Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried out.This has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the day-aheadmarket.The standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique.The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub limitations.Additionally,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.

An improved probabilistic load flow in distribution networks based on clustering and Point estimate methods

Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simulation(MCS)method.However,a challenge of the clustering methods is that the statistical characteristics of the output random variables are obtained with low accuracy.This paper presents a hybrid approach based on clustering and Point estimate methods.In the proposed approach,first,the sample points are clustered based on the𝑙-means method and the optimal agent of each cluster is determined.Then,for each member of the population of agents,the deterministic load flow calculations are performed,and the output variables are calculated.Afterward,a Point estimate-based PLF is performed and the mean and the standard deviation of the output variables are obtained.Finally,the statistical data of each output random variable are modified using the Point estimate method.The use of the proposed method makes it possible to obtain the statistical properties of output random variables such as mean,standard deviation and probabilistic functions,with high accuracy and without significantly increasing the burden of calculations.In order to confirm the consistency and efficiency of the proposed method,the 10-,33-,69-,85-,and 118-bus standard distribution networks have been simulated using coding in Python®programming language.In simulation studies,the results of the proposed method have been compared with the results obtained from the clustering method as well as the MCS method,as a criterion.

Using an Alternative Method to Estimate the Status of Iodine Nutrition in Pregnant Women

Iodine is an element that is essential for the synthesis of thyroid hormones.Adequate intake of dietary iodine has been recognized as a critical factor for maintaining health.It is a well-known fact that iodine deficiency can impede the production of thyroid hormones in both the mother and fetus,which increases the risk of brain damage in the fetal stage.

An Efficient Reliability-Based Optimization Method Utilizing High-Dimensional Model Representation and Weight-Point Estimation Method

The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the efficiency of RBDO algorithm,which hinders their application to high-dimensional engineering problems.To address these issues,this paper proposes an efficient decoupled RBDO method combining high dimensional model representation(HDMR)and the weight-point estimation method(WPEM).First,we decouple the RBDO model using HDMR and WPEM.Second,Lagrange interpolation is used to approximate a univariate function.Finally,based on the results of the first two steps,the original nested loop reliability optimization model is completely transformed into a deterministic design optimization model that can be solved by a series of mature constrained optimization methods without any additional calculations.Two numerical examples of a planar 10-bar structure and an aviation hydraulic piping system with 28 design variables are analyzed to illustrate the performance and practicability of the proposed method.

Comparative Study of Probabilistic and Least-Squares Methods for Developing Predictive Models

This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.

A new method for the estimation of fine-sediment resuspension ratios in estuaries-taking the turbidity maximum zone of the Changjiang(Yangtze) estuary as an example

Based on the principle of conservative matter removal in estuary,a new method is proposed for estimating the ratio of sediment resuspension in estuaries with fine suspended sediments in the turbidity maximum zone(TMZ) of the Changjiang(Yangtze) estuary during 2005.Results show that there was a range of 18.7%±27.9% to 73.9%±22.5% per annum of total suspended particulate matter(SPM),with an average of 49.2%.Nearly half of the particulate matter in the TMZ originates from sediment resuspension.This indicates that sediment resuspension is one of the major mechanisms involved in formation of the TMZ.Compared with traditional method for calculating these ratios in the estuary,this new method evaluates the dynamic variation of SPM content carried by river runoff from the river mouth to the ocean.The new method produced more reliable results than the traditional one and could produce a better estimation of resuspension flux for particulate matter in estuaries.

Evaluation of estimation methods for meiofaunal biomass from a meiofaunal survey in Bohai Bay

Studies in the coastal area of Bohai Bay,China,from July 2006 to October 2007,suggest that the method of meiofaunal biomass estimation affected the meiofaunal analysis.Conventional estimation methods that use a unique mean individual weight value for nematodes to calculate total biomass may cause deviation of the results.A modified estimation method,named the Subsection Count Method (SCM),was also used to calculate meiofaunal biomass.This entails only a slight increase in workload but generates results of greater accuracy.Results gained using each of these two methods were compared in the present study.The results show that the conventional method generally estimates a meiofaunal biomass.The difference between the two estimation methods was highly significant (P<0.01) for the spring and winter cruises.Furthermore,the estimation method for meiofaunal biomass affected the analysis of horizontal distribution and correlation with environmental factors.These findings highlight the importance of estimation methods for meiofaunal biomass and will hopefully stimulate further investigation and discussion of the topic.

Modified Method for Estimating Organic Carbon Density in Discontinuous Karst Soil Using Ground-Penetrating Radar and Geostatistics

The conventional method which assumes the soil distribution is continuous was unsuitable for estimating soil organic carbon density(SOCD) in Karst areas because of its discontinuous soil distribution. The accurate estimation of SOCD in Karst areas is essential for carbon sequestration assessment in China. In this study, a modified method,which considers the vertical proportion of soil area in the profile when calculating the SOCD, was developed to estimate the SOCD in a typical Karst peak-cluster depression area in southwest China. In the modified method, ground-penetrating radar(GPR) technology was used to detect the distribution and thickness of soil. The accuracy of the method was confirmed through comparison with the data obtained using a validation method, in which the soil thickness was measured by excavation. In comparison with the conventional method and average-soil-depth method,the SOCD estimated using the GPR method showed the minimum relative error with respect to that obtained using the validation method. At a regional scale, the average SOCDs at depths of 0-20 cm and 0-100 cm, which were interpolated by ordinary kriging,were 1.49(ranging from 0.03-5.65) and 2.26(0.09-11.60) kgm-2based on GPR method in our study area(covering 393.6 hm2), respectively. Therefore, the modified method can be applied on the accurate estimation of SOCD in discontinuous soil areas such as Karst regions.

A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method

In this study, a multivariate local quadratic polynomial regression(MLQPR) method is proposed to design a model for the sludge volume index(SVI). In MLQPR, a quadratic polynomial regression function is established to describe the relationship between SVI and the relative variables, and the important terms of the quadratic polynomial regression function are determined by the significant test of the corresponding coefficients. Moreover, a local estimation method is introduced to adjust the weights of the quadratic polynomial regression function to improve the model accuracy. Finally, the proposed method is applied to predict the SVI values in a real wastewater treatment process(WWTP). The experimental results demonstrate that the proposed MLQPR method has faster testing speed and more accurate results than some existing methods.

An Estimation Method of Stress in Soft Rock Based on In-situ Measured Stress in Hard Rock

The law of variation of deep rock stress in gravitational and tectonic stress fields is analyzed based on the Hoek-Brown strength criterion. In the gravitational stress field,the rocks in the shallow area are in an elastic state and the deep,relatively soft rock may be in a plastic state. However,in the tectonic stress field,the relatively soft rock in the shallow area is in a plastic state and the deep rock in an elastic state. A method is proposed to estimate stress values in coal and soft rock based on in-situ measurements of hard rock. Our estimation method relates to the type of stress field and stress state. The equations of rock stress in various stress states are presented for the elastic,plastic and critical states. The critical state is a special stress state,which indicates the conversion of the elastic to the plastic state in the gravitational stress field and the conversion of the plastic to the elastic state in the tectonic stress field. Two cases stud-ies show that the estimation method is feasible.

Calculation of Significant Wave Height Using the Linear Mean Square Estimation Method

Significant wave height is an important criterion in designing coastal and offshore structures.Based on the orthogonality principle, the linear mean square estimation method is applied to calculate significant wave height in this paper.Twenty-eight-year time series of wave data collected from three ocean buoys near San Francisco along the California coast are analyzed.It is proved theoretically that the computation error will be reduced by using as many measured data as possible for the calculation of significant wave height.Measured significant wave height at one buoy location is compared with the calculated value based on the data from two other adjacent buoys.The results indicate that the linear mean square estimation method can be well applied to the calculation and prediction of significant wave height in coastal regions.

Method for array gain and phase uncertainties calibration based on ISM and ESPRIT

A new method for array calibration of array gain and phase uncertainties, which severely degrade the performance of spatial spectrum estimation, is presented. The method is based on the idea of the instrumental sensors method （ISM）, two well-calibrated sensors are added into the original array. By applying the principle of estimation of signal parameters via rotational invariance techniques （ESPRIT）, the direction-of-arrivals （DOAs） and uncertainties can be estimated simultaneously through eigen-decomposition. Compared with the conventional ones, this new method has less computational complexity while has higher estimation precision, what＇s more, it can overcome the problem of ambiguity. Both theoretical analysis and computer simulations show the effectiveness of the proposed method.

WAVE CURRENT FORCE COEFFICIENTS ON INCLINED CYLINDER AND A NEW ESTIMATION METHOD

Based on the review of present force coefficients estimation methods, a new method in the frequency domain, revised cross-spectrum estimation method, is presented in this paper. Some experiments on the wave-current force on inclined cylinders are also described and the wave current force coefficients are estimated by the revised cross-spectrum estimation method. From the results, it is found that the wave and current directions have some regular effect on the coefficients. According to the results, some empirical formulas are obtained for converting the wave-current force coefficients on inclined cylinders into a unified coefficient. Comparisons show that the unified coefficients are in good agreement with other results.

Singularity of Some Software Reliability Models and Parameter Estimation Method

According to the principle, “The failure data is the basis of software reliability analysis”, we built a software reliability expert system (SRES) by adopting the artificial intelligence technology. By reasoning out the conclusion from the fitting results of failure data of a software project, the SRES can recommend users “the most suitable model” as a software reliability measurement model. We believe that the SRES can overcome the inconsistency in applications of software reliability models well. We report investigation results of singularity and parameter estimation methods of experimental models in SRES.

Measuring Herbage Mass by Non-Destructive Methods： A Review

Accurate assessment of herbage mass （HM） in pasture is a key to budgeting forage in grazing systems worldwide. Different non-destructive techniques to measuring pasture yield are commented. The methods compared include visual estimations, manual and electronic pasture meters and remote sensing. All methods are associated with a moderate to high error, showing that some indirect methods of yield estimation are appropriate under most appropriate because many factors as climate variations, soil certain conditions. In general terms, no method was found as the characteristics, plant phenology, pasture management and species composition must be taken into account to make local calibrations from a general model. Best results were found modifying general methods under local calibrations and under local conditions. In order to give farmers the best method to manage adequately their own grazing systems, researchers must select the most suitable technique considering the scale of operation, the desired accuracy and the resources available.

Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method

Source term identification is very important for the contaminant gas emission event. Thus, it is necessary to study the source parameter estimation method with high computation efficiency, high estimation accuracy and reasonable confidence interval. Tikhonov regularization method is a potential good tool to identify the source parameters. However, it is invalid for nonlinear inverse problem like gas emission process. 2-step nonlinear and linear PSO （partial swarm optimization）-Tikhonov regularization method proposed previously have estimated the emission source parameters successfully. But there are still some problems in computation efficiency and confidence interval. Hence, a new 1-step nonlinear method combined Tikhonov regularizafion and PSO algorithm with nonlinear forward dispersion model was proposed. First, the method was tested with simulation and experiment cases. The test results showed that 1-step nonlinear hybrid method is able to estimate multiple source parameters with reasonable confidence interval. Then, the estimation performances of different methods were compared with different cases. The estimation values with 1-step nonlinear method were close to that with 2-step nonlinear and linear PSO-Tikhonov regularization method, 1-step nonlinear method even performs better than other two methods in some cases, especially for source strength and downwind distance estimation. Compared with 2-step nonlinear method, 1-step method has higher computation efficiency. On the other hand, the confidence intervals with the method proposed in this paper seem more reasonable than that with other two methods. Finally, single PSO algorithm was compared with 1-step nonlinear PSO-Tikhonov hybrid regularization method. The results showed that the skill scores of 1-step nonlinear hybrid method to estimate source parameters were close to that of single PSO method and even better in some cases. One more important property of 1-step nonlinear PSO-Tikhonov regularization method is its reasonable confidence interval, which is not obtained by single PSO algorithm. Therefore, 1-step nonlinear hybrid regularization method proposed in this paper is a potential good method to estimate contaminant gas emission source term.

A Simple Method for Source Depth Estimation with Multi-path Time Delay in Deep Ocean

A method of source depth estimation based on the multi-path time delay difference is proposed. When the minimum time arrivals in all receiver depths are snapped to a certain time on time delay-depth plane, time delay arrivals of surface-bottom reflection and bottom-surface reflection intersect at the source depth. Two hydrophones deployed vertically with a certain interval are required at least. If the receiver depths are known, the pair of time delays can be used to estimate the source depth. With the proposed method the source depth can be estimated successfully in a moderate range in the deep ocean without complicated matched-field calculations in the simulations and experiments.

Evaluation of Some Weibull Parameter Estimation Methods for Characterizing Stem Diameter Distribution in a Tropical Mixed Forest of Southern Nigeria

Stem diameter distribution information is useful in forest management planning. Weibull function is flexible, and has been used in characterising diameter distributions, especially in single-species planted stands, the world over. We evaluated some Weibull parameter estimation methods for stem diameter characterisation in （Oban） multi-species Forest in southern Nigeria. Four study sites （Aking, Ekang, Erokut and Ekuri） were selected. Four 2 km-long transects situated at 600 m apart were laid in each location. Five 50m x 50m plots were alternately laid along each transect at 400 m apart （20 plots/location） using systematic sampling technique. Tree growth variables： diameter at breast height （Dbh）, diameters at the base, middle and merchantable limit, total height, merchantable height, stem straightness, crown length and crown diameter were measured on all trees 〉 10 cm to compute model response variables such as mean diameters, basal area and stem volume. Weibull parameters estimation methods used were： moment-based, percentile-based, hybrid and maximum-likelihood （ML）. Data were analysed using descriptive statistics, regression models and ANOVA at α0.05. Percentile-based method was the best for Weibull [location （a）, scale （b） and shape （c）] parameters estimations with mLogL = 116.66±21.89, while hybrid method was least-suitable （mLogL = 690.14±128.81） for Weibull parameters estimations. Quadratic mean diameter （Dq） was the only suitable predictor of Weibull parameters in Oban Forest.

Modelling of Wage and Income Distributions Using the Method of L-Moments

Commonly used statistical procedure to describe the observed statistical sets is to use their conventional moments or cumulants. When choosing an appropriate parametric distribution for the data set is typically that parameters of a parametric distribution are estimated using the moment method of creating a system of equations in which the sample conventional moments lay in the equality of the corresponding moments of the theoretical distribution. However, the moment method of parameter estimation is not always convenient, especially for small samples. An alternative approach is based on the use of other characteristics, which the author calls L-moments. L-moments are analogous to conventional moments, but they are based on linear combinations of order statistics, i.e., L-statistics. Using L-moments is theoretically preferable to the conventional moments and consists in the fact that L-moments characterize a wider range of distribution. When estimating from sample L-moments, L-moments are more robust to the presence of outliers in the data. Experience also shows that, compared to conventional moments, L-moments are less prone to bias of estimation. Parameter estimates obtained using L-moments are mainly in the case of small samples often even more accurate than estimates of parameters made by maximum likelihood method. Using the method of L-moments in the case of small data sets from the meteorology is primarily known in statistical literature. This paper deals with the use of L-moments in the case for large data sets of income distribution （individual data） and wage distribution （data are ordered to form of interval frequency distribution of extreme open intervals）. This paper also presents a comparison of the accuracy of the method of L-moments with an accuracy of other methods of point estimation of parameters of parametric probability distribution in the case of large data sets of individual data and data ordered to form of interval frequency distribution.

Numerical simulation of hydraulic fracturing and associated microseismicity using finite-discrete element method

Hydraulic fracturing （HF） technique has been extensively used for the exploitation of unconventional oiland gas reservoirs. HF enhances the connectivity of less permeable oil and gas-bearing rock formationsby fluid injection, which creates an interconnected fracture network and increases the hydrocarbonproduction. Meanwhile, microseismic （MS） monitoring is one of the most effective approaches to evaluatesuch stimulation process. In this paper, the combined finite-discrete element method （FDEM） isadopted to numerically simulate HF and associated MS. Several post-processing tools, includingfrequency-magnitude distribution （b-value）, fractal dimension （D-value）, and seismic events clustering,are utilized to interpret numerical results. A non-parametric clustering algorithm designed specificallyfor FDEM is used to reduce the mesh dependency and extract more realistic seismic information.Simulation results indicated that at the local scale, the HF process tends to propagate following the rockmass discontinuities; while at the reservoir scale, it tends to develop in the direction parallel to themaximum in-situ stress. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.