A simulation-based two-stage interval-stochastic programming model for water resources management in Kaidu-Konqi watershed,China

This study presented a simulation-based two-stage interval-stochastic programming （STIP） model to support water resources management in the Kaidu-Konqi watershed in Northwest China. The modeling system coupled a distributed hydrological model with an interval two-stage stochastic programing （ITSP）. The distributed hydrological model was used for establishing a rainfall-runoff forecast system, while random parameters were pro- vided by the statistical analysis of simulation outcomes water resources management planning in Kaidu-Konqi The developed STIP model was applied to a real case of watershed, where three scenarios with different water re- sources management policies were analyzed. The results indicated that water shortage mainly occurred in agri- culture, ecology and forestry sectors. In comparison, the water demand from municipality, industry and stock- breeding sectors can be satisfied due to their lower consumptions and higher economic values. Different policies for ecological water allocation can result in varied system benefits, and can help to identify desired water allocation plans with a maximum economic benefit and a minimum risk of system disruption under uncertainty.

Estimation of Daily Global Solar Radiation with Different Sunshine-Based Models for Some Burundian Stations

Sunshine duration (S) based empirical equations have been employed in this study to estimate the daily global solar radiation on a horizontal surface (G) for six meteorological stations in Burundi. Those equations include the Ångström-Prescott linear model and four amongst its derivatives, i.e. logarithmic, exponential, power and quadratic functions. Monthly mean values of daily global solar radiation and sunshine duration data for a period of 20 to 23 years, from the Geographical Institute of Burundi (IGEBU), have been used. For any of the six stations, ten single or double linear regressions have been developed from the above-said five functions, to relate in terms of monthly mean values, the daily clearness index () to each of the next two kinds of relative sunshine duration (RSD): and . In those ratios, G0, S0 and stand for the extraterrestrial daily solar radiation on a horizontal surface, the day length and the modified day length taking into account the natural site’s horizon, respectively. According to the calculated mean values of the clearness index and the RSD, each station experiences a high number of fairly clear (or partially cloudy) days. Estimated values of the dependent variable (y) in each developed linear regression, have been compared to measured values in terms of the coefficients of correlation (R) and of determination (R2), the mean bias error (MBE), the root mean square error (RMSE) and the t-statistics. Mean values of these statistical indicators have been used to rank, according to decreasing performance level, firstly the ten developed equations per station on account of the overall six stations, secondly the six stations on account of the overall ten equations. Nevertheless, the obtained values of those indicators lay in the next ranges for all the developed sixty equations:;;;, with . These results lead to assert that any of the sixty developed linear regressions (and thus equations in terms of and ), fits very adequately measured data, and should be used to estimate monthly average daily global solar radiation with sunshine duration for the relevant station. It is also found that using as RSD, is slightly more advantageous than using for estimating the monthly average daily clearness index, . Moreover, values of statistical indicators of this study match adequately data from other works on the same kinds of empirical equations.

Nonlinear Modeling for a Two-Stage Degradation System Based on Nonhomogeneous Poisson Process

The degradation process modeling is one of research hotspots of prognostic and health management(PHM),which can be used to estimate system reliability and remaining useful life(RUL).In order to study system degradation process,cumulative damage model is used for degradation modeling.Assuming that damage increment is Gamma distribution,shock counting subjects to a homogeneous Poisson process(HPP)when degradation process is linear,and shock counting is a non-homogeneous Poisson process(NHPP)when degradation process is nonlinear.A two-stage degradation system is considered in this paper,for which the degradation process is linear in the first stage and the degradation process is nonlinear in the second stage.A nonlinear modeling method for considered system is put forward,and reliability model and remaining useful life model are established.A case study is given to validate the veracities of established models.

Estimating Critical Clearing Time of Grid Faults Using DA of State-Reduction Model of Power Systems

This paper proposes a critical clearing time (CCT) estimation method by the domain of attraction (DA) of a state-reduction model of power systems using sum of squares (SOS) programming. By exploiting the property of the Jacobian matrix and the structure of the boundary of the DA, it is found the DA of the state-reduction model and that of the full model of a power system are topological isomorphism. There are one-to-one correspondence relationships between the number of equilibrium points, the type of equilibrium points, and solutions of the two system models. Based on these findings, an expanding interior algorithm is proposed with SOS programming to estimate the DA of the state-reduction model. State trajectories of the full model can be transformed to those of the state-reduction model by orthogonal or equiradius projection. In this way, CCT of a grid fault is estimated with the DA of the state-reduction model. The calculational burden of SOS programming in the DA estimation using the state-reduction model is rather small compared with using the full model. Simulation results show the proposed expanding interior algorithm is able to provide a tight estimation of DA of power systems with higher accuracy and lower time costs.

Cost-effectiveness analysis of population-based screening of hepatocellular carcinoma: Comparing ultrasonography with two-stage screening

AIM: To assess the cost-effectiveness of two populationbased hepatocellular carcinoma(HCC) screening programs, two-stage biomarker-ultrasound method and mass screening using abdominal ultrasonography(AUS).METHODS: In this study, we applied a Markov decision model with a societal perspective and a lifetime horizon for the general population-based cohorts in an area with high HCC incidence, such as Taiwan. The accuracy of biomarkers and ultrasonography was estimated from published meta-analyses. The costs of surveillance, diagnosis, and treatment were based on a combination of published literature, Medicare payments, and medical expenditure at the National Taiwan University Hospital. The main outcome measure was cost per lifeyear gained with a 3% annual discount rate. RESULTS: The results show that the mass screening using AUS was associated with an incremental costeffectiveness ratio of USD39825 per life-year gained, whereas two-stage screening was associated with an incremental cost-effectiveness ratio of USD49733 per life-year gained, as compared with no screening. Screening programs with an initial screening age of 50 years old and biennial screening interval were the most cost-effective. These findings were sensitive to the costs of screening tools and the specificity of biomarker screening.CONCLUSION: Mass screening using AUS is more cost effective than two-stage biomarker-ultrasound screening. The most optimal strategy is an initial screening age at 50 years old with a 2-year inter-screening interval.

Vibration Frequency Characteristic Study of Two-stage Excitation Valve Used in Vibration Experiment System

To satisfy the demands of higher frequency and amplitude in hydraulic vibration experiment system,the two-stage excitation valve is presented,and a mathematical model of two-stage excitation valve is established after analyzing the working principle of two-stage excitation valve,then the influence of relevant parameters on the displacement of main spool of two-stage excitation valve is studied by using Matlab/Simulink to calculate and analyze.The results show that the displacement of main spool will be smaller with bigger diameter and more secondary valve ports.When the reversing frequency is higher and the oil supply pressure is lower as well as the axial guide width of valve ports is smaller,the maximum displacement of main spool is smaller.The new two-stage excitation valve is easy to adjust reversing frequency and flow.The high frequency can be achieved by improving the rotation speed of servo motor and adding the number of secondary valve ports;the large flow can be realized by increasing the axial guide width of secondary valve ports and oil supply pressure.The result of this study is of guiding significance for designing the rotary valve for the achievement of higher reversing frequency and larger flow.

Analysis of Optimal Conditions for Two-stage Kalman Estimator

The optimal conditions for two-stage Kalman estimator with random bias of anARMA model is considered in this paper.First,the optimal augmented state Kalman fil-ter and the two-stage Kalman estimator are given.Second,under an algebraic constraint,the equivalence between the two-stage Kalman estimator and the optimal augmented stateKalman filter is proved.Finally,because the given algebraic constraint are restrictive inpractice,the results thus obtained implies that two-stage Kalman estimator is suboptimal.

An Insight into the Second-Harmonic Current Reduction Control Strategies in Two-Stage Converters

Due to the components at twice the fundamental frequency of output voltage in the instantaneous output power of a two-stage single-phase inverter(TSI),the second harmonic current(SHC)is generated in the frontend dc-dc converter(FDC).To reduce the SHC,optimizing the control strategy of the FDC is an effective and costless approach.Fromthe view of visual impedance,this paper conducts an intensive study on the SHC reduction strategies.Origin of the SHC is illustrated first.Then,the equivalent circuit models of the FDC under different control strategies are proposed to analyse the SHC propagation characteristic.The derived model can offer a better insight into how the inductor SHC is affected by the control parameters.According to the derived models,a synthesis of different control strategies is presented and the relevant parameters are listed for control design to achieve better suppression effect.The benefits and limitations of these control strategies are also discussed.Based on the proposed equivalent circuit models,several optimization methods are proposed to enhance the effect.A 1500 VA TSI prototype is built and simulated on MATLAB/Simulink,verifying the effectiveness of the proposed optimization methods.This paper is aimed to provide a guideline for the control design and control optimization of the TSIs.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

CLEARⅠ夜晚模式和Hufnagel-Valley(5/7)模式的存疑

本文分析了几种有代表性的C2n廓线模式,计算了这些模式的相干长度、等晕角以及不同大气层湍流贡献百分比。提出了修正的CLEARⅠ夜晚模式,并对Hufnagel-Valley(5/7)模式进行了讨论。

战略需求导向下的资金集中管理模式变革研究——基于美的集团的纵向案例分析

基于美的集团纵向单案例,分析企业资金集中管理模式随战略需求变化而动态调整的实施效果。研究发现,从机会成长阶段—规模成长阶段—优化升级阶段,美的集团战略需求呈现出“异地扩张—产业扩张—国际化与多元化扩张”的变化态势,其资金集中管理模式经历了“结算中心异地结算模式—数据大集中模式—财务公司”的动态调整,资金集中管理模式对应职能也在动态调整中完成了“异地资金管理能力—跨业务协调能力—金融发展能力”的叠加。美的集团根据战略需求变化动态调整资金集中管理模式是资金集中管理活动成功的关键,这一研究发现对战略需求影响资金集中管理模式的理论解释提供了案例依据,也为集团企业的资金集中管理模式选择提供了启示。

风力发电参与调频市场的机制优化及收益研究

随着风力发电并网容量日益增加,传统能源提供的调频容量已无法满足系统调频需求,目前政策允许风电参与系统调频,但现有调频辅助服务机制无法有效衡量风电参与调频的真实容量,对系统安全稳定运行带来了严峻挑战。在现有调频辅助服务机制基础上,引入风电调频可信度指标及综合调频性能指标对调频市场主体报价进行调整以衡量风电参与调频的真实容量,同时优化现有调频辅助服务机制的补偿细则。针对上述优化部分,建立风电参与调频市场的出清模型。算例结果表明,优化后的机制能够有效衡量风电提供的真实调频容量,促使优质的调频资源优先出清,同时风电可提高调频可信度指标及综合调频性能指标以获得较高的调频收益。

电力现货市场交易运营的未来重大需求与关键技术

中国正在全面开展电力现货市场建设,现货试点地区结合当前市场规则及电力系统现状构建了较为完善的现货市场运营支撑体系,支撑了当前电力现货市场有效运行。随着新能源占比逐步提升的新型电力系统构建及“双碳”目标国家战略的实施,现有的运营技术支撑体系将无法适应未来市场发展需要。文中在总结中国电力现货市场技术实践的基础上,分析了电力现货市场特征发生的变化及面临的技术挑战,探讨了未来在多元主体建模接入、市场出清算法、市场风险分析、可信交易环境、市场仿真等方面的关键技术,部分技术已经取得初步应用。

全国统一电力市场演进过程下省间-省内市场出清及定价模型

为了促进电力资源在省间的余缺互济和在全国范围内的优化配置,我国正有序推进电力市场改革,逐步实现省间-省内市场的耦合和全国统一市场的演进。根据省间-省内市场耦合程度的不同,该文将演进过程划分为三个阶段:分层阶段、松耦合阶段和紧耦合阶段。进一步考虑跨区、跨省以及省内输电的输电费回收问题,跨区直流输电线路与区域内交流输电线路按照传输电量收费,省内线路按照用户负荷用电量收费。构建了面向不同输电费收取方式的出清模型与定价机制,分层阶段采用顺序出清,松耦合阶段采用双层迭代出清,紧耦合阶段采用统一出清,定价机制均采用节点电价定价机制。最后,利用IEEE 39节点和118节点系统进行仿真计算,验证了出清模型和定价机制的有效性,并分析了输电费对出清和定价结果的影响。

基于FY-4A和机器学习的太阳辐照度超短期预测

针对中国西部地区辐射资源充沛但观测资料匮乏的特点,提出一种基于辐照度观测数据、遥感数据、McClear和随机森林算法的太阳辐照度超短期预测方法,并重点分析遥感数据对辐照度预测效果的影响。结果表明:添加遥感数据能够优化不同时间步长的辐照度预测效果,并能显著降低平均绝对百分比误差(MAPE)值高于40%的预测大误差出现概率。同时,添加遥感数据对预测效果的提升随时间步长呈线性增加关系,nRMSE的差值变化范围从2.08%变为13.81%;nMAE的差值从1.64%变化为14.52%;R2的差值随时间步长的变化最为明显,从-0.03变为-0.43。但值得注意的是,添加卫星数据会显著增加模型的建立和超参寻优时间。

计及动态碳排放对新能源消纳市场的价格研究

通过丰富电力市场交易方式,可有效提高新能源消纳水平。针对现有电力市场无法有效控制碳排放的特点,提出计及动态碳排放的电力市场出清方法,将火电碳排放成本动态传导到电价中,从而使部分发电权向新能源转移。同时,当发生弃能时,对弃能机组引入价格因子替换报价,进行电力市场二次出清,深度促进新能源应发尽发,以激励相容机制平衡发电权转受让方收益。采用改进的IEEE 30节点系统进行仿真验证,提出的电力市场交易机制能有效降低火电机组的碳排放并促进风光消纳,验证了该方法的有效性。

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.

Dispersed Wind Power Planning Method Considering Network Loss Correction with Cold Weather

In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.

基于时空路径匹配的城市轨道交通新型清分算法

大多城市选择传统的“分配型”模型对轨道客流进行清分,即按照既定固化比例进行清分,未充分考虑乘客和列车的实际运行状态,导致清分结果存在一定偏差。为进一步研究解决传统清分算法导致的偏差问题,提出基于时空路径匹配的客流清分模型,基于乘客刷卡数据和列车实际运行ATS数据,对乘客个体OD出行过程进行全方案搜索,同时融合手机信令等多元大数据,实现乘客-列车的精准匹配。通过路网建模、乘车方案搜索与匹配,得出的客流清分结果,经采集手机信令数据进行分析验证,证明基于时空路径匹配的清分算法适用于轨道交通客流清分要求,可为轨道交通运营组织、客流调度、应急指挥等提供决策支持。

基于TCGA数据库的肾透明细胞癌线粒体自噬基因预后模型构建及预后标志物鉴定

目的:构建基于TCGA数据库的肾透明细胞癌线粒体自噬基因预后模型,寻找肾透明细胞癌的预后标志物。方法:利用TCGA-KIRC数据集,对线粒体自噬基因进行差异表达分析及单因素Cox分析;在训练集内应用多因素Cox回归模型构建预后风险评估模型;在验证集及完整集中对其预测效果进行评价;对构成该模型的基因进行鉴定。结果:筛选出3个线粒体自噬基因(E2F1、TRAF2和BNIP3)构建风险模型,生存分析结果显示与低风险组相比,高风险组患者的生存期更短且ROC曲线下面积大于0.68(P<0.01);E2F1在肾透明细胞癌中高表达并与预后不良有关。结论:基于线粒体自噬基因构建的肾透明细胞癌预后风险模型可有效预测患者预后;E2F1具有作为预后标记物的潜力。