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The present article provides a comprehensive analysis of the aging phenomenon of transformer insulation and the corresponding changes in insulation performance that occur over time. The present manuscript outlines diverse insulating materials that are employed in transformers, the fundamental aging mechanism of the insulation, the function of space charges in the aging of the insulation, and various simulation methodologies utilized for diagnosing the insulation status of the transformer. The primary focus of this paper is to examine the various numerical techniques employed in modeling and analyzing the aging characteristics of transformer insulation. The mechanisms underlying the accumulation of charge and distortion of electric fields in insulation are explained through the utilization of numerical modeling techniques. This paper focuses on understanding the mechanism of insulation aging, thereby facilitating the optimization and maintenance of insulation performance throughout its operational lifespan. [ABSTRACT FROM AUTHOR]

Oil-impregnated paper condenser transformer bushings are an important part of transformer equipment, and partial discharge (PD) occurred when defects exist on the condenser aluminum foil layers. Firstly, to study the PD process of the oil-paper insulated capacitance graded bushing with the defect of broken aluminum foil, a defective oil-paper bushing discharge sample is constructed to study the PD parameters and capacitance, and to discharge carbonization traces at different voltage levels. Then, in order to verify the process of condenser aluminum foil layer discharge and the space charge variation in the oil-paper insulation system of a sample model, the surface flashovers of a needle-plane discharge model based on the bipolar charge transport model and the hydrodynamic model was built. The simulation, by Transport of Diluted Species physics of COMSOL Multiphysics software, points out the discharge process of aluminum foil electrode caused by space charge action and electric field distortion under an electric field at different voltages. The results of simulation and sample bushing experiments showed that the PD process of the defective condenser foil layer is mainly divided into three stages: tip corona discharge, streamer in oil, and surface flashovers. The voltage amplitude is larger the more electrical branches are discharged and the shorter the discharge time is. The findings of the article have important implications for the discharge of the foil layer inside the oil-paper bushing. [ABSTRACT FROM AUTHOR]

The space charge effect of transformer insulating paper will distort its internal electric field, and its space charge effect will be more prominent under extremely complex working conditions. At present, the micromechanism of charge transport and electric field distortion characteristics of paperboard are not clear under complex working conditions. So in this article, based on the bipolar carrier transport (BCT) model and the dynamic carrier balance model, the effects of high temperature, aging degree, moisture content, and bent degree on charges accumulation in paperboard are studied under dc high voltage by numerical simulation and the measured data needed for simulation. The results show that the higher temperature strengthens the carrier migration, deepens the injection depth of the charge, and aggravates the distortion of electric field intensity in paperboard. The more extended the aging time of paperboard, the greater the internal trap density and the deeper the trap depth, which lead to charges migration difficultly. Increasing water content in the paperboard will accelerate space charges to reach a steady state, while further increasing it will slow down this process, which is related to the shallow trap getting deeper. When the critical part of paperboard is extruded, many traps are introduced, resulting in charges accumulation seriously. The research results can reference the design of paperboard and the maintenance of transformer insulation system. [ABSTRACT FROM AUTHOR]

This paper provides a simulation model for the electronic paper display device, the model can quickly and efficiently simulate the initial steady state of the electronic ink system, thus improving the efficiency of device development. It mainly includes the following contents: Obtaining the parameters of the model, then the built‐in electric field strength is calculated through the model parameter information and the pre‐ constructed built‐in electric field model. The motion of each charged particle is simulated to obtain the simulation relationship between the display gray scale and the applied electric field force. Finally, it make various charged particles reach the force equilibrium state, so as to realize the simulation of the voltage‐gray scale relationship of electronic ink system. [ABSTRACT FROM AUTHOR]

It is known that 1,2,3 benzotriazole (BTA) can effectively limit the oil flow electrification in oil–paper insulation used in HVAC transformers, but it is still unclear whether BTA is applicable to converter transformers. Thus, the effects of BTA on interface charge behaviors and charge migration in oil–paper system under dc stress are investigated in this article. The experimental results show that BTA reduces the interface barrier between oil and oil-immersed paper by altering the charge distribution of the electric double layer (EDL) at the oil–paper interface, which is beneficial to the operation stability of converter transformers in the condition of polarity inversion. However, it is also found that the BTA additive promotes the charge accumulation at oil–paper interface and enhances the local electric field by increasing the carrier mobility of oil–paper insulation. The experimental results in this article provide references for the selection of insulating oil additives for converter transformers. [ABSTRACT FROM AUTHOR]

As an important piece of equipment of ultrahigh-voltage direct current, the valve side of a converter transformer contains a large dc component prone to partial discharge (PD) under the action of a slightly uneven electric field, which can be simulated using a wedge electrode. To study the PD characteristics of oil–paper insulation under a wedge-shaped electrode, a 2-D axisymmetric simulation model was established based on the bipolar-charge transport theory and the fluid dynamics drift-diffusion theory. The microscopic discharge images at nanosecond scale under different dc voltage polarities were calculated, and the change process of the space charge and electric field intensity distribution was obtained. The effects of the voltage amplitude, polarity, and dielectric constant on PD characteristics are discussed. The simulation results show that the discharge pattern and velocity exhibit a notable polarity effect. This polarity effect is related to the adsorption of the cellulose hydroxyl group to negative charge, which leads to the amplitude of the homopolar charge accumulated in negative polarity being greater than that accumulated in positive polarity under the same applied voltage intensity so that the homopolar charge has a greater barrier to the discharge. This type of adsorption is positively correlated with the difference of dielectric constant between the paperboard and insulating oil. The results of this study are of great theoretical significance for elucidating the dc discharge mechanism under wedge electrodes. [ABSTRACT FROM AUTHOR]

Electric‐driven conversion of carbon dioxide (CO2) to carbon monoxide (CO) under mild reaction conditions offers a promising approach to mitigate the greenhouse effect and the energy crisis. Surface engineering is believed to be one of the prospective methods for enhancing the electrocatalytic activity of CO2 reduction. Herein, hydroxyl (OH) groups were successfully introduced to cadmium nanosheets to form cadmium and cadmium hydroxide nanocomposites (i. e. Cd/Cd(OH)2 nanosheets) via a facile two‐step method. The as‐prepared Cd/Cd(OH)2/CP (CP indicates carbon paper) electrode displays excellent electrocatalytic activity for CO2 reduction to produce CO. The Faradaic efficiency of CO reaches 98.3 % and the current density achieves 23.8 mA cm−2 at −2.0 V vs. Ag/Ag+ in a CO2‐saturated 30 wt% 1‐butyl‐3‐methylimidazole hexafluorophosphate ([Bmim]PF6)‐65 wt% acetonitrile (CH3CN)‐5 wt% water (H2O) electrolyte. And the CO partial current density can reach up to 71.6 mA cm−2 with the CO Faradaic efficiency of more than 85 % at −2.3 V vs. Ag/Ag+, which stands out against Cd/CP, Cd(OH)2/CP, and Cd/CdO/CP electrodes. The excellent electrocatalytic performance of the Cd/Cd(OH)2/CP electrode can be attributed to its unique structural properties, suitable OH groups, perfect interaction with electrolyte, abundant active sites and fast electron transfer rate. [ABSTRACT FROM AUTHOR]

Vehicles are getting equipped in technology, communication between vehicle and the user is getting better. The vehicular network is an emerging technology to provide mobile users with the flexibility to use various services such as entertainment and navigation on wheels. The users are served with data related to their travel in terms of road map, weather updates, traffic congestions, radio services, social network applications and place of interest. The data communication takes place with the help of Smart On-board Unit (SOBU) present on the vehicle. There is a requirement of compound data computations with firm latency. Vehicle Edge computing (VEC) is the emerging technology that has serves at the edge in the neighbourhood of vehicle that enables data offloading. There will be much of energy consumption and latency resulting from offloading and computations. In this paper smart offloading scheme is proposed that will efficiently harvest the energy and reduce the energy consumption problem. As a performance statistic, the execution cost is used, which accounts for both execution delay and task failure. And also, the offloading scheme is analyzed for single user and multiuser by simulations and the results are compared graphically for battery energy level, average execution cost and channel mode select parameters respectively. The factors affecting both Single-user and multi-user computation are identified from the results. Experimental results show that offloading schemes proposed for single user and multi user work better than the other state-of-the-art algorithms for vehicular networks. This single-user analysis will be a benchmark for developing auto pilot vehicle for people with special needs and multi-user analysis will help in developing to all kinds of Electric Vehicle communication. [ABSTRACT FROM AUTHOR]

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This paper studies how to integrate the smart charging of large-scale electric vehicles (EVs) into the generation and storage expansion planning (GSEP), while analyzing the impact of smart charging on the GSEP of a real power system in south China. For this purpose, a random simulation-based method is first developed to provide the tractable formulations of the adjustable charging load and reserve provision from EVs. This method avoids the unrealistic assumption that EVs drive and charge every day, which often exists in prior relevant approaches. Based on the random simulation, this paper proposes a novel GSEP optimization model which incorporates the weekly adjustable charging load of EVs. In the proposed model, the total charging load of EVs can be co-optimized with the investment and operational decisions of various generation and storage units. This GSEP model is applied to a provincial power system in south China. The numerical results show that the implementation of smart charging can significantly alter the decisions of GSEP. As the participation rate of smart charging improves from 0% to 90%, there is an additional 1,800 MW installation in wind and solar power, while the need to build new batteries is noticeably reduced; also, depending on the level of EV uptake, the annualized total system cost decreases by 5.11%-7.57%, and the curtailment of wind and solar power is reduced by 10.34%-19.64%. Besides, numerical tests reveal that the traditional assumption that EVs drive and charge every day can mislead the evaluation of adjustable charging load and overestimate the daily charging power peak by averagely 24.72%. [ABSTRACT FROM AUTHOR]

In order to achieve low carbon emissions in the power grid, the impact of new energy grid connections on the power grid should be reduced, as well as the peak-to-valley load difference caused by large-scale electric vehicle grid connections. This paper proposes a two-tier, low-carbon optimal dispatch master–slave game model involving virtual power plant operators as well as electric vehicle operators. Firstly, the carbon flow is tracked based on the proportional sharing principle, and the carbon emission factor during the charging and discharging process of electric vehicles is calculated. Secondly, the node carbon potential and time-sharing tariff are used to guide and change the charging behaviour of electric vehicles and to construct a master–slave game model for low-carbon optimal scheduling with the participation of multiple subjects, with economic scheduling at the upper level of the model and demand response scheduling at the lower level. Finally, the IEEE30 node system is used as an example to verify that the method adopted in this paper can effectively reduce the peak-to-valley difference of loads, reduce the carbon emissions of the grid, and reduce the cost of each participating entity. [ABSTRACT FROM AUTHOR]

Accurately assessing the state of health (SOH) of lithium batteries is of great significance for improving battery safety performance. However, the current assessment for SOH suffers from the difficulty of selecting health features and the lack of uncertainty using data-driven methods. To this end, this paper proposes a health state assessment method for lithium-ion batteries based on health feature extraction and an improved Informer model. First, multiple features that can reflect the SOH of lithium-ion batteries were extracted from the charging and discharging time, the peak value of incremental capacity curve (ICC), and the inflection point value of differential voltage curve, etc., and the correlation between multiple health features and the health state was evaluated by gray correlation analysis. Then, an improved Informer model is proposed to establish a health state estimation method for lithium-ion batteries. Finally, the proposed algorithm is tested and validated using publicly available battery charge/discharge datasets and compared with other algorithms. The results show that the method in this paper can realize high-precision SOH prediction with a root-mean-square error (RMSE) of 0.011, and the model fit reaches more than 98%. [ABSTRACT FROM AUTHOR]

Charging electric vehicles (EVs) is of great concern both for future vehicle owners and grid operators, with charging at home being the preferred solution by 90% of owners. In addition, the supplied electricity needs to be clean in order to reduce emissions. This paper presents solutions for charging EVs at home using renewable electricity that fulfils such needs. It discusses: (1) the current landscape; (2) the latest hardware developments in the fields of renewable sources and storage; (3) software optimization for home energy management; (4) residential charging station standards and incentives offered by governments; (5) the evolution towards designing smart homes with low energy consumption from the grid; (6) case studies of particular interest. [ABSTRACT FROM AUTHOR]

The forecasting of charging demand for electric vehicles (EVs) plays a vital role in maintaining grid stability and optimizing energy distribution. Therefore, an innovative method for the prediction of EV charging load demand is proposed in this study to address the downside of the existing techniques in capturing the spatial–temporal heterogeneity of electric vehicle (EV) charging loads and predicting the charging demand of electric vehicles. Additionally, an innovative method of electric vehicle charging load demand forecasting is proposed, which is based on the weighted measurement fusion unscented Kalman filter (UKF) algorithm, to improve the accuracy and efficiency of forecasting. First, the data collected from OpenStreetMap and Amap are used to analyze the distribution of urban point-of-interest (POI), to accurately classify the functional areas of the city, and to determine the distribution of the urban road network, laying a foundation for modeling. Second, the travel chain theory was applied to thoroughly analyze the travel characteristics of EV users. The Improved Floyd (IFloyd) algorithm is used to determine the optimal route. Also, a Monte Carlo simulation is performed to estimate the charging load for electric vehicle users in a specific region. Then, a weighted measurement fusion UKF (WMF–UKF) state estimator is introduced to enhance the accuracy of prediction, which effectively integrates multi-source data and enables a more detailed prediction of the spatial–temporal distribution of load demand. Finally, the proposed method is validated comparatively against traffic survey data and the existing methods by conducting a simulation experiment in an urban area. The results show that the method proposed in this paper is applicable to predict the peak hours more accurately compared to the reference method, with the accuracy of first peak prediction improved by 53.53% and that of second peak prediction improved by 23.23%. The results not only demonstrate the high performance of the WMF–UKF prediction model in forecasting peak periods but also underscore its potential in supporting grid operations and management, which provides a new solution to improving the accuracy of EV load demand forecasting. [ABSTRACT FROM AUTHOR]

Supercapacitors (SCs) are an emerging energy storage technology with the ability to deliver sudden bursts of energy, leading to their growing adoption in various fields. This paper conducts a comprehensive review of SCs, focusing on their classification, energy storage mechanism, and distinctions from traditional capacitors to assess their suitability for different applications. To investigate the voltage response of SCs, the existing electrical equivalent circuits are further studied. The analysis is carried forward with the parameter of impedance, which has not so far been addressed. Impedance analysis is essential for a better understanding of SCs as capacitors work on alternating source of supply. The paper also highlights the applications of SCs in electric automobiles and charging stations, showcasing their advantages such as fast charging and higher power density compared to traditional capacitors. Additionally, other applications in areas like the military, medicine, and industry are discussed, demonstrating the versatility of SC technology. [ABSTRACT FROM AUTHOR]

The increasing demand for wireless power transfer (WPT) systems for electric vehicles (EVs) has necessitated advancements in charging solutions, with a particular focus on speed and efficiency. However, power transfer efficiency is the major concern in static and dynamic wireless charging (DWC) design. Design consideration and improvements in all functional units are necessary for an increase in overall efficiency of the system. Recently, different research works have been presented regarding DWC at the power converter, coil structure and compensators. This paper provides a comprehensive review of power converters incorporating high-order compensation topologies, demonstrating their benefits in enhancing the DWC of EVs. The review also delves into the coupling coil structure and magnetic material architecture, pivotal in enhancing power transfer efficiency and capability. Moreover, the high-order compensation topologies used to effectively mitigate low-frequency ripple, improve voltage regulation, and facilitate a more compact and portable design are discussed. Furthermore, optimal coupling and different techniques to achieve maximum power transfer efficiency are discussed to boost magnetic interactions, thereby reducing power loss. Finally, this paper highlights the essential role of these components in developing efficient and reliable DWC systems for EVs, emphasizing their contribution to achieving high-power transfer efficiency and stability. [ABSTRACT FROM AUTHOR]

With the increasing prominence of environmental and energy issues, electric vehicles (EVs) as representatives of clean energy vehicles have experienced rapid development in recent years, and the charging load has also exhibited statistical characteristics. Accurate prediction of EV charging load is crucial to improve grid load dispatch and intelligent level. However, current research on EV charging load prediction still faces challenges such as data reliability, complexity and variability of charging behavior, uncertainty, and lack of standardization methods. Therefore, this paper proposes an electric vehicle charging load prediction method based on spectral clustering and deep learning network (SC-CNNLSTM). Firstly, to address the insufficient amount of EV charging load data, this paper proposes to use Monte Carlo simulation to sample and simulate historical load data. Then, in order to identify the internal structure and patterns of charging load, the sampled and simulated dataset is clustered using spectral clustering, dividing the data into different clusters, where each cluster represents samples with similar charging load characteristics. Finally, based on the different sample features of each cluster, corresponding CNN-LSTM models are constructed and trained and predict using the respective data. By modifying the model parameters, the prediction accuracy of the model is improved. Through comparative experiments, the proposed method in this paper has significantly improved prediction accuracy compared to traditional prediction methods without clustering, thus validating the effectiveness and practicality of the method. [ABSTRACT FROM AUTHOR]

The safety of battery energy storage systems (BES) is of paramount importance for societal development and the wellbeing of the people. This is particularly true for retired batteries, as their performance degradation increases the likelihood of thermal runaway occurrences. Existing early warning methods for BES thermal runaway face two main challenges: mechanism-based research methods only consider a single operating state, making their application and promotion difficult; while data-driven methods based on supervised learning struggle with limited sample sizes. To address these issues, this paper proposes a data- driven early warning method for BES thermal runaway. The method utilizes unsupervised learning to create a framework that measures BES differences through reconstruction errors, enabling effective handling of limited samples. Additionally, ensemble learning is employed to enhance the method's stability and quantify the probability of BES experiencing thermal runaway. To accurately capture the time-varying behaviors of BES, such as voltage, temperature, current, and state of charge (SOC), and detect performance differences in BES before and after thermal runaway, a bidirectional long short-term memory (Bi-LSTM) network with an attention mechanism is utilized. This approach effectively extracts features from training data. Subsequently, a Case study was conducted using the actual operation data of retired lithium batteries to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The development and use of single‐phase electric energy router (SPEER) makes the household power system operate more reliably and bring economic income at the same time. This paper presents an economic energy optimization management method of SPEER with historical data of previous day and real‐time data in the grid‐connected mode of SPEER, which can be realized by DSP. First, based on the exchange power between the SPEER and the grid, the optimal charging and discharging power of the battery to make up for the energy difference between the previous day and the real time is obtained through the particle swarm intelligence algorithm, and the optimal economic model of SPEER operation is established. Then, on the hardware experimental platform, DSP is used to realize the real‐time optimization, and the optimal economic optimization algorithm proposed in this paper is implemented and verified on hardware experimental platform. The contradiction between the complexity of optimization algorithm and the limitation of hardware resources is solved in the proposed energy management system. [ABSTRACT FROM AUTHOR]

While they do not appear to report the values, they do introduce the possibility of using an error distance that they refer to as the Singular Value Decomposition (SVD) ratio, which they define as Equation (1): (1) HT ht where HT ht is the coil current reference obtained using COMSOL and HT ht is that from the proposed approach. Electromagnetics is the hidden "glue" that holds together almost all electrical and electronic engineering applications. The basis of electromagnetics is electromagnetic force, one of the fundamental forces of nature; from this force, we can derive electromagnetic fields. [Extracted from the article]

This paper presents an advanced Wireless Power Transfer (WPT) system for electric vehicles (EVs) featuring Active Load Impedance Matching (ALIM) at the rectification stage. Unlike traditional synchronous rectification, ALIM dynamically adjusts load impedance, optimizing energy transfer efficiency and reducing thermal stresses, system costs, and mass. The system incorporates two circuits optimized for distinct frequency bands: one operates below 10 kHz using standard copper wiring for cost-effectiveness, and the other at 85 kHz, which significantly reduces the mass of the onboard coil and magnetic circuit while ensuring interoperability according to SAE J2954 standard. Our approach enhances charging efficiency across various operating conditions, improves thermal management, and minimizes maintenance costs. Additionally, it enables partial compensation for vehicle misalignment and ground assembly impedance, further boosting efficiency and interoperability. Experimental results demonstrate a notable increase in efficiency and reduction in system mass, confirming the superiority of the ALIM-equipped WPT system over conventional solutions. This paper underscores the potential of ALIM to advance the scalability, efficiency, and economic viability of wireless EV charging technology, promoting broader adoption and sustainability in EV infrastructures. By providing a comprehensive solution that addresses key challenges in wireless charging, our work paves the way for more efficient and cost-effective EV charging systems. [ABSTRACT FROM AUTHOR]

Slow-charging electric vehicle (EV) loads are single-phase loads in the power distribution network (PDN). The random access of these EVs to the network brings to the forefront the split-phase voltage quality issues. Therefore, a two-layer EV charging strategy considering split-phase voltage quality is proposed in this paper. Issues with voltage unbalance (VU), split-phase voltage deviation (VD), and split-phase voltage harmonics (VHs) are included in the optimization objective model. An upgraded version of the multi-objective non-dominated sorting genetic algorithm (NSGA-II) is used in the inner layer of the model and to pass the generated EV phase selection scheme to the outer layer. The outer layer consists of a split-phase harmonic current algorithm based on the forward–backward generation method, and feeds the voltage quality calculation results to the inner layer. After several iterations, the optimal EV phase selection scheme can be obtained when the inner layer algorithm satisfies the convergence condition. The results gained for the example indicate that the suggested EV charging approach can effectively handle the PDN's split-phase voltage quality. Furthermore, it enhances the energy efficiency of PDN operations and promotes further energy consumption. [ABSTRACT FROM AUTHOR]

The advancement of new energy vehicles (NEVs) represents a strategic initiative to combatting climate change, mitigating the energy crisis, and fostering green growth. Using provincial panel data from China between 2017 and 2022, in this study, we applied machine learning techniques for sentiment analysis of textual reviews, used word frequency statistics to explore consumers' views on the attributes of new energy vehicles, and constructed a consumer sentiment index to study the impact of consumer sentiment on NEV sales. Considering the dependence of NEVs on a charging station, this paper explores the nonlinear impact of the popularity of charging stations on the relationship between consumer sentiment and sales of new energy vehicles. The findings indicate the potential for enhancement in the areas of space, interior design, and comfort of NEVs. Additionally, consumer sentiment was found to facilitate the diffusion of NEVs, with this effect being heterogeneous across different educational backgrounds, income levels, and ages. Furthermore, the availability of per capita public charging stations was shown to significantly reduce range anxiety and encourage consumer purchasing behavior. [ABSTRACT FROM AUTHOR]

Due to the extensive use of fossil fuels, energy conservation and sustainable transportation have become hot topics. Electric vehicles (EVs), renowned for their clean and eco-friendly attributes, have garnered considerable global attention and are progressively being embraced worldwide. However, disorganized EV charging not only reduces charging station efficiency but also threatens power grid stability. In this low-carbon era, photovoltaic storage charging stations offer a solution that accommodates future EV growth. However, due to the significant instability in both the charging load and photovoltaic power generation within charging stations, it is critical to maximize local photovoltaic power consumption and minimize the impact of disorganized EV charging on the power grid. This paper formulates an intelligent scheduling strategy for electric heavy trucks within charging stations based on typical photovoltaic output data. The study focuses on a photovoltaic storage charging station in an industrial zone in Xinjiang. While considering the electricity procurement cost of the charging station, the aim is to minimize fluctuations in the electricity procurement load. A simulation analysis was conducted using MATLAB 2021a software, and the results indicated that, compared to an uncoordinated charging strategy for electric heavy trucks, the proposed strategy reduced electricity procurement costs by CNY 1348.25, decreased load fluctuations by 169.45, and improved the utilization efficiency of photovoltaic energy by 30%. A statistical analysis was also used to support the reduction in electricity procurement costs and load variations. Finally, a sensitivity analysis of the weight factors in the objective function was performed, proving that the proposed strategy effectively reduces electricity procurement costs and improves the utilization efficiency of photovoltaic energy. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze how global research cooperation patterns in the secondary battery industry have changed over recent years and to identify the evolution in the focus of research. To this end, network analysis was performed using the nationality information of the authors of a 10-year multinational joint research paper related to lithium-ion batteries. Furthermore, keyword analysis and topic modeling were performed using the abstract data from the study. The results of this study confirm that some countries that are not well-known in the field, such as Australia, Spain, and France, showed high centrality, compared with the level of cooperation scale. Additionally, six research topics were identified. According to a comparison over the first half of the decade, no difference was observed in the appearance of keywords indicating high energy density and conductivity with lithium, a key mineral. Keyword distribution was high for topics like battery charging and discharging in the first half of the decade, and for next-generation battery materials, such as solid electrolytes, lithium metal anodes, and lithium–sulfur batteries in the second. These results provide insights into the establishment of research and development (R&D) cooperation strategies by countries and pre-planning by companies in the battery industry. [ABSTRACT FROM AUTHOR]

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours. [ABSTRACT FROM AUTHOR]

Load models have a significant influence on power system simulation. However, current load modeling approaches can hardly satisfy the diversity and time- varying characteristics of loads [including electric vehicles (EVs) and battery storage] in terms of model accuracy and computing efficiency. An online modeling method for composite load models based on measurement information is proposed in this paper. Firstly, the dominant factors in load model output are analyzed based on the active subspace of parameter space. Then the clustering algorithm is applied to cluster the large number of underlying loads based on the characteristics of load daily output curves. Finally, the underlying loads are equivalently aggregated from the low voltage levels to the high voltage levels to construct the composite load model. Simulation results obtained based on PSCAD/EMTDC demonstrate that the load model constructed by the proposed approach can accurately reflect the actual load characteristics of a power system. [ABSTRACT FROM AUTHOR]

The accuracy of voltage transformer (VT) measurements is imperative for the security and reliability of power systems and the equitability of energy transactions. The integration of a substantial number of electric vehicles (EVs) and their charging infrastructures into the grid poses new challenges for VT measurement fidelity, including voltage instabilities and harmonic disruptions. This paper introduces an innovative transformer measurement error prediction model that synthesizes Multivariate Variational Mode Decomposition (MVMD) with a deep learning framework integrating Bidirectional Temporal Convolutional Network and Multi-Head Attention mechanism (BiTCN-MHA). The paper is aimed at enhancing VT measurement accuracy under fluctuating load conditions. Initially, the optimization of parameter selection within the MVMD algorithm enhances the accuracy and interpretability of bi-channel signal decomposition. Subsequently, the model applies the Spearman rank correlation coefficient to extract dominant modal components from both the decomposed load and original ratio error sequences to form the basis for input signal channels in the BiTCN-MHA model. By superimposing predictive components, an effective prediction of future VT measurement error trends can be achieved. This comprehensive approach, accounting for input load correlations and temporal dynamics, facilitates robust predictions of future VT measurement error trends. Computational example analysis of empirical operational VT data shows that, compared to before decomposition, the proposed method reduces the Root-Mean-Square Error (RMSE) by 17.9% and the Mean Absolute Error (MAE) by 23.2%, confirming the method's robustness and superiority in accurately forecasting VT measurement error trends. [ABSTRACT FROM AUTHOR]

This paper presents a novel class of interior solutions for anisotropic stars under the imposition of a self-similar symmetry. This means proposing exact solutions to the Einstein field equations to describe charged matter distribution with radiation flow. The Einstein–Maxwell system by employing specific choices of mass function is formulated to describe the gravitational collapse of charged, anisotropic, spherically symmetric distributions using the Schwarzschild metric. Two ordinary differential equations governing the dynamics are derived by matching a straightforward solution of the symmetry equations to the charged exterior (Reissner–Nordström–Vaidya). Models with satisfactory physical behavior are constructed by extensively exploring self-similar solutions for a set of parameters and initial conditions. Finally, the paper presents the evolution of physical variables and the collapsing radius, demonstrating the inevitable collapse of the matter distribution. [ABSTRACT FROM AUTHOR]

To improve the ergonomic comfort of electric vehicle charging ports, this paper analyzed the height, angle, position and other layout factors of the charging ports from the perspectives of ergonomics considering human body size, comfortable range of human activities, charging pile and charging gun parameters, and proposed a charging port layout scheme, which was verified and evaluated. In addition, the paper offered design proposals for charging operational visibility and operation space, which served as reference for charging ports of electric vehicle. [ABSTRACT FROM AUTHOR]

This paper presents a charging architecture for the Reconfigurable Cascaded Multilevel converter, which was specifically designed for electric vehicle (EV) powertrain applications. The RCMC topology is capable of executing power conversion and actively managing battery systems concurrently. The active battery management is achieved using the Reconfigurable Battery Module, which regulates the serial connection of cells via a switch pattern. In this paper, the RCMC is directly interfaced with an AC three-phase power system, facilitating the dynamic control over battery cells charging. Its inherent design allows for the implementation of various charging algorithms, customizable to specific requirements, without necessitating additional intermediary power stages. Firstly, an overview of the RCMC topology is given, and an analysis to define the optimal filter inductance is carried out. Subsequently, after the AC system characteristics are explained, two charging algorithms are presented and described: one prioritizes State of Charge (SOC) balancing among battery cells, while the other focuses on minimizing power losses. Moreover, a time estimation computation for the RCMC is carried out considering a two-level AC charging station. The result is compared with the time required for a conventional battery pack. The results show a reduction of 10 s in charging time for a mere 20% increase in SOC. Finally, the experimental setup is presented and used to validate the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper examines the extent to which it is possible to replace conventional light commercial vehicles in the heating, ventilation and air conditioning and plumbing trade with battery electric vehicles with an unchanged usage profile. GPS trackers are used to record the position data of 22 craft vehicles with combustion engines from eleven companies over the duration of one working week. Within this paper, various assumptions (battery capacity and average consumption) are made for battery electric vehicles and the charging power on the company premises. The potential of battery electric vehicles is evaluated based on the assumption that they are charged only on company premises. Using the collected data and the assumptions made, theoretical state of charge curves are calculated for the vehicles. The driving profiles of the individual vehicles differ greatly, and the suitability of battery electric vehicles should be considered individually. Battery capacity, vehicle energy consumption and charging power at the company have a substantial influence on the suitability of battery electric vehicles. Furthermore, there are differences between vehicles that can charge on the company premises at night and those that cannot or can only do so on some days. [ABSTRACT FROM AUTHOR]

Fueled by pressing global climate concerns, the integration of large-scale renewable distributed generation sources, including distributed wind power and photovoltaics, along with electricity substitution loads into the distribution network has been accelerated to diminish carbon emissions. This shift introduces significant challenges and necessitates the advanced operation and control of distribution systems to accommodate these changes effectively. Against this backdrop, there is a growing expectation for an open and scalable central control mode, equipped with compatible interfaces, to offer a visionary development platform for the grid. This platform is anticipated to meet the evolving needs of future distribution system development, ensuring adaptability and forward compatibility. The aforementioned platform requires open, scalable, and interface-compatible models of key distribution network equipment as its foundation. To address the challenges presented, this paper proposes a data–physics-driven modeling approach for automating simulations in distribution systems. This method employs a simplified and standardized system of linear differential equations with undetermined coefficients to capture the common physical characteristics of specific device types. The models designed through this approach are notably open, allowing for real-time data to refine undetermined coefficients and accurately depict the dynamic behavior of equipment over various periods. Their scalability also stands out, rendering them apt for large-scale distribution network simulations. The paper elaborates on models for distributed photovoltaic, wind turbine, energy storage, and electric vehicle, and demonstrates their application within an IEEE-33 node distribution network topology built on Python. [ABSTRACT FROM AUTHOR]

The power grid and transportation network are coupled by the charging behavior of electric vehicles. Based on the coupled power-transportation network model, this paper first analyzes the effect of the distribution system operator's (DSO) electricity selling price on guiding the charging behavior of electric vehicles in the transportation network and then builds the DSO's optimal pricing formulation. Considering the competition between multiple charging network operators (CNOs), this paper establishes a game model between CNOs and solves it iteratively through the best response dynamic method. An approximation method using the elasticity matrix is proposed to speed up the solution by reducing the multi-layer optimization to a single layer one in each iteration, with its effectiveness validated through numerical tests. Furthermore, the paper discusses the issue of the prisoner's dilemma that arises among CNOs and explores the potential impact of their cooperative strategies on the overall system. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to reduce the impact of disordered charging of large-scale electric vehicles (EVs) on the grid. EV is great significance for environmental protection, energy conservation and emission reduction to replace fuel vehicles with EVs. However, as a kind of random mobile load, large-scale integration into the power grid may lead to power quality problems such as line overload, line loss increase and voltage reduction. This paper realizes the orderly charging of electric vehicles and the safe operation of the distribution network by optimizing the dispatching scheme. Design/methodology/approach: This paper takes the typical IEEE-33 node distribution system as the research object, adopts the improved particle swarm optimization algorithm and takes the minimum operation cost, the minimum environmental pollution, the minimum standard deviation of daily load, the minimum peak valley difference of load, the minimum node voltage offset rate and the minimum system grid loss rate as the optimization objectives. Findings: Controlling the disordered charging of large-scale electric vehicles by optimizing the dispatching algorithm can realize the full consumption of renewable energy and the safe operation of the power grid. Originality/value: Results show that the proposed scheme can realize the transfer of charging load in time and space, so as to stabilize the load fluctuation of distribution grid, improve the operation quality of power grid, reduce the charging cost of users and achieve the expected research objectives. [ABSTRACT FROM AUTHOR]

Traditional current and voltage strategies proposed for the single-phase rectifier with pulse width modulation (PWM) used in the AC charging system for electric vehicles, have a large response time and the percentage of total harmonic distortion (THD) of the line current is more than 2%, which causes a reduction of the power factor (PF). The present paper focuses on the robust sliding mode current–voltage control (SMCVC) design for the single-phase rectifier with PWM. This SMCVC strategy used the grid current directly without any rotating coordinate transformations and reduced the complexity computational of the implementation algorithm and allows to satisfy the following desired performances, low THD of the input current and the PF is around one in a faster response time. In this paper, the proposed control strategy is compared and verified with proportional integral (PI) control and proportional integral sliding mode current control (PI-SMCC) by Matlab/Simulink. At the end of this paper, simulation examples are given to illustrate our approach and to show that our proposed method provides very good results with respect to some literature results. [ABSTRACT FROM AUTHOR]

In order to eliminate the potential safety hazard that arises when metal foreign objects intervene in the wireless charging area of electric vehicles, this paper proposes that a metal foreign object detection method be applied to the wireless charging system of electric vehicles based on the optimal design of the array detection coil. Firstly, the equivalent circuit model of the metal foreign object detection system is established, then the principle of the foreign object detection system is analyzed, and the scale factor β is introduced as the optimization index of the detection coil. Secondly, the change of the scale factor β with the circuit parameters is analyzed and the appropriate circuit parameters are compared and selected. Thirdly, on the basis of the planar square spiral coil, Ansys Maxwell finite element simulation software is used to optimize its structural parameters, combination mode, and resonant circuit, as well as design the anti-series and anti-parallel enhanced detection coil sets with the decoupling and elimination of detection blind spots. Finally, the feasibility of the proposed detection method of metal foreign objects is verified by experiments. The results show that the two array detection coil sets can detect small-sized common metal foreign objects such as paper clips and the proposed double-layer reinforced structure can significantly improve the detection sensitivity of the system. [ABSTRACT FROM AUTHOR]

The long queuing time for electric vehicles to charge under intelligent network connection leads to low distribution efficiency. Therefore, this paper proposes a strategy to predict the probability of queues forming for electric vehicles arriving at charging stations under intelligent network connection. Both the dynamic demand of customers and the characteristics of the alternating influence of charging vehicles should be considered when studying such problems. Based on the above problem characteristics, a real-time dynamic charging selection strategy is developed by predicting the probability of other vehicles in the region going to the charging station. A distribution path optimization model based on intelligent network connection and queuing theory is proposed for electric logistics vehicles in charging mode, taking into account the time window constraint and the influence of charging vehicles when using intelligent network connection for path planning. The objective is to minimize the total cost, and the route for electric logistics vehicles is adjusted in real time. This is solved by an improved hybrid genetic-annealing algorithm. The experimental results show that this paper obtains real-time dynamic road information and charging information with the help of intelligent network connection. It predicts the queuing probability of electric vehicles by combining with queuing theory, which can help select a more suitable charging location and timing for electric logistics vehicles. This can effectively avoid peak periods and reduce waiting times. By comparing with other models, this paper's model can save the distribution cost of electric vehicles. [ABSTRACT FROM AUTHOR]

The effects of Electromagnetic Interference (EMI) in Inductive Power Transfer (IPT) for electric vehicle charging circuits are elaborated with Common Mode (CM) emission. Electromagnetic Compatibility (EMC) has to be sustained in the circuits such that it is prone to the effects of interferences, noises and disturbances. These issues can be addressed at the initial product development stage, with accurate identification of the parameters that influence the CM emission in the circuit. The analysis of EMI focuses on three features, namely Source of EMI, Coupling path and Receptor. In the IPT model, the primary source of the EMI is the power converter used for high-frequency supply to the inductive coils. The power converter switches and the heat sink with its Printed Circuit Board (PCB) design are predominant in power electronic circuits. This paper facilitates the reduction of EMI without including complex filters. The system is tested for the violated standard SAEJ2954. In addition, the article corroborates precognitive modeling by implementing compensation networks used in IPT applications and their effectiveness in reducing the leakage current caused by the converters and the coils. The paper insights the air gap variation of the coil with the power transfer efficiency and its influence on electromagnetic interferences. The Fast Fourier Transform (FFT) analysis used in PSIM shows leakage current and harmonic reduction with the compensation network and Pulse Width Modulation (PWM) technique. The fabrication model for the front end power supply to the IPT is tested. The electrical noise emission is estimated using predictive modeling. [ABSTRACT FROM AUTHOR]