Modeling,Simulation,and Risk Analysis of Battery Energy Storage Systems in New Energy Grid Integration Scenarios

Energy storage batteries can smooth the volatility of renewable energy sources.The operating conditions during power grid integration of renewable energy can affect the performance and failure risk of battery energy storage system(BESS).However,the current modeling of grid-connected BESS is overly simplistic,typically only considering state of charge(SOC)and power constraints.Detailed lithium(Li)-ion battery cell models are computationally intensive and impractical for real-time applications and may not be suitable for power grid operating conditions.Additionally,there is a lack of real-time batteries risk assessment frameworks.To address these issues,in this study,we establish a thermal-electric-performance(TEP)coupling model based on a multitime scale BESS model,incorporating the electrical and thermal characteristics of Li-ion batteries along with their performance degradation to achieve detailed simulation of grid-connected BESS.Additionally,considering the operating characteristics of energy storage batteries and electrical and thermal abuse factors,we developed a battery pack operational riskmodel,which takes into account SOCand charge-discharge rate(Cr),using amodified failure rate to represent the BESS risk.By integrating detailed simulation of energy storage with predictive failure risk analysis,we obtained a detailed model for BESS risk analysis.This model offers a multi-time scale integrated simulation that spans month-level energy storage simulation times,day-level performance degradation,minutescale failure rate,and second-level BESS characteristics.It offers a critical tool for the study of BESS.Finally,the performance and risk of energy storage batteries under three scenarios—microgrid energy storage,wind power smoothing,and power grid failure response—are simulated,achieving a real-time state-dependent operational risk analysis of the BESS.

Ripple-effect analysis for operational architecture of air defense systems with supernetwork modeling

In order to solve the problem that the ripple-effect analy- sis for the operational architecture of air defense systems （OAADS） is hardly described in quantity with previous modeling approaches, a supernetwork modeling approach for the OAADS is put for- ward by extending granular computing. Based on that operational units and links are equal to different information granularities, the supernetwork framework of the OAADS is constructed as a “four- network within two-layer” structure by forming dynamic operating coalitions, and measuring indexes of the ripple-effect analysis for the OAADS are given combining with Laplace spectral radius. In this framework, via analyzing multidimensional attributes which inherit relations between operational units in different granular scales, an extended granular computing is put forward integrating with a topological structure. Then the operation process within the supernetwork framework, including transformation relations be- tween two layers in the vertical view and mapping relations among functional networks in the horizontal view, is studied in quantity. As the application case shows, comparing with previous modeling approaches, the supernetwork model can validate and analyze the operation mechanism in the air defense architecture, and the ripple-effect analysis can be used to confirm the key operational unit with micro and macro viewpoints.

Operational Modal Analysis of a Ship Model in the Presence of Harmonic Excitation

A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.

Novel Investigation of Stochastic Fractional Differential Equations Measles Model via the White Noise and Global Derivative Operator Depending on Mittag-Leffler Kernel

Because of the features involved with their varied kernels,differential operators relying on convolution formulations have been acknowledged as effective mathematical resources for modeling real-world issues.In this paper,we constructed a stochastic fractional framework of measles spreading mechanisms with dual medication immunization considering the exponential decay and Mittag-Leffler kernels.In this approach,the overall population was separated into five cohorts.Furthermore,the descriptive behavior of the system was investigated,including prerequisites for the positivity of solutions,invariant domain of the solution,presence and stability of equilibrium points,and sensitivity analysis.We included a stochastic element in every cohort and employed linear growth and Lipschitz criteria to show the existence and uniqueness of solutions.Several numerical simulations for various fractional orders and randomization intensities are illustrated.

Analysis and Dynamics of Fractional Order Mathematical Model of COVID-19in Nigeria Using Atangana-Baleanu Operator

We propose a mathematical model of the coronavirus disease 2019(COVID-19)to investigate the transmission and control mechanism of the disease in the community of Nigeria.Using stability theory of differential equations,the qualitative behavior of model is studied.The pandemic indicator represented by basic reproductive number R0 is obtained from the largest eigenvalue of the next-generation matrix.Local as well as global asymptotic stability conditions for the disease-free and pandemic equilibrium are obtained which determines the conditions to stabilize the exponential spread of the disease.Further,we examined this model by using Atangana–Baleanu fractional derivative operator and existence criteria of solution for the operator is established.We consider the data of reported infection cases from April 1,2020,till April 30,2020,and parameterized the model.We have used one of the reliable and efficient method known as iterative Laplace transform to obtain numerical simulations.The impacts of various biological parameters on transmission dynamics of COVID-19 is examined.These results are based on different values of the fractional parameter and serve as a control parameter to identify the significant strategies for the control of the disease.In the end,the obtained results are demonstrated graphically to justify our theoretical findings.

Comprehensive Evaluation Model of Reservoir Operation Based on Improved Set Pair Analysis

A comprehensive evaluation model based on improved set pair analysis is established. Considering the complexity in decision-making process, the model combines the certainties and uncertainties in the schemes, i.e., identical degree, different degree and opposite degree. The relations among different schemes are studied, and the traditional way of solving uncertainty problem is improved. By using the gray correlation to determine the difference degree, the problem of less evaluation indexes and inapparent linear relationship is solved. The difference between the evaluation parameters is smaller in both the fuzzy comprehensive evaluation model and fuzzy matter-element method, and the dipartite degree of the evaluation result is unobvious. However, the difference between each integrated connection degree is distinct in the improved set pair analysis. Results show that the proposed method is feasible and it obtains better effects than the fuzzy comprehensive evaluation method and fuzzy matter-element method.

Simulation Modeling and Analysis of Operator-Machine Ratio

Based on a simulation model of a semiconductor manufacturer, operator-machine ratio （OMR） analysis is made using work study and time study. Through sensitivity analysis, it is found that labor utilization decreases with the increase of lot size. Meanwhile, it is able to identify that the OMR for this company should be improved from 1 ： 3 to 1 ： 5. An application result shows that the proposed model can effectively improve the OMR by 33%.

Dynamic Modeling of Heterogeneous Spatial Analysis for Energy and Natural Resources in Grid Integration Environment

With the high-tech industrialization of earth observation satellite remote sensing and the implementation of digital earth strategy,the energy and natural resources have been decided to be the key research fields in China.In these fields,from the model based on topology data,through simple feature data model to rule-based data model,the basic spatial analysis algorithms have been developed

Symbolic Macromodeling for Statistical Simulation of Operational Amplifiers

Symbolic circuit simulator is traditionally applied to the small-signal analysis of analog circuits. This paper establishes a symbolic behavioral macromodeling method applicable to both small-signal and large-signal analysis of general two-stage operational amplifiers （op-amps）. The proposed method creates a two-pole parametric macromodel whose parameters are analytical functions of the circuit element parameters generated by a symbolic circuit simulator. A moment matching technique is used in deriving the analytical model parameter. The created parametric behavioral model can be used for op-amps performance simulation in both frequency and time domains. In particular, the parametric models are highly suited for fast statistical simulation of op-amps in the time-domain. Experiment results show that the statistical distributions of the op-amp slew and settling time characterized by the proposed model agree well with the transistor-level results in addition to achieving significant speedup.

Modal macro-strain identification from operational macro-strain shape under changing loading conditions

To develop modal macro-strain （ MMS ） identification techniques and improve their applicability in a continuous health monitoring system for civil infrastructures, the concept of operational macro-strain shape （OMSS） and the corresponding identification method are proposed under unknown ever-changing loading conditions, and the MMS is then obtained. The core of the proposed technique is mainly based on the specific property that the macro-strain transmissibility tends to be independent of external excitations at the poles of the system and converges to a unique value. The proposed method is verified using the experimental data from a three-span continuous beam excited by an impact hammer at different locations. The identified results are also compared with the commonly used methods, such as the peak- picking （PP） method, the stochastic subspace identification （SSI） method, and numerical results, in the case of unknown input forces. Results show that the proposed technique has unique merits in accuracy and robustness due to its combining multiple tests under changing loading conditions, which also reveal the promising application of the distributed strain sensing system in identifying MMS of operational structures, as well as in the structural health monitoring （SHM） field.

应用Open Meta-Analysis软件实现诊断准确性试验的Meta分析

Open Meta-Analysis软件是用于二分类数据、连续型数据以及诊断数据Meta分析的开放软件,该软件提供了四种模型来执行诊断准确性试验的Meta分析,即诊断随机效应模型、倒方差混合效应模型、双变量模型和分层综合受试者工作特征曲线法,其中前两者为单变量模型,只能执行单个指标合并,而后两者为双变量模型,能够对灵敏度和特异度之间的负相关性进行综合分析。本文以实例就Open Meta-Analysis软件实现诊断准确性试验Meta分析做相关简述。

Resilience based importance measure analysis for SoS

In a system of systems(SoS),resilience is an important factor in maintaining the functionality,stability,and enhancing the operation effectiveness.From the perspective of resilience,this paper studies the importance of the SoS,and a resilience-based importance measure analysis is conducted to provide suggestions in the design and optimization of the structure of the SoS.In this paper,the components of the SoS are simplified as four kinds of network nodes:sensor,decision point,influencer,and target.In this networked SoS,the number of operation loops is used as the performance indicator,and an approximate algorithm,which is based on eigenvalue of the adjacency matrix,is proposed to calculate the number of operation loops.In order to understand the performance change of the SoS during the attack and defense process in the operations,an integral resilience model is proposed to depict the resilience of the SoS.From different perspectives of enhancing the resilience,different measures,parameters and the corresponding algorithms for the resilience importance of components are proposed.Finally,a case study on an SoS is conducted to verify the validity of the network modelling and the resiliencebased importance analysis method.

Estimates of EAST Operation Window with LHCD by Using a Core-SOL-Divertor Model

An experimental advanced superconducting tokamak （EAST） operation window with the lower hybrid current drive （LHCD） in H-mode is estimated by using a eore-SOL-divertor （C-S-D） model validated by the present EAST divertor experiments. The operation window consists of four limits including two usual limits, one of which is the maximum allowable heat load onto the divertor plate, and two additional limits associated with the LHCD. The predictive EAST operation window is not qualified to fulfill its mission for high input power. To extend the operation window, gas puffing and impurity seeding are presented as two effective methods. In addition, the effect of the LHCD current on the operation window is also discussed. Our numerical analysis results provide a reference for the safe operation of EAST experiments with LHCD in future.

Second-Order Adjoint Sensitivity Analysis Methodology for Computing Exactly Response Sensitivities to Uncertain Parameters and Boundaries of Linear Systems: Mathematical Framework

This work presents the “Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology (2nd-CASAM)” for the efficient and exact computation of 1st- and 2nd-order response sensitivities to uncertain parameters and domain boundaries of linear systems. The model’s response (i.e., model result of interest) is a generic nonlinear function of the model’s forward and adjoint state functions, and also depends on the imprecisely known boundaries and model parameters. In the practically important particular case when the response is a scalar-valued functional of the forward and adjoint state functions characterizing a model comprising N parameters, the 2nd-CASAM requires a single large-scale computation using the First-Level Adjoint Sensitivity System (1st-LASS) for obtaining all of the first-order response sensitivities, and at most N large-scale computations using the Second-Level Adjoint Sensitivity System (2nd-LASS) for obtaining exactly all of the second-order response sensitivities. In contradistinction, forward other methods would require (N2/2 + 3 N/2) large-scale computations for obtaining all of the first- and second-order sensitivities. This work also shows that constructing and solving the 2nd-LASS requires very little additional effort beyond the construction of the 1st-LASS needed for computing the first-order sensitivities. Solving the equations underlying the 1st-LASS and 2nd-LASS requires the same computational solvers as needed for solving (i.e., “inverting”) either the forward or the adjoint linear operators underlying the initial model. Therefore, the same computer software and “solvers” used for solving the original system of equations can also be used for solving the 1st-LASS and the 2nd-LASS. Since neither the 1st-LASS nor the 2nd-LASS involves any differentials of the operators underlying the original system, the 1st-LASS is designated as a “first-level” (as opposed to a “first-order”) adjoint sensitivity system, while the 2nd-LASS is designated as a “second-level” (rather than a “second-order”) adjoint sensitivity system. Mixed second-order response sensitivities involving boundary parameters may arise from all source terms of the 2nd-LASS that involve the imprecisely known boundary parameters. Notably, the 2nd-LASS encompasses an automatic, inherent, and independent “solution verification” mechanism of the correctness and accuracy of the 2nd-level adjoint functions needed for the efficient and exact computation of the second-order sensitivities.

老年髋部骨折手术延迟的影响因素分析及风险预测模型构建

目的:探讨老年髋部骨折手术延迟的影响因素,构建老年髋部骨折手术延迟风险预测模型。方法:选取2019年11月至2022年11月采用手术治疗的老年髋部骨折患者的病例资料进行研究,将纳入研究的患者按照2∶1的比例随机分为训练集(用于模型构建)和验证集(用于模型验证)。从病历系统中提取纳入患者的信息,包括年龄、性别、体质量指数、骨折类型、美国麻醉医师协会(American Society of Anesthesiologists, ASA)分级、伤前日常活动能力(activities of daily living, ADL)、是否服用影响凝血功能的药物、入院至手术时间、手术方式,是否合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、肝功能不全、肾功能不全、电解质紊乱、尿酮体异常、下肢静脉血栓、凝血功能异常,以及入院后血清肿瘤坏死因子-α、C反应蛋白水平等。将训练集中的患者根据入院至手术时间分为早期手术组(入院至手术时间<48 h)和延迟手术组(入院至手术时间≥48 h)。先对2组患者的相关信息进行单因素对比分析,再对单因素分析中组间差异有统计学意义的因素进行多因素Logistic回归分析及多重共线性诊断;采用R软件基于贝叶斯网络模型构建老年髋部骨折手术延迟风险预测模型,并采用Netica软件进行贝叶斯网络模型推理。采用受试者操作特征(receiver operating characteristic, ROC)曲线评价老年髋部骨折手术延迟风险预测模型的区分度,采用校准曲线评价老年髋部骨折手术延迟风险预测模型的校准度。结果:(1)分组结果。共纳入老年髋部骨折患者318例,训练集212例、验证集106例。根据入院至手术时间,训练集中早期手术组78例、延迟手术组134例。(2)老年髋部骨折手术延迟影响因素的单因素分析结果。2组患者ASA分级、是否服用影响凝血功能的药物及是否合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、电解质紊乱、凝血功能异常的比较,组间差异均有统计学意义(χ~2=3.862,P=0.049;χ~2=26.806,P=0.000;χ~2=29.852,P=0.000;χ~2=21.743,P=0.000;χ~2=25.226,P=0.000;χ~2=5.415,P=0.020;χ~2=11.683,P=0.001;χ~2=14.686,P=0.000;χ~2=6.057,P=0.014)。(3)老年髋部骨折手术延迟影响因素的多因素分析及多重共线性诊断结果。多因素Logistic回归分析结果显示,服用影响凝血功能的药物及合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、电解质紊乱、凝血功能异常均是老年髋部骨折手术延迟的影响因素[β=0.328,P=0.000,OR=5.112,95%CI(2.686,9.728);β=0.322,P=0.000,OR=5.425,95%CI(2.884,10.203);β=0.302,P=0.000,OR=3.956,95%CI(2.189,7.148);β=0.312,P=0.000,OR=4.560,95%CI(2.476,8.398);β=0.291,P=0.021,OR=1.962,95%CI(1.108,3.474);β=0.296,P=0.001,OR=2.713,95%CI(1.520,4.844);β=0.303,P=0.000,OR=3.133,95%CI(1.729,5.679);β=0.296,P=0.015,OR=2.061,95%CI(1.154,3.680)];多重共线性诊断结果显示,上述影响因素均不存在共线性(VIF=1.134,VIF=1.266,VIF=1.465,VIF=1.389,VIF=1.342,VIF=1.183,VIF=1.346,VIF=1.259)。(4)基于贝叶斯网络模型的老年髋部骨折手术延迟风险预测模型的构建与推理结果。基于贝叶斯网络模型构建的老年髋部骨折手术延迟风险预测模型包括8个节点、8条有向边。模型显示,服用影响凝血功能的药物及合并精神障碍、呼吸系统疾病、电解质紊乱、凝血功能异常直接影响手术延迟的发生,合并心功能不全、高血压、糖尿病间接影响手术延迟的发生;推理结果显示,患者合并心功能不全、凝血功能异常及精神障碍时,手术延迟发生率为64.1%。(5)老年髋部骨折手术延迟风险预测模型的评价结果。采用训练集数据进行老年髋部骨折手术延迟风险预测模型评价,ROC曲线下面积为0.861[P=0.000,95%CI(0.810,0.912)],灵敏度为91.29%,特异度为93.35%;校准曲线显示其一致性指数为0.866[P=0.000,95%CI(0.702,0.943)];采用验证集数据进行老年髋部骨折手术延迟风险预测模型评价,ROC曲线下面积为0.848[P=0.000,95%CI(0.795,0.901)],灵敏度为91.62%,特异度为92.46%;校准曲线显示其一致性指数为0.879[P=0.000,95%CI(0.723,0.981)]。结论:服用影响凝血功能的药物以及合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、电解质紊乱、凝血功能异常均为老年髋部骨折手术延迟的影响因素,基于上述因素构建的老年髋部骨折手术延迟风险预测模型具有较高的应用价值。

光储直流微电网多运行工况稳定性分析

直流微电网的结构越来越复杂,运行工况也越来越多,而现有稳定性分析方法主要针对单一工况。因此研究适用于多工况运行系统的通用稳定性分析方法非常必要。首先,依据不同的运行工况建立光储直流微电网各单元的阻抗模型,利用稳态分析法结合劳斯判据推导得出系统稳定时负载功率及母线电容的边界条件;其次,通过重新定义系统环路增益表达式,得到更具通用性的基于母线电压控制变换器(BVCC)和母线电流控制变换器(BCCC)的稳定性判据,并利用该判据对系统多种运行工况进行稳定性分析,借助所得边界条件对稳定裕度较低的工况计算合适的系统参数,保证系统能够处于稳定运行状态;最后,搭建系统的仿真模型,通过对3种典型工况仿真分析,验证边界条件及稳定性分析结论的准确性。

隐变量模型及其在贝叶斯运营模态分析的应用

贝叶斯FFT算法是运营模态分析的最新一代算法,以其准确性高、计算速度快、可有效进行不确定性度量等优点受到广泛关注。然而,现有贝叶斯FFT算法针对不同情况(稀疏模态、密集模态、多步测试等)需采用不同优化算法,且编程实现极为复杂。为此,本文旨在提出针对不同情况的贝叶斯FFT算法的统一框架,并实现模态参数的高效求解;视结构模态响应为隐变量,建立贝叶斯模态识别单步测试和多步测试的隐变量模型框架;针对提出的隐变量模型运用期望最大化算法实现各种情况下模态参数的统一贝叶斯推断,利用隐变量解耦模态参数优化过程,采用Louis等式间接求取似然函数的Hessian矩阵。通过两个实际工程测试案例,并与现有方法对比,验证所提方法的准确性和高效性。分析结果表明,本文所提算法与现有方法结果相同,但其推导简单、易编程,尤其对于密集模态识别问题具有明显的计算优势。本文为贝叶斯模态识别建立起统一的隐变量模型框架,在很大程度上简化原本繁琐且冗长的推导过程,提高计算效率,同时也为应用变分贝叶斯、吉布斯采样等算法求解贝叶斯模态识别问题提供了可能。

某F综合医院临床科室运行效率评价研究——基于超效率DEA-Malmquist模型

目的分析某F综合医院部分临床科室投入产出数据,探讨在强化医院精细化管理的时代背景下,如何合理配置资源,提高各科室运行效率。方法应用数据包络分析的超效率模型和Malmquist指数对2019年1月—2022年12月期间福建省某F综合性三甲医院30个临床科室的投入产出数据进行分析。结果2019—2022年30个临床科室DEA有效的数量分别为11、11、14和14个;Malmquist指数均值为1.095,全要素生产率年平均增长9.5%。结论样本医院整体运行效率呈逐渐提高的状态,结合超效率DEA和Malmquist指数的结果,可以有针对性地推进医院精细化管理,加强资源配置的合理性,提高科室人均效能。

基于STIRPAT模型的山东省居住建筑运行碳排放影响因素研究

为有效实施建筑领域节能减排,实现双碳目标,以2005-2021年山东省居住建筑运行阶段碳排放数据为研究对象,利用STIRPAT模型对人口数量、人均GDP、人均消费支出、人均可支配收入、城镇化水平、居住建筑面积、单位建筑面积能耗、能源消耗强度和能源结构等影响碳排放的因素进行岭回归分析。结果表明:人口数量、人均GDP、人均消费支出、人均可支配收入、城镇化水平、居住建筑面积、单位建筑面积能耗和能源消耗强度等的增加会促使碳排放量的增加,但能源结构的增加会抑制碳排放量的增加,其中人口数量是影响碳排放的关键因素。据此提出山东省居住建筑节能减排的建议和对策。