The immediate charging mode of electric vehicle battery swap stations can easily exacerbate the peak-valley difference in grid load and the imbalance in the utilization of battery energy storage resources,thereby restricting the economic operation of the swap stations and the security of the grid.To address this issue,this paper constructs a mixed-integer linear programming model with the objective of minimizing the cost of charging and purchasing electricity,while considering the constraints of battery state coordination and resource balance.It also proposes a battery charging optimization strategy based on dynamic time windows and battery collaborative management.Through the dynamic time window mechanism,the charging tasks are temporally and spatially shifted,and combined with battery state collaborative management,the overall charging load distribution is optimized while meeting the users' battery swap demands,reducing the cost of purchasing electricity and enhancing the security of the grid.Compared with the rapid response of the immediate charging mode,this strategy focuses more on system-level resource balance, providing a feasible solution for large-scale battery swap stations to participate in power demand response.Taking an actual battery swap station in a certain area as the verification object,the load regulation effect and comprehensive benefits of different time window lengths were evaluated.The results show that this strategy significantly improves load balance and reduces the cost of charging and purchasing electricity.In addition,through sensitivity analysis,the intluence laws of key parameters on system performance were revealed,providing a practical optimization method and decision support for large-scale battery swap stations to participate in power demand response,which is of great significance for promoting the coordinated development of the electric vehicle industry and the grid.

As an environment-friendly vehicle,electric vehicle has achieved large-scale development,at the same time,the access of a large number of charging loads has caused a great impact on the traffic network and distribution network.Due to the lack of charging infrastructure and the disorderly charging of car owners,car owners may also face the problem of waiting in line for a long time during the charging peak.For this reason,an electric vehicle booking charging guidance strategy considering dynamic traffic information and available charging resources of charging stations is proposed in this paper.Firstly.the user travel characteristics.dynamic traffic network.charging station and distribution network are modeled；secondly,a global optimal path planning algorithm considering cross-period road resistance update is proposed；then,based on the maximum acceptable load of charging stations in the region,a hierarchical adjustment model of dynamic service charge is established according to the real-time load rate of each charging station.Finally,combined with the charging needs of car owners,a reservation charging guidance strategy aiming at the lowest charging cost,total travel time and slow charging cost is established,and the optimal charging scheme is planned for car owners.Ihe simulation results show that the proposed reservation charging guidance strategy can effectively improve the charging satisfaction of car owners,reduce the load variance between charging stations,stabilize the load fluctuation of distribution network,and maintain the safe and stable operation of distribution network.

Aiming at the simulation problem of electric vehicle charging load in time and space distribution,this paper proposes a simulation method of electric vehicle charging load based on geographic information and multi-level trip chain.By establishing a regional road network model and dividing functional blocks,the vehicle movement trajectory is combined with road information to construct a complete mobile chain model of travel starting point-multi-destination stay-return path'.The path planning algorithm is used to simulate the user's daily travel mode,and the space-time simulation model of charging demand is constructed.The results show that this method can effectively simulate the charging load change trend of different regional types and urban functional areas,and accurately identify the electricity consumption characteristics of residential areas,work areas and other scenes.Compared with the traditional single trip chain model,the multi trip chain model constructed in this paper can more accurately simulate the spatial and temporal distribution characteristics of electric vehicle charging load.

In response to the issue of large-scale failures in power grids with a high proportion of new energy caused by severe sandstorms and the rapid recovery after a significant decline in the output of new energy units, this paper proposes a collaborative optimization method for line fault repair and new energy unit cleaning aimed at enhancing the resilience of transmission networks. Firstly, a high-proportion new energy grid model incorporating concentrated solar power plants and energy storage systems is constructed, which can effectively characterize the dynamic changes in the output power of concentrated solar power plants and energy storage after load variations. Then, the impact of severe sandstorm weather on new energy output and transmission line faults is quantitatively characterized. Further considering factors such as system grid strength and source-load imbalance, a post-disaster transmission line repair and unit cleaning sequence model is proposed with the objective function of maximizing system load recovery. Finally, the effectiveness of the proposed method is verified through an improved IEEE-30 node system, demonstrating that the proposed resilience enhancement strategy can accelerate system recovery, reduce system load loss, and achieve resilience enhancement in power grids with a high proportion of new energy.

Driven by the "dual-carbon"strategic goals,the penetration of renewable energy in power grids continues to increase,placing unprecedented pressure on the regulation capability of power systems.Most existing flexibility assessment and planning methods for power systems focus primarily on supply-demand balance,while paying insufficient attention to the role of transmission capacity in ensuring flexibility.To address this issue,this paper proposes a joint storage-transmission planning method that considers both resource and transmission flexibility margins.First,starting from the system operation mechanism,the influence of transmission network capability on meeting flexibility requirements is analyzed,and the node-level flexibility demand is mapped onto transmission lines.On this basis,two quantitative indices-resource flexibility margin and transmission flexibility margin-are constructed to enable multidimensional flexibility assessment.Subsequently,a joint storage-transmission planning model incorporating both resource and transmission flexibility margins is developed,in which energy storage deployment and transmission expansion are treated as coordinated decision variables to achieve a balance between flexibility enhancement and economic optimization.Finally,simulations based on an improved IEEE RTS-24 bus system are conducted.The results demonstrate that wc piopuscu icuou call mpiuve ucAwuy iaigis wic coiuvig wolal sysicm cust,cuccuvcly aucviaung upciauvnal stress caused by renewable energy fluctuations,and enhancing the accommodation capability of new energy sources.

In the high-proportion renewable energy (High Proportion Renewable Energy, HPRE) power system, thermal power units are largely replaced, and the contradiction between supply and demand of system daily regulation capacity is becoming increasingly prominent. Configuring multi-time scale regulation resources on demand can effectively alleviate the above contradiction. However, the strong uncertainty and volatility brought by large-scale wind power and photovoltaic access make it difficult to evaluate the daily regulation capacity requirements of the system. In this paper, a multi-time scale assessment method for daily regulation capacity demand is proposed for HPRE power system. Firstly, based on the perspective of 'adjustable' matching 'non-adjustable' intra-day power balance, a scenario set generation method is proposed to describe the uncertainty of non-adjustable power through Gaussian mixture model (Gaussian Mixture Model, GMM) and K-means clustering. Secondly, according to the hourly fluctuation characteristics of daily non-adjustable power, a multi-time scale evaluation system of daily regulation capacity demand is established. Then, a multi-time scale decoupling evaluation model of daily regulation capacity demand based on Haar wavelet coefficient is constructed by quantifying demand intensity with regulation power and regulation electricity as indicators. Finally, the example results show that the proposed method can provide some reference for the planning and allocation of multi-time scale adjustment resources.

Wind, solar resource-rich areas usually appear a variety of new energy (wind, solar) co-generation, in-depth study of wind-solar joint power fluctuation characteristics of power grid safety operation is of great significance. In this paper, based on the measured data of large-scale wind power base in the northern part of Shanxi Province, the time-frequency characteristics of wind and light co-generation power are empirically analyzed by using db6 discrete wavelet transform. First, the wavelet transform is used to decompose the output power of different power generation clusters, and at the same time, the fluctuation rate, the extreme value difference and the fluctuation confidence interval index are calculated for each frequency band. Then, the integrated weight coefficient method is used to analyze the impact of the integrated fluctuation characteristic indexes of each frequency band on the power grid. Finally, based on the results of the time-frequency characterization of different wind farms in the northern part of Shanxi, the integrated fluctuation characteristic indexes of each frequency band of the wind-solar co-generation cluster are compared with those of the single wind/photovoltaic cluster, and the optimal capacity allocation ratio of the wind-solar co-generation cluster is obtained. The results show that compared with single wind/photovoltaic power generation cluster, the overall volatility of wind/photovoltaic co-generation cluster is smaller, which is more conducive to the safe operation of the power grid, and there exists an optimal ratio of wind/photovoltaic installed capacity.

With the increasing number of electric vehicles,the substantial charging demand has imposed certain impacts on the power grid while introducing challenges for grid dispatching.Consequently,predicting the charging load of electric vehicles has gradually become a research hotspot.To address the randomness of electric vehicle charging loads and the accuracy issues in deterministic predictions,an interval prediction method based on FMD-LSTM-Bootstrap is proposed.Using real historical electric vehicle charging load data collected,the dataset is preprocessed to eliminate erroneous data caused by equipment factors.The processed data is then divided into training and testing sets according to a certain ratio.Aiter tuning with the training set,the testing set is used to validate the prediction pertormance of the FMD-LSTM-Bootstrap method.The performance of the prediction intervals is further verified by combining the average bandwidth and interval coverage rate.Simulation results demonstrate the superiority of the proposed algorithm.

Aiming at the problems of large demand side load fluctuations, high carbon emission base, and conflicting interests of multiple parties in the integrated energy system of multi microgrids with electricity and hydrogen energy interaction, this paper proposes an optimization of the integrated energy system of multi microgrids with zero carbon target considering self elasticity coefficient and peak shaving fluctuation incentives. Firstly, the benefit objectives and decision variables of each interaction party are analyzed, and an upper level cooperative game framework is constructed with the energy system operator of multi microgrids as the leader, and a lower level one master multi slave game model with user aggregator and shared energy storage operator as followers. Secondly, considering the dispersion coefficient of demand side loads, Pearson correlation coefficient with regional distribution networks, and load self elasticity coefficient, an incentive based and price based comprehensive demand response model is constructed. Finally, genetic algorithm was used to solve the problem, and simulation analysis was conducted. The comparison of the four schemes showed that the proposed strategy not only considers the benefits of multiple agents under the constraint of zero carbon emissions, but also improves the ability of demand side load fluctuations and grid peak shaving and valley filling through the reduction of system dispersion coefficient and Pearson correlation coefficient.

Aiming at the current mismatch between new energy output and base load,a set of multi-level refined dynamic peaking compensation mechanism is proposed；an additional dynamic incentive mechanism is proposed between aggregators and grid trading centers to set the peaking compensation price according to the peaking shortfall, which guides the aggregators to integrate the dispatchable resources to provide peaking auxiliary services for the grid； and a response compensation apportionment strategy is proposed between the aggregators and the electric vehicle users.It also proposes a response compensation sharing strategy between aggregators and EV users,which defines the concept of sharing coefficient from the dimensions of dispatchable capacity and load transfer demand during the access time to explore the dispatch potential of the user group.At the optimization level of charging and discharging scheduling scheme,Adjusted Grey Wolf Optimization （AGWO）is used to solve the control model with the objectives of maximizing the revenue of EV aggregator and minimizing the load fluctuation of the system；combined with the user dispatch response model,it is proved that the proposed strategy can tap the potential of EV dispatching according to the demand of grid,which makes the management more flexible.Combined with the user dispatch response model,the proposed strategy proves that it can exploit the potential of EV dispatch according to the grid demand,manage vehicle charging and discharging more flexibly,reduce the charging cost of EV users,increase the revenue of aggregators, reduce the net load fluctuation,and increase the rate of new energy consumption.

A high percentage of new energy is accessed, which decreases the system's inertia and speeds up response times. This can lead to several issues, such as poor unit-tripping regulation strategy and an erroneous evaluation of the system stability boundary. The traditional unit-tripping regulation strategy is no longer sufficient for the current power system. To ensure quick stable operation after optimal unit-tripping regulation, provide a boundary energy-based power system transient stability unit-tripping strategy. First, define the individual machine energy function to determine the system's boundary energy. through calculating and analyzing the boundary energy, assess the system's stability using the stability index; Second, create the generator sensitivity index and determine how the unit's active power and stability index relate to one another, and through the calculation of the sensitivity index, screen out the units that need to be regulated as a priority. Use the optimization model to calculate the minimum required unit-tripping amount to restore system stability, and take corresponding unit-tripping operation based on the judgment results; Finally, it has been verified that this method can effectively regulate unit-tripping quantity to achieve the goal of restoring stable system operation in actual power system.

High-Frequency Transformer (HFT) is a core component of power electronic transformers and dual-active-bridge DC-DC converters. The distribution characteristics of its temperature and stress fields are crucial for system reliability and environmental friendliness. In this study, a 5kHz/10kVA HFT was analyzed using finite element simulation software. A full-chain multiphysics coupling simulation covering electromagnetic, fluid, thermal, structural, and acoustic domains was conducted to obtain the distribution patterns of electromagnetic, temperature, stress, and other multiphysics fields under nonsinusoidal excitation. The results reveal that the core loss of the high-frequency transformer is 52.7W, with high-voltage and low-voltage winding losses of 13.23 and 3.01W, respectively. The maximum magnetic flux density under sinusoidal excitation reached 0.746T, while under square wave excitation it reached 0.923T. The hotspot temperature was located approximately two-thirds along the core column at 115℃, with the lowest temperature recorded at 92.3℃ beneath the high-voltage winding. Forced air cooling demonstrates significant cooling efficiency below 4m/s wind speed, with cooling rates slowing thereafter. At 4m/s wind speed, core stress amplitudes under sinusoidal and square wave excitation were 2.81×106 and 3.65×106N/m2, respectively, with sound pressure level amplitudes of 78.1 and 94.9dB. When further considering the effect of thermal stress, under natural air cooling and square wave excitation, the core stress rises to 3.92×106N/m2, the vibration acceleration increases to 64.3m/s2, and the maximum sound pressure level reaches 97.3dB, representing increases of 7.4%, 6.8%, and 2.4dB, respectively, compared with the forced air cooling condition. Owing to its rich harmonic content, square wave excitation leads to significantly greater deformation, stress and noise than sinusoidal excitation, while thermal stress, as a secondary factor, further exacerbates vibration and noise levels. Through multiphysics simulation, the influence mechanism of waveform excitation and thermal stress on the comprehensive performance of transformers is revealed, providing a theoretical basis for multi-objective optimization of high-frequency transformers.

With the continuous increase in the penetration rate of new energy sources,distribution networks are facing problems such as unbalanced temporal and spatial distribution of source-load and insufficient flexible adjustment capability of the network.To address this issue,this paper proposes a joint planning method for soft open points （SOP）and electrochemical energy storage （EES）in distribution networks that comprehensively considers the source-load matching characteristics and flexibility of the network.Firstly,source-load matching indexes reflecting the utilization rate of the SOP and the adaptability of EES to the uncertainty of the net load are constructed,to characterize the priority of configuring the SOP on the tie line and the incremental benefit of the ESS capacity configuration.On this basis, the flexibility profit index is integrated into the objective function,with the maximization of annual profit as the optimization goal.A joint planning model for the optimal configuration of SOP and EES is established by comprehensively considering the regulatory role of SOP and the operational constraints of the distribution network.With reference to the source-load matching index,an internal and external nested algorithm considering the priority of SOP configuration is proposed to solve the proposed planning model.Simulation calculations are carried out using the improved IEEE 33-bus system as an example to verify the effectiveness of the proposed model and method.

To address the challenges in determining the appropriate burial depth and achieving uniform current distribution of grounding electrodes for ultra-high voltage direct current (UHVDC) transmission projects in complex terrain conditions, this study takes the receiving-end converter station of the ±800kV Hami-Chongqing transmission project as a representative case. The performance optimization measures for China's first branched UHVDC grounding electrode were comprehensively investigated. An electrical performance simulation model was developed using CDEGS, and the effects of grounding electrode burial depth, shallow soil resistivity, and the number of segments on grounding characteristics were systematically analyzed. The research shows that increasing the burial depth of the grounding electrode and reducing the shallow soil resistivity can effectively limit the ground step voltage, and the optimal number of segments that balances current sharing performance and economy was determined through simulation. Field tests show that the maximum step voltage increases by more than 40% under dry conditions compared with the surface wet period, indicating that dry surface conditions should be the key reference for the design margin of grounding electrode burial depth. The current division difference among the 11 grounding electrode branches is only 1.8%, which proves the rationality of the core parameter segment number setting in the grounding electrode design scheme. The research results can provide theoretical basis and engineering reference for the design and optimization of UHVDC grounding electrodes in complex terrain.

The Doubly-Fed Induction Generator (DFIG) is the most widely used wind generator in wind power generation. Its rotational speed is decoupled from the grid frequency. The DFIG participates in the system frequency regulation through measures such as overspeed load shedding control and adjustment of the pitch angle. Such measures can indeed reserve a part of the active power to participate in the primary frequency regulation of the system, but it will reduce the power generation efficiency of the generator set, make the control of the turbine pitch angle operate frequently, and increase the maintenance cost. In order to improve the power generation efficiency of wind turbines, improve the possibility of wind turbines participating in primary frequency regulation, and enhance the operational stability, according to the characteristics of doubly-fed wind turbines, an energy storage device is configured for a single wind turbine to participate in the primary frequency regulation of the system, and a dynamic proportional coefficient frequency regulation strategy is proposed. From the perspective of State of Charge (SOC), combined with the characteristics of virtual inertia control and virtual droop control, dynamic coefficients are allocated for the two frequency regulation methods to reduce the frequency deviation and frequency change rate caused by load disturbance. Simulation results verify that the doubly-fed wind turbine equipped with supercapacitors has significantly improved primary frequency regulation capability, providing broad ideas for the modification of already operating doubly-fed wind turbines.

To cope with the uncertainty brought by wind power integration,this paper proposes a hybrid energy storage optimization configuration strategy that combines supercapacitors and lithium batteries for coordinated stabilization,in order to improve the stabilization effect of wind power fluctuations and take into account the impact of stabilization dead zones.Based on typical daily wind power data,ensemble empirical mode decomposition （EEMD）is applied to decompose wind power signals into direct grid connected components and energy storage smoothing components；Secondly,secondary EEMD is used to decompose the energy storage power into the high-frequency component smoothed by supercapacitors and the low-frequency component smoothed by lithium batteries.A control strategy that takes into account the smoothing dead zone is proposed to address the impact of frequent charging and discharging on energy storage life,with the aim of extending equipment lifespan.This article constructs three capacity optimization configuration models that take into account the smoothing dead zone,and obtains the comprehensive cost and state of charge of the energy storage system by solving them in different scenarios.Evaluate the depth of charge and discharge using rainflow counting method and calculate the equivalent cycle life.Finally,the effectiveness of the strategy was verified by comparing the number of charge and discharge cycles and energy storage life.

To address the problems of complex backgrounds,small defect regions,susceptibility to adverse weather conditions,and the resulting low image clarity and loss of detail in transmission line insulator images,an improved SSD Single Shot MultiBox Detector）network is designed for efficient detection of insulator defects.By performing detection on feature maps at different levels,SSD can capture multi-scale targets and is more suitable for detecting small objects and subtle defects.An improved generative adversarial network is adopted to enhance image quality under rainy, snowy,foggy,and nighttime conditions.MobileNetV3 is used to replace VGG16 to achieve model light weighting and improve detection speed.In addition,CARFB （Coordinate Attention and Receptive Field Block）and SE （Squeeze-and-Excitation）attention modules are introduced into the feature layers to enhance the recognition ability for small defects such as cracks and contamination.Experimental results show that the proposed method achieves a detection precision of 93.4%and an mAP mean Average Precision）of 95.1%，enabling efficient detection of insulator defects while maintaining high mAP and recall.

Angle steel serves as a core load-bearing component of the main/diagonal members in transmission towers, and its local buckling behavior of angle steel directly affects the bearing capacity and safety of the tower structure, which is key issue in power engineering design. Based on the two-dimensional improved Fourier series method, this study conducts analytical solution research on the local buckling problem of angle steel under practical engineering scenarios. In the solving procedure, after performing Stokes' transformation on the selected trial function, the local buckling problem of angle steel can be converted into solving a set of linear algebraic equations, which significantly reduces the difficulty of theoretical solution during the engineering design phase and provides an efficient analysis tool for on-site engineers. Through numerical example comparisons, the critical load and buckling mode obtained in this study match well with existing analytical results and numerical simulation results, demonstrating the reliability of the proposed method in practical engineering applications. Based on the obtained analytical solutions, a quantitative analysis is further carried out on the commonly used angle steel section parameters in engineering, clarifying the quantitative influence laws of key geometric parameters such as aspect ratio and width-thickness ratio on the local buckling resistance of angle steel. This research provides theoretical support for the section selection, dimension optimization, and buckling-resistant design of angle steel used in the main and diagonal members of transmission towers.