bluuebunny/crossref_metadata_2025 huggingface dataset:
Entangled States Are Harder to Transfer than Product States
Authors: Apollaro Tony J. G. , Lorenzo Salvatore , Plastina Francesco , Consiglio Mirko , Życzkowski Karol
Published: December 2022

The distribution of entangled states is a key task of utmost importance for many quantum information processing protocols. A commonly adopted setup for distributing quantum states envisages the creation of the state in one location, which is then sent to (possibly different) distant receivers through some quantum channels. While it is undoubted and, perhaps, intuitively expected that the distribution of entangled quantum states is less efficient than that of product states, a thorough quantification of this inefficiency (namely, of the difference between the quantum-state transfer fidelity for entangled and factorized states) has not been performed. To this end, in this work, we consider n-independent amplitude-damping channels, acting in parallel, i.e., each, locally, on one part of an n-qubit state. We derive exact analytical results for the fidelity decrease, with respect to the case of product states, in the presence of entanglement in the initial state, for up to four qubits. Interestingly, we find that genuine multipartite entanglement has a more detrimental effect on the fidelity than two-qubit entanglement. Our results hint at the fact that, for larger n-qubit states, the difference in the average fidelity between product and entangled states increases with increasing single-qubit fidelity, thus making the latter a less trustworthy figure of merit.
Security Risk Assessment Approach for Distribution Network Cyber Physical Systems Considering Cyber Attack Vulnerabilities
Authors: Zhou Buxiang , Sun Binjie , Zang Tianlei , Cai Yating , Wu Jiale , Luo Huan
Published: December 2022

With the increasing digitalization and informatization of distribution network systems, distribution networks have gradually developed into distribution network cyber physical systems (CPS) which are deeply integrated with traditional power systems and cyber systems. However, at the same time, the network risk problems that the cyber systems face have also increased. Considering the possible cyber attack vulnerabilities in the distribution network CPS, a dynamic Bayesian network approach is proposed in this paper to quantitatively assess the security risk of the distribution network CPS. First, the Bayesian network model is constructed based on the structure of the distribution network and common vulnerability scoring system (CVSS). Second, a combination of the fuzzy analytic hierarchy process (FAHP) and entropy weight method is used to correct the selectivity of the attacker to strike the target when cyber attack vulnerabilities occur, and then after considering the defense resources of the system, the risk probability of the target nodes is obtained. Finally, the node loads and node risk rates are used to quantitatively assess the risk values that are applied to determine the risk level of the distribution network CPS, so that defense strategies can be given in advance to counter the adverse effects of cyber attack vulnerabilities.
Forecasting for Chaotic Time Series Based on GRP-lstmGAN Model: Application to Temperature Series of Rotary Kiln
Authors: Hu Wenyu , Mao Zhizhong
Published: December 2022

Rotary kiln temperature forecasting plays a significant part of the automatic control of the sintering process. However, accurate forecasts are difficult owing to the complex nonlinear characteristics of rotary kiln temperature time series. With the development of chaos theory, the prediction accuracy is improved by analyzing the essential characteristics of time series. However, the existing prediction methods of chaotic time series cannot fully consider the local and global characteristics of time series at the same time. Therefore, in this study, the global recurrence plot (GRP)-based generative adversarial network (GAN) and the long short-term memory (LSTM) combination method, named GRP-lstmGAN, are proposed, which can effectively display important information about time scales. First, the data is subjected to a series of pre-processing operations, including data smoothing. Then, transforming one-dimensional time series into two-dimensional images by GRP makes full use of the global and local information of time series. Finally, the combination of LSTM and improves GAN models for temperature time series prediction. The experimental results show that our model is better than comparison models.
Estimation of Large-Dimensional Covariance Matrices via Second-Order Stein-Type Regularization
Authors: Zhang Bin , Huang Hengzhen , Chen Jianbin
Published: December 2022

This paper tackles the problem of estimating the covariance matrix in large-dimension and small-sample-size scenarios. Inspired by the well-known linear shrinkage estimation, we propose a novel second-order Stein-type regularization strategy to generate well-conditioned covariance matrix estimators. We model the second-order Stein-type regularization as a quadratic polynomial concerning the sample covariance matrix and a given target matrix, representing the prior information of the actual covariance structure. To obtain available covariance matrix estimators, we choose the spherical and diagonal target matrices and develop unbiased estimates of the theoretical mean squared errors, which measure the distances between the actual covariance matrix and its estimators. We formulate the second-order Stein-type regularization as a convex optimization problem, resulting in the optimal second-order Stein-type estimators. Numerical simulations reveal that the proposed estimators can significantly lower the Frobenius losses compared with the existing Stein-type estimators. Moreover, a real data analysis in portfolio selection verifies the performance of the proposed estimators.
A Bayesian Mixture Cure Rate Model for Estimating Short-Term and Long-Term Recidivism
Authors: de la Cruz Rolando , Fuentes Claudio , Padilla Oslando
Published: December 2022

Mixture cure rate models have been developed to analyze failure time data where a proportion never fails. For such data, standard survival models are usually not appropriate because they do not account for the possibility of non-failure. In this context, mixture cure rate models assume that the studied population is a mixture of susceptible subjects who may experience the event of interest and non-susceptible subjects that will never experience it. More specifically, mixture cure rate models are a class of survival time models in which the probability of an eventual failure is less than one and both the probability of eventual failure and the timing of failure depend (separately) on certain individual characteristics. In this paper, we propose a Bayesian approach to estimate parametric mixture cure rate models with covariates. The probability of eventual failure is estimated using a binary regression model, and the timing of failure is determined using a Weibull distribution. Inference for these models is attained using Markov Chain Monte Carlo methods under the proposed Bayesian framework. Finally, we illustrate the method using data on the return-to-prison time for a sample of prison releases of men convicted of sexual crimes against women in England and Wales and we use mixture cure rate models to investigate the risk factors for long-term and short-term survival of recidivism.
Solving Spatial Vision Difficulties Related to the Instruction of Welded Joints by Using PDF-3D
Authors: Arroyave-Espinosa Luis Miguel , Antón-Sancho Álvaro , Ariza-Echeverri Edwan Anderson , Vergara Diego
Published: December 2022

In this work, exploratory quantitative research on the opinion of the students of a course on Geometry of Welded Joints, in an introductory welding course of secondary technical education curriculum, on the didactic use of PDF-3D in the development of the course is carried out. For this purpose, a survey designed by the authors was used, which the students answered once the study of the subject had been completed. As a result, it has been found that the valuations of the virtual tool PDF-3D are very high, mainly in relation to motivation and ease of use. The importance that students attach to the ease of solving the spatial comprehension difficulties inherent to welded joints -and the imperfections linked with them- positively influences the ratings of this educational technology. On the other hand, this study reveals that students prefer a mixed methodology in which the presence of virtual resources is greater than that of the master class. Finally, the results obtained suggest that, given the lack of knowledge on the part of teachers of the potential of PDF-3D in the educational field, it would be advisable to offer specific training on the didactic use of PDF-3D to technical education professors.
Gaussian Mixture with Max Expectation Guide for Stacked Architecture of Denoising Autoencoder and DRBM for Medical Chest Scans and Disease Identification
Authors: Jamjoom Mona , Mahmoud Abeer M. , Abbas Safia , Hodhod Rania
Published: December 2022

Artificial intelligence (AI), in particular deep learning, has proven to be efficient in medical diagnosis. This paper introduces a new hybrid deep learning model for pneumonia diagnosis based on chest CT scans. At the core of the model, a Gaussian mixture is combined with the expectation-maximization algorithm (EMGMM) to extract the regions of interest (ROI), while a convolutional denoising autoencoder (DAE) and deep restricted Boltzmann machine (DRBM) are combined for the classification. In order to prevent the model from learning trivial solutions, stochastic noises were added as an input to the unsupervised learning phase. The dataset used in this work is a publicly available dataset of chest X-rays for pneumonia on the Kaggle website; it contains 5856 images with 1583 normal cases and 4273 pneumonia cases, with an imbalance ratio (IR) of 0.46. Several operations including zooming, flipping, shifting and rotation were used in the augmentation phase to balance the data distribution across the different classes, which led to enhancing the IR value to 0.028. The computational analysis of the results show that the proposed model is promising as it provides an average accuracy value of 98.63%, sensitivity value of 96.5%, and specificity value of 94.8%.
A Deep Learning-Based Encrypted VPN Traffic Classification Method Using Packet Block Image
Authors: Sun Weishi , Zhang Yaning , Li Jie , Sun Chenxing , Zhang Shuzhuang
Published: December 2022

Network traffic classification has great significance for network security, network management and other fields. However, in recent years, the use of VPN and TLS encryption had presented network traffic classification with new challenges. Due to the great performances of deep learning in image recognition, many solutions have focused on the deep learning-based method and achieved positive results. A traffic classification method based on deep learning is provided in this paper, where the concept of Packet Block is proposed, which is the aggregation of continuous packets in the same direction. The features of Packet Block are extracted from network traffic, and then transformed into images. Finally, convolutional neural networks are used to identify the application type of network traffic. The experiment is conducted using captured OpenVPN dataset and public ISCX-Tor dataset. The results shows that the accuracy is 97.20% in OpenVPN dataset and 93.31% in ISCX-Tor dataset, which is higher than the state-of-the-art methods. This suggests that our approach has the ability to meet the challenges of VPN and TLS encryption.
Real-Time Dynamic and Multi-View Gait-Based Gender Classification Using Lower-Body Joints
Authors: Azhar Muhammad , Ullah Sehat , Ullah Khalil , Rahman Khaliq Ur , Khan Ahmad , Eldin Sayed M. , Ghamry Nivin A.
Published: December 2022

Gender classification based on gait is a challenging problem because humans may walk in different directions at different speeds and with varying gait patterns. The majority of investigations in the literature relied on gender-specific joints, whereas the comparison of the lower-body joints in the literature received little attention. When considering the lower-body joints, it is important to identify the gender of a person based on his or her walking style using the Kinect Sensor. In this paper, a logistic-regression-based model for gender classification using lower-body joints is proposed. The proposed approach is divided into several parts, including feature extraction, gait feature selection, and human gender classification. Different joints’ (3-dimensional) features were extracted using the Kinect Sensor. To select a significant joint, a variety of statistical techniques were used, including Cronbach’s alpha, correlation, T-test, and ANOVA techniques. The average result from the Coronbach’s alpha approach was 99.74%, which shows the reliability of the lower-body joints in gender classification. Similarly, the correlation data show a significant difference between the joints of males and females during gait. As the p-value for each of the lower-body joints is zero and less than 1%, the T-test and ANOVA techniques demonstrated that all nine joints are statistically significant for gender classification. Finally, the binary logistic regression model was implemented to classify the gender based on the selected features. The experiments in a real situation involved one hundred and twenty (120) individuals. The suggested method correctly classified gender using 3D data captured from lower-body joints in real-time using the Kinect Sensor with 98.3% accuracy. The proposed method outperformed the existing image-based gender classification systems.
The Correlation between Proximal and Remote Sensing Methods for Monitoring Soil Water Content in Agricultural Applications
Authors: Romano Elio , Bergonzoli Simone , Bisaglia Carlo , Picchio Rodolfo , Scarfone Antonio
Published: December 2022

Water shortages have increasingly become a global issue due to the acceleration of climate change. The consumption of freshwater can be reduced to a minimum using water irrigation techniques that are based on conservative methods. For example, one of these is precision irrigation, or PI, which uses advanced digital technology to regulate the amount of water used. The aim is to use the least amount of water necessary for a given purpose. This approach keeps consumption to a minimum while the amount remains effective for its purpose. It is also important to note that the variability which occurs in soil and crops will create different types of conditions. These different conditions will need to be studied so as to determine the correct and adequate dynamics for a water management approach that is efficient. In this study, three investigation methods were developed and compared. The first evaluation was performed on outputs from the geoelectric reading of Automatic Resistivity Profiling (ARP). A second evaluation was performed in real time via a sensor network placed in the soil for the duration of two growing seasons of two different crops. The last evaluation was carried out by using maps of spectral indices obtained by the Sentinel 2 satellites. The correlations between the three methods were evaluated to verify if satellite information may have significant potential in the use of water management in varying conditions. From the results obtained, some correlations have been found from the observations of the three systems under study. This has given a positive input towards using satellite maps which are integrated with simplified proximal sensor networks. The outcome of this technique can improve the efficiency of how to manage water distribution on cultivated land.
Global Temperature and Carbon Dioxide Nexus: Evidence from a Maximum Entropy Approach
Authors: Macedo Pedro , Madaleno Mara
Published: December 2022

The connection between Earth’s global temperature and carbon dioxide (CO2) emissions is one of the highest challenges in climate change science since there is some controversy about the real impact of CO2 emissions on the increase of global temperature. This work contributes to the existing literature by analyzing the relationship between CO2 emissions and the Earth’s global temperature for 61 years, providing a recent review of the emerging literature as well. Through a statistical approach based on maximum entropy, this study supports the results of other techniques that identify a positive impact of CO2 in the increase of the Earth’s global temperature. Given the well-known difficulties in the measurement of global temperature and CO2 emissions with high precision, this statistical approach is particularly appealing around climate change science, as it allows the replication of the original time series with the subsequent construction of confidence intervals for the model parameters. To prevent future risks, besides the present urgent decrease of greenhouse gas emissions, it is necessary to stop using the planet and nature as if resources were infinite.
A Perspective on the Overarching Role of Hydrogen, Ammonia, and Methanol Carbon-Neutral Fuels towards Net Zero Emission in the Next Three Decades
Authors: Liu Haifeng , Ampah Jeffrey Dankwa , Zhao Yang , Sun Xingyu , Xu Linxun , Jiang Xueli , Wang Shuaishuai
Published: December 2022

Arguably, one of the most important issues the world is facing currently is climate change. At the current rate of fossil fuel consumption, the world is heading towards extreme levels of global temperature rise if immediate actions are not taken. Transforming the current energy system from one largely based on fossil fuels to a carbon-neutral one requires unprecedented speed. Based on the current state of development, direct electrification of the future energy system alone is technically challenging and not enough, especially in hard-to-abate sectors like heavy industry, road trucking, international shipping, and aviation. This leaves a considerable demand for alternative carbon-neutral fuels such as green ammonia and hydrogen and renewable methanol. From this perspective, we discuss the overarching roles of each fuel in reaching net zero emission within the next three decades. The challenges and future directions associated with the fuels conclude the current perspective paper.
Mathematical Modeling and Simulation of a Compound Parabolic Concentrators Collector with an Absorber Tube
Authors: Shoeibi Habib , Jarrahian Azad , Mehrpooya Mehdi , Assaerh Ehsanolah , Izadi Mohsen , Pourfayaz Fathollah
Published: December 2022

CPC solar collectors are a combination of new technologies that make it possible to generate heat from radiant solar energy by transferring heat between the absorber and the fluid. This study was performed based on heat transfer equations by proposing a mathematical model, as reported in the literature. A compound parabolic concentrators solar collector (CPC) numerical model was simulated and coded in Aspen HYSYS and MATLAB software and validated by comparing its results with other researchers and experimental results. The simulated mathematical model includes a two-dimensional numerical model to describe the thermal and dynamic behavior of the fluid inside the CPC solar collector absorber tube. Numerical simulations of the fluid flow equations inside the CPC solar collector absorber tube, along with the energy equation for the absorber tube wall, coating, insulation and reflector, and solar collector heat analysis, were performed repeatedly in MATLAB and Aspen HYSYS software. This method is the most appropriate and reliable method for solving equations for numerical convergence. The experimental results of the parabolic concentrated solar collector (CPC) were used to evaluate and validate the numerical model. A solar compound parabolic concentrators collector (CPC) with short reflectors was used. This collector includes a cylindrical absorber with a real density ratio of 1.8, a reception angle of 22 degrees and a length of 2.81 m, a width of 0.32 m, and an opening of 0.1764 m. Analysis and uncertainty of the proposed model were performed with the measured sample. In the thermal efficiency analysis, the average deviation of the model from the experimental results of other researchers was equal to 7%, for increasing the temperature by 9 °C. According to these results, a good correlation between numerical results and experimental results for this proposed model has been obtained.
Techno-Economic and Environmental Analysis of Renewable Mix Hybrid Energy System for Sustainable Electrification of Al-Dhafrat Rural Area in Oman
Authors: Al Abri Abdullah , Al Kaaf Abdullah , Allouyahi Musaab , Al Wahaibi Ali , Ahshan Razzaqul , Al Abri Rashid S. , Al Abri Ahmed
Published: December 2022

Affordable and clean energy for any rural community is crucial for the sustainable development of the community and the nation at large. The utilization of diesel-based power generation is one of the barriers to the sustainable development of these communities. Such generations require fuel that has a volatile market price and emits massive greenhouse gas emissions. This paper presents the design, modeling, and simulation of a hybrid power system for a rural area in the Sultanate of Oman that aims to reduce daily consumption of diesel fuel and greenhouse gas emissions. Hybrid Optimization of Multiple Energy Resources (HOMER) is utilized to model multiple energy mix hybrid systems and to propose the best optimal energy mix system for a selected community. In addition, Electrical Transient Analyzer Program (ETAP) software is employed to assess hybrid system operational performances, such as bus voltage profiles and active and reactive power losses. This study revealed that the PV–wind–diesel system is the optimal energy mix hybrid microgrid for the Al-Dhafrat rural area in Oman, with a net present cost of USD 14.09 million. Compared to the currently operating diesel-based system, the deployment of this microgrid can reduce the levelized cost of energy, diesel fuel consumption, and greenhouse gas emissions per year by 54.56%, 70.44%, and 70.40%, respectively. This study confirms that the Sultanate of Oman has a substantial opportunity to install a hybrid microgrid system for rural diesel-based communities to achieve sustainable development in the country.
Transient Calculation Studies of Liquid–Solid Collision in Jet Descaling
Authors: Hu Shu , Teng Zhaohui , Huang Shan , Li Na , Zhou Qulan
Published: December 2022

Sichuan is gradually being transformed and is utilizing groundwater and thermal resources. However, this investigation found that the high mineralization rate of geothermal resources in the Sichuan Basin is common and efficient, and environmentally friendly descaling technology is the key to promoting the utilization of thermal resources in low-yield oil and gas wells. Due to the high efficiency, low cost, and lack of pollution of high-pressure jet descaling, it has attracted more and more attention recently, but the mechanism of jet descaling is still unclear. The key to jet descaling is the stress concentration in the scale caused by the impact of droplets from the jet. In this paper, the process of jet descaling is simplified as a 2D droplet–scale collision with a detailed theoretical analysis of the stress on the scale. A circular droplet was simulated to impact the surface of the scale. By using numerical methods for transient calculations, we couple the pressure of the droplets and the scale strain. We acquired transient equivalent stress fields inside scales and pressure distributions inside the water droplet. As a result of the impact, areas of high stress in the scale appeared. Due to the stress superposition, the highest stress is concentrated in two areas: the contact edge and the shaft. These results can identify the mechanism for high-pressure jet descaling and help improve the efficiency of high-pressure water-jet descaling.
Simulation Analysis of Emptying the Explosives in Projectiles with Electromagnetic Heating
Authors: Qiao Zhiming , Xiang Hongjun , Cao Genrong , Qiao Zhibo , Lv Qing’ao , Yuan Xichao , Chen Lei
Published: December 2022

This paper concerns a new method for projectile disposal by emptying the explosives in projectiles with electromagnetic heating. It explains the basic principles of the emptying technology via electromagnetic heating. A multiphysical analysis model coupled with an electromagnetic, thermal, fluid and phase transition model is established, and the explosive melting simulation is conducted based on this model. The dynamic phase transition process of the explosive from solid to liquid is simulated, and the electric field, magnetic field and thermal field distribution characteristics during the process are analyzed. Furthermore, the effect of excitation current characteristics on the phase transition of the explosive is given, which shows that the explosive melting process is controllable by setting the excitation current amplitude or frequency. This paper provides a new method for the disposal of end-of-life projectiles, which is more controllable, safe and environmentally friendly.
Empirical Study on the Low-Carbon Economic Efficiency in Zhejiang Province Based on an Improved DEA Model and Projection
Authors: Luo Hongyun , Lin Xiangyi
Published: December 2022

Low-carbon economic efficiency is an important indicator that can be used to measure the quality of regional economic development. In this study, an improved DEA model is introduced into the calculation of low-carbon economic efficiency in Zhejiang Province. Using the actual data of nine prefecture-level cities in Zhejiang Province from 2015 to 2020, the low-carbon economic efficiency of each prefecture-level city is calculated. The result is that the overall low-carbon economic efficiency of Zhejiang Province indicates a trend of first falling and then rising, and the low-carbon economic efficiencies of different prefecture-level cities largely differ. The causes of six inefficient DMUs (prefecture-level cities) are analyzed using projection. The improved DEA model is used to determine the “expansion coefficient” of the input and output of three DMUs (prefecture-level cities) with relatively low-carbon economic efficiency. The research results provide a strong basis and support for the development of a low-carbon economy for Zhejiang Province.
Influence of Bismaleimide HVA-2 Grafting on the Direct Current Dielectric Properties of XLPE
Authors: Zhang Chengcheng , Wang Sen , Zhao Hong , Wang Xuan
Published: December 2022

In this paper, N, N’-m-phenylene dimaleimide (HVA-2) grafted crosslinked polyethylene (XLPE) insulation materials with different HVA-2 contents were prepared. The grafting, crosslinking, and crystalline structure were characterized by Fourier-transform infrared spectroscopy (FTIR), Soxhlet extraction, and differential scanning calorimetry (DSC), respectively. The space charge distribution, direct current (DC) breakdown strength, and DC conduction current density were tested and the electronic structure was calculated from first-principles. HVA-2 grafting modification can significantly reduce the accumulation of space charges and the conduction current density of XLPE, but have a negative effect on DC breakdown strength. The polar groups of the grafted HVA-2 anchored on XLPE by the grafting reaction can introduce deep traps densely and evenly in XLPE, which would capture and scatter charge carriers, thus reducing the carrier concentration and mobility and further improving the space charge distribution and reducing conduction current density. However, the grafting of HVA-2 can increase the crosslinking extent of XLPE to make the crystallinity decrease and the crystallization inhomogeous, leading to a certain decrease in the breakdown strength of the grafted XLPE.
Hierarchical Surrogate-Assisted Evolutionary Algorithm for Integrated Multi-Objective Optimization of Well Placement and Hydraulic Fracture Parameters in Unconventional Shale Gas Reservoir
Authors: Zhou Jun , Wang Haitao , Xiao Cong , Zhang Shicheng
Published: December 2022

Integrated optimization of well placement and hydraulic fracture parameters in naturally fractured shale gas reservoirs is of significance to enhance unconventional hydrocarbon energy resources in the oil and gas industry. However, the optimization task usually presents intensive computation-cost due to numerous high-fidelity model simulations, particularly for field-scale application. We present an efficient multi-objective optimization framework supported by a novel hierarchical surrogate-assisted evolutionary algorithm and multi-fidelity modeling technology. In the proposed framework, both the net present value (NPV) and cumulative gas production (CGP) are regarded as the bi-objective functions that need to be optimized. The hierarchical surrogate-assisted evolutionary algorithm employs a novel multi-fidelity particle-swarm optimization of a global–local hybridization searching strategy where the low-fidelity surrogate model is capable of exploring the populations globally, while the high-fidelity models update the current populations and thus generate the next generations locally. The multi-layer perception is chosen as a surrogate model in this study. The performance of our proposed hierarchical surrogate-assisted global optimization approach is verified to optimize the well placement and hydraulic fracture parameters on a hydraulically fractured shale gas reservoir. The proposed surrogate model can obtain both the NPV and CPG with satisfactory accuracy with only 500 training samples. The surrogate model significantly contributes to the convergent performance of multi-objective optimization algorithm.
Optimal Design and Comparative Analysis of a PV/Mini-Hydropower and a PV/Battery Used for Electricity and Water Supply
Authors: Falama Ruben , Skarka Wojciech , Doka Serge
Published: December 2022

This work proposed an optimal design of PV-system-based water-pumped energy storage for both electricity and water supply. A case study was considered in a rural community in Cameroon. The parameters of the assessment of the system were reliability, represented in the present work by the system supply deficiency (SSD), and economic accessibility, represented by the levelized cost of energy (LCOE). The obtained results showed that for 0% SSD, the optimal configuration of the system was composed of 438 PV modules of 235 W, an immersed solar motor pump of 35 kW, a hydroelectric turbine of 51.7 kW, an upper reservoir of 2307.1 m3, an inverter of 25.27 kW, and a total dynamic head of 88 m. The corresponding LCOE to this configuration is 0.224 USD/kWh. The economic accessibility of the designed system was evaluated by comparison with a PV-system-based battery energy storage. The optimal design configuration of the studied PV-system-based battery energy storage was a PV generator (120 PV modules of 235 W), solar motor pump (15 kW), upper reservoir (590.4 m3), battery capacity (351.78 kWh), inverter (25.27 kW), and total dynamic head (81 m). The corresponding LCOE to this configuration was 0.1857 USD/kWh. Although the PV-system-based battery storage appeared to be economically more cost-effective than the PV-system-based water-pumped energy storage, the sensitivity analysis revealed that there was the possibility for the PV-system-based water-pumped energy storage to be economically more profitable than the PV-system-based battery energy storage. This economic outperformance occurred when the project lifetime was a multiple of 7.5 years or when the costs of the storage components were reduced from 20% to 60%.
Approach to Multi-Timescale Optimization for Distributed Energy Resources Clusters Considering Flexibility Margin
Authors: Li Bo , Ou Weichao , Chen Tingwei , Li Gaoming , Chen Yuanrui , Liu Junfeng
Published: December 2022

The disordered access of massively distributed energy resources (DERs) brings great challenges to the operation stability of the power grid. This paper puts forward the concept of a cluster, which gathers DERs in large quantities, small capacities, dispersion and disorder to form a large, centralized and orderly whole, namely cluster, with certain incentive measures. In this paper, a multi-timescale optimization method of day-ahead planning and intra-day rolling optimization is proposed according to the characteristics of aggregated clusters and the requirements of China’s power grid architecture. Specifically, the day ahead model is proposed in two steps: the first step is to establish an optimization model with the goal of optimal fitting the target load curve and maximizing the utilization of DERs; The second step is to establish a potential game model considering the reasonable distribution of cluster benefits. Taking the minimum percentage of output correction of each cluster as the objective, considering the deviation of load forecasting and the deviation of day ahead instruction execution, an intra-day rolling optimization model is established. Finally, the application scenario of cluster participation in power grid auxiliary peak shaving is simulated and verified. The simulation results show that the cluster collaborative optimization method proposed in this paper can effectively reduce the load peak valley difference and maximize the use of cluster resources. The optimization tasks can be reasonably allocated while ensuring the stable and reliable operation of the power grid.
A Numerical Analysis of the Thermal Energy Storage Based on Porous Gyroid Structure Filled with Sodium Acetate Trihydrate
Authors: Beer Martin , Kudelas Dušan , Rybár Radim
Published: December 2022

The present paper deals with the evaluation of the unique design of the thermal energy storage unit and its impact on the overall heat exchange efficiency. The proposed thermal energy storage unit consists of a gyroid thermally conductive structure, the voids of which are filled with sodium acetate trihydrate. The presented concept is focused on the use in the field of heavy machinery, where it is possible to accumulate and re-use waste heat from internal combustion engines from the cooling liquid or lubricating products. The evaluation of designs took place through numerical simulations on three models characterized by different levels of the introduction of the gyroid structure into the design. From the design point of view, the gyroid structure was considered as an object produced by additive manufacturing methods from a thermally conductive filament based on a thermoplastic polymer, which enables considerable simplification of production compared to the use of suitable anti-corrosion metals. A comparison of the essential thermophysical parameters in the process of charging and discharging of the proposed thermal energy storage unit quantified a significant increase in the rate of the charging, respectively, of the discharging process, manifested by a rapid increase in the temperature of the sodium acetate trihydrate volume, respectively, of the output temperature of the heat transfer medium that removes the accumulated heat for further use.
Effects of Temperature and Chemical Speciation of Mineral Elements on PM10 Formation during Zhundong Coal Combustion
Authors: Sun Qiaoqun , Zhao Zhiqi , Wang Shizhang , Zhang Yu , Da Yaodong , Dong Heming , Wen Jiwang , Du Qian , Gao Jianmin
Published: December 2022

Particulate matter (PM) pollution from coal combustion is a leading contributor to the influence of atmospheric visibility, photochemical smog, and even global climate. A drop tube furnace was employed to explore the effects of temperature and chemical speciation of mineral elements on PM formation during the combustion of Zhundong coal. Chemical fractionation analysis (CFA), X-ray fluorescence (XRF), and inductively coupled plasma-atomic emission spectrometry (ICP-AES) were used to investigate the chemical and physical characteristics of the solid samples. It can be indicated that the combustion of similarly sized coal particles yielded more PM10 when the combustion temperature was increased from 1000 to 1400 °C. Zhundong coal is fractionated with deionized water, ammonium acetate, and hydrochloric acid, and pulverized coal, after fractionation, is burned to study the influence of mineral elements with different occurrence forms, such as water-soluble mineral elements, exchangeable ion elements, hydrochloric acid soluble elements and acid-insoluble elements, on the formation of particles. The results show that water-soluble salts play an important role in forming ultrafine particles (PM0.2); Fe, Ca, and other elements in organic form are distributed in flue gas through evaporation during pulverized coal combustion. When the flue gas temperature decreases, PM1 is formed through homogeneous nucleation and heterogeneous condensation, resulting in the distribution of these two elements on PM1. Different fractionation methods do not significantly affect the distribution of Si and Al in the PM1–10 combustion process.
Method of Geomechanical Parameter Determination and Volumetric Fracturing Factor Simulation under Highly Stochastic Geologic Conditions
Authors: Ding Dongmei , Wu Yongbin , Xia Xueling , Li Weina , Zhang Jipeng , Liu Pengcheng
Published: December 2022

In order to accurately predict geomechanical parameters of oil-bearing reservoirs and influencing factors of volumetric fracturing, a new method of geomechanical parameter prediction combining seismic inversion, well logging interpretation and production data is proposed in this paper. Herein, we present a structure model, petrophysical model and geomechanical model. Moreover, a three-dimensional geomechanical model of a typical reservoir was established and corrected using history matching. On this basis, a typical well model was established, 11 influencing factors of volume fracturing including formation parameters and fracturing parameters were analyzed and their impact were ranked, and the oil recovery rate and the accumulated oil production before and after optimal fracturing were compared. The results show that with respect to formation parameters, reservoir thickness is the main influencing factor; interlayer thickness and stress difference are the secondary influencing factors; and formation permeability, Young’s modulus and Poisson’s ratio are the weak influencing factors. For a pilot well of a typical reservoir, the optimized fracture increased production by 7 tons/day relative to traditional fracturing. After one year of production, the method increased production by 4 tons/day relative to traditional fracturing, showing great potential in similar oil reservoirs.
On the Optimal Shape and Efficiency Improvement of Fin Heat Sinks
Authors: Zullo Federico , Giorgi Claudio
Published: December 2022

In this paper, we analyze the values of the entropic efficiency of longitudinal fins by investigating the coupling between the function describing the fin profile and the corresponding steady-state temperature distribution along the fin. By starting from different boundary conditions, we look at the distribution temperature maximizing the efficiency of the fin. From this temperature distribution and by requesting that the fin must comply with natural physical constraints, such as the maximum fin thickness, we obtain an optimal profile for a purely convective fin and a convecting–radiating fin. For different boundary conditions and for a maximum fin thickness equal to four (in dimensionless units), both the profiles are increasing starting from the fin base until they reach the maximum value and then decrease to zero at the tip. Analytic and numerical results, together with different plots, are presented.
Review on Recent Strategies for Integrating Energy Storage Systems in Microgrids
Authors: Kandari Ritu , Neeraj Neeraj , Micallef Alexander
Published: December 2022

Energy security and the resilience of electricity networks have recently gained critical momentum as subjects of research. The challenges of meeting the increasing electrical energy demands and the decarbonisation efforts necessary to mitigate the effects of climate change have highlighted the importance of microgrids for the effective integration of renewable energy sources. Microgrids have been the focus of research for several years; however, there are still many unresolved challenges that need to be addressed. Energy storage systems are essential elements that provide reliability and stability in microgrids with high penetrations of renewable energy sources. This study provides a systematic review of the recent developments in the control and management of energy storage systems for microgrid applications. In the early sections, a summary of the microgrid topologies and architectures found in the recent literature is given. The main contributions and targeted applications by the energy storage systems in the microgrid applications is defined for each scenario. As various types of energy storage systems are currently being integrated for the reliable operation of the microgrids, the paper analyses the properties and limitations of the solutions proposed in the recent literature. The review that was carried out shows that a hybrid energy storage system performs better in terms of microgrid stability and reliability when compared to applications that use a simple battery energy storage system. Therefore, a case study for a DC microgrid with a hybrid energy storage system was modelled in MATLAB/Simulink. The presented results show the advantages of hybrid energy storage systems in DC microgrids.
AC-DC DAB Converter with Power Factor Correction
Authors: Guzmán Pablo , Vázquez Nimrod , Liserre Marco , Orosco Rodolfo , Vaquero Joaquín , Hernández Claudia
Published: December 2022

AC-DC conversion is required in many applications, and in some of them isolation is strictly required, certainly while maintaining characteristics such as a high power factor and low input current THD. In this paper, an AC-DC converter, comprising a full-bridge diode rectifier and a dual-active-bridge (DAB) converter, is utilized to fulfill these characteristics. The used modulation makes the converter behave as a resistive load, maintaining the output voltage constant while achieving a high power factor and low input current THD. The operation of the converter is simple, as only two voltage sensors are required, and no inner current loop is utilized. A low-power prototype was experimentally tested to corroborate the proposal.
Declining Discount Rates for Energy Policy Investments in CEE EU Member Countries
Authors: Buła Rafał , Foltyn-Zarychta Monika
Published: December 2022

Energy policy investments are usually evaluated using a cost-benefit analysis (CBA), which requires an estimation of the social discount rate (SDR). The choice of SDR can be crucial for the outcome of the appraisal, as energy-related investments generate long-term impacts affecting climate change. Once discounted, these impacts are highly sensitive to slight changes in the value of the SDR. Some countries (the UK and France) switched from a constant SDR to the declining rate scheme—a solution that limits the impact sensitivity. To our knowledge, none of the CEE countries apply DDR in CBA. While a constant SDR is a relatively well-established approach, declining SDRs are estimated to be used much less frequently, particularly for CEE EU member countries and energy policies. The rationale for the decline can rest on uncertainty over future discount rates, as shown by the approach developed by Weitzman and Gollier, which extends the classical Ramsey model. We applied this approach in our paper, as the Ramsey formula is the prevailing formula for EU countries’ SDR estimates. We estimated a flat SDR via the Ramsey formula with Gollier’s “precautionary term”, and next, we calculated Weitzman’s certainty equivalent rates for the 500-year horizon. Ramsey’s SDRs, obtained using consumption growth rates dating back to 1996, varied between 6.77% for Lithuania and 2.95% for Czechia and declined by 0.15% on average (Gollier’s term). Declining SDRs for the longest horizon dropped to approx. 0.5% (from 0.35% for Bulgaria to 0.67% for Poland), and the descent is deeper and faster when forward SDRs (following the UK Green Book approach) were considered (0.01% to 0.04%). The results are important for long-term policies regarding energy and climate change in CEE EU member countries, but they are still dependent on fossil fuels and experience an investment gap to fulfil EU climate goals.
Carbon Circular Utilization and Partially Geological Sequestration: Potentialities, Challenges, and Trends
Authors: Hou Zhengmeng , Luo Jiashun , Xie Yachen , Wu Lin , Huang Liangchao , Xiong Ying
Published: December 2022

Enhancing carbon emission mitigation and carbon utilization have become necessary for the world to respond to climate change caused by the increase of greenhouse gas concentrations. As a result, carbon capture, utilization, and storage (CCUS) technologies have attracted considerable attention worldwide, especially in China, which plans to achieve a carbon peak before 2030 and carbon neutrality before 2060. This paper proposed six priorities for China, the current world’s largest carbon emitter, to achieve its dual carbon strategy in the green energy transition process. We analyzed and summarized the challenges and potentialities of conventional carbon utilization (CU), carbon capture utilization (CCU), and CCUS. Based on the current development trend, carbon dioxide capture, circular utilization, and storage (CCCUS) technology that integrates carbon circular utilization and partial sequestration, with large-scale underground energy storage were proposed, namely biomethanation. Technically and economically, biomethanation was believed to have an essential contribution to China’s renewable energy utilization and storage, as well as the carbon circular economy. The preliminary investigation reveals significant potential, with a corresponding carbon storage capacity of 5.94 × 108 t~7.98 × 108 t and energy storage of 3.29 × 1012 kWh~4.42 × 1012 kWh. Therefore, we believe that in addition to vigorously developing classical CCUS technology, technical research and pilot projects of CCCUS technology that combined large-scale underground energy storage also need to be carried out to complete the technical reserve and the dual-carbon target.
Investment Risk and Energy Security Assessment of European Union Countries Using Multicriteria Analysis
Authors: Kozłowska Justyna , Benvenga Marco Antônio , Nääs Irenilza de Alencar
Published: December 2022

Investment opportunities are analyzed from the perspective of the variables that influence risk. The present study analyzes some energy characteristics using data from the Eurostat Data Browser. First, we identified a gap in energy research. Second, we proposed a multicriteria analysis using the analytic hierarchy process (AHP). An algorithm was developed to simulate how experts think to determine pairwise comparisons. A procedure identified the levels of importance of each criterion and alternative based on extracted data from the Eurostat website. The method was used to rate countries according to data regarding their energy policy results. The present study shows that applying the AHP method is possible without expert support and using data regarding the theme studied. The results show that Malta and Estonia are the most suitable countries to receive investments since they are presently at the top of the energy security ranking. The selected set of criteria seems to properly correspond with the assessment of the sector security as far as risk investment is concerned. The results of the current study may represent a base to support investment decision-making in the energy sector of EU countries.