. This paper proposes a collaborative planning method for low-carbon DES with networks, which is focusing on the characteristics of EV charging behavior. First of all, based on the quantitative analysis of the temporal and spatial distribution of charging load of four typical types of EV, including private vehicle, public vehicle, taxi and bus, the Monte Carlo method is used to construct the charging load modeling of EV in the region area. In addition, the low-carbon DES is modeled equipped with photovoltaic (PV) and power to gas (P2G) devices, and the Prim algorithm is adopted to search for the optimal network layout of energy transmission pipelines in the region area. Under carbon trading mechanism, aiming at the lowest annualized total cost of the regional station-network, a station-network collaborative planning modeling strategy for the capacity allocation of low-carbon DES with networks is established. Finally, the case study for a project in Beijing China is implemented on MATLAB, to prove the effectiveness of the method proposed in the paper. The time-of-use electricity price scheme changes the temporal and spatial characteristics of EV load. For example, more than 2 MW the EV load can be brought into the valley price range, which reduces the carbon transaction cost and energy purchase cost of DES by 50 thousand yuan almost when peak-valley electricity price difference is close to 1.2 yuan. At the same time, the growth of EV charging load(the penetration rate of EV can reach up to 20%) in the planning area has not increased the investment and operation-maintenance costs of DES equipment.

Influence of Battery Aging on the Operation of a Charging Infrastructure

The increasingly widespread use of electric vehicles requires proper planning of the charging infrastructure. In addition to the correct identification of the optimal positions, this concerns the accurate sizing of the charging station with respect to energy needs and the management of power flows. In particular, if we consider the presence of a renewable energy source and a storage system, we can identify strategies to maximize the use of renewable energy, minimizing the purchase costs from the grid. This study uses real charging data for some public stations, which include “normal” chargers (3 kW and 7 kW) and “quick” ones (43 kW and 55 kW), for the optimal sizing of a photovoltaic system with stationary storage. Battery degradation due to use is included in the evaluation of the overall running costs of the station. In this study, two different cost models for battery degradation and their influence on energy flow management are compared, along with their impact on battery life.

FlexHM: A Practical System for Heterogeneous Memory with Flexible and Efficient Performance Optimizations

With the rapid development of cloud computing, numerous cloud services, containers, and virtual machines have been bringing tremendous demands on high-performance memory resources to modern data centers. Heterogeneous memory, especially the newly released Optane memory, offer appropriate alternatives against DRAM in clouds with the advantages of larger capacity, lower purchase cost, and promising performance. However, cloud services suffer serious implementation inconvenience and performance degradation when using hybrid DRAM and Optane memory. This article proposes FlexHM, a practical system to manage transparent heterogeneous memory resources and flexibly optimize memory access performance for all VMs, containers, and native applications. We present an open-source prototype of FlexHM in Linux with several main contributions. First, FlexHM raises a novel two-level NUMA design to manage DRAM and Optane memory as transparent main memory resources. Second, FlexHM provides flexible and efficient memory management, helping optimize memory access performance or save purchase costs of memory resources for differential cloud services with customized management strategies. Finally, the evaluations show that cloud workloads using 50% Optane slow memory on FlexHM can achieve up to 93% of the performance when using all-DRAM, and FlexHM provides up to 5.8× improvement over the previous heterogeneous memory system solution when workloads use the same ratio of DRAM and Optane memory.

Decision on Mixed Trading between Medium- and Long-Term Markets and Spot Markets for Electricity Sales Companies under New Electricity Reform Policies

The introduction of the new round of electricity reform policies has made the electricity sales companies’ trading environment increasingly complex. In the medium- and long-term market and spot market, following the new policy-oriented optimization of trading decisions is the focus of electricity sales companies. The main objective of this study is to consider the impact of the latest policies of China’s current electricity reform on each subject of electricity trading and to propose a method for electricity sales companies to make optimal decisions on renewable energy source (RES) power and conventional energy source (CES) power mixed with power trading in the medium- and long-term and spot markets to improve the efficiency of electricity market trading, promoting the consumption of renewable energy and helping the synergistic development of the electricity market and the tradable green certificate (TGC) market. This paper first discusses the impact of the new electricity reform policies on the transactions of various subjects in the electricity market and constructs the model of the consumer utility function, the profit model of an electricity sales company, and the profit model of power generators with energy storage. Considering the complex power supply and demand relationship among the various subjects of the electricity market, a game model is established for the decision on mixed trading between the medium- and long-term market, the spot market, and the tradable green certificate market to minimize the comprehensive power purchase cost of an electricity sales company. To reduce the decision-making risk caused by the uncertainty of spot price, the prophet model is used to predict the spot price; finally, through the analysis of the decision-making model of the electricity sales companies, the optimal transaction decisions of the electricity sales companies in different trading periods and different scenarios are solved. The test results show that the proposed model can significantly improve the profitability of the electricity sales companies and provide a decision-making reference for electricity sales companies to participate in the medium- and long-term market and spot market.

Will Climate Change Affect Photovoltaic Performances? A Long-Term Analysis from 1971 to 2100 in Italy

This work aims to assess the impacts of climate change on photovoltaic (PV) electricity in two Italian cities, with different latitudes and Köppen–Geiger climate classifications. This was undertaken using the recent EURO-CORDEX set of high-resolution climate projections and PV power generation models, implemented on TRNSYS software. Data for two variables (surface air temperature and solar radiation) were analysed over a long period from 1971 to 2100. For future periods, two of the Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) used in the Intergovernmental Panel on Climate Change Fifth Assessment Report were considered. In both RCP scenarios and both locations, it is estimated that the yearly PV energy produced in the future period will not undergo significant variations on average given that the rate of decrease is foreseen almost constant; instead, a slight reduction in the PV energy was detected in the past period. It can be concluded that the PV market in Italy will grow in the next years considering that the reduction in the foreseen PV purchase costs will be also supported by the slight positive effect of climate change on PV manufacturability.

PRAGMATIST: A tool to prioritize foot-and-mouth disease virus antigens held in vaccine banks.

Antigen banks have been established to supply foot-and-mouth disease virus (FMDV) vaccines at short notice to respond to incursions or upsurges in cases of FMDV infection. Multiple vaccine strains are needed to protect against specific FMDV lineages that circulate within six viral serotypes that are unevenly distributed across the world. The optimal selection of distinct antigens held in a bank must carefully balance the desire to cover these risks with the costs of purchasing and maintaining vaccine antigens. PRAGMATIST is a semi-quantitative FMD vaccine strain selection tool combining three strands of evidence: (1) estimates of the risk of incursion from specific areas (source area score); (2) estimates of the relative prevalence of FMD viral lineages in each specific area (lineage distribution score); and (3) effectiveness of each vaccine against specific FMDV lineages based on laboratory vaccine matching tests (vaccine coverage score). The output is a vaccine score, which identifies vaccine strains that best address the threats, and consequently which are the highest priority for inclusion in vaccine antigen banks. In this paper, data used to populate PRAGMATIST are described, including the results from expert elicitations regarding FMD risk and viral lineage circulation, while vaccine coverage data is provided from vaccine matching tests performed at the WRLFMD between 2011 and 2021 (n = 2,150). These data were tailored to working examples for three hypothetical vaccine antigen bank perspectives (Europe, North America, and Australia). The results highlight the variation in the vaccine antigens required for storage in these different regions, dependent on risk. While the tool outputs are largely robust to uncertainty in the input parameters, variation in vaccine coverage score had the most noticeable impact on the estimated risk covered by each vaccine, particularly for vaccines that provide substantial risk coverage across several lineages.

An Empirical Study on the Impact of China's E-commerce Development on Export Trade

At home and abroad, this paper will concentrate on the perspective of the development of electronic commerce and the export trade development, draw lessons from advanced literature at home and abroad, and analyzes its limitations, establishing electronic commerce the influence mechanism of our country foreign trade export, and to our country's current e-commerce and the development of our country foreign trade status and the existing problems are studied. Finally, according to China's current policy and e-commerce market environment, put forward the strategy and suggestions to further expand e-commerce activities.China's e-commerce activities do have an important impact on China's export, and with the improvement of China's e-commerce level, the growth of trade volume will play a positive role in promoting China's export. Through the development of e-commerce activities, can significantly improve the export of a country's foreign trade, thus promoting the country's international trade. E-commerce can reduce international trade barriers between suppliers and the national consumers, through the logistics, information flow and cash flow, greatly reduces the country's purchase cost, reduce the cost of international suppliers, simplify the international market, improve the international suppliers and cooperation between countries, to produce more trade opportunities, promote the export of international trade.