On Distributed Object Storage Architecture Based on Mimic Defense

With the advent of the era of big data,cloud computing,Internet of things,and other information industries continue to develop.There is an increasing amount of unstructured data such as pictures,audio,and video on the Internet.And the distributed object storage system has become the mainstream cloud storage solution.With the increasing number of distributed applications,data security in the distributed object storage system has become the focus.For the distributed object storage system,traditional defenses are means that fix discovered system vulnerabilities and backdoors by patching,or means to modify the corresponding structure and upgrade.However,these two kinds of means are hysteretic and hardly deal with unknown security threats.Based on mimic defense theory,this paper constructs the principle framework of the distributed object storage system and introduces the dynamic redundancy and heterogeneous function in the distributed object storage system architecture,which increases the attack cost,and greatly improves the security and availability of data.

Design and Implementation of ZTE Object Storage System

This paper introduces the basic concepts and features of an obiect storage system. It also introduces some related standards, specifications, and implementations for several existing systems. ZTE＇ s Object Storage System （ZTE OSS） was designed by Tsinghua University and ZTE Corporation and is designed to manage large amounts of data. ZTE OSS has a scalable architecture, some open source components, and an efficient key-value database. ZTE OSS is easy to scale and highly reliable. Experiments show that ZTE OSS performs well with mass data and heavy

Performance Analysis and Framework Optimization of Open Source Cloud Storage System

More and more embedded devices, such as mobile phones, tablet PCs and laptops, are used in every field, so huge files need to be stored or backed up into cloud storage. Optimizing the performance of cloud storage is very important for Internet development. This paper presents the performance evaluation of the open source distributed storage system, a highly available, distributed, eventually consistent object/blob store from Open Stack cloud computing components. This paper mainly focuses on the mechanism of cloud storage as well as the optimization methods to process different sized files. This work provides two major contributions through comprehensive performance evaluations. First, it provides different configurations for Open Stack Swift system and an analysis of how every component affects the performance. Second, it presents the detailed optimization methods to improve the performance in processing different sized files. The experimental results show that our method improves the performance and the structure. We give the methods to optimize the object-based cloud storage system to deploy the readily available storage system.

Data Utilization-Based Adaptive Data Management Method for Distributed Storage System in WAN Environment

Recently,research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase.Physical expansion limits exist for traditional standalone storage systems,such as I/O and file system capacity.However,the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data location.And this leads to system performance degradation due to low locality occurring in a Wide Area Network(WAN)environment with high network latency.This problem hinders deploying distributed storage systems to multiple data centers over WAN.It lowers the scalability of distributed storage systems to accommodate data storage needs.This paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system performance.The proposed distributed storage method monitors data utilization and locality to classify data temperature as hot,warm,and cold.With assigned data temperature,the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment,leading to overall system performance degradation.This paper also conducts simulations to evaluate the proposed and existing distributed storage methods.The result shows that our proposed method reduced latency by 38%compared to the existing method.Therefore,the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods,such as consistent hashing.

JSON-ASR:A lightweight data storage and exchange format for automatic systematic reviews of TCM

Objectives:The aim of this study was to investigate and develop a data storage and exchange format for the process of automatic systematic reviews(ASR)of traditional Chinese medicine(TCM).Methods:A lightweight and commonly used data format,namely,JavaScript Object Notation(JSON),was introduced in this study.We designed a fully described data structure to collect TCM clinical trial information based on the JSON syntax.Results:A smart and powerful data format,JSON-ASR,was developed.JSON-ASR uses a plain-text data format in the form of key/value pairs and consists of six sections and more than 80 preset pairs.JSON-ASR adopts extensible structured arrays to support the situations of multi-groups and multi-outcomes.Conclusion:JSON-ASR has the characteristics of light weight,flexibility,and good scalability,which is suitable for the complex data of clinical evidence.

EB XML Modeler and SMART XML Storage Theory

Current electronic business activities involve a vast number of trading partners and different electronic documents.Usually different enterprises use different document formats.This is the well-understood problem of the lack of information interoperability between applications in the e-business arena.It is required to construct an enterprise information model to harmonize different system data structures into a unified data structure.The purpose of this paper is to develop an E-Business information modeler,which will be a good tool to exchange the information between different business enterprises.This is a new approach to the well-understood problem of the lack of information interoperability between applications in the e-business arena.It will be a more flexible and interoperable way of standardizing Business Semantics.During the transformation,SMART theory is widely used in our system.SMART is meta-model to formally specify information structures in object orientation.It provides an object-oriented environment to express the canonical information model with one semantic structure,that is,class.SMART consists of a collection of views:there is a core view which we called canonical view,and also there are many different kinds of views around the core view.We can transform the core view to the around view as we need.

Selecting China＇s strategic petroleum reserve sites by multi-objective programming model

An important decision for policy makers is selecting strategic petroleum reserve sites. However, policy makers may not choose the most suitable and efficient locations for strategic petroleum reserve（SPR） due to the complexity in the choice of sites. This paper proposes a multi-objective programming model to determine the optimal locations for China＇s SPR storage sites. This model considers not only the minimum response time but also the minimum transportation cost based on a series of reasonable assumptions and constraint conditions. The factors influencing SPR sites are identified to determine potential demand points and candidate storage sites. Estimation and suggestions are made for the selection of China＇s future SPR storage sites based on the results of this model. When the number of petroleum storage sites is less than or equals 25 and the maximum capacity of storage sites is restricted to 10 million tonnes, the model＇s result best fit for the current layout scheme selected thirteen storage sites in four scenarios. Considering the current status of SPR in China,Tianjin, Qingdao, Dalian, Daqing and Zhanjiang, Chengdu,Xi＇an, and Yueyang are suggested to be the candidate locations for the third phase of the construction plan. The locations of petroleum storage sites suggested in this work could be used as a reference for decision makers.

基于集合经验模态分解和多目标遗传算法的火-多储系统调频功率双层优化

针对分布于区域电网不同网络节点的多座储能电站参与电网调频功率调度问题,该文提出一种基于集合经验模态分解(EEMD)和多目标遗传算法(MOGA)的火-多储系统调频功率双层优化策略。该策略包含火-储调频功率优化层和多储能电站调频功率优化层:上层计及火-储调配资源各自优势及剩余调频能力,构建火-储调频功率优化分配模型,完成火-储调频功率的分配;下层引入关于调频成本和荷电状态(SOC)的自适应权重系数,以调频成本最低和SOC均衡为优化目标,完成调频功率在多储能电站之间的分配。仿真结果表明,所提策略可以提升区域电网调频效果并降低调频成本,均衡控制多个储能电站的调频成本和SOC,可以防止经济性较好的储能电站长期处于SOC越限边缘状态,提升储能电站参与调频的积极性和可持续性。

基于源荷匹配的梯级水风光蓄系统优化运行研究

在“双碳”目标下,我国亟需进行能源结构转型,提高可再生能源在电力系统中的比例。调节性水电站和抽蓄电站具有良好的调蓄能力,可以作为调节电源与风光电源联合运行,从前端减缓风光不稳定出力对电力系统的冲击,促进新能源集中上网消纳。因此,充分利用梯级水电站及抽水蓄能电站的调节性能,制定合理的一体化调度策略,有利于在发电侧实现水风光蓄打捆送出和协同消纳,助力“双碳”目标实现。基于对梯级水风光蓄互补发电系统在短期调度中电网侧负荷需求和梯级水电水资源利用率的考虑,构建以源荷差异最小、梯级水电蓄能增量最大为目标,以常规水电站、风光电站、抽水蓄能电站安全运行条件为约束的短期互补调度模型,并采用粒子群-动态规划内外层嵌套算法对模型进行求解。选取西南某流域常规梯级水电站、抽水蓄能电站及周边风光电站组成的水风光蓄互补发电系统为研究对象进行模拟计算,结果表明:(1)抽水蓄能电站倾向于在梯级电站水头较低时选择泄水发电,而在梯级电站水头较高时选择抽水或维持当前水位不变;(2)所构建模型有效提高了系统总出力曲线与电网负荷曲线的匹配度,实现了梯级水电站之间负荷与水量的合理分配,一定程度上减少了梯级水电站效益损耗,提高了水能利用率,增加了梯级水电蓄能增量,有利于源网协同运行和梯级电站之间的稳定运行。

基于频率响应特性的储能辅助电网调频方法

新能源渗透率的提高增加了电网频率控制的复杂度,储能辅助电网调频能在一定程度上缓解该问题,但受储能运行的安全性与经济性约束,要求调频措施更具针对性。文中对此展开研究,提出一种基于频率响应特性的储能辅助电网一次调频方法。首先,在储能辅助电网调频模型基础上,选择惯性加下垂的储能辅助电网调频综合控制方法,通过电网频率变化率(rate of change of frequency,RoCoF)、频率偏差与调频需求的关联性分析,设计基于频率响应特性的调频需求分区规则;然后,根据不同调频需求对应的分区判断,对储能有功输出方式进行动态调整,以响应调频需求的不确定性,并在此基础上,针对调频需求与储能出力需求、储能出力强弱与其循环使用寿命间的矛盾关系,通过多目标优化问题的设计与求解来予以平衡;最后,仿真结果验证了所提方法能够在保证电网调频效果的基础上,有效降低储能充放电深度。

基于NSGA-Ⅱ的飞轮-火电联合二次调频最优负荷分配策略

为了在提升飞轮-火电联合二次调频性能的同时控制调频成本、减少飞轮损耗,面向飞轮-火电联合系统,提出一种考虑自动发电控制(AGC)指令及电网频率偏差的基于多目标遗传算法的负荷分配策略。该策略以电网调频偏差、飞轮-火电出力能量和、飞轮损耗为三层最小化目标,考虑火电机组爬坡率及其负荷备用、飞轮荷电状态及其功率等约束条件,基于改进的非支配排序遗传算法(NSGA-Ⅱ),通过AGC功率指令变化速度及频率偏差动态调整飞轮与火电出力权重系数,以频率偏差及其变化方向调整飞轮虚拟下垂、惯性系数,求解飞轮-火电出力比例、飞轮下垂系数和飞轮惯性系数。结合中国宁夏某电厂飞轮储能系统辅助火电机组调频现场AGC指令等历史数据,在双区域两机系统模型上进行仿真实验,结果表明该负荷分配策略有效地减小了火电二次调频偏差和调节时间,提升了调频性能,同时也有效地控制了系统出力能量及飞轮损耗。

基于改进蝙蝠算法的多目标移动储能调度

在大规模电动汽车的随机充电等因素的影响下,电网峰谷差等问题突出。首先,计算所需多类型移动储能(电动汽车、移动储能车、氢燃料发电车)调度功率,考虑交通能耗,建立各类移动储能模型。其中,针对电动汽车交通能耗,根据电价对用户参与意愿的影响,建立相应的补贴体系。其余类型则只计及交通能耗成本。然后,建立以的配电网负荷峰谷差、新能源利用率、配电网运行成本为目标,利用超平面的概念自适应地确定不同目标函数的权重,将多目标归一化,建立三类移动储能协调调度模型。而后,使用改进蝙蝠算法求解,得到多类型移动储能协同调度的方案。其中,基本蝙蝠算法引入柯西变异逆累积分布函数等改进,得到改进蝙蝠算法,该算法有效提升优化速度、全局搜索能力。最后,在IEEE33节点系统中,进行仿真验证,结果证明了该调度方案的有效性。

考虑综合经济性的风光配电网储能优化配置

在配电网中配置储能系统可以有效缓解风光接入对配电网产生的影响,提升对风光的消纳能力、减少电压偏差等,而储能接入的位置和容量不同会对配电网产生不同的影响。首先分析储能建设过程中的主要费用,考虑基于分时电价下的网损成本和峰谷差收益,以年综合成本、配电网的日节点电压偏差和最小为目标,建立配电网储能选址定容模型,采用改进多目标遗传二代算法(improved multi-objective nondominated sorting genetic Ⅱ algorithm, INSGA-Ⅱ)对模型进行求解,利用改进理想点决策法(improved ideal point based decision, IIPBD)来选取最优配置方案,最后在IEEE-33节点模型中对方案进行验证。仿真结果表明:使用INSGA-Ⅱ算法进行求解时最终决策出的储能配置方案实现了成本最小,并且显著提升了电压的稳定性,验证了改进后算法的优良性和所提出的储能优化配置方案的有效性。

考虑调节费用补偿的海水抽蓄优化定容策略

通过海上风电与海水抽蓄联合运行参与电网的调峰调度,可有效促进海上风电消纳,助力电网调峰,但需要进行海水抽蓄多目标优化定容研究,解决海水抽蓄成本回收问题以及多目标之间的主观偏好与客观冲突对优化决策的影响等关键问题。为此,本文提出了考虑调节费用补偿的海水抽蓄多目标优化定容方法,通过海水抽蓄调节费用双向补偿机制,保障抽蓄调节利益;考虑风电不确定性,计及风蓄联合出力跟踪电网负荷误差最小、海上风电运营效益最大和海水抽蓄收益最大,建立风蓄联合运行多目标优化模型,实现多主体利益共赢。以双向补偿电价、海水抽蓄上水库容量、海水抽蓄机组容量为优化决策变量,通过NSGA-Ⅱ方法优化得到Pareto前沿解集;通过K-means算法对Pareto前沿解集进行聚类作为备选方案;采用层次分析法-熵权法进行主、客观赋权;通过TOPSIS方法将备选方案与理想方案比较排序选出最优解。仿真算例结果验证了本文海水抽蓄优化定容方案能够兼顾新能源消纳效果与多主体利益共赢。

混合抽蓄-风-光多能互补系统容量配置研究

混合抽蓄-风-光多能互补系统具有广阔应用前景,其容量配置需面临复杂水力水量、电力电量关系表征问题,同时其经济性评价需考虑电力市场规则。本研究提出了运行优化与容量配置决策两阶段优化思路,首先提出了适应中长期市场的月度统一电力送出模式,其次构建了混合抽蓄-风-光多能互补系统中长期运行多目标优化模型,然后基于海量容量情景模拟获取离散决策空间,最后从中优选得到最优新能源容量配置及运行方案。黄河上游清洁能源基地算例结果表明:失负荷风险接受度高、中、低分别对应新能源容量配置为混合抽蓄容量的3.2~3.9倍、2.4~3.0倍、1.6~2.1倍;系统月尺度打包并网电量峰谷比介于1.36~1.45,说明系统内各电源在中长期尺度的互补性较好。

江苏省江水北调工程水量调度

江水北调工程以向江苏境内工农业及城乡生活供水为主,同时保障改善洪泽湖、骆马湖生态,但工程运行现状存在运行成本高、湖泊弃水量大等不足。为此,采用模拟和优化技术相结合的方式,以供水缺额最小、湖泊水位超限制最小和泵站运行成本最低为优先级设定目标函数,构建江水北调工程年水量调度概化模型,开展不同水平年、不同补湖水位以及高水低用3种调度情景分析。结果表明:骆马湖调蓄库容小,调水运行期使得水位波动频繁,中运河泵站会同频开启补湖,且容易达到输水能力上限;适当降低洪泽湖补湖水位,可充分利用湖泊富余水量,使调水成本降低;完全禁止骆马湖高水低用会加重洪泽湖以下梯级线路供水任务的运行负担,同时增加骆马湖的弃水量,适当允许高水低用可使成本减小,减少泵站抽水任务。建立的模型以及相关研究成果可为江水北调工程调度运行提供一定参考。

基于PSTSO多目标优化储能系统功率分配

为提高电池储能系统的功率分配合理性,提出基于状态优先的金枪鱼群优化PSTSO(priority of status tuna swarm optimization)算法的储能系统功率分配策略。首先设定了3个储能系统功率分配的评价指标,其次建立储能系统的运行成本、储能单元的健康状态SOH(state-of-health)损失、储能系统的荷电状态SOC(state-of-charge)一致性的数学模型,最后在满足系统功率平衡和SOC上、下限约束条件下,采用PSTSO算法进行功率分配。算例分析结果表明,所提策略可以有效减少电池单元充放电次数,降低电池单元的容量损耗,且保证储能系统的SOC一致性好。

提升光储充电站运行效率的多目标优化配置策略

光储充电站的运行效率直接影响到其经济效益及电网侧的电能质量。针对在进行容量配置时对运行效率考虑不足会导致非必要的电能损耗,文中提出一种提升光储充电站运行效率的多目标优化配置策略。通过分析光储充电站变换器与内源线路功率损耗对于运行效率的影响,提出充电站的运行效率评估指标与计算方法,并讨论光储充电站运行效率对其容量配置的影响。建立以充电站经济效益、运行效率、电网侧峰谷供电功率补偿能力最佳为优化目标的多目标容量优化配置策略。针对优化目标特性,提出一种改进二代非支配排序遗传算法得到优化策略求解方法。选取中国西南地区某典型光储充电站运营场景,通过算例验证了优化策略的有效性与优越性。