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[Display omitted] • Graphene/silver nanoparticles coating exhibited timely transfer of electrons and heat due to the 3D electric conductive network. • The aluminum film played the dual functions of electromagnetic interference shielding and heat transfer. • The material showed excellent electrical conductivity and electromagnetic interference shielding effectiveness of 4431 S/m and 92.29 dB, respectively. • The material exhibited outstanding mechanical properties with tensile strength of 32 MPa and elongation at break of 6.65%. Rubber and plastic are widely acted as substrates in flexible electromagnetic shielding (EMS) materials. However, these materials have several drawbacks in practical applications, such as potential environmental concerns and difficulties in degrading. In this work, a biodegradability paper-based material composed of electromagnetic wave loss layer (graphene/ silver nanoparticles (AgNPs) coating) and reflective layer (aluminum film layer) was successfully fabricated. The electromagnetic wave loss layer constructed three-dimensional (3D) electric conductive network to facilitate the timely transfer of electrons and heat energy obtained from electromagnetic waves. Meanwhile, the reflective layer received electrons and heat from the electric conductive network and make a small quantity of transmission wave back to the wave loss layer. The resulting material exhibited an ultrahigh electromagnetic interference shielding effectiveness (EMI SE) of 92.29 dB within 8–13 GHz, electrical conductivity of 4431 S/m, mechanical properties with a tensile strength of 32 MPa and elongation at break of 6.65%. Compared to the traditional EMS materials, the composite material integrated with excellent EMI SE, heat transfer performance, and weatherability, which has potential applications in microelectronics, high integrated circuits, and flexible electronic fields. [ABSTRACT FROM AUTHOR]

Conference organizers often face the following problem: Given a collection of submitted papers, select a subset to be presented at the conference. The bulk of the work often amounts to assembling a pool of reviewers and then sending each submitted paper to several reviewers. We present in this paper a technique for finding a good assignment of papers to reviewers. An important feature of the solution we find is that each paper is sent to at least one reviewer who is "as expert as possible" for that paper. A major component of the problem is modeled as a bottleneck version of a capacitated transshipment problem. [ABSTRACT FROM AUTHOR]

Electric pylons are crucial components of power infrastructure, requiring accurate detection and identification for effective monitoring of transmission lines. This paper proposes an innovative model, the EP-YOLOv8 network, which incorporates new modules: the DSLSK-SPPF and EMS-Head. The DSLSK-SPPF module is designed to capture the surrounding features of electric pylons more effectively, enhancing the model's adaptability to the complex shapes of these structures. The EMS-Head module enhances the model's ability to capture fine details of electric pylons while maintaining a lightweight design. The EP-YOLOv8 network optimizes traditional YOLOv8n parameters, demonstrating a significant improvement in electric pylon detection accuracy with an average mAP@0.5 value of 95.5%. The effective detection of electric pylons by the EP-YOLOv8 demonstrates its ability to overcome the inefficiencies inherent in existing optical satellite image-based models, particularly those related to the unique characteristics of electric pylons. This improvement will significantly aid in monitoring the operational status and layout of power infrastructure, providing crucial insights for infrastructure management and maintenance. [ABSTRACT FROM AUTHOR]

A run‐of‐the‐river power plant is a renewable energy source used for electricity production. Its power output depends on the varying water flow rate over time, which can impact the reliability of the electric network. Previous research has not studied the effects of water flow rate and temperature variations on the hazard rate of the plant's components. This paper addresses this gap by investigating the impact of these variations on the reliability of electric networks with run‐of‐the‐river power plants. The analysis considers the hazard rates of the plant's components, incorporating the relationship between hazard rate and temperature based on the Arrhenius law. Parameters such as power output, current, power loss, operating temperature, and hazard rate are calculated for different water flow rates and ambient temperatures. Numerical simulations on a test system are conducted to examine the influence of water flow rate and temperature on the reliability indices of the electric network. The results demonstrate that water flow rate and temperature significantly affect the hazard rates of run‐of‐the‐river power plants. This highlights the need to consider these factors in the reliability analysis of electric networks incorporating these renewable resources. [ABSTRACT FROM AUTHOR]

Traditionally, electric power systems are subject to uncertainties related to equipment availability, topological changes, faults, disturbances, behaviour of load, etc. In particular, the dissemination of distributed generation (DG), especially those based on renewable sources, has introduced new challenges to power systems, adding further randomness to the management of this segment. In this context, stochastic analysis could support planners and operators in a more appropriate manner than traditional deterministic analysis, since the former is able to properly model the power system uncertainties. The objective of this work is to present recent achievements of one of the most important techniques for stochastic analysis, the Monte Carlo Method (MCM), to study the technical and operational aspects of electric networks with DG. Besides covering the DG topic itself, this paper also addresses emerging themes related to smart grids and new technologies, such as electric vehicles, storage, demand response, and electrothermal hybrid systems. This review encompasses more than 90 recent articles, arranged according to the MCM application and the type of analysis of power systems. The majority of the papers reviewed apply the MCM within stochastic optimization, indicating a possible trend. [ABSTRACT FROM AUTHOR]

Driven by energy shortages and climate concerns, the electric vehicles are popular around the world with their energy-saving and environmentally friendly advantages. As electric vehicle batteries (EVBs) mainly use lithium batteries, and the batteries' performance decreases with the increase of charging times, a large number of batteries are entering the end-of-life (EoL) stage. Recycling and reuse of EVBs are effective ways to reduce environmental pollution and promote resources utilization and is now a top priority. Building a recycling network is the foundation of battery recycling. However, there are few studies on battery recycling networks and the construction of recycling networks is expensive, which impedes the sustainable development of electric vehicles. Based on this, recycling network design is critical for EVB recycling. This paper first analyzes three strategies to deal with used batteries: remanufacturing, reuse, and recycling materials. Secondly, an EVB recycling network model is developed with the objective of minimizing the total cost and carbon emissions. The model solves the problem of siting the centers in the network and the vehicle routing in the recycling process. Finally, the model was applied to GEM (a Chinese company dedicated to circular economy) and validated using a greedy algorithm. In addition, the results show that logistics costs and operating costs account for the majority of the recycling network total expense, at 48.45% and 31%, respectively. Therefore, if companies want to further reduce the cost of EVB recycling, they should reduce logistics costs and operating costs. In summary, this paper provides a decision-making approach for EVB recycling enterprises to carry out recycling and reuse, and offers advice on how to promote the sustainable economic and environmental development of the electric vehicle battery industry. [ABSTRACT FROM AUTHOR]

This paper proposes a reliability evaluation method of a distribution network with electric vehicles (EVs) and distributed generations (DGs) based on network equivalence and sequential Monte Carlo. Unlike traditional studies, the proposed method not only can effectively simplify the complexity structure of the distribution network with EVs and DGs, but also can establish a multidimensional reliability evaluation system for the distribution network with EVs and DGs. Firstly, the related models of DGs and EVs are established respectively in our study. Based on the minimum path method, the impact of each component fault on each load point is analyzed, and the failure impact analysis (FIA) table is constructed. Then, a simplified method for the structure of the distribution network with EVs and DGs based on network equivalence is proposed and validated. Moreover, the reliability evaluation method of the distribution network with EVs and DGs is given based on the sequential Monte Carlo. Finally, an illustrative example of the improved IEEE RBTS BUS6 F4 feeder system, as well as multiple dimensions discussions, are elaborated in detail to demonstrate the effectiveness and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

A set S ⊆ V is referred to as a k-fault-tolerant power-dominating set of a given graph G = (V , E) if the difference S ∖ F remains a power-dominating set of G for any F ⊆ S with | F | ≤ k , where k is an integer with 0 ≤ k < | V | . The lowest cardinality of a k-fault-tolerant power-dominating set is the k-fault-tolerant power-domination number of G, denoted by γ P k (G) . Generalized Petersen graphs G P (m , k) and generalized cylinders S G are two well-known graph classes. In this paper, we calculate the k-fault-tolerant power-domination number of the generalized Petersen graphs G P (m , 1) and G P (m , 2) . Also, we obtain γ P k (G) for the subclasses of cylinders S C m and S B m . [ABSTRACT FROM AUTHOR]

In this paper, a new model for charging infrastructure placement with latency optimization is presented. Nodal charging latency coefficients are calculated including the traffic flow over a candidate location and charging time of the charging technology to be installed. Faster charging and lower traffic flow reduce charging latency and vice versa. To exceed optimization problem's complexity, set-cover modeling is applied to model the road network and the electric vehicle driving trajectories comprising the drivers' behavior. As a result, optimal number and layout of charging locations are found for the minimal charging infrastructure latency, subject to the charging reliability constraint. Numeric results illustrate integration of the charging latency as part of optimal charging infrastructure placement modeling. An optimal locations layout is shown for applying the optimization model to a 10 × 10 discrete grid with driving trajectories. A comparison is made with a reference model that is user-centric and quality of service based to oversee advantages of the newly infrastructure-centric planning model by minimizing charging latency. Differences are found in optimal layouts with the change of optimal locations. [ABSTRACT FROM AUTHOR]

Knowledge on the utilities hidden in the wall, e.g., electric lines or water pipes, is indispensable for work safety and valuable for planning. Since most of the existing building stock originates from the pre-digital era, no models as understood for Building Information Modeling (BIM) exist. To generate these models often labor-intensive procedures are necessary; however, recent research has dealt with the efficient generation and verification of a building's electric network. In this context, a reliable measurement method is a necessity. In this paper we test different measurement techniques, such as point-wise measurements with hand-held devices or area-based techniques utilizing thermal imaging. For this purpose, we designed and built a simulation environment that allows various parameters to be manipulated under controlled conditions. In this scenario the low-cost handheld devices show promising results, with a precision between 92% and 100% and a recall between 89% and 100%. The expensive thermal imaging camera is also able to detect electric lines and pipes if there is enough power on the line or if the temperature of the water in the pipe and the environment's temperature are sufficiently different. Nevertheless, while point-wise measurements can directly yield results, the thermal camera requires post-processing in specific analysis software. The results reinforce the idea of using reasoning methods in both the do-it-yourself and commercial sector, to rapidly gather information about hidden installations in a building without prior technical knowledge. This paves the way for, e.g., exploring the possibilities of an implementation and presentation in augmented reality (AR). [ABSTRACT FROM AUTHOR]

The scaled access of electric vehicles (EVs) exacerbates load fluctuations in distribution networks, which is not conducive to the stable and economic operation of the distribution networks. At present, user satisfaction degree is generally low. To avoid this problem, this paper proposed a two-layer cooperative optimization of flexible interconnected distribution networks considering EV user satisfaction degree. First, the EV user satisfaction degree model is established by considering EV users' charging waiting time, charging power, and other indicators. At the same time, an EV charging mode switching model is constructed based on the number of EVs entering the network and their battery charge state. On this basis, the Monte Carlo algorithm is used to generate the results of the daily distribution of EV loads taking into account the user satisfaction degree, so as to improve the load ratio of the transformer in the distribution network. Further, a two-layer cooperative optimization of flexible interconnected distribution networks considering electric vehicle user satisfaction degree is developed with the daily operating cost of each network as the optimization objective. Finally, a flexible interconnected power distribution network consisting of three power distribution networks is used as an example for validation. The results show that this method is effective in improving EV user satisfaction degree and reducing the peak-to-valley ratio of the system load while taking into account the safe and economic operation of the distribution network, which greatly improves the reliability and economy of the operation of the flexible interconnected power distribution network. [ABSTRACT FROM AUTHOR]

In this paper, a non-linear programming method allowing for the optimization of the structure of high-current circuits that supply resistance-arc furnaces was presented. In the case of resistance-arc furnaces, two types of asymmetries most often occur: structural and operational ones. The structural asymmetry is related to the construction of a bifilar high-current busduct, which leads to the so-called short network. Knowing the parameters of the high-current busduct allows one to determine the operating characteristics of the arc furnace. It is also necessary to know the energy consumed in individual steps of the arc furnace operation. The method proposed in this paper makes it possible to establish guidelines for the modernization of a short network in order to eliminate asymmetry. The presented method was verified on a real object by conducting experimental tests on a furnace with a power of 12 MVA. Experimental tests were first carried out for a furnace with asymmetry, and then, by conducting simulation tests, guidelines for changing the design of the short network were determined. The measurements carried out after the modernization of the short network confirmed that the furnace was in a symmetrical operating condition and confirmed the correctness of the calculation method proposed in this paper. [ABSTRACT FROM AUTHOR]

The evaluation and estimation of the electric and magnetic field (EMF) intensity in the vicinity of overhead transmission lines (OHTL) is of paramount importance for residents' healthcare and industrial monitoring purposes. Using artificial intelligence (AI) techniques makes researchers able to estimate EMF with extremely high accuracy in a significantly short time. In this paper, two models based on the Artificial Neural Network (ANN) have been developed for estimating electric and magnetic fields, i.e., feed-forward neural network (FFNN) and cascade-forward neural network (CFNN). By performing the sensitivity analysis on controlling/hyper-parameters of these two ANN models, the best setup resulting in the highest possible accuracy considering their response time has been chosen. Overall, the CFNN achieved a significant 56% reduction in Root Mean Squared Error (RMSE) for the electric field and a 5% reduction for the magnetic field, compared to the FFNN. This indicates that the CFNN model provided more accurate predictions, particularly for the electric field than the proposed methods in other recent works, making it a promising choice for this application. When the model is trained, it will be tested by a different dataset. Then, the accuracy and response time of the model for new data points of that layout will be evaluated through this process. The model can predict the fields with an accuracy near 99.999% of the actual values in times under 10 ms. Also, the results of sensitivity analysis indicated that the CFNN models with triple and double hidden layers are the best options for the electric and magnetic field estimation, respectively. [ABSTRACT FROM AUTHOR]

Renewable energy sources have a multifaceted impact on power grids, ranging from the reliability and quality of electricity to the selective impact on equipment. While renewables used to be distributed in distribution networks, now their capacity is commensurate with thermal power plants and their impact on the grid should not be underestimated. According to the statistics on the interruption of the bulk electric networks, one of the main reasons for emergency shutdowns of extra high-voltage power lines are single-phase short circuits. The problem of mathematical modeling of the limit modes in terms of static stability is very relevant to the design and operation of electric power systems (EPS). Calculations of limit modes have both an independent value and a component of other electrical engineering tasks related to ensuring the required level of reliability and cost-effectiveness of the operation of the united PS. Despite the great degree of development of issues of planning and control of electric modes, system accidents associated with unacceptable loads of network elements occur in the Ukrainian energy industry. Non-phase modes regularly occur in electric power systems, which can lead to an unacceptable load of intersystem network elements, which imposes significant restrictions on their throughput. Single-phase short circuits are more than 95% of other damage that occurs in the line. The use of single-phase auto reclose on the transmission lines allows disconnecting only the damaged phase for a short period of time and not the entire transmission line. This action preserves the transit of electricity along the line and prevents the violation of the stability of parallel operation. To achieve this, the current-free pause of the single-phase auto reclose should last as short as possible. On the other hand, an important task to be solved when using single-phase auto reclose is to choose the minimum duration of the current-free pause necessary for its success. The problem studied in this paper deals with the safety and correct operation of transmission lines (TS) of the Ukrainian bulk power system in special conditions (not predictable, changing due to frequent attacks). For a quickly changing configuration, the power grid uses switches, and in the case of ultra-high voltage, the TS needs to solve the problem of secondary arc currents and recovering stresses in the place of arc burning after its extinction. One of the methods of reducing secondary arc currents and recovering stresses in the place of arc burning after its extinction is the implementation of single-phase automatic reclosing (SPAR). The main theoretical result of the paper is a proposed mathematical model of a compensated power transmission line based on the use of matrix n-poles, which makes it possible to model in detail stationary power transmission modes, including the SPAR mode. The proposed mathematical model of three-phase power transmission has been created using phase coordinates and can be used for the analysis of complex asymmetric modes. The main practical result of the paper is physically interpreted simplified models of three-phase TS, which can be used for the study of resonant overvoltages and currents of the feeding arc in non-full-phase circuit SPAR. The conclusion that can be drawn from the obtained results points out which line lengths must take into account the influence of longitudinal asymmetry when choosing the inductive resistance, i.e., take into account the dependence of the mode parameters on the location of the damaged phase. The observed results show that the largest values of the multiplicity of overvoltages will take place in phase B. The novelty of the work is the developed technique that makes it possible to determine in advance, depending on the disconnected phase of the line, the values of the primary conductivities of the STC (static thyristor compensators) and the corresponding angles of control of the thyristor switches, which satisfy almost complete compensation of the secondary arc at any point of the line in the specific condition (hard) of the bulk power system operation. [ABSTRACT FROM AUTHOR]

GRAPH JIN XU received the B.S. degree in electrical engineering from Sichuan University, Chengdu, China, in 2013, and the Ph.D. degree in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2019. Modern power and energy systems are integrating more and more renewable energy sources, most of which are interfaced through power electronic converters. The proposed decoupling method is compared with other established methods using the simulation examples of IEEE 34 and IEEE 123 node test feeders, and is found to be more accurate and efficient as well as more suitable for the parallel computation of distribution network simulations. This Special Issue is aimed at identifying the latest advances in computer modelling and simulation methods for the transient simulation that accommodate the multi-scale and stochastic nature of modern power systems. [Extracted from the article]

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities. [ABSTRACT FROM AUTHOR]

As electric vehicles (EVs) continue to advance, there is a growing emphasis on sustainability, particularly in the area of effectively managing the lifecycle of EV batteries. In this study, an efficient and novel optimization model was proposed for designing a circular supply chain network for EV batteries. In doing so, a comprehensive, bi-objective, mixed-integer linear programming model was employed. It is worth noting that the current model outlined in this paper involved both forward and reverse flows, illustrating the process of converting used batteries into their constituent materials or repurposing them for various applications. In line with the circular economy concept, the current model also minimized the total costs and carbon emission to develop an inclusive optimization framework. The LP-metric method was applied to solve the presented bi-objective optimization model. We simulated six problems with different sizes using data and experts' knowledge of a lithium-ion battery manufacturing industry in Canada, and evaluated the performance of the proposed model by simulated data. The results of the sensitivity analysis process of the objective functions coefficients showed that there was a balance between the two objective functions, and the costs should be increased to achieve lower emissions. In addition, the demand sensitivity analysis revealed that the increase in demand directly affects the increase in costs and emissions. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization approach for day-ahead reactive power planning to improve voltage security in transmission networks. The problem is formulated as a voltage-secure multi-period optimal reactive power dispatch (MP-ORPD) problem. The optimization approach searches for optimal set-points of dynamic and static reactive power (var) resources. Specifically, the output includes set-points for switching shunts, transformer taps, and voltage magnitudes at the regulated buses. The primary goal is to maximize the dynamic reactive power reserve of the system, by minimizing the reactive power supplied by synchronous generators. As the size of the MP-ORPD problem increases significantly with increasing number of contingencies and time periods, efficiency is crucial for practical applications. In this paper, a decomposition technique based on consensus and alternating optimization, where integer variable targets are obtained via MILP, is used to partition the MP-ORPD problem into a set of subproblems, which can be solved in parallel to reduce the computation time. The proposed MP-ORPD problem and its solution algorithm are integrated into the EPRI-VCA software. The results of various power networks of large electric utilities in the Eastern interconnection demonstrate the effectiveness of the proposed algorithm in providing preventive control schedules. [ABSTRACT FROM AUTHOR]

Mean hitting time for random walks on a network, defined as the average of hitting times over all possible pairs of nodes, has found a large variety of applications in many areas. In this paper, we first prove that among all $N$ -node networks, the complete graph has the minimum mean hitting time $N-1$ , which scales linearly with network size. We then study a random walk mobility model with location heterogeneity, modeled by scale-free networks, which are ubiquitous in realistic systems such as P2P networks. For this purpose, we consider random walks on two sparse scale-free small-world networks. By using the connection between random walks and electrical networks, we derive explicit formulas about mean hitting time for both networks, the dominant scaling of which exhibits the same behavior as that of complete graphs. This paper demonstrates that heterogeneous sparse networks can have low-mean hitting time with a behavior similar to that of dense complete graphs, which is instrumental in designing networks, where search is fast between any pair of nodes. [ABSTRACT FROM AUTHOR]

In recent years, electric vehicles have been widely promoted by virtue of environmental protection and pollution-free advantages. In the future, they are expected to become a mainstream means of transportation. However, as new services and business models become more and more closely related to the distribution network, the traditional AC distribution district has limited ability to respond to the large-scale access of electric vehicles. Therefore, it is becoming increasingly important to improve the ability to respond to electric vehicle access, reduce the negative impact of large-scale electric vehicle access on the grid, and predict and calculate the uncertainty caused by electric vehicle load access. Therefore, this paper proposes the concept of flexible distribution district, constructs the security operational boundary model for flexible distribution district, and uses "security margin percentage" as the measurement mechanism to provide dispatchers with an intuitive system operating status. In addition, this paper also proposes an effective uncertainty quantification method for the problem that it is difficult to measure the changes of bus voltage and line current at network nodes when large-scale electric vehicles are connected to the network. Numerical results demonstrate both the extensibility and the practicality of the proposed security operational boundary. In addition, the proposed uncertainty quantification method can reflect the impact of load changes on the characteristics of the power grid, which is helpful to study the improvement significance of adjusting electric vehicle load changes to distribution network optimization. [ABSTRACT FROM AUTHOR]

In recent years, the increased distributed generation (DG) capacity in electric distribution systems has been observed. Therefore, it is necessary to research existing structures of distribution networks as well as to develop new (future) system structures. There are many works on the reliability of distribution systems with installed DG sources. This paper deals with a reliability analysis for both present and future medium voltage (MV) electric distribution system structures. The impact of DG technology used and energy source location on the power supply reliability has been analyzed. The reliability models of electrical power devices, conventional and renewable energy sources as well as information and communications technology (ICT) components have been proposed. Main contribution of this paper are the results of performed calculations, which have been analyzed for specific system structures (two typical present network structures and two future network structures), using detailed information on DG types, their locations and power capacities, as well as distribution system automation applied (automatic stand-by switching on—ASS and automatic power restoration—APR). The reliability of the smart grid consisting of the distribution network and the coupled communications network was simulated and assessed. The observations and conclusions based on calculation results have been made. More detailed modeling and consideration of system automation of distribution grids with DG units coupled with the communication systems allows the design and application of more reliable MV network structures. [ABSTRACT FROM AUTHOR]

In order to change the centralized operation framework of the active distribution network with electric heating loads (EHLs), a distributed optimization method is proposed for the coordinated operation of the active distribution network with EHLs. Firstly, considering the thermal delay effect and heat loss of the thermal system, a centralized optimization operation model for active distribution networks with EHLs is established. Then, based on the centralized optimization operation model, it is rephrased as a standard sharing problem, and a distributed optimization operation model for the EHL active distribution network is established based on the alternating direction multiplier method (ADMM) solution. In the process of solving ADMM, dynamic step correction was further considered. By updating the steps during the iteration process, the number of iterations was reduced, and the convergence and computational efficiency of ADMM were improved. Finally, the effectiveness of the distributed coordinated operation method proposed in this paper was simulated and verified by constructing an IEEE33 distribution system. The results showed that the proposed distributed coordinated operation method has strong robustness to the randomness of the number of distributed units and parameters, and EHLs participating in coordinated operation can expand the consumption space of wind power and photovoltaic power, and improve the economic efficiency of system operation. [ABSTRACT FROM AUTHOR]

The successful introduction of battery electric trucks heavily depends on public charging infrastructure. But even as the first trucks capable of long-haul transportation are being built, no coherent fast-charging networks are yet available. This paper presents a methodology for assessing fast charging networks for electric trucks in Germany from the literature. It aims to establish a quantitative understanding of the networks' performance and robustness to deviations from idealized system parameters and identify crucial charging sites from a transportation planning perspective. Additionally, the study explores the quantification of adaptation effects displayed by agents in response to charging site outages. To achieve these objectives, a comprehensive methodology incorporating infrastructure, vehicle and operational strategy modeling, simulation, and subsequent evaluation is presented. Factors such as charging station locations, C-rates, mandatory rest periods, and vehicle parameters are taken into account, along with the distribution of traffic according to publicly available data. The study aims to offer a comprehensive understanding of charging networks' performance and resilience. This will be applied in a case study on two proposed networks and newly created derivatives. The proposed network offers over 99% coverage for long-haul transport but leads to a time loss of approximately 7% under reference conditions. This study advances the understanding of the performance and resilience of proposed charging networks, providing a solid foundation for the design and implementation of robust and efficient charging infrastructure for electric trucks. [ABSTRACT FROM AUTHOR]

There are several methods are used to calculate the power quality indicators. These objectives are now served by the most recent soft computing theories, such as neural networks, fuzzy time series, fuzzy logic theory, and other techniques. The fuzzy logic theory is one of the methods used to calculate the power quality indicators for the distribution electric network in this paper. It resolves the issue of forecasting the voltage values on distribution electrical networks' buses and enhances the quality of electricity in the face of fluctuating consumer loads and uncertainty. This paper proposed a device that has the ability for monitoring the quality of electricity domestically which leads to getting rid of many problems such as voltage deviations and asymmetrical faults. The fuzzy logic theory is implemented to develop the algorithm for this purpose. The actual data is recorded at the distribution transformer and implement the fuzzy logic theory by using a mathematical model as well as the simulation on MATLAB for optimizing the power quality indicators with the help of fuzzy logic theory and results are discussed at the end of the paper. [ABSTRACT FROM AUTHOR]

The current paper defines a framework for the introduction of automated frequency restoration reserve services, enabled by vehicle-to-grid technology, into the business model of an entity owning and operating a network of semi-public Electric Vehicle Supply Equipment. It assesses the profitability of this introduction by performing a case study based on the real-life electric vehicle charging data from the EVSE network located in a hospital parking lot. From the results of the study, it is clearly visible that the introduction of vehicle-to-grid-enabled automated frequency restoration reserve services has a significant positive incremental profitability; however, this is heavily dependent on the plug-in ratio of the charging network, determined by electric vehicle users' behavior. [ABSTRACT FROM AUTHOR]

An electric railway system is a typical single-phase grid-connected converter system, and the low-frequency oscillation (LFO) phenomenon in electric railway systems has been widely reported around the world. Previous research has indicated that the LFO is a small-signal instability issue caused by impedance mismatching between the traction network system and electric trains. Therefore, this paper proposes an improved q-axis current control method to reshape the train's impedance. The proposed method can be implemented easily by relating a reverse q-axis reactive current directly to the reference of the q-axis current under the dq current decoupled control. Moreover, considering the additional q-axis reactive current control, a small-signal impedance model of a train–network system is built. The impedance-based analysis results indicate that the proposed q-axis reactive current feedback control can increase the magnitude of the train's impedance, which is beneficial to enhancing the system's stability. Finally, this paper employs experimental results to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the sensorless voltage control schemes for the promising wound-rotor brushless doubly-fed induction generator (BDFIG) in both the stand-alone and grid-connected systems. For sensorless target, a new rotor position observer using the space vector flux relations of BDFIG is suggested. The proposed sensorless algorithm does not require any additional observers for obtaining the generator speed by directly detecting the rotor position, which may reduce the cost of the overall control system. For the voltage control purpose, a direct voltage control (DVC) scheme is applied for the stand-alone systems. However, this control strategy did not handle the control of the generated-voltage phase angle and also did not consider the phase sequence issue which are very essential provisions for the smooth connection target in grid-connected applications. Hence, DVC strategy cannot guarantee all the intended provisions for a soft and smooth connection process with the grid side and therefore, an effective grid-synchronization algorithm is proposed. A comprehensive analysis to confirm the functionality of the proposed control strategies is discussed through both simulation and experimental results. The presented study in this paper verifies the efficacy of the proposed schemes for sensorless voltage control target of BDFIG in both the stand-alone and grid-connected applications with a good transient behavior. [ABSTRACT FROM AUTHOR]

The article announces the appointment of Russell Bent as the new Area Editor of the Network Optimization: Algorithms and Applications area of the INFORMS Journal on Computing. The article also provides a description of the Network Optimization Area, which focuses on networks in various fields such as social, natural, physical, and engineering sciences. It highlights the importance of combining computational methods with impactful demonstrations on application domains. Additionally, the article announces the winners of the INFORMS Journal on Computing Test of Time Paper Award for the years 1998-2002, which was awarded to the paper "Lifted Cover Inequalities for 0-1 Integer Programs: Computation" by Zonghao Gu, George L. Nemhauser, and Martin W. P. Savelsbergh. The paper is recognized for its contribution to the practical performance of mixed integer-programming solvers. [Extracted from the article]

In a low-voltage electric distribution network, arc fault presents a high energy density electricity-discharging phenomenon between conductors, which is often caused by aging of electric facilities, loose contacts and terminals, or insulation failure due to internal and external destructions. A large amount of heat may be created during this discharging, which will further cause the risk of fire hazards to mitigate in the residential environment. Currently, many utility grid operators and electricity users are still devoted to seeking effective detection technology for arc fault protection. This paper proposes a hybrid approach that combines discrete wavelet transform (DWT), empirical mode decomposition (EMD), and dynamic time warping (DTW) methods for low-voltage AC series arc fault detection. In DWT, it uses time–frequency domain characteristics of the arc current signal to extract the occurrence of arc fault. In EMD, it decomposes the complex arc fault current signal into a finite intrinsic mode (IMF) signal; then, instantaneous amplitude of IMF signal is obtained by Hilbert–Huang transform (HHT) as a feature for arc fault identification. Firstly, the results of arc fault detections depend on the results from DWT and EMD. When both two methods detect different results, DTW method will be activated, using the similarity measurements between normal and arc fault current waveforms as an assistant measure to determine the occurrence of arc fault. The performance of the proposed approach is tested and validated using various electric appliances, and the results show that the proposed approach can effectively detect low-voltage AC series arc fault. [ABSTRACT FROM AUTHOR]

The mathematical model of a fragment of a high-voltage electric network is developed in this paper. The network consists of a long power line with distributed parameters and an equivalent three-phase active-inductive load. Neumann and Robin—Poincare boundary conditions were used to identify the boundary conditions for the long line equation. The parameter output voltage (voltage at the end of the line) is introduced into the paper for further universal use of the developed line model. On the basis of the developed mathematical model, the program code is written in the algorithmic language Visual Fortran. By means of it, oscillograms of transient electromagnetic processes of voltages and currents in the form of spatial, temporal and temporal-spatial distributions during remote two-phase short circuits in the transmission line of high voltage are obtained. Two transient electromagnetic processes are analyzed in the present work. The first one was analyzed during the switching on of the line to the normal mode of operation with the subsequent transition to the emergency mode. The second one was analyzed during the switching on the line in the mode of a remote two-phase short circuit to the ground. The results of transient electromagnetic process simulation in the form of analyzed figures are shown. All the results presented in this paper were obtained exclusively using numerical methods. [ABSTRACT FROM AUTHOR]

Editorial. Discusses the changes in the publication of papers in the "IEEE Transactions on Microwave Theory and Techniques". Criteria for publication; Decision not to accept short papers.

In this part of the paper (Part II), available technical solutions to minimize or prevent wildfires caused by power networks are reviewed in more detail. These methods include fundamental changes to power system equipment, periodic checkups and maintenance, identification of faults via voltage/current waveforms, changes to protection and relaying settings and coordination, and installation of new power system apparatus. The strengths and drawbacks of each method are discussed. The discussion section brainstorms some future possible solutions that smart-grid technologies can enable. The paper concludes that there has neither been enough research in universities nor enough investment by industry to develop viable preventive solutions for wildfires that initiate from the daily operation of electrical power grids. This, in the authors’ opinion, is the crucial time for investment in research and development to tackle the lethal wildfire problem while electric power networks offer “the lowest hanging fruits to pick.” [ABSTRACT FROM AUTHOR]

Recently, electric vehicles (EVs) have received more and more attention, but the problem of the insufficient range is still the main reason that hinders electric vehicles to travel long distances. Under the premise of the battery capacity without technological innovation, the path planning method can ensure the safety and efficiency of electric vehicles in long-distance travel. This paper studies the actual freeway network to optimize the vehicle driving path and give the charging strategy based on the shortest travel time of a single vehicle. In this paper, a path and charging strategy planning model is proposed. In this model, the shortest travel time of a single vehicle is taken as the objective function, and the state of charging equipment in the actual road network and the safe electric quantity are considered as constraints. And the genetic algorithm is used to solve the model. Through case analysis, the rationality and optimization efficiency of the model proposed in this paper are verified. Finally, the sensitivity analysis of the three parameters of traffic volume, temperature, and travel speed is carried out with the Shanghai-Nanjing freeway network. The experimental results can get the nodes with the highest service pressure in the network, which can provide a theoretical basis for charging nodes' expansion in the freeway network in the future. [ABSTRACT FROM AUTHOR]

Intro to the ARPA-E Grid Optimization Competition In "Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition," we review the state of the art in practical algorithms for scheduling power-systems operations in the short term and the results of the recent competition organized by the U.S. Advanced Research Projects Agency–Energy. We explain the mixed-integer nonlinear formulation used in the competition for nonspecialists in electrical engineering, the context and organization of the competition, and the performance of competitors. We find that the collective approaches and results of competitors provide support for efforts to move nonlinear optimization techniques into industrial applications, as they have proven to be a robust and efficient alternative to current linear approximation techniques. The optimal power-flow problem is central to many tasks in the design and operation of electric power grids. This problem seeks the minimum-cost operating point for an electric power grid while satisfying both engineering requirements and physical laws describing how power travels through the electric network. By additionally considering the possibility of component failures and using an accurate alternating current (AC) power-flow model of the electric network, the security-constrained AC optimal power flow (SC-AC-OPF) problem is of paramount practical relevance. To assess recent progress in solution algorithms for SC-AC-OPF problems and spur new innovations, the U.S. Department of Energy's Advanced Research Projects Agency–Energy organized Challenge 1 of the Grid Optimization (GO) competition. This special issue includes papers authored by the top three teams in Challenge 1 of the GO Competition (Teams gollnlp, GO-SNIP, and GMI-GO). To introduce these papers and provide context about the competition, this paper describes the SC-AC-OPF problem formulation used in the competition, overviews historical developments and the state of the art in SC-AC-OPF algorithms, discusses the competition, and summarizes the algorithms used by these three teams. History: This paper has been accepted for the Operations Research Special Issue on Computational Advances in Short-Term Power System Operations. Funding: I. Aravena and C. G. Petra contributed to this work under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, with support of the Exascale Computing Project [17-SC-20-SC], a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. The contributions of D. K. Molzhan, F. E. Curtis, S. Tu, A. Wächter, E. Wei, and E. Wong were supported by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy [Grant DE-AR0001073]. X. Andy Sun acknowledges the continued support of ARPA-E [Award DE-AR0001089] and the National Science Foundation [Award 1751747]. The Pacific Northwest National Laboratory (PNNL) information, data, or work presented herein was funded in part by ARPA-E under Award 13/CJ000/09/03. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/opre.2022.0315. [ABSTRACT FROM AUTHOR]

The paper proposes a simple compact thermal model of a large LED module. This model takes into account self-heating in each of the LEDs contained in such a module and mutual thermal couplings between each pair of these LEDs. It also allows determining the waveforms of the junction temperature of each of the LEDs. In each moment of time, the temperature distributions in the LED module in the form of values of the junction temperature of all the diodes contained in this module are calculated using the proposed model. The developed thermal model has the form of an electric network and it contains a much smaller number of components than thermal models given in the literature. The form of the developed model and the method of determining the values of its parameters as well as the results of calculations and measurements confirming the practical usefulness of this model are presented. [ABSTRACT FROM AUTHOR]

Benford's law can be used as a method to detect non-natural changes in data sets with certain properties; in our case, the dataset was collected from electricity metering devices. In this paper, we present a theoretical background behind this law. We applied Benford's law first digit probability distribution test for electricity metering data sets acquired from smart electricity meters, i.e., the natural data of electricity consumption acquired during a specific time interval. We present the results of Benford's law distribution for an original measured dataset with no artificial intervention and a set of results for different kinds of affected datasets created by simulated artificial intervention. Comparing these two dataset types with each other and with the theoretical probability distribution provided us the proof that with this kind of data, Benford's law can be applied and that it can extract the dataset's artificial manipulation markers. As presented in the results part of the article, non-affected datasets mostly have a deviation from BL theoretical probability values below 10%, rarely between 10% and 20%. On the other side, simulated affected datasets show deviations mostly above 20%, often approximately 70%, but rarely lower than 20%, and this only in the case of affecting a small part of the original dataset (10%), which represents only a small magnitude of intervention. [ABSTRACT FROM AUTHOR]

With the accelerating transportation electrification, increasing numbers of electric vehicles (EVs) are connected to distribution networks via electric vehicle charging stations (EVCSs). Due to the uncertain charging behaviors of EV users, concentrated high-power EV charging during peak hours can result in significant undervoltage issues, affecting the regular operation of distribution networks and having detrimental effects on regular EV charging. Conventional EV charging scheduling methods are conducted in a centralized way, requiring extensive investment in communication infrastructure and intensive computing power. Accordingly, this paper proposes a novel decentralized voltage control scheme with the participation of EVs and EVCSs. The proposed control scheme first divides the distribution network into clusters based on a modified modularity index. Then, it applies the rolling optimization-based control of the EVs and EVCSs within each cluster to solve the regional voltage problems. The optimization window length is self-adapted based on the monitored states of the grid-connected EVs and EVCSs. Case studies show that the proposed decentralized voltage control strategy could effectively address the undervoltage violation issues and alleviate the pressure on peak shaving during peak hours, while satisfying the charging requirements of EV users. [ABSTRACT FROM AUTHOR]

The necessity for environmentally friendly transportation systems has prompted the proliferation of electric vehicles (EVs) in low-voltage (LV) distribution networks. However, large-scale integration and simultaneous charging of EVs can create power quality challenges for the distribution grid. It is therefore important to assess the impact of connecting EVs for charging in existing distribution networks and determine the hosting capacity (HC) of such a network. This paper uses a deterministic time-series method and stochastic method based on a simplified Monte Carlo simulation to estimate the HC of single-phase and three-phase EV charging, respectively, for a South African low-voltage distribution network containing 21 households. Voltage drop and equipment loading are the performance indices (PI) considered for the impact assessment and HC estimation in this study. The impact assessment result confirms that increasing EV charging penetration will result in a corresponding movement of the PIs toward the allowable limits. The results show that the HC is 5–8 three-phase EVs charging simultaneously for the worst-case scenario and 9–13 EVs for the best-case scenario. Furthermore, the single-phase HC for the popular 3.7 kW EV charger is 15 and 8 EVs for the best-case and worst-case scenarios, respectively. The result showing the seasonal variation in HC and for other EV charging power is also presented. The difference in HC for the worst-case and best-case scenarios portrays the effect that the location of charging has on the HC. [ABSTRACT FROM AUTHOR]

The fifth paper, entitled "Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement Learning" [[5]], puts forward a backscatter communication model based on business priority and cognitive network. Mobile ad hoc networks (MANETs) represent a featured domain of wireless networks, which are well-differentiated from cellular network infrastructure and have attracted decades of research effort. The fourth paper, entitled "EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid" [[4]], proposes a smart grid communication network architecture, based on cloud computing and fog computing to effectively manage, and dispatch the charging and discharging behavior of electric vehicles (EVs) in the smart grids (SGs). [Extracted from the article]

In this paper, static voltage stability assessments and their improvement using analytical and optimization techniques with different loading scenarios are described. In this study, the 400 kV, 230 kV, and 132 kV Ethiopian electric power networks are modeled using Power System Analysis Toolbox (PSAT), and the load flow analysis is carried out using MATLAB source code. Then, different voltage stability indices, such as the user-defined advanced voltage stability index (AVSI), modified voltage stability index (MVSI), and fast voltage stability index (FVSI), are used to identify the voltage unstable buses. Besides this, particle swarm optimization (PSO) is also used to find the optimal size and placement of the Static Synchronous Compensator (STATCOM) for the most severe buses identified by voltage stability indices. The result presents three special arguments: whether the most severe buses that are ranked at the top based on the voltage stability indices are optimal buses for compensation placement or not, the voltage stability improvement, and power loss reduction with the introduction of STATCOM. Accordingly, it is observed that the weakest bus that is identified through the stability indices may not be the optimal placement of the compensation device. It is also indicated that, with the introduction of STATCOM to the optimal selected bus, the network power loss is reduced from 46.65 MW to 33.32 MW and its voltage magnitude in all buses is also improved. All bus voltages are within the IEEE standard acceptable limit. [ABSTRACT FROM AUTHOR]

Zafarana Wind Park is considered the largest wind farm in Middle East. By the year 2022, Zafarana wind farm will inject 545 MW to Egypt national electric network (ENEN). This wind farm has been connecting to ENEN in stages (eight stages) from ten years ago. Each stage represents a project of wind farm. Hence, the impact of the emergency condition of ENEN on the performance of the installed stage of Zafarana Wind Park should be studied. Moreover, determining the possible method to reduce the side effects of the emergency condition of ENEN on the installed stage of Zafarana Wind Park becomes an important issue. In this paper, a combination between Static Synchronous Series Compensator (SSSC) and crowbar (Cro-SSSC) is used to enhance the performance of the one installed stage of Zafarana Wind Park. The studied stage of Zafarana Wind Park is Zafarana Z1 wind farm (Z1). Zafarana Z1 wind farm represents that first stage of Zafarana Wind Park that was connected to ENEN. Hence, the paper takes Zafarana Z1 wind farm as the studied case to examine the impact of Cro-SSSC. The crowbar is used to protect the power components of the SSSC from the high current during grid fault condition. Also, in this paper multi-objective lightning attachment procedure optimization algorithm (LAPO) and the sine cosine algorithm (SCA) are used to evaluate and tune the control gains of SSSC and the value of crowbar's resistance. This step is important in order to determine the values of the control's gains of SSSC and the value of crowbar resistance which suit the studied system (one installed stage of Zafarana Wind Park). The sliding mode control (SMC) is used as an example of nonlinear control of SSSC. Also, a hybrid of LAPO and SMC is used to improve the performance of Zafarana Z1 during faults. The results of LAPO, SCA and SMC are compared during three-phase fault and single line to ground fault applied to the studied system. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for detection, classification, and characterization of voltage sags and swells in electricity networks using three-phase voltage ellipse parameters. The proposed method employs the instantaneous magnitude of three-phase voltage signals in three axes, which are separated from each other by 120 ^\circ . Thus, the resultant rotating vector, namely, the three-phase voltage vector, traces an ellipse. Then, the parameters of the ellipse, which include minor axis, major axis, and inclination angle, are used to develop the proposed algorithm for classification and characterization of voltage sags and swells. The proposed method is validated using real data of the IEEE working group and some measured real data of the Belgian transmission grid. The method is further tested for the sags and swells generated due to balanced and unbalanced faults at different buses, in test distribution networks (radial and mesh type) embedded with distributed generation and in a practical distribution network of Australia. This paper also demonstrates the proposed algorithm as a real-time sag/swell monitoring tool. [ABSTRACT FROM PUBLISHER]

The novel cascade-mitigation scheme developed in Part I of this paper is implemented within a receding-horizon model predictive control (MPC) scheme with a linear controller model. This present paper illustrates the MPC strategy with a case-study that is based on the IEEE RTS-96 network, though with energy storage and renewable generation added. It is shown that the MPC strategy alleviates temperature overloads on transmission lines by rescheduling generation, energy storage, and other network elements, while taking into account ramp-rate limits and network limitations. Resilient performance is achieved despite the use of a simplified linear controller model. The MPC scheme is compared against a base-case that seeks to emulate human operator behavior. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the optimal power flow (OPF) problem. We have recently shown that a convex relaxation based on semidefinite programming (SDP) is able to find a global solution of OPF for IEEE benchmark systems, and moreover this technique is guaranteed to work over acyclic (distribution) networks. The present work studies the potential of the SDP relaxation for OPF over mesh (transmission) networks. First, we consider a simple class of cyclic systems, namely weakly-cyclic networks with cycles of size 3. We show that the success of the SDP relaxation depends on how the line capacities are modeled mathematically. More precisely, the SDP relaxation is proven to succeed if the capacity of each line is modeled in terms of bus voltage difference, as opposed to line active power, apparent power or angle difference. This result elucidates the role of the problem formulation. Our second contribution is to relate the rank of the minimum-rank solution of the SDP relaxation to the network topology. The goal is to understand how the computational complexity of OPF is related to the underlying topology of the power network. To this end, an upper bound is derived on the rank of the SDP solution, which is expected to be small in practice. A penalization method is then applied to the SDP relaxation to enforce the rank of its solution to become 1, leading to a near-optimal solution for OPF with a guaranteed optimality degree. The remarkable performance of this technique is demonstrated on IEEE systems with more than 7000 different cost functions. [ABSTRACT FROM PUBLISHER]

This paper summarizes simulation based technical feasibility and cost benefit analyses of a range of off-the-shelf electrical losses reduction intervention solutions in the Isle of Wight (IoW) 11 kV distribution network in the United Kingdom (UK), assessed over a period of 45 years. Based on presented results, the paper provides a brief commentary on the merits, drawbacks, risks and recommendations associated with each considered network intervention. Among considered network interventions, the transformer auto stop-start scheme, where one of the transformers among a pair at a primary substation would be switched depending on the substation's varying load, was found to be the optimal solution, i.e., in reducing technical losses significantly and may also have a reasonable Net Present Value (NPV) with a good rate of Return on Investment (RoI) when fully implemented. [ABSTRACT FROM PUBLISHER]

The detection of audio tampering plays a crucial role in ensuring the authenticity and integrity of multimedia files. This paper presents a novel approach to identifying tampered audio files by leveraging the unique Electric Network Frequency (ENF) signal, which is inherent to the power grid and serves as a reliable indicator of authenticity. The study begins by establishing a comprehensive Chinese ENF database containing diverse ENF signals extracted from audio files. The proposed methodology involves extracting the ENF signal, applying wavelet decomposition, and utilizing the autoregressive model to train effective classification models. Subsequently, the framework is employed to detect audio tampering and assess the influence of various environmental conditions and recording devices on the ENF signal. Experimental evaluations conducted on our Chinese ENF database demonstrate the efficacy of the proposed method, achieving impressive accuracy rates ranging from 91% to 93%. The results emphasize the significance of ENF-based approaches in enhancing audio file forensics and reaffirm the necessity of adopting reliable tamper detection techniques in multimedia authentication. [ABSTRACT FROM AUTHOR]

Electric vehicle technology is a crucial technology for achieving sustainable energy transformation, which is of great significance to climate change and promotes sustainable development. This paper attempts to study the transnational R&D cooperation of electric vehicles. According to the authorized data of transnational co-patents from the United States Patent and Trademark Office (USPTO), a social network analysis method is employed, and a detailed study of transnational co-patent networks in electric vehicles is conducted, including the construction of network, the analysis of nationality distribution of co-patent inventors, the analysis of structural characteristics and important nodes of network in different stages from the perspective of inventors and countries. The research results show that the cooperative groups formed by inventors are independent of each other and have not yet formed a large network; the degree of transnational cooperation in developed countries far exceeds that in developing countries; the US and Germany are the dual-core in the transnational co-patent networks; the breadth and intensity of transnational cooperation are strengthening, and the regional borders are less and less restrictive. As for the existing problems, authoritative inventors could organize large international R&D cooperation institutions to gather dispersed inventors together and connect them into a large inventors' network; developing countries are encouraged to seek partners through the network, actively participate in transnational R&D cooperation, and developed countries are encouraged to hold global technological innovation events. [ABSTRACT FROM AUTHOR]

Large-scale development of small-scale generation, and facilities based on this, with their integration into existing distribution networks, qualitatively change the modes and tasks of network management and transform previously passive electric networks into active ones. Features of parameters and modes of small-scale generation, insufficient observability and manageability in centralized management determine the need to use a decentralized multi-agent control of the modes of such networks. First of all, this applies to emergency management, which includes automatic restoration of the integrity and normal operation of the network. This paper presents a set of specialized methods for emergency management of active power grid modes and the results of a study of their effectiveness on mathematical and physical models that confirm the feasibility of using decentralized emergency management and network recovery management. In particular, this includes: a method of emergency proactively balanced separation of grid energy districts along one of the a priori fixed network cross-sections in the event of disturbances with the transition to island mode, and a method for two-stage restoration of the integrity and normal network mode with decentralized synchronization of active parts on remote network switches. In the case of the decentralized remote synchronization of active parts, it is proposed to use special control of the excitation and speed of generators to create conditions for the successful operation of automatic reclosing devices with synchronization detection. It is essential for emergency management in active networks with small generation to reject the concept of ensuring the reliability of power supply through maintaining the integrity of the network in favor of the concept of an emergency-balanced breakdown of the network into balanced areas with the subsequent automatic restoration of integrity. To conduct research on the physical model, a prototype of distributed system emergency automation has been developed that does not use data transmission tools, which ensures its high cybersecurity and the feasibility of decentralized management. [ABSTRACT FROM AUTHOR]

In recent years, the interest in electric vehicles (EVs) in the research community has been growing, particularly in the context of decarbonization. Additionally, there is a growing increase in their number, leading to massive energy demand on the charging stations (CSs). Energy trading management for CSs puts great pressure on the power grid and is a stimulating challenge in smart cities. In this paper, we propose an innovative market formulation in which autonomous vehicles and smart charging and discharging stations are motivated to cooperate dynamically with changing roles. In order to mathematically formulate the energy trading market, we adopt a double auction strategy that is repeated in steps. In this strategy, EVs and CSs participate by buying and selling energy. The investigated problem has high complexity, and thus, multi-objective optimization is employed so as to encapsulate the opposing objectives that the EVs and CSs have. Multi-objective optimization leads to a fairer and more efficient market operation. The performance of the presented approach is investigated through analytical and experimental results. More specifically, the proposed algorithm achieves up to 52.5 % reduction in energy consumption. The performance evaluation proves that the suggested strategy offers both fairness and significant energy benefits, encouraging both electric vehicles and charging stations to take part in a double auction energy trading system. [ABSTRACT FROM AUTHOR]

This paper reports the design and optimization of a permanent magnet-based spring. The aim of the optimization, performed using a particular form of the self-organizing map (SOM) algorithm, was to determine the dimensions of a ring PM-based spring with a force–displacement curve similar to a desired one. For each step in the optimization process, a spring composed of different ring-shaped magnets was analyzed using a semi-analytical model. Its characteristic was compared with the desired one to search for a minimum cost function obtained by subtracting the evaluated and the desired force–displacement curve. The resulting algorithm was efficient in the design of a spring with a desired characteristic. The geometry obtained was used to study an electrodynamic damper based on the exploitation of the interaction between the moving magnet of the spring and a conductive cylinder. A parametric analysis was performed: the damping effect grows when the cylinder thickness increases and decreases with the gap between the cylinder and the magnets. Also, the cylinder thickness needed to reduce to one the number of overshoots in the moving magnet's position decreases with the gap increase. Computations were performed using the research code EN4EM (Electric Network 4 ElectroMagnetics) developed by the authors. [ABSTRACT FROM AUTHOR]