Aiming at the uncertainty of the time distribution of charging demand in vehicle-to-grid (V2G) scheduling and the uncertainty of output under the penetration of high proportion of new energy, which makes it difficult to balance the load of the grid, a V2G collaborative optimization strategy driven by electricity price response under a high proportion of new energy access is proposed. Firstly, a V2G collaborative optimization model driven by electricity price response for high proportion new energy access scenarios is constructed, which guides the charging and discharging behavior of V2G charging piles and electric vehicles by regulating electricity prices, and is optimized with multiple goals to minimize the total cost of the power grid, minimize the peak-to-valley difference of grid load, and maximize user benefits. Secondly, the non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ) algorithm is fused with the Wasserstein generative adversarial network(WGAN) to establish a dynamic adjustment algorithm with non-dominated sorting and crowding distance selection elite solution reward function for solving the model. Case analysis shows that compared with the traditional NSGA-Ⅲ algorithm, the proposed NSGA-Ⅲ WGAN algorithm can effectively reduce the operating cost of the power grid, improve the new energy consumption capacity of the power grid, stabilize the peak-to-valley difference of the load of the power grid, and improve the benefits of users through efficient global search and local fine-tuning capabilities.

Electric vehicle(EV) mobile charging stations(MCS) face challenges including demand prediction errors, insufficient multi-objective coordination, and lack of multi-agent interaction mechanisms, necessitating multidimensional collaborative optimization. An IGDT-based dual-strategy DQN optimization method for MCS scheduling is proposed. First, a multi-objective optimization framework is constructed to achieve coordinated balance with the goals of minimizing total system costs, minimizing the average user waiting time, and improving the satisfaction rate of charging demand. Second, by combining IGDT to quantify uncertainty parameters in charging demand, a dynamic scheduling strategy is designed. Under high-uncertainty scenarios(such as holiday demand spikes), a robustness model is activated to prioritize to ensure uninterrupted basic services in high-demand areas. Under low-uncertainty periods(such as weekday commutes periods), an opportunistic model is applied to optimize costs through route planning and off-peak charging, accommodating different scenario-based decision preferences. Finally, an improved dual-strategy deep reinforcement learning DQN algorithm is used to solve the model, and its effectiveness is verified through simulation experiments. Case analysis shows that, compared to the traditional actor-critic(AC) algorithm, the proposed DQN model can effectively reduce MCS operating costs and reasonably meet EV users' charging demands by dynamically selecting robust or opportunistic decision-making.

To address the expansion planning of electric vehicle(EV) charging stations and improve the utilization of charging piles, a method for calculating the simultaneity factor is proposed. A Gaussian mixture model(GMM) and a Monte Carlo algorithm-based queuing model is used to simulate user charging behavior, thereby calculating the optimal vehicle-to-pile ratio and simultaneity factor. The influence of multi-scenario factors on the simultaneity factor is investigated, and a charging facility design framework for different scenarios in residential and commercial areas is established. By analyzing charging power characteristic curves of private vehicles, the charging simultaneity factor for diverse scenarios is measured. Case study results demonstrate the effectiveness and feasibility of the proposed method, providing valuable insights for the planning and construction of EV charging stations.

Traditional vulnerability management systems often face issues such as fragmented management frameworks, risks of data tampering, difficulties in tracing repair responsibilities, and insufficient incentives for participants, leading to inefficiency and hard-to-supervise of vulnerability remediation processes. To address these challenges, this study proposes a vulnerability management system for power enterprises based on the decentralized architecture, immutability, and full-chain traceability of block chain technology. The system integrates smart contracts, token-driven incentive mechanisms, responsibility assignment mechanisms, and distributed storage technology via the InterPlanetary File System (IPFS). Through systematic analysis and demonstration of multiple functional modules, including vulnerability data notarization, remediation progress tracking, responsibility tracing, and reward-punishment incentives, the proposed system can ensure high efficiency and closed-loop management of vulnerability management in power enterprises within a block chain environment, providing a novel solution for strengthening vulnerability management in the power sector.

Ice covering on insulators is a major natural disaster that poses a threat to the safe operation of power grids, especially posing a severe challenge to the high-altitude and heavily ice-covered areas that strategic projects such as the West-to-East Power Transmission pass through. For this reason, this article systematically reviews the core research progress in the field of insulator icing. Firstly, it elaborates on the physical mechanism of icing and the coupling influence laws of its interaction with water droplet movement, environmental parameters, and electric fields. Then, the flashover characteristics of icing insulators are analyzed to reveal their nonlinear voltage characteristics and the development process of arcs. Finally, it summarizes and evaluates the applicability and limitations of three types of anti-icing measures, that is, structural optimization, active de-icing, and passive protection. The article points out that current research still lacks in the aspects of microscopic icing physics, multi-physical field coupling models, and insulators adaptability in extreme environments. In the future, efforts should be focused on the construction of intelligent anti-icing technology and the construction of a full-chain risk early warning system to support the reliable operation of future power grids.

Aiming at the galloping problem of iced overhead conductors, a three-dimensional dynamic model of iced conductors is established based on the finite element simulation method, and the mechanical and electrical characteristics of conductor galloping under different wind speeds, wind directions, and airflow attack angles are systematically analyzed. The study finds that ice thickness and wind speed are the most sensitive parameters affecting galloping. On this basis, a galloping early warning system integrating 5G communication, sensor monitoring, and intelligent classification algorithms is proposed. The system employs an support vector machine (SVM) model to predict meteorological conditions prone to galloping in the area and combines an AdaBoost classifier to achieve a graded early warning for transmission line galloping risk. This system, with its multi-source perception and intelligent decision-making capabilities, effectively enhances the accuracy in early warning and prevention and control levels of the power grid against galloping disasters.

The heat generation characteristics of LiFePO₄(LFP) batteries during thermal runaway(TR) are directly tied to their safety performance in practical applications. Using 60A·h LFP batteries as the research subject, this study focuses on comparing heat generation processes under different test environments, detailing combustion evolution laws of the anode, separator, and cathode, systematically revealing the TR mechanism of the battery, and obtaining the total heat released from TR and combustion under adiabatic, room temperature, and pure oxygen conditions. Results indicate that LFP battery TR can be divided into four stages: heating, self-heating (initial temperature 105 ℃), internal short circuit(initial temperature 136 ℃), and TR (trigger temperature 200 ℃). In a room temperature environment, the anode responds fastest to combustion and has the highest heat release peak. The total heat release of battery TR under the three conditions reaches 430.26 kJ, 1 035.84 kJ, and 20 135.13 kJ, respectively. The findings can provide valuable support for formulating TR suppression strategies in energy storage stations and for designing fire protection measures.

The characteristics of lithium iron phosphate battery fires in energy storage systems both at home and abroad are analyzed, and typical accident cases of various faults are listed. The differences between energy storage battery fires and automotive power battery fires are compared. The advantages and disadvantages of current research on fluorine-based fire extinguishing agents, water-based fire extinguishing agents, aerosols, liquid nitrogen, and carbon dioxide fire extinguishing technologies at home and abroad are summarized. The research progress on the combustion and explosion mechanism, explosion prevention and explosion suppression technologies of large-capacity lithium iron phosphate battery energy storage systems at home and abroad is summarized. The viewpoint of "intrinsic safety to prevent fires", "early warning and active regulation to avoid thermal runaway", and "fire suppression and explosion suppression to ensure safety as a last resort" for the safety defense of lithium-iron battery energy storage systems is proposed, providing technical guidance for the prevention and control of fire and explosion in the domestic lithium iron phosphate energy storage industry.

To address the significant errors associated with conventional neural network models in predicting the fusibility of coal ash with complex composition, a novel weighted coupled neural network model (WCN) is proposed. It is particularly characterized by the modification of the traditional feedforward neural network based on the competitive reaction mechanisms of the coal ash Si-Al-Ca-Fe systems. Specifically, the silica-to-alumina ratio (S/A) and calcium-to-iron ratio (C/F) of coal ash are incorporated as feature items into a weighted network, whose outputs are then coupled with those of the component network, enabling the neural network to have flexible and adjustable prediction weights, thereby enhancing the model's adaptability and prediction accuracy. Comparison of the prediction results of coal ash softening temperature by different models demonstrates that the maximum errors of the WCN model in the prediction of the test set is below 60 ℃, outperforming the reproducibility requirement (80 ℃) for coal ash fusion characteristics in the Chinese National Standard(GB/T 219—2008). Furthermore, compared to the conventional eXtreme Gradient Boosting(XGBOOST) and back propagation neural network(BPNN) models, the WCN model improves prediction accuracy for complex high-alkali coal(e.g., Zhundong coal) ash by 32.8% and 83%, respectively. The study shows that the WCN model achieves significant improvements in both coal ash adaptability and prediction accuracy, demonstrating considerable application value.

Under the background of "carbon peaking and carbon neutralization", it is necessary for the construction of China's power market to fully consider the rapid development of new energy and the need for low-carbon emission reduction. It is urgent to clarify the economic relationship between the power and carbon markets and study the transmission effect of carbon costs to electricity prices. To address this issue, a qualitative analysis of the interaction mechanism between the power and carbon markets is first conducted, concluding that carbon emission costs will ultimately be passed on to power purchasers in the form of electricity prices. Using the Cournot equilibrium model, the formula for the transmission of carbon costs to electricity prices is derived, leading to the conclusion that the transmission rate of regional carbon prices to electricity prices needs to be calculated based on the elasticity of the slope of the electricity inverse demand function. Subsequently, a multiple linear regression model is used to construct the regional electricity demand function, enabling the regional carbon cost transmission rate to be implemented at the data level. Finally, through a case study using data from seven carbon pilot regions as samples, the price elasticity of electricity demand for both regional and national levels is obtained, and the carbon cost transmission functions for each region are estimated.

With the large-scale integration of distributed new energy into distribution networks, the main grid power flow weakens, causing excessively high and hard-to-regulate static voltage. However, the reactive power compensation capacity of static var generator (SVG) in existing centralized photovoltaic (PV) power plants remains underutilized. To solve these issues, a reactive power compensation strategy with active adjustment for PV-integrated power plants based on an improved holomorphic embedding method (HEM) is proposed. The traditional holomorphic embedding power flow (HEPF) method is modified to build an improved HEPF model, which fully utilizes the reactive power compensation capacity of centralized PV power plants and optimizes the siting and sizing of additional reactive power compensation devices. This strategy enhances power system voltage stability and reduces the investment cost of such devices. Simulations on a modified IEEE 39-bus system show that the power system voltage stability is effectively improved, and the total installed capacity of reactive power compensation devices is reduced by approximately 20%.

With the expansion of application scenarios and the intensification of working environments, the failure issues of metallized film capacitors have become increasingly prominent, emerging as a critical constraint on the reliability of power systems. Starting from the two dimensions of film material failure and metallized film capacitor device failure, this paper systematically analyzes the failure mechanisms of metallized film capacitors, with a focus on elaborating the intrinsic connections and fundamental differences between microscopic film failure and macroscopic device failure, and summarizes the evolution patterns of metallized film capacitor performance under multi-field effects including electric field, temperature, and mechanical stress. Finally, failure protection strategies and technical improvements are proposed to provide a theoretical foundation for the reliability design and lifetime prediction of metallized film capacitors.

A two-layer optimization scheduling model for the power system is proposed to address the diversity of load side carbon reduction methods and insufficient synergy of source side market measures in low-carbon dispatch. The model is based on demand response guided by node carbon potential and considers the linkage mechanism between green certificate trading and carbon emission trading. Firstly, based on carbon emission flow theory, a load-side carbon emission flow model is established, and combined with the demand response mechanisms, a low-carbon demand response framework for the load side is constructed. Secondly, by analyzing the operating principles of the carbon market and green certificate market, a carbon trading model and a green certificate trading model are established on the source side, and the carbon reduction attributes of green certificates are utilized to establish a connection between the two, completing the design of the interaction mechanism between the two. Then, a two-layer optimization scheduling model for the power system is constructed based on the low-carbon demand response and green certificate carbon trading linkage mechanism. The upper layer is the economic optimization dispatch model on the source side of the power system, and the lower layer is the low-carbon optimization dispatch model for the load side. Finally, a case study is conducted using the IEEE 14 node system to validate the effectiveness of the modle. The simulation results show that this scheduling strategy can fully exploit the potential of load side carbon reduction on the load side, enhance the interaction of market means on the source side, and promote the low-carbon transformation of the system.

Starting from an on-site misoperation accident of the distance protection for a wind farm transmission line, this paper analyzes the causes of such misoperation from the perspective of criterion mechanism, and conducts a comparative analysis on the special protection behaviors under this fault scenario. On this basis, performance tests and criterion comparisons on the distance protection devices of mainstream relay protection manufacturers are further carried out through methods such as on-site fault recording playback and RTDS simulation tests. The analysis reveals the mechanism by which the wind farm transmission lines affect the distance protection, and finally corresponding strategies are formulated at the level of protection criteria in combination with on-site realities.

To address the contradiction between dynamic response and steady-state performance in traditional fixed moment of inertia strategies of virtual synchronous generators (VSG) and the power oscillation problems, a virtual synchronous generator control strategy based on adaptive segmented moment of inertia is proposed. By analyzing the oscillation mechanism, the inertia adjustment interval are dynamically divided. When power fluctuations are severe, the moment of inertia is reduced to improve control speed. When power fluctuations have a slowing trend, the moment of inertia is increased to improve stability. Through this optimized design, VSG exhibits good parameter robustness in various operating modes, effectively suppressing low-frequency oscillations of active power and improving the accuracy of transient process control. Finally, MATLAB/Simulink simulation verifies the feasibility and correctness of the proposed method.

To address the critical challenges in power standard management including poor real-time information, weak knowledge correlation, and prominent service passivity, a dynamic knowledge service system based on the collaborative drive of distributed monitoring, natural language processing, and knowledge graph is proposed. The system relies on a distributed monitoring engine to achieve second-level data collection from multiple source heterogeneous sources, reducing the standard update detection delay from 120 hours (with manual inspection) to 5.8 hours. Through dynamic knowledge graph construction technology driven by deep learning, the system breaks through the semantic limitations of traditional keyword search, increasing the knowledge association recall rate to 96.2%. It also pioneers a proactive early warning mechanism empowered by graph reasoning, promoting the service model to shift from passive response to on-demand push. After a high-concurrency pressure test of 1 752 requests per second, the system's 95% request response time remains stably below 300 ms, achieving coordinated optimization in terms of timeliness, completeness, and robustness. This system provides a reusable technical path for the power industry to build an enterprise-level "digital standard library".

In recent years, with the high proportion of power electronic equipment access, the data scale on the edge side of the power distribution system has increased sharply. Adaptive compressed sensing has gradually become one of the key technologies to alleviate the bandwidth pressure on the cloud edge. The corresponding distribution master station needs to reasonably decompress it to meet the delay requirements of differentiated business. Therefore, an adaptive allocation method of computing power resources for distribution master station adapted to the dynamic compressed sensing on the edge side is proposed. Firstly, a distribution master station decompression system model driven by computing power resources is established. Then, considering the differentiated business delay requirements, a computing power resource allocation revenue function is designed, and based on this, the corresponding optimization problem is designed. Finally, the original optimization problem is decoupled into the computing power resource allocation layer and the time section setting layer. The KKT(Karush-Kuhn-Tucker) condition and the variable step size reallocation mechanism are adopted to solve the corresponding sub-problems of each layer respectively, thereby achieving efficient solution of the original optimization problem. Simulation analysis shows that the proposed method can fully explore the utility of computing power resource allocation through a unique variable step size reallocation mechanism, flexibly adapt to the dynamic compression process on the edge side, and ensure the state perception ability of the distribution master station on the edge side.

The installation position angle of the three-phase switch-type Hall position sensors for permanent magnet synchronous motors is prone to offset during mass production and installation, resulting in significant measurement errors in the closed-loop feedback speed and position signals of the motor vector control. To address the above problems, a rotor position and speed estimation method for permanent magnet synchronous motors based on Hall vector filtering is proposed. First, the two-phase orthogonal components of the Hall three-phase switch signal are obtained through coordinate transformation, and a full-order state observer with harmonic feedback calculation is constructed in the form of Hall vectors. An adaptive vector filter is introduced to extract the fundamental signal from it, thereby filtering out the low-order harmonic interference generated by the Hall sensor offset and obtaining a more accurate rotor position and speed. Simulation and experiments verify that this method can still accurately estimate the rotor position and speed after the Hall sensor is offset, reducing the estimation error and ensuring the normal and stable operation of the motor vector control system.

Traditional signal processing methods often fail to effectively capture the complex nonlinear characteristics of lightning surge current signals in distribution networks lightning fault identification. To overcome this limitation, a method for lightning surge current identification in distribution networks based on an improved Transformer network with multi-dimensional feature inputs is proposed. Leveraging the self-attention mechanism of the Transformer network, intricate dependencies and patterns are extracted from multi-dimensional features, and a model classification module is incorporated to accurately classify lightning surge currents, significantly enhancing identification accuracy. The simulation model is built on the Simulink platform to validate the proposed algorithm. Experimental results demonstrate that the proposed method achieves an identification accuracy of 99.12% in classifying lightning surge currents, verifying the effectiveness of the approach.

In order to meet the multiscale latency requirements of differentiated services while maintaining resource redundancy, it is necessary for the relay nodes involved in the multi-hop data transmission paths of computational load source-destination to coordinate the allocation of bandwidth resources. A collaborative bandwidth resource reservation method for distribution substations based on joint multiscale latency constraints is proposed. First, a system model for cooperative bandwidth resource reservation at distribution substations, adapted to computational load spatiotemporal transfer, is established. Next, an optimization problem for single-substation bandwidth resource allocation under given bandwidth constraints and aimed at multi-scale delay requirements is designed. Finally, under differentiated available bandwidth constraints, a cooperative reservation optimization problem for multi-substation bandwidth resources is formulated. An adaptive water-filling strategy is designed to solve this problem based on the feedback of the optimal solutions for resource allocation at each substation. Simulations analysis show that the proposed method, via its unique resource reservation mechanism, flexibly adapts to random access and latencysensitive characteristics of heterogeneous distribution services, ensures the spatiotemporal transfer capabilities of computational load indistribution substations, and facilitates the efficient utilization of distributed compute resources.