Low-Carbon Dispatch of an Integrated Energy System Considering Confidence Intervals for Renewable Energy Generation

Addressing the insufficiency in down-regulation leeway within integrated energy systems stemming from the erratic and volatile nature of wind and solar renewable energy generation,this study focuses on formulating a coordinated strategy involving the carbon capture unit of the integrated energy system and the resources on the load storage side.A scheduling model is devised that takes into account the confidence interval associated with renewable energy generation,with the overarching goal of optimizing the system for low-carbon operation.To begin with,an in-depth analysis is conducted on the temporal energy-shifting attributes and the low-carbon modulation mechanisms exhibited by the source-side carbon capture power plant within the context of integrated and adaptable operational paradigms.Drawing from this analysis,a model is devised to represent the adjustable resources on the charge-storage side,predicated on the principles of electro-thermal coupling within the energy system.Subsequently,the dissimilarities in the confidence intervals of renewable energy generation are considered,leading to the proposition of a flexible upper threshold for the confidence interval.Building on this,a low-carbon dispatch model is established for the integrated energy system,factoring in the margin allowed by the adjustable resources.In the final phase,a simulation is performed on a regional electric heating integrated energy system.This simulation seeks to assess the impact of source-load-storage coordination on the system’s low-carbon operation across various scenarios of reduction margin reserves.The findings underscore that the proactive scheduling model incorporating confidence interval considerations for reduction margin reserves effectively mitigates the uncertainties tied to renewable energy generation.Through harmonized orchestration of source,load,and storage elements,it expands the utilization scope for renewable energy,safeguards the economic efficiency of system operations under low-carbon emission conditions,and empirically validates the soundness and efficacy of the proposed approach.

Measures of Variability for Qualitative Variables Using the R Software

Although there are many measures of variability for qualitative variables, they are little used in social research, nor are they included in statistical software. The aim of this article is to present six measures of variation for qualitative variables of simple calculation, as well as to facilitate their use by means of the R software. The measures considered are, on the one hand, Freemans variation ratio, Morals universal variation ratio, Kvalseths standard deviation from the mode, and Wilcoxs variation ratio which are most affected by proximity to a constant random variable, where the measures of variability for qualitative variables reach their minimum value of 0. On the other hand, the Gibbs-Poston index of qualitative variation and Shannons relative entropy are included, which are more affected by the proximity to a uniform distribution, where the measures of variability for qualitative variables reach their maximum value of 1. Point and interval estimation are addressed. Bootstrap by the percentile and bias-corrected and accelerated percentile methods are used to obtain confidence intervals. Two calculation situations are presented: with a sample mode and with two or more modes. The standard deviation from the mode among the six considered measures, and the universal variation ratio among the three variation ratios, are particularly recommended for use.

Computing Confidence Intervals for the Postal Service’s Cost-Elasticity Estimates

This paper provides methods for assessing the precision of cost elasticity estimates when the underlying regression function is assumed to be polynomial. Specifically, the paper adapts two well-known methods for computing confidential intervals for ratios: the delta-method and the Fieller method. We show that performing the estimation with mean-centered explanatory variables provides a straightforward way to estimate the elasticity and compute a confidence interval for it. A theoretical discussion of the proposed methods is provided, as well as an empirical example based on publicly available postal data. Possible areas of application include postal service providers worldwide, transportation and electricity.

基于数据优化和CQR-CNN-BiGRU模型的光伏功率超短期区间预测

现有光伏功率区间预测算法通常侧重于提高覆盖率和缩小预测区间,未能充分考虑实际调度中的经济成本和风险成本,从而限制了这些算法在调度决策方面的实际应用。引入了“最优置信度”概念,提出了一种创新的光伏功率区间预测方法,将数据优化与CQR算法以及CNN-BiGRU神经网络模型相结合。首先对经预处理后的光伏功率序列进行优化,采用分解-重构的思想,将光伏功率序列分解为趋势分量、周期分量和随机分量;然后结合多个气象因素,分别输入经改进麻雀搜索算法优化的CQRCNN-BiGRU神经网络中,建立各自的区间预测模型,叠加三个分量的区间预测结果,实现光伏功率的区间预测。仿真结果表明,所提方法的预测区间能够更接近预设的最优置信度,同时能够快速、有效地获得更高质量的预测区间。

基于区间概率不确定集的微电网两阶段自适应鲁棒优化调度

光伏出力的随机性和负荷用电的波动性对微电网的优化调度影响显著,为此提出了预测-调节-决策一体化的策略框架。基于高斯过程回归(Gaussian process regression,GPR)将光伏出力和负荷用电典型日历史数据自适应生成的置信区间与鲁棒优化中不确定集的构建相结合,建立了基于区间概率不确定集的自适应鲁棒优化调度模型。首先,通过GPR生成自适应鲁棒优化调度模型中不确定集的固定项,然后调节决策环节所考虑的风险水平以确定不确定集中的波动项,进而确定衡量不同调度保守度下的不确定集边界;接着采用预测区间质量评测指标来考核各个不确定集所对应的区间优劣。最后,通过改进的IEEE-37节点微电网系统验证了所提模型在有效抵御光伏出力和负荷用电波动的同时保持较低的运行成本。

基于峰值特性判定模型更新的鲁棒视觉跟踪算法

为解决传统的模型更新算法在视觉跟踪中出现遮挡、光照变化以及自身旋转等情况下存在的鲁棒性较差问题,提出一种利用峰值特性对模型进行选择性更新的鲁棒视觉跟踪算法.该算法首先通过粒子滤波跟踪确定目标位置,接着利用当前模型在当前帧跟踪的结果位置附近进行局部穷搜索,然后通过检测到的峰值分布确定目标置信度的数值矩阵,最后采用峰值旁瓣比阈值判断法确定是否更新当前模型.仿真结果表明:所提算法能够对目标模型进行有效更新,与对比算法比较,在应对视觉跟踪中常见的遮挡、光照变化以及自身旋转等情况时,总体上能够达到更好的跟踪效果.

Role of IL-17 gene polymorphisms in osteoarthritis:A meta-analysis based on observational studies

BACKGROUND Osteoarthritis(OA)is a chronic complex multifactorial joint disease,and a major degenerative form of arthritis.Existing studies on the association between polymorphisms of the IL-17 gene and the risk of OA in different populations have yielded conflicting findings.AIM To investigate the association between polymorphisms of the IL-17 gene and the risk of OA.METHODS We conducted a meta-analysis by systematically searching databases,including PubMed,EMBASE,MEDLINE,Cochrane Library,and Google Scholar to evaluate this association by calculating pooled odds ratios with 95%confidence intervals.Moreover,subgroup analyses stratified by ethnicity and OA type were also conducted.RESULTS In a total of 6 citations involving 8 studies(2131 cases and 2299 controls),4 single nucleotide polymorphisms were identified.Of these 4 polymorphisms,2(rs2275913,rs763780)were common in five case-control studies.Together,the pooled results revealed that the A allele and genotype AA/GA of the rs2275913 polymorphism,and the C allele and genotype CC of the rs763780 polymorphism in the IL-17 gene increased the risk of OA.Furthermore,stratification analyses by ethnicity and OA type showed that the rs2275913 polymorphism increased the risk of OA among Asians and in knee/hip OA,respectively.In addition,stratification analyses also revealed that the rs763780 polymorphism increased OA risk among both Asians and Caucasians in knee/hip OA.CONCLUSION The rs763780 polymorphism of the IL-17F gene increased the risk of OA,whereas the rs2275913 polymorphism of the IL-17A gene increased the risk of OA only among Asians.Due to the limitations of this study,these findings should be validated in future studies.

Optimization on the High Precision 3D Seismic Acquisition Parameters in Leijia Tight Sandstone Oil Area

The main targets of seismic exploration research in Leijia carbonatite tight sandstone oil area of Liaohe depression are thin reservoirs prediction and minor faults identification, which is one of the important representatives of complex exploration objects in Liaohe depression. High precision 3D seismic exploration has significantly improved the ability of thin reservoirs prediction and minor faults identification of this area. Reducing the cost of high precision 3D seismic exploration through optimizing the acquisition parameters is very important for the next step exploration and development of Liaohe depression and similar areas. Based on high precision 3D seismic acquisition data in Leijia tight sandstone oil area, multiple sub-geometries are obtained with different bin sizes, different folds, different aspect ratio, different line intervals by extracting receiver points and shot points, and PSTM processing is performed respectively, obtained PSTM datasets of the sub-geometries, extract time slices, amplitude slices along the layer, coherent slices and so on. We evaluate the data results of the sub-geometries from the aspects of signal-to-noise ratio, thin reservoirs resolution, acquisition footprint and so on. Considering the exploration cost and data effect of each sub-geometry, the optimal direction of the main parameters of high precision seismic exploration in Liaohe depression is put forward, and the acquisition effect of adjacent area by the optimized parameters is given.

WSNs中基于占空比间隔优化的延迟约束路由算法

针对WSNs中现有路由算法存在的各种不足,提出了一种基于占空比间隔优化的延迟约束路由算法;具体来说,在提出的算法中,首先,将端到端延迟分布估计为占空比间隔和潜在转发器数量的函数,在给定的网络模型和参数下,其分布可以近似地估计;然后,选择满足延迟约束成功率要求的占空比间隔最大值,每个节点独立地调度其休眠和唤醒时间,发送端节点将数据包转发给潜在转发器中最先唤醒的节点,从而确保数据包以要求的概率即DCSR到达接收器,同时最大化占空比间隔;仿真实验结果表明,提出的路由算法不仅能够满足要求的DSCR,并在ETE延迟、数据包交付率和实际得到的DCSR方面都优于现有的先进算法。

基于随机子空间法的海上风电机组模态阻尼比识别与评估

海上风电机组在整机载荷计算时必须考虑整机与基础的阻尼比。但现有条件下,模态阻尼比的识别精度偏低,工程应用上,模态阻尼比测试值存在离散性,短期测试难以得到有统计意义的结果。为此提出一种针对海上风电机组的阻尼比评估方案,采用运行模态分析(Operational Modal Analysis,OMA)方法对广东沿海某风电场某型号风机进行1个月的持续测试,应用随机子空间法识别阻尼比,确定了阻尼比测试值的概率分布模型,研究了阻尼比离散程度与采样数据时间、测试样本数量、风速、湍流等因素的关系。结果发现随着采样时间的增加,置信区间上下边界均有增大的趋势,阻尼比离散程度在低风速高湍流下较大,随着风速增加而降低。给出阻尼比分布模型与95%置信区间,将阻尼比识别值置信范围降低到0.1%。所提方案降低了测试时间成本,为产品设计研发提供了具备统计意义的实测数据支持。

风电–火电–抽水蓄能联合优化机组组合模型

大规模间歇式能源出力的不确定性给电网的安全经济运行提出新的挑战,研究间歇式能源接入下的多源联合优化机组组合模型对提高电网运行的安全性和促进间歇式能源的消纳具有重要意义。基于对风电不确定性及抽水蓄能运行机理分析,采用置信区间法进行风电不确定性建模,并提出新的系统调节能力约束,从而建立了考虑风电不确定性影响及抽蓄水头变化影响的联合优化机组组合模型。采用6节点系统验证模型的有效性,研究了抽蓄容量及置信区间选择对发电成本、机组组合及其可靠性的影响。结果显示,风电、火电装机不变的情况下,要保证机组组合的可靠性达99%以上,抽蓄发电装机占比分别为5.6%和10.5%时,与无抽蓄时相比可以分别减少16和17个开机时段,发电成本可以分别降低4.6%和6.8%。通过算例的论证,表明该机组组合模型能够实现多元能源的协调优化和给定置信区间内的风电的有效消纳。

安全性与经济性协调的鲁棒区间风电调度方法

鲁棒调度模型可以求解出在不确定性参数发生变化,总是可行的优化解。但是,这类模型的问题是结果过于保守。为此,提出了满足一定置信水平的鲁棒区间调度模型,通过设置置信水平实现经济与安全的协调。分析了风电出力不确定度取值与置信水平的数学关系,以此作为风电出力区间收缩的依据。采用线性规划的对偶理论将所提的双层优化模型转化为单层的非线性规划模型求解,并采用内点法实现有效求解。在IEEE RTS 24节点测试系统上的算例结果证明了该方法的有效性。

智能电网中无线传感器网络通信链路可靠性置信区间预测

实时准确的无线链路质量预测是保证智能电网厂、站区域通信链路可靠性的必要信息。无线链路质量信噪比时间序列所表现出的非线性和非平稳随机性的叠加是影响预测准确性的主要因素。为此,提出一种通信链路可靠性置信区间预测方法,通过对无线链路质量的信噪比序列近似解耦处理,将其分解为非线性序列和非平稳随机序列,采用小波神经网络建立信噪比非线性序列和非平稳随机方差序列的预测模型,并用预测结果计算通信链路可靠性置信区间上、下界。最后,在实际的智能电网环境中验证了所提出的算法和结果。

计及备用容量优化配置的风火联合随机经济调度模型

风电的随机波动性对电力系统的安全造成了严重的影响,备用容量能够起到降低风电入网风险的作用。在风电全额入网的前提下,考虑风速和负荷预测误差的概率特点,以置信水平确定旋转备用需求,以最大负荷的10%确定事故备用需求,在此基础上采用bootstrap抽样法,提高置信区间的估算精确度。将发电成本与备用成本计入目标函数,建立风电和火电联合运行的随机经济调度模型,并对模型中整数变量进行处理,以降低求解难度。通过算例求解,分析了不同时段各机组承担的发电和备用任务情况,以及不同置信水平下的经济性。

纯电动汽车变速器传动比区间优化

针对纯电动汽车传统传动比优化所得结果为确定值,而变速器齿轮配齿所得传动比往往偏离该确定值的问题,根据某典型行驶工况,在整车参数已定的情况下,对驱动电机进行匹配选择。以传动比为变量,结合两参数换挡规律,提出一种传动比区间优化方法。优化结果表明,该方法所得传动比的最大可行区间既能解决齿轮配齿问题,又能使电机驱动系统工作在高效区。

动力总成悬置系统频率和解耦率的稳健优化方法

由于测量误差、安装误差及老化等原因,动力总成悬置的刚度存在一定程度的误差或波动,从而悬置系统的频率和解耦率必然有一定程度的不确定性。考虑到通常容易得到悬置刚度的变化范围,在不需了解其统计特性的情况下,采用区间数描述悬置刚度、悬置系统的频率及解耦率的不确定性。给出了计算悬置系统频率和解耦率变化范围的改进区间截断方法,并验证了其计算精度。为提高悬置系统频率和解耦率的稳健性,提出一种区间型稳健优化方法(简称区间优化)对悬置刚度进行稳健设计。对某轿车悬置系统的频率和解耦率进行了稳健优化,结果表明,对于该悬置系统,稳健优化方法可以较大幅度地提高悬置系统侧倾和俯仰方向频率的稳健性,避免了悬置系统与其它零部件产生共振。与确定性优化相比,悬置系统在垂直方向和绕发动机曲轴扭转方向解耦率稍有降低,但能够满足悬置系统解耦布置的要求。

两正态总体方差比的优化置信区间

用传统方法得到的两正态分布方差比的置信区间显然不是最短的,因而就此意义而言也不是最佳的.本文得到优化后的置信区间,并将它与传统的置信区间比较.结果表明:优化后的最短置信区间比原置信区间有较明显的改进.

基于序列二次规划的粒子滤波算法

针对传统粒子滤波(PF)没有引入当前信息,并存在粒子退化的问题,提出了一种基于序列二次规划(SQP)多级优化的PF算法。首先,基于残差分布特性采用置信区间剔除较大偏差粒子,调整粒子权值分布;然后,将重采样后的粒子映射到集合U,根据集合U中各粒子复制次数建立多级优化模型,通过SQP求解模型的参数值,当前后两级模型优化参数差异小于门限时,输出最后一级优化参数为滤波结果;最后,为防止过度采样导致粒子退化,利用滤波值及其协方差采样新粒子。仿真实验表明:SQP-PF算法在跟踪精度,粒子多样性方面优于传统PF算法。

医院科室间病床合理分配的创新论证思路探讨

在新病区病床分配问题的研究中,从统计角度入手,通过对床位利用情况、疾病谱变化、人均工作效率、病床经济效益四个方面的实例论证分析,为医院病床配置提供决策依据,并可在不同医院间推广应用。

基于改进蚁群优化算法的QoS区间数服务组合方法

已有的Qo S服务组合方法由于无法准确量化区间型Qo S属性,且存在忽视Qo S属性中的数据分布特征和用户Qo S需求表达不准确的问题,导致其组合结果与用户理想结果存在较大偏差。为此,基于改进的蚁群优化算法,提出一种Qo S属性区间数的服务组合方法。从服务本身和用户体验两方面出发,应用区间数形式的用户满意度和Qo S效用函数构造服务组合的目标函数,并通过改进的蚁群信息素更新策略和参数选择策略加快蚁群收敛速度,在满足用户全局Qo S约束的基础上,找出用户满意度高、整体性能好的组合服务。实验结果表明,该方法能够有效提高服务组合的效率和成功率。