Phase field model for electric-thermal coupled discharge breakdown of polyimide nanocomposites under high frequency electrical stress

In contrast to conventional transformers, power electronic transformers, as an integral component of new energy power system, are often subjected to high-frequency and transient electrical stresses, leading to heightened concerns regarding insulation failures. Meanwhile, the underlying mechanism behind discharge breakdown failure and nanofiller enhancement under high-frequency electrical stress remains unclear. An electric-thermal coupled discharge breakdown phase field model was constructed to study the evolution of the breakdown path in polyimide nanocomposite insulation subjected to high-frequency stress. The investigation focused on analyzing the effect of various factors, including frequency, temperature, and nanofiller shape, on the breakdown path of Polyimide(PI) composites. Additionally, it elucidated the enhancement mechanism of nano-modified composite insulation at the mesoscopic scale. The results indicated that with increasing frequency and temperature, the discharge breakdown path demonstrates accelerated development, accompanied by a gradual dominance of Joule heat energy. This enhancement is attributed to the dispersed electric field distribution and the hindering effect of the nanosheets. The research findings offer a theoretical foundation and methodological framework to inform the optimal design and performance management of new insulating materials utilized in high-frequency power equipment.

Preparation and Anti⁃arc Erosion Property of Ag Matrix Electrical Contact Material Reinforced by Novel Ti_(2)Cd

Due to their outstanding electrical contact properties,Cd-containing silver-matrix electrical contact materials can meet the requirements of high stability and long life for military defense and aerospace applications.In order to further reduce the Cd content under the premise of meeting the high-performance requirements,in this study,high-purity intermediate Ti_(2)Cd powder of MAX phase(Ti_(2)CdC)was synthesized with a pressureless technique and then applied to reinforce the Ag matrix.The Cd content of the as-prepared Ag/Ti_(2)Cd composites was actually reduced by 38.31%compared with conventional Ag/CdO material.Based on the systematic study of the effect of heat treatment temperature on the physical phase,morphology,interface and comprehensive physical properties of Ag/Ti_(2)Cd composites,the preferred samples(heat treated at 400°C for 1 h)showed high density(97.77%),low resistivity(2.34μΩ·cm),moderate hardness(90.8HV),high tensile strength(189.9 MPa),and exhibited good electrical contact performance after 40000 cycles of arc discharging under severe conditions(DC 28 V/20 A).The results of microscopic morphological evolution,phase change and elemental distribution of the electrical contact surface show that the combination of high stability of Ti_(2)Cd reinforcing phase,good interfacial bonding with Ag matrix and improved melt pool viscosity in the primary stage of arc erosion,results in low and stable contact resistance(average value 13.20 mΩ)and welding force(average value 0.6 N),low fluctuation of static force(2.2-2.5 N).The decomposition and absorption energy of Ti_(2)Cd and the arc extinguishing effect of Cd vapor are the main reasons for the stable arcing energy and arcing time of electric contacts in the late stage of arc erosion.

Electrical characteristics of new three-phase traction power supply system for rail transit

A novel three-phase traction power supply system is proposed to eliminate the adverse effects caused by electric phase separation in catenary and accomplish a unifying manner of traction power supply for rail transit.With the application of two-stage three-phase continuous power supply structure,the electrical characteristics exhibit new features differing from the existing traction system.In this work,the principle for voltage levels determining two-stage network is dissected in accordance with the requirements of traction network and electric locomotive.The equivalent model of three-phase traction system is built for deducing the formula of current distribution and voltage losses.Based on the chain network model of the traction network,a simulation model is established to analyze the electrical characteristics such as traction current distribution,voltage losses,system equivalent impedance,voltage distribution,voltage unbalance and regenerative energy utilization.In a few words,quite a lot traction current of about 99%is undertaken by long-section cable network.The proportion of system voltage losses is small attributed to the two-stage three-phase power supply structure,and the voltage unbal-ance caused by impedance asymmetry of traction network is less than 1‰.In addition,the utilization rate of regenerative energy for locomotive achieves a significant promotion of over 97%.

Significantly Improved High-Temperature Energy Storage Performance of BOPP Films by Coating Nanoscale Inorganic Layer

Biaxially oriented polypropylene(BOPP)is one of the most commonly used commercial capacitor films,but its upper operating temperature is below 105℃due to the sharply increased electrical conduction loss at high temperature.In this study,growing an inorganic nanoscale coating layer onto the BOPP film's surface is proposed to suppress electrical conduction loss at high temperature,as well as increase its upper operating temperature.Four kinds of inorganic coating layers that have different energy band structure and dielectric property are grown onto the both surface of BOPP films,respectively.The effect of inorganic coating layer on the high-temperature energy storage performance has been systematically investigated.The favorable coating layer materials and appropriate thickness enable the BOPP films to have a significant improvement in high-temperature energy storage performance.Specifically,when the aluminum nitride(AIN)acts as a coating layer,the AIN-BOPP-AIN sandwich-structured films possess a discharged energy density of 1.5 J cm^(-3)with an efficiency of 90%at 125℃,accompanying an outstandingly cyclic property.Both the discharged energy density and operation temperature are significantly enhanced,indicating that this efficient and facile method provides an important reference to improve the high-temperature energy storage performance of polymer-based dielectric films.

Traction power systems for electrified railways:evolution,state of the art,and future trends

Traction power systems(TPSs)play a vital role in the operation of electrified railways.The transformation of conventional railway TPSs to novel structures is not only a trend to promote the development of electrified railways toward high-efficiency and resilience but also an inevitable requirement to achieve carbon neutrality target.On the basis of sorting out the power supply structures of conventional AC and DC modes,this paper first reviews the characteristics of the existing TPSs,such as weak power supply flexibility and low-energy efficiency.Furthermore,the power supply structures of various TPSs for future electrified railways are described in detail,which satisfy longer distance,low-carbon,high-efficiency,high-reliability and high-quality power supply requirements.Meanwhile,the application prospects of different traction modes are discussed from both technical and economic aspects.Eventually,this paper introduces the research progress of mixed-system electrified railways and traction power supply technologies without catenary system,speculates on the future development trends and challenges of TPSs and predicts that TPSs will be based on the continuous power supply mode,employing power electronic equipment and intelligent information technology to construct a railway comprehensive energy system with renewable energy.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Numerical Study on Reduction in Aerodynamic Drag and Noise of High-Speed Pantograph

Reducing the aerodynamic drag and noise levels of high-speed pantographs is important for promoting environmentally friendly,energy efficient and rapid advances in train technology.Using computational fluid dynamics theory and the K-FWH acoustic equation,a numerical simulation is conducted to investigate the aerodynamic characteristics of high-speed pantographs.A component optimization method is proposed as a possible solution to the problemof aerodynamic drag and noise in high-speed pantographs.The results of the study indicate that the panhead,base and insulator are the main contributors to aerodynamic drag and noise in high-speed pantographs.Therefore,a gradual optimization process is implemented to improve the most significant components that cause aerodynamic drag and noise.By optimizing the cross-sectional shape of the strips and insulators,the drag and noise caused by airflow separation and vortex shedding can be reduced.The aerodynamic drag of insulator with circular cross section and strips with rectangular cross section is the largest.Ellipsifying insulators and optimizing the chamfer angle and height of the windward surface of the strips can improve the aerodynamic performance of the pantograph.In addition,the streamlined fairing attached to the base can eliminate the complex flow and shield the radiated noise.In contrast to the original pantograph design,the improved pantograph shows a 21.1%reduction in aerodynamic drag and a 1.65 dBA reduction in aerodynamic noise.

Bifunctional TiO_(2-x)nanofibers enhanced gel polymer electrolyte for high performance lithium metal batteries

Exploration of advanced gel polymer electrolytes(GPEs)represents a viable strategy for mitigating dendritic lithium(Li)growth,which is crucial in ensuring the safe operation of high energy density Li metal batteries(LMBs).Despite this,the application of GPEs is still hindered by inadequate ionic conductivity,low Li^(+)transference number,and subpar physicochemical properties.Herein,Ti O_(2-x)nanofibers(NF)with oxygen vacancy defects were synthesized by a one-step process as inorganic fillers to enhance the thermal/mechanical/ionic-transportation performances of composite GPEs.Various characterizations and theoretical calculations reveal that the oxygen vacancies on the surface of Ti O_(2-x)NF accelerate the dissociation of Li PF_6,promote the rapid transfer of free Li^(+),and influence the formation of Li F-enriched solid electrolyte interphase.Consequently,the composite GPEs demonstrate enhanced ionic conductivity(1.90m S cm^(-1)at room temperature),higher lithium-ion transference number(0.70),wider electrochemical stability window(5.50 V),superior mechanical strength,excellent thermal stability(210℃),and improved compatibility with lithium,resulting in superior cycling stability and rate performance in both Li||Li,Li||Li Fe PO_(4),and Li||Li Ni_(0.8)Co_(0.1)Mn_(0.1)O_(2)cells.Overall,the synergistic influence of nanofiber morphology and enriched oxygen vacancy structure of fillers on electrochemical properties of composite GPEs is comprehensively investigated,thus,it is anticipated to shed new light on designing high-performance GPEs LMBs.

Enhanced High-Temperature Energy Storage Performance of All-Organic Composite Dielectric via Constructing Fiber-Re in forced Structure

Optimizing the high-temperature energy storage characteristics of energy storage dielectrics is of great significance for the development of pulsed power devices and power control systems.Selecting a polymer with a higher glass transition temperature(T_(g))as the matrix is one of the effective ways to increase the upper limit of the polymer operating temperature.However,current high-T_(g)polymers have limitations,and it is difficult to meet the demand for high-temperature energy storage dielectrics with only one polymer.For example,polyetherimide has high-energy storage efficiency,but low breakdown strength at high temperatures.Polyimide has high corona resistance,but low high-temperature energy storage efficiency.In this work,combining the advantages of two polymer,a novel high-T_(g)polymer fiber-reinforced microstructure is designed.Polyimide is designed as extremely fine fibers distributed in the composite dielectric,which will facilitate the reduction of high-temperature conductivity loss for polyimide.At the same time,due to the high-temperature resistance and corona resistance of polyimide,the high-temperature breakdown strength of the composite dielectric is enhanced.After the polyimide content with the best high-temperature energy storage characteristics is determined,molecular semiconductors(ITIC)are blended into the polyimide fibers to further improve the high-temperature efficiency.Ultimately,excellent high-temperature energy storage properties are obtained.The 0.25 vol%ITIC-polyimide/polyetherimide composite exhibits high-energy density and high discharge efficiency at 150℃(2.9 J cm^(-3),90%)and 180℃(2.16 J cm^(-3),90%).This work provides a scalable design idea for high-performance all-organic high-temperature energy storage dielectrics.

Design of Protograph LDPC-Coded MIMO-VLC Systems with Generalized Spatial Modulation

This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.

Low profile cavity-embedded ultra-wideband UHF array antenna with end-fire beams

A low-profile,vertically polarized,ultra-wideband array antenna with end-fire beams operating in an ultra-high frequency(UHF)band is developed in this paper.The array antenna consists of 1×16 log-periodic top-hat loaded monopole antenna arrays and is feasible to embed into a shallow cavity to further reduce the array height.Capacitance is introduced in the proposed antenna element to reduce profile height and the rectangular top hats are carefully designed to minimize the transverse dimension.Simulated results show that when the antenna array operates in a frequency range of 300 MHz-900 MHz,the end-fire radiation pattern achieves±45°scanning range in the horizontal plane.Then prototypes of the proposed end-fire antenna element and a uniformly spaced linear array(1×2)are fabricated and validated.The end-fire antenna array should be suitable for airborne applications where low-profile and conformal scanning phased antenna arrays with end-fire radiations are required.This design is attractive for airborne platform applications that are used to search,discover,identify,and scout the aerial target with vertically polarized beams.

Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning

BACKGROUND Lymphovascular invasion(LVI)and perineural invasion(PNI)are important prognostic factors for gastric cancer(GC)that indicate an increased risk of metastasis and poor outcomes.Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment deci-sions.However,prior models using conventional computed tomography(CT)images to predict LVI or PNI separately have had limited accuracy.Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion.We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients.AIM To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately.METHODS This study used a retrospective dataset involving 257 GC patients(training cohort,n=172;validation cohort,n=85).First,several clinical indicators,including serum tumor markers,CT-TN stages and CT-detected extramural vein invasion(CT-EMVI),were extracted,as were quantitative spectral CT parameters from the delineated tumor regions.Next,a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters.A logistic regression(LR)-based nomogram model was subsequently constructed to predict LVI/PNI status,and its performance was evaluated using the area under the receiver operating characteristic curve(AUC).RESULTS In both the training and validation cohorts,CT T3-4 stage,CT-N positive status,and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant(P<0.05).LR analysis of the training group showed preoperative CT-T stage,CT-EMVI,single-energy CT values of 70 keV of venous phase(VP-70 keV),and the ratio of standardized iodine concentration of equilibrium phase(EP-NIC)were independent influencing factors.The AUCs of VP-70 keV and EP-NIC were 0.888 and 0.824,respectively,which were slightly greater than those of CT-T and CT-EMVI(AUC=0.793,0.762).The nomogram combining CT-T stage,CT-EMVI,VP-70 keV and EP-NIC yielded AUCs of 0.918(0.866-0.954)and 0.874(0.784-0.936)in the training and validation cohorts,which are significantly higher than using each of single independent factors(P<0.05).CONCLUSION The study found that using portal venous and EP spectral CT parameters allows effective preoperative detection of LVI/PNI in GC,with accuracy boosted by integrating clinical markers.

Hyphae-mediated bioassembly of carbon fibers derivatives for advanced battery energy storage

Ingenious design and fabrication of advanced carbon-based sulfur cathodes are extremely important to the development of high-energy lithium-sulfur batteries,which hold promise as the next-generation power source.Herein,for the first time,we report a novel versatile hyphae-mediated biological assembly technology to achieve scale production of hyphae carbon fibers(HCFs)derivatives,in which different components including carbon,metal compounds,and semiconductors can be homogeneously assembled with HCFs to form composite networks.The mechanism of biological adsorption assembly is also proposed.As a representative,reduced graphene oxides(rGOs)decorated with hollow carbon spheres(HCSs)successfully co-assemble with HCFs to form HCSs@rGOs/HCFs hosts for sulfur cathodes.In this unique architecture,not only large accommodation space for sulfur but also restrained volume expansion and fast charge transport paths are realized.Meanwhile,multiscale physical barriers plus chemisorption sites are simultaneously established to anchor soluble lithium polysulfides.Accordingly,the designed HCSs@rGOs/HCFs-S cathodes deliver a high capacity(1189 mA h g^(-1)at 0.1 C)and good high-rate capability(686 mA h g^(-1)at 5 C).Our work provides a new approach for the preparation of high-performance carbon-based electrodes for energy storage devices.

The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real environments.In this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

Osteoarthritis(OA)is a debilitating degenerative disease affecting multiple joint tissues,including cartilage,bone,synovium,and adipose tissues.OA presents diverse clinical phenotypes and distinct molecular endotypes,including inflammatory,metabolic,mechanical,genetic,and synovial variants.Consequently,innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches.Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints,causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues.This issue has led to standardization difficulties and hindered the success of clinical trials.Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues,encompassing DNA,RNA,metabolites,and proteins,as well as their chemical properties,elemental composition,and mechanical attributes,can contribute to a more comprehensive understanding of the disease subtypes.Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment,providing a more holistic view of cellular function.Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various-omics lenses,such as genomics,transcriptomics,proteomics,and metabolomics,with spatial data.This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates.Furthermore,advanced imaging techniques,including high-resolution microscopy,hyperspectral imaging,and mass spectrometry imaging,enable the visualization and analysis of the spatial distribution of biomolecules,cells,and tissues.Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes.This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis.It explores their applications,challenges,and potential opportunities in the field of OA.Additionally,this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.

Generation of input spectrum for electrolysis stack degradation test applied to wind power PEM hydrogen production

Hydrogen production by proton exchange membrane electrolysis has good fluctuation adaptability,making it suitable for hydrogen production by electrolysis in fluctuating power sources such as wind power.However,current research on the durability of proton exchange membrane electrolyzers is insufficient.Studying the typical operating conditions of wind power electrolysis for hydrogen production can provide boundary conditions for performance and degradation tests of electrolysis stacks.In this study,the operating condition spectrum of an electrolysis stack degradation test cycle was proposed.Based on the rate of change of the wind farm output power and the time-averaged peak-valley difference,a fluctuation output power sample set was formed.The characteristic quantities that played an important role in the degradation of the electrolysis stack were selected.Dimensionality reduction of the operating data was performed using principal component analysis.Clustering analysis of the data segments was completed using an improved Gaussian mixture clustering algorithm.Taking the annual output power data of wind farms in Northwest China with a sampling rate of 1 min as an example,the cyclic operating condition spectrum of the proton-exchange membrane electrolysis stack degradation test was constructed.After preliminary simulation analysis,the typical operating condition proposed in this paper effectively reflects the impact of the original curve on the performance degradation of the electrolysis stack.This study provides a method for evaluating the degradation characteristics and system efficiency of an electrolysis stack due to fluctuations in renewable energy.

Detection Algorithm of Laboratory Personnel Irregularities Based on Improved YOLOv7

Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed.Therefore,the current management of personnel behavior mainly relies on institutional constraints,education and training,on-site supervision,etc.,which is time-consuming and ineffective.Given the above situation,this paper proposes an improved You Only Look Once version 7(YOLOv7)to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy.First,to better capture the shape features of the target,deformable convolutional networks(DCN)is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed.Second,to enhance the extraction of important features and suppress useless features,this paper proposes a new convolutional block attention module_efficient channel attention(CBAM_E)for embedding the neck network to improve the model’s ability to extract features from complex scenes.Finally,to reduce the influence of angle factor and bounding box regression accuracy,this paper proposes a newα-SCYLLA intersection over union(α-SIoU)instead of the complete intersection over union(CIoU),which improves the regression accuracy while increasing the convergence speed.Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes,with an increase of 2.92%in the precision rate,4.14%in the recall rate,0.0356 in the weighted harmonic mean,3.60%in the mAP@0.5 value,and a reduction in the number of parameters and complexity.Compared with the mainstream algorithm,the improved algorithm has higher detection accuracy,faster convergence speed,and better actual recognition effect,indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios.

Robust optimal dispatch strategy of integrated energy system considering CHP-P2G-CCS

Integrated energy systems(IESs)can improve energy efficiency and reduce carbon emissions,essential for achieving peak carbon emissions and carbon neutrality.This study investigated the characteristics of the CHP model considering P2G and carbon capture systems,and a two-stage robust optimization model of the electricity-heat-gascold integrated energy system was developed.First,a CHP model considering the P2G and carbon capture system was established,and the electric-thermal coupling characteristics and P2G capacity constraints of the model were derived,which proved that the model could weaken the electric-thermal coupling characteristics,increase the electric power regulation range,and reduce carbon emissions.Subsequently,a two-stage robust optimal scheduling model of an IES was constructed,in which the objective function in the day-ahead scheduling stage was to minimize the start-up and shutdown costs.The objective function in the real-time scheduling stage was to minimize the equipment operating costs,carbon emission costs,wind curtailment,and solar curtailment costs,considering multiple uncertainties.Finally,after the objective function is linearized with a ψ-piecewise method,the model is solved based on the C&CG algorithm.Simulation results show that the proposed model can effectively absorb renewable energy and reduce the total cost of the system.

Ascertaining the influences of auxiliary qubits on the Einstein–Podolsky–Rosen steering and its directions

Einstein–Podolsky–Rosen(EPR) steering is an example of nontrivial quantum nonlocality and characteristic in the non-classical world.The directivity(or asymmetry) is a fascinating trait of EPR steering,and it is different from other quantum nonlocalities.Here,we consider the strategy in which two atoms compose a two-qubit X state,and the two atoms are owned by Alice and Bob,respectively.The atom of Alice suffers from a reservoir,and the atom of Bob couples with a bit flip channel.The influences of auxiliary qubits on EPR steering and its directions are revealed by means of the entropy uncertainty relation.The results indicate that EPR steering declines with growing time t when adding fewer auxiliary qubits.The EPR steering behaves as damped oscillation when introducing more auxiliary qubits in the strong coupling regime.In the weak coupling regime,the EPR steering monotonously decreases as t increases when coupling auxiliary qubits.The increases in auxiliary qubits are responsible for the fact that the steerability from Alice to Bob(or from Bob to Alice) can be more effectively revealed.Notably,the introductions of more auxiliary qubits can change the situation that steerability from Alice to Bob is certain to a situation in which steerability from Bob to Alice is certain.