Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks

Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emis-sions and electricity expenses of base stations.However,renewable energy is inherently stochastic and inter-mittent,imposing formidable challenges on reliably satisfying users'time-varying wireless traffic demands.In addition,the probability distribution of the renewable energy or users’wireless traffic demand is not always fully known in practice.In this paper,we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations,the battery charging and discharging rates,and the energy purchased from the grid under the constraint of a limited battery size at each base station.In solving the formulated non-convex chance-constrained stochastic optimization problem,a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and covariance.Using this ambiguity set,the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization approach.Furthermore,a low-complexity distributionally robust chance-constrained energy management algo-rithm,which requires only interval sets of the mean and covariance of stochastic parameters,is proposed.The results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity,energy cost,and reliability.

Research and Design of IPv6 Network Management and Operations Support System

IPv6 is the foundation of the development of Next Generation Internet (NGI). An IPv6 network management and operations support system is necessary for real operable NGI. Presently there are no approved standards yet and relevant equipment interfaces are not perfect. A Network Management System (NMS) at the network layer helps implement the integrated management of a network with equipment from multiple vendors, including the network resources and topology, end-to-end network performance, network failures and customer Service Level Agreement (SLA) management. Though the NMS will finally realize pure IPv6 network management, it must be accommodated to the management of relevant IPv4 equipment. Therefore, modularized and layered structure is adopted for the NMS in order to implement its smooth transition.

Identification of Key Links in Electric Power Operation Based-Spatiotemporal Mixing Convolution Neural Network

As the scale of the power system continues to expand,the environment for power operations becomes more and more complex.Existing risk management and control methods for power operations can only set the same risk detection standard and conduct the risk detection for any scenario indiscriminately.Therefore,more reliable and accurate security control methods are urgently needed.In order to improve the accuracy and reliability of the operation risk management and control method,this paper proposes a method for identifying the key links in the whole process of electric power operation based on the spatiotemporal hybrid convolutional neural network.To provide early warning and control of targeted risks,first,the video stream is framed adaptively according to the pixel changes in the video stream.Then,the optimized MobileNet is used to extract the feature map of the video stream,which contains both time-series and static spatial scene information.The feature maps are combined and non-linearly mapped to realize the identification of dynamic operating scenes.Finally,training samples and test samples are produced by using the whole process image of a power company in Xinjiang as a case study,and the proposed algorithm is compared with the unimproved MobileNet.The experimental results demonstrated that the method proposed in this paper can accurately identify the type and start and end time of each operation link in the whole process of electric power operation,and has good real-time performance.The average accuracy of the algorithm can reach 87.8%,and the frame rate is 61 frames/s,which is of great significance for improving the reliability and accuracy of security control methods.

A Stream Control Transmission Protocol Based OAM System of 3G Cellular Network

OAM (Operations, Administration and Maintenance) system is a very impo rtant component of 3G cellular network. In order to acquire overall managemen t, fast response and steady operation, an SCTP (Stream Control Transmission Prot ocol) based OAM, i.e., SOAM system was proposed. SOAM implements new characters of SCTP such as multi-stream, enforced SACK and heartbeat mechanism on its tran sport layer. These characters help SOAM decrease the message transmission delay and accelerate the link failure detection. Besides, a new component named SOAM agent was introduced to improve the operation efficiency of SOAM. The experim ental results prove the proposed SOAM system achieves better performance on sign aling transmission compared with conventional TCP based OAM system.

Development Methodologies for Network Softwarization: A Comparison of DevOps, NetOps, and Verification

This white paper explores three popular development methodologies for network softwarization: DevOps, NetOps, and Verification. The paper compares and contrasts the strengths and weaknesses of each approach and provides recommendations for organizations looking to adopt network softwarization.

Optimal Operation of Flexible Distribution Networks for Security Improvement Considering Active Management

This paper presents a stochastic optimal operation problem of gas turbine integrated distribution networks in the presence of active management schemes,which is formulated as a multi-objective chance-constrained mixed integer nonlinear programming problem.The control variables are the on-load tap-changer tap position,the power provided by the distributed generation(DG),the DG power factor angle,the load participating in demand side management and the switch status.The objectives defined in this paper are to simultaneously minimize the expectation cost and variation coefficient of security distance.Uncertainties related to DG output and load fluctuation and fault power restoration under contingencies are also considered in the optimization problem.The collaboration of normal boundary intersection and the dynamic niche differential evolution algorithm is proposed to handle the optimal operation mode.Simulation results are presented and demonstrate the effectiveness of the proposed model.Compared with the operation result without the consideration of security,the security-constrained operation can reduce the expectation cost.Therefore,the proposed optimization is reasonable and valuable.

An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

Recently,various mobile apps have included more features to improve user convenience.Mobile operating systems load as many apps into memory for faster app launching and execution.The least recently used(LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low.However,the LRUbased cached app termination does not distinguish between frequently or infrequently used apps.The app launch performance degrades if LRU terminates frequently used apps.Recent studies have suggested the potential of using users’app usage patterns to predict the next app launch and address the limitations of the current least recently used(LRU)approach.However,existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again.In this paper,we present a new approach for predicting future app launches by utilizing the relaunch distance.We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction(M2ARP).M2ARP utilizes past app usage patterns to predict the relaunch distance.It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.

Price-Based Power Allocation in Two-Tier Spectrum Sharing Heterogeneous Cellular Networks

Attributable to the using of the same spec-trum resources,heterogeneous cellular networks have serious interference problems,which greatly restricts the performance of the network.In this paper,the price-based power allocation for femtocells underlaying a macrocell heterogeneous cellular network is investigated.By ex-ploiting interference pricing mechanism,we formulate the interference management problem as a Stackelberg game and make a joint utility optimization of macrocells and femtocells.Specially,the energy consumption of macrocell users and the transmission rate utility of femtocell users are considered in this utility optimization problem.In the game model,the macrocell base station is regarded as a leader,which coordinates the interference from femtocell users to the macrocell users by pricing the inter-ference.On the other hand,the femtocell base stations are modelled as followers.The femtocell users obtain their power allocation by pricing.After proving the existence of the Stackelberg equilibrium,the non-uniform and uniform pricing schemes are proposed, and distributed interference pricing algorithm is proposed to address uniform interference price problem. Simulation results demonstrate that the proposed schemes are effective on interference management and power allocation.

中国油气管网运行关键技术及展望

管道是连接油气产业链上下游的关键纽带,是现代能源体系和现代综合交通体系的重要组成部分。在油气体制深化改革和“碳达峰、碳中和”战略目标下,油气管道正朝着大型化、网络化、多元化的方向发展,为大规模复杂管网的运行管理带来了新的机遇和挑战。基于我国油气管网发展现状,聚焦系统分析、仿真优化、运行监测和新型管道输送4个关键领域,剖析了油气管网运行相关研究的发展动态以及面临的难点,包括管网系统可靠性和韧性评估技术、管网运行仿真和调运优化技术、管道本体监测与修复技术以及氢气、甲醇、液氨、LNG管道输送技术等。针对“全国一张网”新阶段和“多能源互补”新动向,总结了数字孪生、智能调控、智能预警、多网融合等前瞻性技术攻关方向,旨在推动我国油气管网安全、高效、绿色转型。最后,提出展望:现阶段,需要立足我国能源资源禀赋,加快适应新形势下的管网运营模式,发挥油气管网在“全国一张网”发展新阶段下的能源大动脉作用;未来,需要稳步推进多元介质的管网灵活输送,挖掘油气管网在综合立体交通和综合能源系统中的发展潜力。

基于机器学习的主动配电网能量管理与运行控制

随着分布式资源和灵活负荷广泛接入,配电网正演变成为主动配电网,其能量管理与运行控制面临着巨大挑战:1)海量分布式资源并网使得调控需求大增,同时引入了大量随机性使得运行风险增加,需要挖掘其主动支撑能力;2)分布式资源量大且异动频繁,难以及时维护,配电网模型精度差,基于精确建模的运行控制和优化调度技术的工程应用困难。为应对上述挑战,文中介绍了基于机器学习的理论和方法,提出了“测-辨-控”一体化的主动配电网能量管理与运行控制技术体系,实现少/免模型维护的运行控制与优化调度。同时,分析了以下核心技术:1)配电网弱/无模型实时调控技术,实现自律优化;2)分布式资源集群自适应动态控制技术,实现对电网的主动支撑;3)风险量化的概率优化调度方法,实现风险与经济的平衡。最后,简要介绍了适应含极高比例分布式资源的主动配电网的能量管理与运行控制系统架构。

基于互信息粒子群优化-长短期记忆神经网络医疗设备运行质量预测模型的慢性呼吸系统疾病诊疗设备智能管理研究

目的:基于互信息粒子群优化(PSO)-长短期记忆(LSTM)神经网络构建医疗设备运行质量预测模型,辅助慢性呼吸系统疾病诊疗设备智能管理。方法:采集设备基本数据、使用数据、维修数据和性能数据进行去噪和标准化处理,构建基于PSO-LSTM神经网络医疗设备运行质量预测模型(简称PSO-LSTM模型),制定设备使用、维护、维修及报废的智能管理方案。选取2019年8月至2023年7月新疆维吾尔自治区人民医院呼吸科临床在用的139台医疗设备,将2019年8月至2021年7月的67台设备采用经验管理模式,2021年8月至2023年7月的72台设备采用智能管理模式。计算传统循环神经网络(RNN)、LSTM神经网络模型训练集和测试集与PSO-LSTM神经网络模型的预测准确性,对比两种管理模式设备管理质量和设备使用操作与技术保障人员以及患者或家属对两种管理模式的管理满意度。结果:PSO-LSTM模型训练集预测准确性的平均绝对百分比误差(MAPE)值和均方根差(RMSE)值分别为0.014和0.008,测试集分别为0.032和0.018,均低于RNN和LSTM模型。采用智能管理模式的设备平均故障频次、平均开机率、管理成本平均增幅、平均维护执行率及平均报废合规率分别为(0.99±0.85)次/年、(95.74±2.16)%、(1.72±1.28)%、(96.49±1.97)%和(97.59±1.49)%,平均故障频次和管理成本平均增幅低于经验管理模式,平均开机率、平均维护执行率和平均报废合规率高于经验管理模式,差异有统计学意义(t=3.297、3.469、2.394、4.187、3.503,P<0.05);设备使用操作与技术保障人员及患者或家属对采用智能管理模式的设备性能、运行质量、管理方式、管理成本以及诊疗效果满意度评分分别为(94.73±1.85)分、(93.38±3.15)分、(93.48±2.02)分、(94.35±2.34)分和(95.14±2.07)分,均高于经验管理模式,差异有统计学意义(t=4.131、3.827、5.716、3.430、3.173,P<0.05)。结论:基于PSO-LSTM神经网络医疗设备运行质量预测模型能更准确地评估设备运行状况,提高医疗设备临床运行质量,改善临床服务满意度。

面向6G的数字孪生网络综述:架构、应用和挑战

数字孪生网络(digital twin network,DTN)是一种利用数字孪生(digital twin,DT)技术创建物理网络设施的孪生虚拟数字网络。通过DT技术,DTN将从多个方面提升6G网络的能力,包括智能故障管理、智能网络优化和管理、网络智慧内生,助力实现极简化、智能化和自动化的网络。通过对DTN进行全面研究,描述了DTN的主要特征和定义,阐述了DTN的架构、协议及接口和生命周期管理,并阐述其在模型管理与部署、资源管理与调度、安全与隐私保护、智能网络运维和智能交通这几个方面的应用,分析了DTN的挑战与未来。

我国水库大坝安全挑战与运维思考

水库大坝是国家水网的重要调蓄结点,是防御水旱灾害、优化水资源配置、复苏河湖生态环境、提升国家水安全保障能力的“重器”。新形势下我国水库大坝运行管理面临的潜在风险日益突出,针对运行管理信息化、智能化水平不高,安全保障技术装备存在薄弱环节,应急管理水平与技术能力不足等问题与挑战,从大坝防洪韧性、安全管理、诊断加固、应急处置等方面提出安全对策,旨在为提升大坝安全水平提供有益思路。

银行业DAO数字化安全运营体系研究

在网络威胁呈爆发式增长的当下,随着业务模式数字化重塑与业务持续性增长,银行业面临因网络安全防线持续扩大所导致的安全设备冗杂、安全运营任务繁重、实战能力不足等问题.对银行业金融机构在安全运营中所面临的挑战进行分析,提出了融合平战一体化安全运营机制的银行业DAO(defence,ability and operation)数字化安全运营体系,重点研究纵深化防护基础、原子化能力中枢、数字化运营总台3层次架构,以及针对常态化、高强度、无间断防护目标的平战一体机制实施路径.

适应四网融合的市域(郊)铁路列控系统互联互通设计方案研究

近年来,“四网融合”发展已成为国家对轨道交通发展的重大战略,信号系统互联互通是实现“四网融合”的关键条件。从信号系统应用情况看,近期市域(郊)铁路信号系统列控制式大多数采用CTCS2+ATO,城市轨道交通线路信号系统主要采用CBTC,有必要对这两种列控系统互联互通设计方案进行研究。依托实际工程应用需求,采用重点内容举例和类比分析方法。首先,分析了互联互通土建及设备系统需要具备的工程条件,并对两种典型列控系统情况进行说明;之后,结合国内互联互通线路接驳及车站配线工程需求,阐述互联互通主要设计原则、制式转换最关键的共管区设计要点,以举例分析方式对停车和不停车切换的典型运营场景进行说明;最后,根据不同的设备设置方式,从调度指挥、车载列控和联锁设备几个方面,提出了完全独立和完全融合两种系统互联互通的建议方案;系统阐述互联互通需要具备的工程条件、共管区结合列控系统统一布置的设计要点,按两套车载设备同时在线和作业流程研究典型运营场景,提出了完全独立和完全融合两种方案的调度信息处理、车辆接口方式和联锁处理策略。

考虑配电网脆弱性的储能双层优化配置模型

针对大规模分布式电源并网时配电网调控能力不足导致的配网脆弱性差、供需不平衡及运维成本增加等问题,立足储能设备的灵活响应特性,提出1种计及储能配置和运行耦合的双层协同优化模型。上层模型考虑储能系统配置成本、网络运行损耗成本和主网购电成本,探究储能最优配置方案;下层模型以优化电网综合脆弱性为目标感知电网运行的改善潜力,修正上层储能配置方案并制定储能能量管理策略。引入混沌理论及动态惯性权重策略优化算法性能,以改进的混沌粒子群算法求解优化模型。算例分析表明,所提储能配置方案及运行优化策略有效改善了电网运行脆弱性,提高了配电网运行水平。

基于SAP的财务共享中心运营管理能力评估研究

运营管理能力是财务共享中心优劣的最直观体现,当前针对财务共享中心运营管理能力评估的研究较少,文中提出一种基于卷积神经网络的财务共享中心运营管理能力评估方法。先通过调研、查阅相关资料等方式了解CM公司财务共享中心的现状,并选取其运营管理的关键性要素建立评估指标体系;随后,将数据进行归一化处理以便于评估模型的建立;再基于各指标权重采用卷积神经网络法建立评估模型。将该方法运用于CM公司财务共享中心2021-2022年度运营管理能力评估中,该方法具有可行性,有助于提高企业财务共享中心运营管理的质量。

基于RPA技术的网络安全运营自动化实践应用研究

研究了围绕网络与信息系统的全生命周期,面向网络安全运营全流程场景,以机器人流程自动化(robotic process automation,RPA)技术和自适应网络安全技术建立能力框架,以期解决网络安全运营工作人工依赖性高、网络安全能力平台相对割裂、网络安全运营工作要点缺失等普适性痛点问题。构建了基于RPA技术的网络安全运营自动化平台,实现主要日常性网络安全工作的自动化转型,并将剩余人力资源配置到暂时不能自动化的领域,如业务层网络安全分析、业务网络安全渗透、漏洞挖掘、自动化运营脚本编制等方面,最终达到缓解人力不足,解决人员缺失,避免网络安全工作覆盖面不全、效能不高等目的。

新时代高职大学生网络素养教育路径刍探

网络素养包括网络技能素养、网络安全素养、网络规范素养、网络学习素养等内容。新时代高职大学生网络素养教育有助于规范大学生的网络行为,使其树立正确的“三观”,有助于优化网络环境,形成风清气正的网络空间。在具体实践中,高校要鼓励大学生加强自我教育,提高网络自律能力,建议互联网主体加强运营与监管,营造安全的网络环境,同时在校园普及网络素养知识,积极提升大学生的网络素养。

基于SDN架构的高校数据中心设计方案

某高校随着规模的扩大,多元化的业务系统对服务器和存储资源需求增多。当前高校各业务系统独占服务器和存储,无法共享资源,扩容成本高,运维管理难度大。文章基于SDN架构,从核心区、DMZ区、运维管理区、网络安全管理区阐述了数据中心的设计方案,该方案充分利用物理资源,实现业务快速上线,提升了数据中心的可靠性和安全性,可视化的管理运维降低了运维难度。该项目实施后,缩短了新业务部署周期,降低了扩容成本和人工运维成本。