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- Abstract：ObjectivesThe control of greenhouse gas emissions, mainly CO2, under the “carbon peak and carbon neutrality” target has received high attention. In 2023, China’s CO2 emissions ranked first in the world, with 12.6 billion tons. Among them, the steel industry is one of the main sources of CO2 emissions, accounting for about 15% of China’s CO2 emissions. China’s crude steel production accounts for more than half of the world’s total, and reducing carbon emissions in the steel industry requires consideration of China’s national conditions and energy utilization characteristics. Therefore, it is necessary to study the carbon capture and utilization technologies suitable for China’s steel industry.MethodsThe emission characteristics of CO2 in the steel production process are analyzed and the research progress of existing carbon capture technologies are summarized, with a focus on the application of absorption, adsorption, and membrane separation methods in CO2 capture of blast furnace gas. In addition, the utilization of top gas recycling oxygen blast furnace technology is explored and its coupling potential with absorption and adsorption technologies is analyzed, in order to achieve low-carbon transformation in the steel industry.ConclusionsThe use of top gas recycling oxygen blast furnace technology can be combined with absorption or adsorption methods to reduce carbon consumption and carbon emissions of the blast furnace, and the effective utilization of steel plant waste heat can help improve the economic feasibility of carbon capture. The integration of carbon capture and utilization in the steel industry can be realized by the combination of converter CO2 injection, steel slag mineralization and utilization, and tempering co-production of CO2 to produce chemical products technology. It can not only reduce the carbon emissions in the steel industry, but also improve the industry’s economic and environmental benefits through capacity optimization.Keywords：carbon peak;carbon neutrality;carbon capture, utilization and storage (CCUS);carbon emissions;steel industry;top gas recycling oxygen blast furnace;chemical absorption;adsorption separation;membrane separation65|23|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesConsidering the premise of clean and low-carbon system, the pricing of energy products in the integrated energy system is closely related to the economic interests of market users and the profitability of energy operators. Exergic economic analysis of integrated energy system (IES) containing power to gas-carbon capture power plant (P2G-CCPP) is proposed.MethodsFrom the perspective of exergy, both the quantity and quality of energy were taken into account, exergy values are calculated for different energy flows, and a sub-model of exergy flow calculation is established. The low carbon economy of the system is introduced into the exergic economic analysis. The carbon emission trading cost, carbon capture energy cost and carbon storage cost are included in the non-energy cost. The exergic economic cost distribution model is established. Compared with traditional pricing methods, the paper considered the impact of carbon cost and energy grade difference on the exergic cost of the system, reflecting the principle of good quality and good price.ResultsSimulation examples show that the P2G-CCPP linkage system can improve the low carbon emission reduction ability of the system and realize the reasonable cost allocation.ConclusionsThe integrated energy system with P2G-CCPP units can effectively reduce the total system cost and the unit cost of cooling, heating and power, enhance the energy conservation and emission reduction approaches of carbon utilization and gas production and consumption, and has significant economic and social benefits.Keywords：integrated energy system;power to gas;carbon capture power plants;exergy flow;exergoeconomy;cost allocation9|5|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
Carbon Neutrality
- Abstract：ObjectivesIn large-scale multi-energy complementary bases, there are multiple types of resources, significant fluctuations in new energy output, complex operating conditions, and diverse demands of owners, leading to great challenges in the actual operation optimization. Therefore, it is necessary to design and develop operation scheme of the wind-solar-thermal-storage coupled system according to engineering practice.MethodsThe operation model of large-scale new energy bases is established. Taking economic efficiency, carbon emission, load fluctuation rate, and new energy accommodation rate as the core optimization objectives, this study examines the three-stage multi-mode operation optimization scheme by comprehensively considering various operation requirements and equipment flexibility under different time scales. Combined with actual engineering cases, the typical days of the year and each season are analyzed to verify the effectiveness of the scheme.ResultsThe dispatching method can effectively balance multiple optimization objectives, give consideration to equipment flexibility and diverse requirements, and improve the economic efficiency and operational friendliness of large-scale new energy bases.ConclusionsThe proposed scheme can effectively solve the multi-mode and multi-stage problems of large-scale new energy base operation optimization. It is suggested that the operators sign power export curve contracts based on the output characteristics of new energy in different seasons to improve the economic efficiency. The research results can provide engineering case reference and technical support for operation of the same type of large-scale new energy bases.Keywords：multi-energy complementation;new energy base;wind-solar-thermal-storage;new energy accommodation rate;operation optimization;low-carbon transformation;operation mode9|6|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesHigh-temperature solid oxide electrolysis cell (SOEC) technology is regarded as one of the key technologies to achieve sustainable hydrogen production due to its advantages of high efficiency, environmental friendliness, and waste heat utilization. Therefore, the industrialization development of SOEC technology is systematically reviewed and prospected, so as to provide multiple application scenarios for water electrolysis-based hydrogen production in China.MethodsFirst, the research progress, market applications, and challenges encountered in the process of industrialization of SOEC technology at home and abroad are analyzed in depth. Second, the application potential and related technical solutions of SOEC technology in different application scenarios are evaluated. Finally, the future development prospects and trends of SOEC technology are discussed based on the main SOEC technology manufacturers and related projects at home and abroad.ConclusionsSOEC technology is a key way to achieve sustainable hydrogen production. Its industrialization process urgently requires removing barriers to coordinated development across upstream and downstream industrial chains. At the same time, in order to achieve its large-scale application, it is necessary to solve a series of critical technical challenges through technological innovation. With the maturity of the technology, SOEC is expected to be widely used in the chemical industry, distributed power generation, and other fields, making a significant contribution to meeting global energy demands and achieving decarbonization goals.Keywords：hydrogen energy;renewable energy;water electrolysis;hydrogen production technology;high-temperature solid oxide electrolysis cell (SOEC);industrialization analysis;commercial application;application scenarios8|6|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesTo make full use of historical information, maximize the optimization of model effect and improve the accuracy of photovoltaic (PV) power prediction, a short-term PV power prediction method considering weather-coupled similar days is proposed.MethodsFirstly, fuzzy C-means clustering is used to divide the dataset into different weather types, and the correlation weight factors are calculated according to the affiliation and features selection of the day to be predicted for each weather type, and the similarity of the historical days is calculated by grey relation analysis, and similar days with weather coupling are selected. The similar days are decomposed into modal components with different frequencies by variational mode decomposition to realize further denoising. Secondly, in order to fully utilize the nonlinear fitting ability of the model, the red-tailed hawk algorithm (RTHA) is used to optimize the hyperparameters of the bidirectional long short-term memory (BiLSTM) network model, and the RTHA-BiLSTM model is constructed to predict the modal components. Finally, taking the actual data of a power plant in Jiangsu Province as an example, the simulation experiment is carried out to verify the effectiveness of the proposed method.ResultsIn sunny, cloudy and rainy scenarios, compared with the method without similar days, the proposed method reduces the root mean square error (RMSE) by 9.1%, 6.1%, 2.9% and 11.1%, 6.5%, 13.9% in the single model and the combined model, respectively.ConclusionsThe proposed method can effectively improve the prediction accuracy of PV power, has good robustness and strong prediction ability, and can better cope with the prediction tasks in different scenarios.Keywords：photovoltaic (PV) power generation;power prediction;fuzzy C-means clustering;grey relation analysis;similar days;red-tailed hawk algorithm6|7|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesIn cold regions, the icing problem on wind turbine blades significantly reduces power generation efficiency and increases safety risks, making accurate icing prediction technology crucial. To improve the accuracy of ice accretion prediction for wind turbine blades, this study proposes an ice accretion prediction model based on multi-layer perceptron neural network.MethodsThe study combines orthogonal experiments with computational fluid dynamics to collect ice accretion feature data for wind turbine blades under different operating conditions. Based on these data, two prediction models are developed: multiple linear regression and multi-layer perceptron neural network.ResultsPerformance evaluations using metrics such as average relative error and maximum relative error reveal that the ice accretion prediction model based on multi-layer perceptron neural network achieves an average relative error of less than 7% and a maximum relative error of less than 20% in predicting both ice mass and maximum ice thickness for glaze ice. For rime ice, the model achieves an average relative error of less than 3% and a maximum relative error of less than 13%. By comparison, the multi-layer perceptron neural network model outperforms the multiple linear regression model in terms of relative error and other metrics.ConclusionsThis study provides a novel and more accurate method for ice accretion prediction in the wind power industry, contributing to improved safety and efficiency in wind power generation.Keywords：wind power generation;neural network;multi-layer perceptron;wind turbine blade icing;rime ice;glaze ice;prediction model;wind farm7|5|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
New Energy
- Abstract：ObjectivesIn order to provide directional reference and strategic basis for promoting the standardization and normative development of new energy storage systems in China, it is necessary to evaluate the completeness and applicability of existing energy storage system standards, identify the advantages and deficiencies within the current standard system, and clarify specific standardization requirements arising from industrial development trends and emerging technologies.MethodsFirst, a standard framework for energy storage systems is developed, encompassing five domains: fundamental and generic standards, performance requirements, communication functions, testing and experimentation, and safety requirements. Second, relevant energy storage system standards from both domestic and international sources are systematically compared. Subsequently, key technical standards issued in recent years are analyzed with a focus on discerning their similarities and differences, followed by an in-depth interpretation. Finally, based on the analysis of current status, a strategic roadmap for standard development in key industrial sectors is proposed.ConclusionsCurrently, the development of China’s energy storage standard system has preliminarily established a framework covering major technical aspects, which is relatively well-developed in areas such as fundamental and generic standards as well as performance requirements. However, deficiencies such as a lack of standards or insufficient coordination persist in areas including communication protocols and fire safety regulations. In addition, discrepancies in key technical indicators among different standards remain, constraining the standardization and interoperability of energy storage systems in large-scale applications.Keywords：energy storage system;status of standards;system framework;standard comparison;fundamental and generic standards;performance requirements;testing and experimentation;safety requirements5|4|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesFlywheel energy storage that assists thermal power units to participate in frequency regulation is widely used in the field of grid frequency regulation. In view of the fact that the current commonly used energy storage control strategies fail to fully consider the operating requirements of frequency in different stages of change, an adaptive frequency regulation control strategy for flywheel energy storage-thermal power units is proposed that considers the effects of positive and negative inertia.MethodsConsidering the effects of positive and negative inertia control on frequency recovery at different stages of frequency variation, this study proposes an adaptive frequency control method for a thermal power-energy storage coupled system under dynamic operating conditions. This method fully integrates the advantages of inertia control and droop control, and adaptively adjusts the control strategy according to the system frequency deviation and the real-time state of charge (SOC) of energy storage.ResultsUnder step disturbance, the proposed strategy reduces the maximum dynamic frequency deviation by 2.8%, the steady-state deviation by 2.9%, and the settling time by 17 s, while the change curve of SOC becomes smooth. Under continuous disturbance, the proposed strategy can reduce the frequency deviation by 8.1% and reduce the output of thermal power units by 52.8%. The SOC of the energy storage system can be maintained within a good range throughout the command period.ConclusionsThe proposed strategy can improve the frequency regulation performance of the system, reduce unit output fluctuations, and take into account the state of charge management of the energy storage system.Keywords：new energy;flywheel energy storage;droop control;frequency regulation;positive and negative inertia;adaptive switch;state of charge5|3|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesWhen assisting the frequency regulation of the thermal power units, battery energy storage faces difficulties in coping with high-amplitude and high-frequency disturbances. To address these issues, a hybrid energy storage strategy for participation in secondary frequency regulation control, based on hierarchical adaptive control, is proposed.MethodsThe outer layer discusses the relationship between system residual energy and the proportional factor, proposing an adaptive proportional factor control strategy based on mathematical function methods. This strategy reasonably allocates regional control signals based on the system’s current frequency regulation capacity. Meanwhile, the inner layer optimizes the output control of the generation units and energy storage outputs. The unit output is controlled using a variable fuzzy PI controller, and energy storage output is controlled using a two-layer fuzzy control with variable time constant low-order filtering. This ensures that energy storage is not overcharged or over-discharged while adaptively allocating the frequency regulation signals. Simulation analysis is conducted using continuous load disturbances.ResultsCompared with the traditional fixed K method, the proposed adaptive proportional factor control strategy reduces the maximum frequency deviation by 2.2%, reduces the energy storage contribution by 31.7%, and demonstrates better maintenance performance of state of charge.ConclusionsThe proposed control strategy can optimize the system’s frequency control and provide a new approach for maintaining grid frequency safety.Keywords：hybrid energy storage;thermal power unit;secondary frequency regulation;control strategy;adaptive proportional factor;state of charge;system residual energy5|5|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesVirtual synchronous generator (VSG) technology is extensively utilized for its capability to regulate damping and inertia during inverter grid connection. However, line impedance is commonly present, and voltage and current distortions are prone to occur during the pre-synchronization process of disconnection and grid connection. Therefore, the control structure and pre-synchronization method of VSG are optimized.MethodsA photovoltaic power generation system and hybrid energy storage are used to replace a traditional DC source, simulating actual conditions. Virtual active power and virtual current are incorporated into the conventional VSG model to compensate for phase and amplitude differences during grid connection, thereby achieving frequency and voltage regulation. To address the issue of selecting PD control parameters in a traditional linear active disturbance rejection control (LADRC) system, fuzzy control is employed to adaptively adjust these PD parameters online. Furthermore, LADRC is introduced into the active power loop for secondary frequency regulation, which enhances system robustness.ResultsThe feasibility of the proposed method is verified through MATLAB/Simulink simulation, which demonstrates the excellent performance of the pre-synchronization control system.ConclusionsThe fuzzy LADRC enables rapid and smooth grid connection, suppresses grid-side current surges, and reduces frequency oscillations, thereby improving the stability of the grid-connected inverter-grid interaction system.Keywords：virtual synchronous generator (VSG);microgrid;distributed energy;pre-synchronization;impedance;virtual current;fuzzy linear active disturbance rejection control;photovoltaic storage system9|5|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesIn order to effectively improve the energy storage characteristics and system efficiency of compressed air energy storage (CAES) systems, the performance of each component and their coupling characteristics are modeled and analyzed.MethodsIn this study, for CAES systems, the standard thermal resistance method is applied to take into account the transfer characteristics of the heat exchange components. Combined with the gas storage device and the work component model, this study establishes a comprehensive thermodynamic model integrating heat transfer, gas storage, and work coupling, along with an energy and exergy analysis model. The influence of the mass flow rate of energy storage and release on the system performance during compression and expansion is evaluated. The pattern of influence of the relationship between the maximum gas storage pressure and volume of the gas storage reservoir on the system energy storage efficiency and energy storage density is revealed.ResultsWith the increase of the mass flow rate of energy storage and release, the total energy consumption of the compressor first decreases and then increases, and the output work of the expander first increases and then decreases. When the mass rates of energy storage and release are 1.2 and 1.3, respectively, the system efficiency reaches the maximum value of 53.65%. In addition, the exergy loss of the first-stage compression and expander in the system is the largest, and the exergy loss of the oil-gas heat exchanger and the compressor accounts for 69% of the total exergy loss of the system.ConclusionsThe established model and the research findings provide important guidance for CAES operational strategy.Keywords：compressed air energy storage;heat exchanger;thermal resistance;exergy loss;mass flow;system efficiency;gas storage device7|3|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
State of Health Estimation for Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy
Abstract：ObjectivesLithium-ion batteries, as key components in electric vehicles and energy storage systems, require accurate prediction of their state of health (SOH) to ensure system reliability, extend battery life, and optimize energy management. However, in actual operation, the performance of batteries deteriorates due to multiple factors. Therefore, a high-precision SOH prediction method based on electrochemical impedance spectroscopy (EIS) data is proposed.MethodsExperiments are conducted using EIS data, and the linear Kramers-Kronig algorithm is employed to preprocess the EIS data. A convolutional neural network (CNN), bi-directional long short-term memory (BiLSTM) network, and an attention-based combination model are adopted as prediction models. The real part, imaginary part, and magnitude of the impedance from the EIS data are used as inputs. CNN is utilized to extract important features, which are then integrated with the BiLSTM network model to predict the SOH. Additionally, the Attention mechanism and Dropout algorithm are applied to optimize the model.ResultsThe prediction of SOH of lithium-ion batteries at different temperatures and the comparison with the CNN-BiLSTM and BiLSTM models show that the CNN-BiLSTM-Attention model has better SOH prediction effects at 25, 35, and 45 ℃, with accuracy improvements of 46.1% and 77.9% respectively compared to the two models.ConclusionsSOH prediction based on EIS data is feasible. The CNN-BiLSTM-Attention combination model is able to achieve accurate and efficient prediction of the SOH of lithium-ion batteries, demonstrating strong practicality.Keywords：new energy;electric vehicle;lithium-ion batteries;state of health;electrochemical impedance spectroscopy;convolutional neural network;bi-directional long short-term memory;prediction model11|3|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
Energy Storage
- Abstract：ObjectivesTo enhance the heat transfer and cooling performance of blade tips and explore how curved micro-ribs induce cooling air flow and affect heat transfer performance, this study uses a numerical solution of three-dimensional Reynolds-averaged Navier-Stokes (RANS) equations and standard k-ω turbulence models. The effects of streamwise micro-rib film cooling arrangements on heat transfer and cooling performance of cavity turbine blade tips are analyzed.MethodsBased on the cavity blade tip of the first stage in GE-E3 high-pressure turbine, and following previous experience, a film cooling arrangement (case 1) is designed with large-diameter film holes at the leading and trailing edges, and two small-diameter film holes near the pressure side at the mid-chord region. Then, two additional small-diameter film holes are added near suction side at the mid-chord region where cooling air is scarce, and four mid-chord film holes are arranged at three positions to investigate the effect of hole position. For these three cooling arrangements, a curved micro-rib with 30% arc length is added to explore the effect of rib structure.ResultsCompared to case 1, placing the micro-rib at the leading edge of the cavity reduces blade tip leakage flow by 0.9% and total pressure loss at the outlet cross-section of flow passage by 0.5%. Positioning the micro-rib near the trailing edge results in an 8% reduction in blade tip heat transfer coefficient, and a 16% reduction when the micro-rib surface is excluded. Additionally, the streamwise micro-rib film cooling arrangement at this position achieves a film cooling effectiveness of 0.43 and optimal cooling uniformity.ConclusionsThe research findings can provide valuable references for the coupled design of high-performance blade tip structures in gas turbines.Keywords：gas turbine;streamwise micro-rib;cavity blade tip;heat transfer coefficient;film cooling effectiveness;cooling performance;Reynolds-averaged Navier-Stokes (RANS) equation6|3|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesWith the rapid increase in the installed capacity of renewable energy generation, the requirements for the operational flexibility of coal-fired power units are becoming increasingly stringent. In this context, the control of key thermal parameters of the units becomes more challenging, and the system energy efficiency decreases during transient load change processes. Therefore, it is necessary to develop a multi-parameter coordinated control strategy to balance load change rates with energy efficiency levels.MethodsBased on the GSE simulation platform, this study constructs and validates a dynamic model of a 350 MW supercritical coal-fired power unit. Additionally, a boiler system heat storage model incorporating the heat storage of the working fluid and pipeline metal is established. On the basis of adopting high-pressure heater extraction steam throttling, an optimized control strategy is proposed that takes into account the change patterns of internal heat storage within the boiler system.ResultsDuring the load increase from 30% to 50% turbine heat acceptance (THA), high-pressure heater extraction steam throttling increases the maximum load increase rate of the unit from 1.5%Pe/min (where Pe represents the rated load) to 2.5%Pe/min. However, the average standard coal consumption rate during the transient process reaches 314.81 g⋅(kW⋅h)-1. Under the premise of meeting the power standards, the optimized control strategy achieves an average coal savings of 0.67 g⋅(kW⋅h)-1 during the transient process.ConclusionsThe optimized control strategy can not only significantly increase the load increase rate but also efficiently improve the system energy efficiency, providing strong support for the efficient and stable operation of coal-fired power units.Keywords：coal-fired power generation;coal-fired boiler;deep peak shaving;low load;ultra-supercritical unit;energy consumption analysis;boiler heat storage;transient load change process7|3|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesOver the past 30 years, the conflict between electricity and coal prices has persisted in China. To straighten out the relationship between coal and electricity prices and to promote the comprehensive, coordinated, and sustainable development of the coal and power industries, it is necessary to establish evaluation indicators and price adjustment formulas for quickly assessing the rationality of electricity and coal prices for coal-fired power plants, so as to provide a new set of indicators for economic decision-making analysis for coal-fired power plants.MethodsFirst, the energy value analysis method is applied to investigate the physical process of converting coal energy into electric energy in coal-fired power plants. Then, the intrinsic relationship between coal and electricity prices is derived through mathematical deduction. Finally, the calculation formulas for indicators such as the coal-to-electricity energy price ratio and the coal-electricity price linkage are derived.ResultsIndicators such as the critical coal price, critical coal-to-electricity energy price ratio, reasonable coal-to-electricity energy price ratio, reasonable on-grid electricity price, and reasonable coal price are proposed. The reasonable coal-to-electricity energy price ratio for different units ranging from 300 to 1000 MW is calculated to be 3.68‒4.17.ConclusionsThe coal-to-electricity energy price ratio and related indicators provide a new method for power plants and relevant government departments to evaluate the rationality of electricity and coal prices. They can provide a scientific basis for government departments to formulate reasonable on-grid tariffs, for coal-fired power plants to develop scientific operational strategies, and for plants to set bidding prices.Keywords：coal-fired power plants;coal price;electricity price;economic analysis;coal-to-electricity energy price ratio;power generation cost;coal-electricity price linkage9|5|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesAiming at the problem that the acoustic emission (AE) signal induced by the cavitation of a hydraulic turbine is disturbed by background noise and the fault is difficult to identify, a state recognition method based on energy-entropy features and Harris hawks optimization (HHO) algorithm combined with 3-fold cross-validation (3Fold) to optimize stacked denoising auto encoder (SDAE) is proposed.MethodsFirst, the variational mode decomposition algorithm is used to decompose the signal, and then a series of intrinsic mode functions are obtained. Second, the energy and entropy features of the two intrinsic mode functions are extracted, which have the largest correlation coefficient with the original signal. A 12-dimensional feature vector is constructed and then the vector is imported into the recognition model. Third, the HHO algorithm combined with 3Fold is used to optimize the hyper-parameters of SDAE. Finally, the optimal parameters obtained by the HHO-3Fold-SDAE algorithm and other algorithms are input into the model for comparison.ResultsCompared with other algorithms, the HHO-3Fold-SDAE algorithm has smaller loss rate and accuracy variance, and higher average accuracy. Compared with SDAE, its test set average accuracy is increased by 6%. Compared with HHO-SDAE, its test set average accuracy is increased by 4% and accuracy variance is decreased by 17%.ConclusionsThe proposed method can be used to classify and recognize AE signals induced by cavitation of hydraulic turbine, and provide reference for the condition monitoring of hydraulic machinery.Keywords：hydropower;hydraulic turbine;cavitation state recognition;Harris hawks optimization (HHO) algorithm;stacked denoising auto encoder (SDAE);entropy8|4|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
Power Generation and Environmental Protection
- Abstract：ObjectivesTimely and accurate detection of line-break faults is crucial for ensuring the normal and safe operation of distribution networks. However, the application of traditional criteria for single-phase line-break fault detection in active distribution networks has certain limitations. Therefore, a single-phase line-break fault detection method based on an improved support vector machine and integrating multiple electrical characteristic quantities is proposed.MethodsFirst, an electrical characteristic index system incorporating startup criteria, traditional criteria, and active criteria is established. Second, the startup criteria are processed using the switching quantization method. Subsequently, kernel principal component analysis is employed to eliminate low-contribution feature indicators from the characteristic index system excluding the startup criteria. Finally, the dimensionality-reduced data are input into the support vector machine, and the sparrow search algorithm is utilized to optimize the parameters of the support vector machine, thereby obtaining the line-break fault detection model.ResultsSimulation case studies conducted on a modified IEEE 15-node model demonstrate that the proposed method effectively realizes dimensionality reduction of the characteristic quantities and improves the detection accuracy by 8.87% compared to using a single criterion.ConclusionsThe proposed method solves the problem that traditional criteria for single-phase line-break fault detection are prone to failure, and can effectively achieve fault detection under different scenarios.Keywords：active distribution network;single-phase line-break;support vector machine;sparrow search algorithm;kernel principal component analysis7|5|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesTo ensure stable and smooth energy flow between AC and DC subnets and address the degradation of interlinking converter control performance caused by uncertain switching of high-power AC or DC loads, a power coordination control strategy for interlinking converters based on a second-order linear active disturbance rejection controller is proposed.MethodsBased on the transmission power of the converters, a current observer is utilized to estimate the disturbance caused by high-power load switching in the AC or DC subnet. Then, the disturbance is compensated in the feedback channel of the control loop to ensure that the frequency of the AC subnet and the bus voltage of the DC subnet can always remain within permissible ranges during high-power load switching. Finally, an electromagnetic transient simulation model of an isolated AC-DC hybrid microgrid is established based on MATLAB/Simulink to verify the effectiveness of the proposed ADRC-based power coordination control strategy.ResultsA comparison with traditional control strategies shows that the proposed control strategy improves the reliability and stability of the microgrid.ConclusionsWhen a large power load is switched on in an independent microgrid, the proposed control strategy can accurately regulate the active power flowing through the interconnected converter in real time, ensuring that the AC subnet frequency and DC bus voltage are within the stable operating range, to solve the problem of using traditional PI controllers. The problem of system instability caused by lagging tracking large load switching.Keywords：AC/DC microgrid;active disturbance rejection control (ADRC);droop control;interlinking converter;voltage instability;load switching9|7|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesWith the increase in electrical equipment and frequent natural disasters, the demand for distribution network resilience is increasing. Therefore, a strategy of optimizing distributed resource cooperative allocation based on typhoon disasters is proposed.MethodsFirst of all, the power supply recovery potential of electric vehicle users participating in typhoon disasters is fully considered, and the power grid failure and electric vehicle pre-disaster scheduling model under typhoon disaster is constructed. Then, aiming at the lowest load curtailment cost and network fault risk cost, the optimal allocation model of distributed renewable energy is established. Finally, taking the IEEE 33-node system as an example, three scenarios are set up for comparative study.ResultsThe comparative analysis results of the three scenarios show that the proposed strategy can simultaneously achieve the lowest percentage of load curtailment reduction and the sum of branch cumulative over-limit power. Among them, the power loss reduction percentage is 95.53%, and the sum of branch cumulative over-limit power is 29.23%.ConclusionsIn the case of typhoon disaster, the proposed strategy can effectively restore the system load curtailment, improve the elasticity of the power grid, and reduce the operation risk of the system.Keywords：distributed resources;electric vehicle dispatch;typhoon disaster;grid resilience;multi-source coordination;distribution network;vehicle-to-grid (V2G)11|5|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesTo better integrate high-density photovoltaic (PV) energy, energy storage devices are integrated into distribution networks to achieve peak shaving and valley filling of electrical loads and alleviate the effect of distributed PV on grid voltage.MethodsThrough appropriate selection of energy storage scheduling and distributed PV output strategies, a multi-objective optimization method is used to optimize the power flow of distribution networks, aiming to minimize network losses and voltage deviations. A comprehensive evaluation system is established using the analytic hierarchy process-entropy weight method, and an optimization model is developed using a particle swarm optimization algorithm to obtain the optimal scheduling strategy for PV and energy storage coordination.ResultsCalculations and analysis are conducted on a 30-bus simulation model and practical cases. It is demonstrated that the energy storage system effectively mitigates PV fluctuations, ensures voltage stability at buses, and improves energy utilization efficiency. Compared with traditional algorithms, the proposed method shows significant advantages in power quality and network loss control.ConclusionsThe method enables efficient and stable operation of distribution networks, reducing system network losses.Keywords：distribution network optimization;high-density photovoltaic;energy storage;multi-target optimization;PV-storage coordination;particle swarm algorithm;optimal scheduling;comprehensive evaluation7|4|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
- Abstract：ObjectivesAggregating distributed resources within the regional grid based on virtual power plant (VPP) technology can effectively improve system flexibility with low marginal cost. However, data barriers arising from factors such as information security, the computational efficiency of distributed coordination, and the uncertainty of regulatory resources pose difficulties for VPP auxiliary service decisions. Accordingly, this paper analyzes the external characteristics of VPP as a whole, including interconnection power, reserve capacity, and operating cost. It proposes a trusted quantification method for robust scheduling characteristics of VPP and constructs a collaborative scheduling model for multiple VPPs.MethodsFirst, considering the uncertainty factors in distributed power and demand response, the source-load reserve potential under the network constraint is analyzed to establish a VPP mathematical model. Secondly, combining robust optimization and multi-parameter programming theory, the VPP interconnection power regulation range, flexible reserve capacity, and optimal cost robust feasible domain trusted quantification are realized. An affine relationship is established between the internal resource regulation strategy of the VPP and the external transaction results, thereby completing the VPP equivalence aggregation. Further, a cooperative game model is constructed for the effective interaction between multiple VPPs and the grid. Finally, the effectiveness of the model and method is verified by three test cases.ResultsThe quantified encapsulation model participates in scheduling, has higher computing efficiency, and also protects the privacy of internal information within the VPP. By designing parallel programs, the computational efficiency of the encapsulation model quantification process is further improved.ConclusionsThe quantified encapsulation model participates in scheduling, has higher computing efficiency, and also protects the privacy of internal information within the VPP. By designing parallel programs, the computational efficiency of the encapsulation model quantification process is further improved.Keywords：virtual power plant;trusted quantization;robust optimization;multi-parameter programming;equivalence aggregation;cooperative games8|6|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-03
New Power System
