bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantification and economic analysis of virtual energy storage caused by thermal inertia in buildings
Authors: Shi Weijie , Liu Qingrong , Ruan Yingjun , Qian Fanyue , Meng Hua 
Published: April 2023

Abstract
The virtual energy storage caused by the thermal inertia of the building is the property and can participate in the demand response. However, the quantification of this virtual energy storage part is not clear. To determine the energy flexibility potential of building virtual energy storage, quantitative indicators such as charging and discharging time, and charging and discharging energy are proposed. The building model was created using the building energy simulation software EnergyPlus, and the quantification of each performance indicator of the building’s virtual energy storage was developed. Among them, the optimal charging time for this building model is 30 minutes, and the optimal discharging time is 60 minutes. Under the optimal virtual energy storage operation strategy, the charging energy is 57 kWh and the discharging energy is 84 kWh. Compared with the reference strategy, the building virtual energy storage operation strategy saves 48.76 yuan in single-day cooling electricity cost, with a 3% savings rate. The results show that building virtual energy storage has certain energy flexibility without additional investment and can generate certain economic benefits at the same time.
Thermal Management of Lithium-ion Battery based on Flow and Heat Transfer Entropy Generation Analysis
Authors: Yu Shuai , Xu Yanzhe , Yang Shuai , Leng Feng , Liu Fei , Wang Yunfan , Hu Qiwei , Zhao Yutian , Li Zhoupin , Wang Zhen 
Published: April 2023

Abstract
In order to realize the problem of efficient heat dissipation management of lithium battery modules, the thermal management model of liquid cooled lithium-ion battery modules is studied in this paper. The entropy increase characteristics caused by the flow process and heat transfer process of the lithium-ion battery module under three liquid cooling inlet and outlet modes are analyzed by numerical simulation, and the internal mechanism of the local entropy generation characteristics of the lithium-ion battery module is clarified. The numerical results show that the 3×3 MIMO is the best model considering the heat dissipation and fluidity of the protective fluid. In addition, the results of this study can provide effective design guidance for thermal management of lithium-ion batteries.
Analysis of New Energy Consumption Capacity Considering Photovoltaic Uncertainty
Authors: Sun Jingliao , Hu Changhong , Mei Fuxian , Xiang Yejun , Liu Jinyuan 
Published: April 2023

Abstract
With the active promotion of the double carbon policy, vigorously developing the new energy industry is our important job. Considering the volatility and randomness of distributed photovoltaic forces, abandonment often occurs. This article proposes a new energy consumption capability analysis model that considers distributed photovoltaic uncertainty. First of all, a typical contribution curve of massive scenarios is reduced to generate a typical power curve of distributed photovoltaic; then the model simplifies; finally, based on the random optimization method, the operating cost, photovoltaic construction cost, and abandoned cost of the unit are based on the random optimization method. The above theory is verified by the IEEE 33 standard node, which proves the theory’s effectiveness. The energy storage equipment can improve the configuration capacity of distributed photovoltaic, reduce system operating costs, and improve the ability of new energy.
Comprehensive Reliability Assessment Method for Lithium Battery Energy Storage Systems
Authors: Si Jiandong , Tang Yiqin , Li Xinang , Zhang Linyu 
Published: April 2023

Abstract
Electrochemical energy storage systems have the advantages of fast power response, intensive energy storage, flexible and convenient deployment, but the output characteristics of the battery system directly affect the service effect of the energy storage power plant. Therefore, it is quite necessary to analyze the reliability of battery energy storage and scientifically assess the system’s performance. This paper considers the aging state of the battery storage system as well as sudden failures and establishes a comprehensive reliability assessment method for battery energy storage systems that take into account the battery health index and the impact of thermal runaway failures, which makes up for the shortcomings of existing reliability assessment methods.
Low Carbon Operation Optimization Study of Integrated Energy Systems Considering Interruptible Loads
Authors: Di HuiQiang , Zhang Min , Chang Xiao , Fan Rui , Li Rui 
Published: April 2023

Abstract
Aiming at the consumption of renewable energy, researched controllable load mechanisms and energy storage regulation strategies in an electric-thermal coupled network, and a comprehensive flexible resource model was constructed to optimize the low-carbon operation of the system. Firstly, analyzed the industrial load regulation mechanism of differentiated layout in the regional power grid. Based on the electric-thermal coupled system in the urban energy grid, the regulation model of controllable load and electric/thermal energy storage was established. Then a comprehensive benefit model was constructed with an economic cost, wind and solar usage, and carbon emissions as indicators. Proposed a carbon emission flow topology to describe the carbon emission flow information attached to the energy flow. Finally, combined with the IEEE 33-node distribution network and 45-node heat network coupling system, the improvement effect of the multiple flexible resource response on the comprehensive benefits of the system was analyzed. And the carbon emission flow process of the electricity-thermal network in the typical scenario was visually displayed through topology. The results show that exploring flexible resources can improve the consumption margin of wind and solar energy in the integrated energy system, which verifies the rationality and effectiveness of the proposed method.
Research on input-output evaluation index of high energy consumption main transformer transformation
Authors: Zhang Hongyang , Lu Shaohua , Zhang Ming , Wang Shuo , Gao Jingren 
Published: April 2023

Abstract
In order to quantify the input-output evaluation index of high-energy consumption main transformer transformation projects of power grid enterprises, to provide a real, effective, and quantitative basis for investment decisions, the project input and output indexes are measured from the whole life cycle perspective. The whole life cycle cost, including the initial investment and annual operational maintenance cost, is selected as the input evaluation index, and the four dimensions of safety, economics, efficiency, and greenness are considered. The output evaluation index is proposed, combined with the investment effectiveness of typical projects of production technology reform, the potential output analysis indexes under each dimension are sorted out, the full set of output evaluation indexes of production technology reform projects is constructed, the corresponding characteristic indexes of high energy consumption main transformation projects are selected from the full set of indexes, and mathematical evaluation model of input-output analysis of high energy consumption main transformation projects is built. Verify the model through practical engineering examples.
A prediction model based on an adaptive genetic algorithm and optimized Elman neural network for PV power
Authors: Wang Guanfu , Gao Difei , Jiang Yu , Wang Zhe , Cui Jia , Xu Jiachen 
Published: April 2023

Abstract
Due to the high volatility and randomness of PV power generation, fast change frequency, limited data samples and poor data conditions, the reliability, stability and economy of grid operation are affected seriously. Therefore, the accurate prediction of PV power generation can mitigate the impact of its volatility on the stability of national grid power operations. And it improves PV consumption capacity and enhances the security of microgrid and distribution grid interconnection. This paper first proposed a PV power prediction method. Secondly, through the introduction and analysis of three types of prediction methods, the first type of prediction is selected using statistical analysis and modeling methods. Thirdly, an optimized Elman neural network-based PV power prediction model is proposed to allow accurate prediction of PV power prediction. Finally, an example is given to verify the advantages of the algorithm.
Analysis and treatment of oil chromatography of a 1000kV high voltage parallel reactor
Authors: Wei Kai , Qin Wenping 
Published: April 2023

Abstract
In this paper, the author combined with gas chromatography to a 1000kV high voltage shunt reactor oil chromatography anomaly analysis, preliminary theoretical analysis conclusion, then in the maintenance process through the equipment breakdown and the fault cause, verify the correctness of the theoretical analysis, for the next step of equipment manufacturers equipment manufacturing and process control provides advice.
Research on High-Frequency Data Acquisition Technology in Distributed Photovoltaic Station Areas
Authors: Kang Liyan , Shang Ying , Liu Xinran , Qin Yuewei 
Published: April 2023

Abstract
With the large-scale development of low-voltage distributed photovoltaic in power systems, the safe and effective operation of the power grid has been put forward. The analysis of the operation of distributed photovoltaics depends on the development of high-frequency data acquisition technology. This paper mainly aims at the problem of high-frequency data acquisition for distributed photovoltaic users and proposes a solution to achieve high-frequency data acquisition using high-speed power line carriers and dual-mode communication technology. The high-speed power line carrier is used to achieve minute-level data acquisition. The dual-mode communication technology further improves communication reliability. This paper provides theoretical support and construction experience for data acquisition and analysis of distributed photovoltaic users.
Evaluation of factors influencing subsynchronous oscillation of wind turbine
Authors: Wang Liqiang , Yuan Shuai , Zhang Xiuqi , Wang Qi , Cong Yu 
Published: April 2023

Abstract
Because of the subsynchronous oscillation caused by wind turbines connected to the AC power grid, the causes of subsynchronous oscillation are analyzed, and the frequency impedance test method of a wind turbine is proposed, which can effectively evaluate the impedance characteristics of a wind turbine. On this basis, the research on the influence of the parameters of several network side control links of the wind turbine generator, such as phase lock loop, current loop, and voltage loop, on the impedance characteristics is carried out. Combined with the influencing factors, a targeted improvement scheme for subsynchronous oscillation is proposed to improve the safety and stability of the wind turbine generator grid connection.
Distribution network planning with distributed photovoltaic and electric vehicle charging stations based on multi-population traction differential evolution algorithm
Authors: Wang Daye , Liu Yang , Wang Jing 
Published: April 2023

Abstract
Currently, in the background of a large number of distributed photovoltaic and electric vehicle charging stations with access to the distribution network, this paper presents a distribution network planning method with distributed photovoltaic and electric vehicle charging stations based on multi-population traction differential evolution algorithm. Firstly, this paper considers the curve characteristics of photovoltaic and load, and uses cluster analysis to determine the planning scenery, so that the planning results are more accurate. Then, the distribution network planning model is constructed from the aspects of the minimum investment cost, operation cost and performance index. Then, to improve the speed and the accuracy of planning results, the multi-population traction differential evolution algorithm is used to solve the planning model. Finally, a 10kV line is used as an example to verify the reliability of the proposed method. Then, the traditional differential evolution algorithm and the other two algorithms are compared and analyzed to verify the effectiveness of the proposed planning method.
Identification of Insulator Contamination Status Based on Multi-spectral Image Fusion and Radial Basis Function Neural Network
Authors: Li Lu , Wang Yi , Zhang Yang , Zhang Jian , Wang Wenjing , Wang Jiapeng 
Published: April 2023

Abstract
Insulator contamination monitoring is an important way to avoid insulator contamination and maintain insulation performance. In order to ensure power supply and achieve contactless detection of insulator contamination status, a method is proposed in this paper to identify insulator contamination status by adopting infrared, ultraviolet, and visible multi-spectral image information fusion. An insulator with different contamination states in a number of substations is taken as the research object in this paper. The image segmentation is performed by using the seed region growth method to extract the infrared, ultraviolet and visible features of the insulator surface, and the radial basis function neural network learning algorithm is used to classify and decompose and fuse the images according to their different local area energies. The comparison of the recognition rates using infrared and ultraviolet features with those fused shows that the method has significant advantages and provides a new method for the detection of insulator contamination status.
Fast assessment method of power outage risks caused by extreme cold disasters based on probabilistic graph model
Authors: Wang Zhiqiang , Yan Yunqi , Cui Zhengda , Chen Ying , Wang Dongsheng , Li Xin 
Published: April 2023

Abstract
The extremely cold weather may cause cascading failures of power grid components, resulting in large-scale outages and economic losses. Thus, to make better anti-disaster preparations, it is necessary to assess the system-level risk of the power grid efficiently with extreme weather predictions. This paper proposes a risk assessment method for power grids based on the probabilistic graphical model (PGM). First, the time-varying failure probabilities of power lines are estimated with consideration of the cascading effects of the direct impact of disasters and power flow transfer. Then, load supply reliabilities are fast inferred by solving the probability graph model of power component failure propagations. Finally, the overall outage risk is evaluated by considering the capacity and importance of different loads, which helps to improve anti-disaster decisions. Through theoretical analysis and case studies, the efficacy of the proposed method is verified.
Research on photovoltaic MPPT algorithm based on improved perturbation observation method
Authors: Hu Yuan , Wu Peng , Zheng Junguan , Wang Ning , Zhang Shuai , Li Yan , Zhao Penghui , Gao Ming , Li Tianhao 
Published: April 2023

Abstract
The connection of new energy, mainly solar energy, is closely related to the output of photovoltaic power supply. When the PV curve of photovoltaic output is optimally solved, it is easy to take the local optimal solution as the global optimal solution. Aiming at the shortcoming that the traditional perturbation observation method is only suitable for single peak tracking in maximum power tracking, the characteristics of photovoltaic output power are studied and analyzed, and the perturbation method is used to track the maximum power according to the distribution characteristics of each extreme point. Through the simulation analysis in MATLAB/Simulink environment, the results show that the improved algorithm has high accuracy and is easy to realize, and it has a good effect on solving the global optimal solution of photovoltaic output power.
Calculation of Emission Factors of the Northwest Regional Grid Based on Linear Support Vector Machines
Authors: Peng Yu , Chong Ting , Zhang Xiaoye , Chen Shaolin , Xie Gang 
Published: April 2023

Abstract
Machine learning can perform correlation analysis on a large amount of data and explore their relationship. Thus, the status quo can be analyzed, and the values of specific power industry indicators can be predicted in the future. This paper deduced the industry indicators that affect the grid emission factor by analyzing the grid emission factor algorithm. The study used the support vector machine regression algorithm to model and analyze collected power grid data and discussed the regression effect from five main aspects, including RMSE, MSE, MAE, coefficient and running time. Finally the most fitted regression model parameters were obtained. And the optimal model predicted the emission factors of the Northwest power grid.
Design of a multichannel polychromator for KTX Thomson scattering diagnostic
Authors: Peng Yuan , Jian Zheng , Weiqiang Tan , Peng Wang , Jinlin Xie , Tao Lan , Hong Li , Weixing Ding , Wandong Liu 
Published: April 2023

Abstract
A polychromator is designed based on the transmission volume phase holographic grating spectrometer coupled with avalanched photon diodes through fiber bundle and has been successfully implemented on the Keda Torus eXperiment [Plasma Phys. Control. Fusion
56 (2014) 094009] Thomson scattering system. The polychromator is operated with a 1064 nm laser, and the designed spectral range is from about 1000 nm to 1100 nm. The spectral channels have been optimized with a five-channel design to lower the estimated error of the electron temperature and density within 5% and 3% respectively when the temperature is around 100 eV. With a calibration method about the ratio of the channel average responses using bremsstrahlung radiation, we obtain an electron temperature of 7 eV in a typical experiment with low plasma currents.
A new method for the timber tracing toolbox: applying multi-element analysis to determine wood origin
Authors: Boeschoten Laura E , Vlam Mart , Sass-Klaassen Ute , Rocha Venâncio Meyer-Sand Barbara , Adzkia Ulfa , Bouka Gaël D U , Ciliane-Madikou Jannici C U , Engone Obiang Nestor L , Guieshon-Engongoro Mesly , Loumeto Joël J , Mbika Dieu-merci M F , Moundounga Cynel G , Ndangani Rita M D , Bourobou Dyana Ndiade , Rahman Mohamad M , Siregar Iskandar Z , Tassiamba Steve N , Tchamba Martin T , Toumba-Paka Bijoux B L , Zanguim Herman T , Zemtsa Pascaline T , Zuidema Pieter A 
Published: April 2023

Abstract
To effectively reduce illegal timber trade, law enforcers need forensic methods to independently verify claims of wood origin. Multi-element analysis of traded plant material has the potential to be used to trace the origin of commodities, but for timber it has not been tested at relevant large scales. Here we put this method to the test, by evaluating its tracing accuracy for three economically important tropical timbers: Azobé and Tali in Central Africa (22 sites) and Red Meranti on Borneo (9 sites). Wood samples from 991 trees were measured using Inductively Coupled Plasma Mass Spectrometry and element concentrations were analysed to chemically group similar sites (clustering) and assess accuracy of tracing samples to their origin (Random Forest models). For all three timbers, we found distinct spatial differences in chemical composition. In Central Africa, tracing accuracy was 86%–98% for regional clusters of chemically similar sites, with accuracy depending on the tracing question. These clusters were 50–800 km apart and tracing accuracy was highest when combining the two timbers. Tracing accuracy of Red Meranti on Borneo was 88% at the site level. This high accuracy at a small scale may be related to the short distances at which differences in soil type occur on Borneo. A blind sample analysis of 46 African timber samples correctly identified the origin of 70%–72% of the samples, but failed to exclude 70% of the samples obtained from different species or outside the study area. Overall, these results illustrate a high potential for multi-element analysis to be developed into a timber tracing tool which can identify origin for multiple species and can do so at a within-country scale. To reach this potential, reference databases need to cover wider geographic areas and represent more timbers.
The impact of policy and model uncertainties on emissions projections of the Paris Agreement pledges
Authors: den Elzen Michel G J , Dafnomilis Ioannis , Hof Andries F , Olsson Marika , Beusen Arthur , Botzen W J Wouter , Kuramochi Takeshi , Nascimento Leonardo , Rogelj Joeri 
Published: April 2023

AbstractTracking progress towards the Paris Agreement climate goal requires understanding the 2030 emission levels implied by countries’ National Determined Contributions (NDCs). However, key uncertainties and assumptions impact greenhouse gas (GHG) emission projections implied by the NDCs. This study analyses this impact, both globally and for major emitting countries. We find that the assessed uncertainties markedly affect global GHG emission projections. Full achievement of NDC targets is estimated to result in a range of 46–60 GtCO2eq by 2030 (median estimate: 53 GtCO2eq). The uncertainty in measuring historical emissions, including land-use, as reflected by different datasets is the most important contributing factor. This is followed by two equally important factors globally: socio-economic baseline uncertainty and uncertainty about the emissions implied by current policies in case NDCs are less ambitious than these. Overall, the impact of policy uncertainty (i.e. uncertainty resulting from conditionality of or ranges in NDC targets and uncertainty in emissions resulting from current policies) is about equally important as model/technical uncertainty (i.e. uncertainty in historical emissions and socio-economic baseline variations). This new insight is important for decision makers and researchers because a larger share of the total uncertainty is now attributable to aspects that can be influenced by policy decisions compared to previous analyses of NDC uncertainty.
Studying the Inhibitory Effect of Cinnamon and Ginger Aqueous Extracts Against Rice-Associated Fungi that Produce Aflatoxin
Authors: Al-Alwani Khalid Abbood Kurdi , Al-Nazzal Ahmed Ismail 
Published: April 2023

Abstract
The studying included testing the inhibitory activity of the aqueous extracts of cinnamon (Cinnamomum zylanicum) and ginger (Zingibero fficinale) at three concentrations (50, 100 and 200 mgL-1) on the aflatoxin-producing fungi (Aspergillus lavus and Aspergillus parasiticus) isolated from rice used for daily consumption in Iraq as well as the studying of possibility of reducing the aflatoxins after treating the fungal isolates with aqueous extract of medicinal plants. The studying showed the possibility of isolating and diagnosing three genera of fungi using phenotypic and microscopic characteristics based on taxonomic keys. The results showed the presence of three species of Aspergillus (A. flavus, A. parasiticus and A. niger), in addition to the Penicillium spp. and Fusarium spp. at different appearance rates. Also, the results revealed that the aqueous extract of cinnamon and ginger had inhibitory activity against the aflatoxin producing fungi at a concentration of 200 mgL-1 and that the fungal diameter of the inhibition area was 22 mm for both A. flavus and A. parasiticus.