Volume 40, Issue 6 (2025)
Smart grid
Research on multi-objective planning of power transmission network considering power angle stability
Zifa LIU and Haoyu ZHENG
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.001
With the continuous improvement of the p enetration level of renewable energy in the power transmission network, the negative resistance effect of the unit side caused by the grid connection of new energy is significantly enhanced, which brings severe challenges to the power angle stability of the power system and then restricts the ability of new energy consumption. In this study, the power angle stability characteristics of multi-node power transmission networks are systematically analyzed by establishing a ternary single-port coupling equivalent model including traditional generator sets, new energy access nodes, and equivalent load networks. Different from the traditional stand-alone system analysis method, a static power angle stability margin quantification index suitable for complex power grid structures is innovatively proposed, which can effectively characterize the system operation boundary, tackling the bottleneck posed by the marked nonlinearity of existing static voltage stability indexes and their inadequacy for grid planning optimization. Then, a marginal index linearization strategy based on the improved McCormick envelope method is proposed. By integrating ε constraint theory and the multi-objective mixed integer programming method, a power transmission network expansion planning model with both economy and stability is constructed. In particular, the Pareto optimal solution set screening mechanism is used to realize multi-objective collaborative optimization to ensure the optimal comprehensive performance of the planning scheme. The HRP-38 high-proportion new energy grid planning case is verified, which fully proves the effectiveness and engineering applicability of the model.
Research on mutual support capacity analysis and optimization evaluation of mutual support capacity for interconnected regional power grids
Zhiqiang LIU, Geng CHEN, Aihong TANG, Caifu CHEN, and Lulu MA
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.002
In order to determine the mutual support capacity between regional power grids with complementary characteristics in terms of new energy output and electrical load demand, fully leverage the mutual support advantages of cross-regional interconnected grid capacity, reduce allocation of system reserve capacity, and enhance system operation reliability, the complementary characteristics between new energy output and electrical load demand in interconnected regional power grids and the feasibility of the mutual support between regional power grids are analyzed from the perspective of source-load matching. An algorithm for quantifying the mutual support capacity of the residual power after source-load matching is proposed. To fully exploit mutual support potential while considering economic efficiency, based on the principle that all regional power grids are given priority to participating in internal electric energy interaction, a spatial-temporal mutual support capacity optimization evaluation model of regional grids is constructed, which incorporates constraints such as internal and external power exchange within the system and residual power mutual support capacity after source-load matching, so as to minimize the overall operational cost of interconnected regional power grids. The Cplex solver embedded in MATLAB is employed to perform case simulation analysis and obtain the solution, thereby validating the effectiveness of the proposed model.
Load frequency control of multi-area interconnected power systems under aperiodic DoS attacks
Guanyu LIU and Yonghui LIU
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.003
Based on an event-triggered mechanism, load frequency control of multi-area interconnected power systems under aperiodic denial-of-service (DoS) attacks is studied. First, a mathematical model of the multi-area interconnected power system under aperiodic DoS attacks is established. Then, a switching state observer is designed to estimate the states during the hibernating and active periods of the aperiodic DoS attacks, respectively. To save communication resources, an event-triggered mechanism is introduced into the observer-controller channel. Moreover, based on the state observer, a feedback controller is designed. Finally, the three-area interconnected power system is taken as an example to make a simulation-based verification. The simulation results demonstrate that the proposed control method can restrain the adverse impact of the aperiodic DoS attacks effectively.
Research on transformer fa ult pattern recognition based on double-layer XGBoost algorithm optimiz ed by Borderline SMOTE and NGO
Youcai LI, Weilong PENG, Yuming ZHOU, Qiyan LI, Cheng ZENG, and Xin YANG
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.004
To enhance the diagnostic accuracy of transformer fault, raise the efficiency of model recognition, and mitigate the adverse effects of imbalanced samples on transformer fault recognition models, a transformer fault pattern recognition model with double-layer extreme gradient boosting (XGBoost) based on borderline synthetic minority over-sampling technique (BSMOTE) and northern goshawk optimization (NGO) algorithms is proposed. Firstly, BSMOTE is used to expand minority class samples, and a balanced dataset is obtained. Secondly, the noncoding ratio method is used to establish multi-dimensional feature quantities, and XGBoost is used to determine the optimal feature subset. Then, an NGO algorithm is used to optimize the XGBoost parameters, and a transformer fault diagnosis model is obtained, achieving accurate recognition of transformer faults. Finally, practical cases are adopted to make a simulation analysis of the proposed method. The diagnostic accuracy of the proposed method is 2.88%, 4.03%, 4.44%, and 7.47% higher than that of the recursive feature elimination (RFE), random forest (RF) feature screening, categorical boosting (CatBoost) feature extraction, and the 19-dimensional feature, respectively. The results show that the method proposed in this article has higher fault recognition accuracy, lower misjudgment rate, and stable performance.
Research on distance protection under critical condition based on improved ellipse fitting
Mei LI and Zijian ZHANG
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.005
When disturbances such as sudden load changes, generator tripping, or transmission line faults occur, the system will experience a power swing (PS). In this case, the impedance trajectory detected by the distance protection may encroach into zone Ⅲ of the protection, leading to maloperation or refusal operation of the distance protection and triggering cascade failures. Therefore, it is crucial to accurately distinguish PS and faults. A distance protection method based on improved ellipse fitting is proposed. Firstly, a Lissajous figure (LF) is constructed by using voltage and current signals at the protection mounting location, and a critical condition detection algorithm (CCDA) based on this figure is implemented. Then, the elliptical area and its Hausdorff distance from the fitted signal trajectory are calculated to distinguish the critical conditions and faults of the system. Next, the critical conditions such as various PSs, load intrusion, voltage instability, and faults at the electrical center are simulated and analyzed. The influence of factors including fault location, fault resistance, fault type, and inception angle are also analyzed. Finally, by simulating the case studies, it is demonstrated that the proposed algorithm is applicable to various complex critical conditions and accurately fits signal trajectories; the tripping is avoided even when the observed impedance trajectory intrudes into zone III of the protection; the functions of PS blocking and de-blocking are rapidly recognized and executed. The research results show that the method does not rely on high sampling rates, exhibits easy calculation and good noise immunity, and maintains fast response speed, effectively distinguishing remote PSs and high-resistance faults.
Timing fault identification strategy for weak lines in power grid based on deep reinforcement learning
Songkai LIU, Yukun AI, Pan SU, Yanzhang LI, Yuheng WU, and Wenpei DING
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.006
To effectively prevent cascadin g faults of the power grid caused by line timing faults, an identification method that integrates deep reinforcement learning (DRL) and transient stability constraints is proposed. The core of this method lies in formalizing the identification task as a Markov decision process (MDP) problem, enabling the DRL agent to efficiently screen out the key fault paths that cause system instability through interactive learning with the transient simulation environment. Firstly, a vulnerability index combining Q value and timing cumulative effect is introduced, achieving precise positioning of weak lines. By combining the time-domain transient simulation calculation of the power grid, the key faults that are prone to cause power grid instability are screened out. Then, the line weakness index is proposed through Q learning combined with the cumulative effect of timing faults, and the weak lines in the power grid considering the transient stability constraint under the cumulative effect of timing faults are calculated and obtained. Finally, IEEE 5 node, IEEE 39 node, and IEEE 300 node systems are used to simulate as test cases. The simulation results all verify the applicability of the proposed method in terms of learning efficiency and weak link identification in the power grid. Research results show that the proposed DRL method, based on Q learning, combines optimistic initial guessing and greedy algorithm to achieve selection for critical faults and evaluate the learning efficiency under different line fault conditions, with favorable stability and fast training speed.
Research on oscillation suppression of multi-VSG based on transient inertia and damping optimization
Junxiang LIU, Xiangyang XIA, Yu GONG, Wangfang XU, Shaoyu MA, and Tian XIA
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.007
The parallel operation of multiple virtual synchronous generators (VSGs) in large-scale renewable energy power stations may lead to the system's power frequency oscillation issues. To better coordinate multi-unit operation and enhance oscillation suppression, an oscillation suppression strategy optimizing transient inertia and damping is proposed. Firstly, based on a small-signal model, the oscillation mechanism and the impact of fixed parameters on stability are elucidated. Secondly, the system inertia centwe is introduced as a global coordination reference. Inertia and damping characteristics are optimized based on each VSG's frequency deviation from the inertia center and its rate of change. This accelerates transient energy decay, effectively minimizes frequency differences among VSGs under load disturbances, and significantly reduces system regulation time during the transient process while lowering impact power without compromising steady-state operation. Thirdly, the stability of the proposed strategy is demonstrated using Lyapunov's theorem. Finally, the effectiveness of the proposed strategy is validated through simulations in SIMULINK and on the RT-LAB hardware-in-the-loop platform. This research provides theoretical support and reference for the parallel operation of multiple VSGs within large-scale renewable energy power stations.
Joint regulation strategy of rural distribution networks for renewable energy accommodation and seasonal load supply
Zhumeng SONG, Bao WANG, Jianxiong JIA, Fei WU, Li ZHANG, Chengjia ZHANG, and Xiaodong YANG
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.008
In view of the problems of new energy accommodation and seasonal load supply pressure in a rural distribution network with extensive access to electric vehicles and new energy power generation, a joint regulation strategy is proposed which utilizes soft open points to achieve coordinated cooperation of flexible interconnection of transformers, orderly charging of electric vehicles, and rational economic expansion of transformer capacity, thereby ensuring seasonal load demand in renewable energy distribution networks while enhancing renewable energy accommodation capacity. Firstly, the usage and load characteristics of different transformer districts in rural areas under the background of renewable energy are analyzed. Secondly, the orderly charging model of electric vehicles, the transformer capacity evaluation system model, and the cooperative regulation operation model are established. Thirdly, the constructed nonlinear optimization model is transformed into a second-order cone programming model through convex relaxation and linearization methods. Finally, simulation experiments are carried out by using the improved 33-node distribution network, which confirms the practical rationality and effectiveness of the regulation strategy proposed in this paper. The research findings demonstrate that the proposed strategy can effectively improve the transformer utilization rate in rural areas and enhance the renewable energy accommodation capacity, as well as the seasonal load supply capacity when transformer capacity is insufficient.
Method for enhancing distribution network resilience under typhoon disasters based on multi-scale attention mechanisms and multi-resource coordinated optimization
Guilian WU, Naixuan ZHU, Hao CHEN, Nuoling SUN, and Jinlin LIAO
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.009
In recent years, the increasing frequency of extreme weather events poses a serious threat to the secure operation of distribution networks. To improve the accuracy of typhoon-induced hazard intensity forecasting, an LSTM network enhanced with multi-scale attention (LSTM-MSA) is developed, which overcomes the deficiency of the traditional LSTM model that cannot capture the local attention mechanism. Building on this, a resilience-enhancement strategy is proposed that coordinates diverse flexible resources, including line reinforcement, tie-switch operation, wind power generation, and energy storage system control, to mitigate the impacts of typhoon disasters on the distribution network. Case studies on the IEEE 33-node and IEEE 69-node distribution systems demonstrate the feasibility and effectiveness of the proposed method, providing a new perspective and a practical solution for enhancing the robustness of urban distribution networks.
Weak point identification and risk assessment of distributed photovoltaic access to distribution network based on time-varying Copula function
Huanhuan YE, Linjun ZENG, and Qin YAN
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.010
With the continuous development of distributed photovoltaic (PV), the risk of voltage limit violation and the problem of power quality degradation in the distribution network are becoming increasingly serious. Therefore, a method of weak point identification for distributed PV access to the distribution network based on the time-varying Copula function is proposed, and risk assessment is performed on it. The correlation of distributed PV output is analyzed to identify vulnerabilities in power system component states, and the distribution network reliability is enhanced. First, a time-varying Gaussian Copula function is employed to analyze the force output correlation of distributed PV, achieving complex function sampling and accurately describing the distributed PV correlation. Subsequently, a linearized model derived from the Newton Raphson power flow calculation method is adopted to simplify and solve the complicated nonlinearized problems. Furthermore, a quantitative method based on repeated power flow calculation is proposed for evaluating the reliability of the system, and the utility theory is used for identifying the weak links of components. Finally, simulation experiments are conducted on the proposed method by means of the modified IEEE 33 node system. The results demonstrate that the accuracy of distributed PV output can be improved by the proposed method. Additionally, a quantitative analysis is conducted on the effects of different distributed PV penetration ratios on the operational risks of the power system.
Phase recognition of low-voltage distribution network based on locally linear embedding and Gaussia n mixture model algorithms
Jiayao HONG, Xiangyang XIA, Yunfei LEI, Zejian YI, Hanqin ZHU, and Xiaozhong HU
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.011
In order to solve the problem of inaccurate user phase recognition in low-voltage station areas, a phase recognition method based on local linear embedding (LLE) dimensionality reduction and Gaussian mixture model (GMM) clustering algorithm based on user voltage data is proposed. In this method, the voltage data in the smart meter of the user in the station area is extracted firstly, and the principal component analysis (PCA) method is used to achieve noise reduction and redundancy. Then, the derivative dynamic time warping (DDTW) method is employed to measure the correlation between user voltages. The LLE dimensionality reduction is used to extract the voltage data features of the user, and the GMM clustering algorithm is used to perform phase recognition on the user. Finally, the simulation is verified in the actual station area, and the accuracy is as high as 100%, indicating that the proposed method can effectively solve the phase recognition problem of the user in the station area.
Study on anomaly data detection of voltage in transformer district of distribution network based on TFEC-INNE
Junsheng ZOU, Xiu YANG, Gaiping SUN, Fan YANG, Jun LIU, and Bingbing LU
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.012
A large amount of anomaly data of voltage can be generated during the transformer district operation of distribution network. These anomaly data cannot correctly reflect the operating conditions of the distribution network and can seriously affect the analysis of the voltage characteristics of the transformer district. Therefore, anomaly detection of the voltage operation data in the transformer district is of great significance. Due to the problem of low accuracy of traditional anomaly detection methods, an algorithm, combining trimmed window feature extraction clustering (TFEC) and isolation-based anomaly detection using nearest-neighbor ensembles (INNE), is proposed. The daily features of the initial data are first extracted by using the trimmed window of TFEC. Then, based on principal component analysis (PCA) and K-means, the daily feature data are downgraded and clustered to obtain operation data clusters of initial voltage based on multiple daily fluctuation types. Moreover, the INNE algorithm is used to construct an integrated INNE detector in the data space of each cluster and compute a composite anomaly score for each sample. Finally, the anomaly samples are determined based on the anomaly scores. The main advantages of the model lie in integrating the trimmed window and clustering, which further enhances the advantages of the INNE algorithm in terms of local and global anomaly detection capabilities. By using the actual voltage operation data of the transformer district in a city's distribution network for validation and comprehensively comparing with other algorithms in terms of several evaluation indices, the results show that the TFEC-INNE model improves the detection effects in various anomaly scenarios.
Coordinated reactive voltage control of distribution network with photovoltaic inverter and magnetically controlled reactor based on adaptive voltage partition weights
Haiyun WANG, Qian CHEN, Linyu ZHANG, Xiyu YIN, Zhijian ZHANG, Huayue WEI, and Xiaoyue CHEN
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.013
To utilize the reactive power regulation and voltage control capabilities of reactive power devices and photovoltaic inverters and improve system operation efficiency and safety stability, a coordinated reactive voltage control of the distribution network with photovoltaic inverter and magnetically controlled reactor based on adaptive voltage partition weights is proposed. By detecting the grid-connected point voltage range, the reactive power under the weight calculation integrated strategy is adaptively obtained, and the photovoltaic inverter is controlled. Meanwhile, an optimization model with voltage deviation and active transmission loss as objectives is established to optimize the reactive power of the distribution network containing magnetically controlled reactors and inverters based on the improved quantum particle swarm algorithm. Simulation analysis results of the improved IEEE 33 node system show that the proposed strategy results in a 4.28% reduction in the average grid voltage deviation when the active output is 2 MW. During the coordinated reactive power optimization of the inverter and magnetically controlled reactor, the maximum transmission loss is reduced by 2 663.186 kW using the improved quantum particle swarm algorithm, and the average voltage deviation at the nodes where the photovoltaic is located (10, 15, 24, and 30) is reduced by 1.63%, 2.32%, 0.38%, and 0.49%, respectively. The results show that the proposed method in this paper can effectively improve the grid stability and economic performance.
Power quality disturbance recognition method based on spectrogram and lightweight 2D-DSCNN
Yongqiang LIU, NG Jinmei WA, Xuetao GUAN, and Feng LI
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.014
In response to difficulties in ac curately classifying and recognizing complex power quality disturbance (PQD) signals, this paper proposes a novel PQD recognition method based on spectrogram and lightweight two-dimensional (2D) depth-separable convolutional neural network (2D-DSCNN). Time-frequency analysis is applied to convert PQD signals into spectrograms, so that complex signal data is presented in the form of images. A lightweight 2D-DSCNN model is constructed by using depth-separable convolution technology, and the spectrograms corresponding to different PQD signals are classified and identified. The feasibility and effectiveness of the proposed method are verified through simulation experiments. The experimental results show that the method can effectively recognize and classify various PQD signals with high accuracy and strong anti-noise capability, and the model is lightweight, which is suitable for the deployment of edge devices and real-time monitoring.
Prediction model for power quality of distribution networks based on temporal fusion transformer
Xiao'e HOU, Ning WANG, Shaoan SHOU, Fu LI, Jie ZHANG, and Wenting WEI
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.015
Anomaly detection of power quality is crucial for the reliable operation of the distribution network, and its accuracy is directly related to the stability and security of the power grid system. In practice, it is not only time-consuming and laborious to analyze the anomaly in the field by manpower but also impossible to manage the anomaly in advance. To this end, a prediction model for power quality anomaly of distribution networks, which combines feature screening, signal decomposition, and an attention-based deep neural network, is proposed. First, the fast correlation-based filter (FCBF) algorithm based on maximal information coefficient (MIC) is combined with a feature screening method to select features of the input data. Then, the selected input features and corresponding components are fed into the temporal fusion transformer (TFT) model for prediction, and the prediction results of power quality anomalies such as voltage deviation, frequency deviation, and harmonic distortion rate are output. The complexity and computation time are significantly reduced, and the accuracy of fault diagnosis is potentially improved by the model, compared to conventional models. The real-time monitoring of power quality is realized, and the intelligence and visualization of the daily management of distribution network operation and maintenance are promoted.
Thyristor-based hybrid DC circuit breaker with low-amplitude oscillation current and reclosing detection function
Fangjie GOU, Zhongfei WANG, Dong QIU, Jiang ZHANG, Hongwei SHU, Wenqin HE, and Zhuangxi TAN
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.016
The thyristor-based hybrid DC circuit breaker (THDCB) pre-charges the capacitor through the DC system and adopts LC oscillation to assist fault current commutation and interruption. However, the existing schemes have problems such as a large amplitude of oscillation current and the lack of a reclosing detection function, which limits their application in overhead-line flexible DC grids with high instantaneous fault rates. To address this issue, a THDCB with low-amplitude oscillation current and a reclosing detection function is proposed. A capacitor voltage-divider pre-charging method and a capacitor energy transfer method are adopted to reduce the oscillation current amplitude and to provide the reclosing detection function without increasing additional cost and control complexity. First, the reasons for the high-amplitude oscillation current generated by existing schemes are analyzed, and the capacitor voltage-divider pre-charging and capacitor energy transfer scheme is proposed. Second, the topology of the proposed THDCB is presented, and its operating principles of DC current interruption and reclosing detection are analyzed. Then, parameter influence analysis is carried out, and the selection ranges of capacitance and inductance parameters are determined. Finally, simulation cases and comparative analysis are conducted to verify the effectiveness and techno-economic performance of the proposed scheme. This paper may provide a reference for promoting the development of overhead-line flexible DC grids in China.
Method for anomaly detection of converter valve voiceprints based on improved denoising auto-encoder
Yisheng YU, Zhihong WANG, Yunya ZHOU, Zhanfan ZHOU, Yu YAN, Yu YU, and Mingzhu TANG
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.017
The converter valve, as a key equipment in high-voltage direct current transmission converter stations, may experience an abnormal state when operated for a long time. To address the problem that non-intrinsic background noise in the valve hall may cause voiceprint anomalies and unstable anti-noise performance of the converter valve, an anomaly detection method for the converter valve based on voiceprint signal filter banks (Fbank) features and an improved denoising auto-encoder (IDAE) is proposed. Firstly, the voiceprint data generated during the operation of the converter valve is collected; the Fbank features of the voiceprint data are extracted; the samples containing temporal information through sliding window (SW) processing are obtained. Then, a denoising auto-encoder (DAE) based on environmental noise and dual channels is constructed to train normal samples, and multiple feature thresholds are calculated through fused directional distance (FDD) reconstruction error. Finally, speech, white noise, and industrial background noise with different signal-to-noise ratios are added to the test data for comprehensive performance evaluation. The experimental results show that compared with other anomaly detection models, the proposed method has better performance and stronger noise resistance.
Prediction of carbon emissions and analysis of influencing factors in urban areas considering electricity consumption in key industries
Xing DENG, Wenjun CHEN, and Pu WANG
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.018
In the context of "carbon peaking and carbon neutrality", carbon emission accounting results are an important basis for the government to formulate carbon emission reduction policies. Due to the difficulty in obtaining data required by regional carbon emission calculation, traditional calculation methods depending on these data have weak applicability and low accuracy for carbon emission calculation in urban areas. To this end, a method for predicting carbon emissions and analyzing influencing factors in urban areas considering the electricity consumption in key industries is proposed. The conversion relationship of electricity, energy, and carbon emissions in the regional key enterprises, residential buildings, and transportation is explored. The carbon emissions of various industries in the region are calculated based on the carbon emission data of these enterprises. The dynamic time warping (DTW) model is used to calculate the correlation between these industries and electricity-related carbon emissions in the region, and key electricity consumption industries in the region are selected through box plots. A regional carbon emission prediction model is established based on a long short-term memory (LSTM) network, and the prediction results of regional carbon emissions are obtained. A regional carbon emission influencing factor analysis model is constructed based on stochastic impacts by regression on population affluence and technology (STIRPAT) model. By taking the urban new district in the eastern region as an example, the carbon emissions of this new district in 2022 are predicted, and the main influencing factors of the changes in carbon emissions of the new district are analyzed. A reference is provided for carbon emission prediction in urban areas considering electricity consumption of key industries.
Reliable scheduling of electric vehicles based on user response credibility assessment
Qin WANG, Jian ZHAO, and Haoyang CUI
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.019
Grid dispatch centers can achieve orderly dispatch of electric vehicle (EV) demand response by signing demand response agreements with EV users. However, EV users may violate the agreements by ending charging behaviour early or misreporting dispatchable loads, which affects the effectiveness of the grid scheduling strategy. To improve the reliability of the grid scheduling strategy, a reliable EV scheduling strategy based on user response credibility assessment is proposed. First, the influencing factors of EV user response credibility are analysed, and an evaluation index model of EV user response credibility is constructed from the aspects of integrity, compliance, and support. Second, to more accurately represent the judgment of power grid experts on the weights of EV user response credibility indicators, an improved analytic hierarchy process based on T-spherical fuzzy numbers is proposed to calculate the indicator weights. Third, an optimization scheduling model considering EV user response credibility and power grid load fluctuations is constructed to obtain the EV scheduling strategy. Finally, a numerical example is used to verify the proposed model and strategy. The results show that, compared with the scheduling strategy without considering user response credibility, the proposed strategy can actually dispatch more EV loads, which not only effectively smooths power grid fluctuations but also reduces potential risks and losses to the power grid.
Analysis method of impact of complex electromagnetic signals on measurement errors in single-phase electric energy meters
Hongtao SHEN, Chong LI, Hao WANG, Juchuan GUO, Penghe ZHANG, and Cong WANG
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.020
In complex electromagnetic environments, the diverse array of electromagnetic interference signals with varying modulation schemes leads to degraded performance in single-phase electric energy meters. With the random and dynamic characteristics of complex electromagnetic signals, a parametric model is established for m-sequence dynamic test signals. Subsequently, a structured measurement model for single-phase electric energy meters and a mathematical model for the impact of electromagnetic signals on single-phase electric energy meter errors are developed. The impact of electromagnetic signals on random interference in electric energy meters is then simulated. Finally, through simulation and experimental tests, the impacts of electromagnetic signals on measurement errors in single-phase electricity meters are analyzed. The research findings demonstrate that the proposed methodology effectively quantifies the impact of complex electromagnetic signals on measurement errors in single-phase electric energy meters.
Design and development of non-contact mA-class tunneling magnetoresistance current sensor with high performance
Jicheng YU, Feng ZHOU, Xiaodong YIN, Changxi YUE, Siyuan LIANG, Jiaqi ZHANG, Jiale LIU, Lei LI, and Xiaoxu HU
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.021
Timely and accurate monitoring of leakage current is the foundation for achieving insulation monitoring and fault early warning of electrical equipment and ensuring the safe and stable operation of the power grid. With the wide application of renewable energy and power electronic equipment, the measurement of leakage current needs to achieve mA-level accurate measurement without affecting the normal operation of equipment. Tunneling magnetoresistance (TMR) technology senses the magnetic field generated by the current to be measured through the quantum tunneling effect. Its unique material structure endows it with ultra-low power consumption and weak current detection capabilities, providing core support for the sensor design in this paper. This paper first constructs a current sensing structure suitable for mA-level non-contact measurement based on the working principle of TMR. Second, to address the sensitivity temperature drift problem of TMR sensors in complex temperature environments, an innovative software compensation method based on cubic spline interpolation is proposed. By fitting the output data of the sensor at different temperatures, a continuous sensitivity compensation curve is obtained, significantly improving the temperature stability of the sensor. The magnetic ring and signal processing circuit of the sensor are optimized and designed. Finally, through the development of a prototype for experiments, it is proved that within the current range of ± 250 mA, the designed high-performance non-contact mA-class TMR current sensor can accurately measure the current at 0.2%FS, with a temperature drift coefficient as low as 106.3 ppm/℃, which is approximately 73% lower than that in the traditional hardware compensation method (387.9 ppm/℃). It significantly suppresses the influence of environmental temperature, and the sensitivity is 9.994 V/A, meeting the requirements of weak current measurement in fields such as safe operation of power grids and insulation detection.
Low-carbon demand response optim ization method for integrated energy system based on improved car bon trading mechanism
Xin JIN, Chao LI, Tingzhe PAN, Wei ZHOU, Jiale LIU, and Xinlei CAI
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.022
To address the problems that power customers lack motivation for carbon reduction and that the extensive existing energy consumption patterns prevent the full exploitation of carbon reduction potential on the user side and result in low energy utilization efficiency, an economic optimization method for integrated energy systems is proposed by incorporating a carbon trading mechanism. First, a stepped positive-negative carbon trading mechanism is introduced to guide users to reduce carbon emissions through financial profit and loss. A two-stage optimization is then applied to the power-to-gas device, and combined heat and power with an adjustable heat-to-power ratio is adopted to develop an integrated energy system model based on electricity-hydrogen interaction, thereby improving the energy utilization efficiency of the integrated energy system. Finally, by considering a demand response based on electricity price, a low-carbon demand response optimization method for an integrated energy system based on an improved carbon trading mechanism is proposed to further reduce carbon emissions and economic costs. The comparison results of multiple experimental scenarios show that the proposed method reduces the carbon emissions of the system by 3.97% compared to the stepped carbon trading mechanism, with an additional 1.58% reduction in carbon emissions and a 0.11% reduction in economic costs when combined with demand response.
Simulation and analysis of very fast transient overvoltage in 1 000 kV GIS
Shuheng DAN and Shunli QIAO
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.023
In gas insulated switchgear (GIS), very fast transient overvoltage (VFTO) is easily caused by disconnector operation. In order to simulate and analyze this electromagnetic transient phenomenon and its potential harm, the high-frequency oscillation zero-crossing factor is taken into account on the basis of the traditional method considering zero-crossing arc extinguishing logic. A detailed logical judgment process of arc extinguishing and arc reignition is proposed. A 1 000 kV GIS transient circuit model is constructed, and the simulation analysis on the model is carried out. The research results show that when the disconnector reignites the arc, the load side presents a ladder-shaped voltage response with different widths, and the peak overvoltage appears at the end of the bus, but the VFTO amplitude at the transformer inlet does not reach the insulation breakdown threshold. With the increase of residual voltage at the load side, the frequency of arc ignition also increases.compared with the traditional models, the new model considering high-frequency oscillation zero-crossing arc extinguishing shows that the number of VFTO arc reignition increases by 40 times, and the amplitude increases by 0.27 p.u. The research provides a reference for an in-depth understanding of electromagnetic transient phenomenon in GIS and optimization of substation insulation design.
Clean energy and energy storage
Prediction of direct normal irradiance from photovoltaic power plants based on clustering and RIME-LightGBM
Can ZHOU, Yuca i ZHOU, Yanxiang TAN, Tian XIAO, Qiyue XIE, Zhongli SHEN, Qiang FU, and Yuanheng QIN
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.024
The stability of the power output of the photovoltaic power plants is affected by the intermittency and uncertainty of direct normal irradiance (DNI). In order to solve this problem, a prediction model of DNI based on the clustering and rime optimization algorithm (RIME) optimizing the light gradient boosting machine (LightGBM) is proposed. Firstly, the strongly correlated meteorological parameters of the DNI are determined by the Pearson correlation coefficient, and the historical meteorological data is classified by the mini batch K-means (MBK) clustering algorithm. Then, RIME is used to optimize the hyperparameters of the LightGBM and to establish the prediction model of DNI for different categories of historical meteorological data. The Euclidean distances between the hourly data of the forecast day and the strongly correlated meteorological parameters of each cluster center are used to select the corresponding prediction model for the prediction of the DNI. Finally, by using the historical meteorological data from 2000 to 2019 of a concentrating solar power (CSP) plant in California, USA, the proposed model is validated. The experimental results show that the proposed prediction model can accurately predict the value and variation trend of DNI.
Optimal scheduling method of cross-regional game of flexible resources under high proportion of renewable energy
Xiaofan XIE, Xiaobo HE, Bo LIU, Xuejing XIAO, Guozhou XIAO, and Po HU
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.025
Flexible mutual assistance in resources can be achieved among power grids in different regions, and regions with a high proportion of renewable energy can obtain flexible resources from other regions to ensure the safe and flexible operation of the power grid. Based on this, a cross-regional optimal scheduling method of multivariate flexible resources is proposed considering Stackelberg game. Firstly, the flexible adjustment demand of wind, solar, and other renewable energy and the flexible supply capacity of the typical flexible resources in the source, load, and storage are quantitatively analyzed. An upper-level optimization model for multi-agents within a region is constructed with the goal of minimizing the net benefit of each operator and aggregator. Secondly, the flexible mutual assistance capability of the contact line is analyzed; a lower-level optimal scheduling model for mutual assistance among multi-regional power grids is constructed with the goal of minimizing the operating cost of the power grid in each region; the Stackelberg game is carried out with the upper-level model. Finally, three interconnected IEEE-39 node systems are constructed for case analysis. The results show that the above-mentioned double-layer optimal scheduling model based on the Stackelberg game can effectively reduce the operating cost of the system and improve the overall flexible mutual assistance level of the power grid.
Electric power market
Default risk prediction model of power trading service fees in spot market
Wei YANG, Qi SHI, Zhijian ZENG, Xueliang GONG, Jiaxun LIU, and Xinlei WANG
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.026
With the further reform of the electricity market in China, electricity trading platforms are confronted with more types of market participants, more categories of trading services, and higher trading frequencies. As the core trading intermediary, power trading institutions bear a great number of operating and management costs and face an increasing trading credit risk, namely the risk that market participants may fail to fulfill their contractual obligations on time. The credit risk resides in trading accounts and is closely related to market fluctuations and the financial status of market users. This paper introduces the traditional credit risk measurement model in the international finance field. Based on the electricity market simulation results of a province, the paper utilizes hybrid credit scoring model to quantitatively analyze the default risks that power trading institutions may face in the spot market. The research focuses on the risk of service fee recovery for electricity trading institutions. The simulation results show that default by trading users with exceptionally large trading volumes significantly impacts institutions' cost recovery. Under stress test scenarios, such as the simultaneous default of two users with exceptionally large trading volumes, a substantial cost shortfall could arise. Therefore, a more complete protection plan should be formulated to deal with the possible default of large users in the future.
Peer-to-peer electricity trading method considering supply-demand relationship tariffs and tariff time period division
Renjun ZHOU, Xin PENG, Jingjie HUANG, and Xiaojiao TONG
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.027
A two-layer optimization model of a leader-follower game is constructed for peer-to-peer electricity trading between distributed energy power stations and industrial parks. The profit maximization of the distributed energy station is taken as the goal in the upper layer, with decision variables including tariff time period division, tariff pricing, and the charging/discharging power of on-site energy storage equipment. The difference between electricity supply and demand is used as the state variable, and a certain range of difference is used to determine the valley and peak price periods. A tariff response coefficient is designed, which depends on the demand response effect of the power load elasticity matrix at each period, thereby determining the tariff for each time period. The highest production schedule matching degree and the cost-effectiveness of electricity procurement in industrial parks are taken as the goal in the lower layer, with the load adjustment for demand response as the decision variable. The production scheme matching degree is defined as the degree of consistency between the power variation at each moment of the industrial park after participating in the demand response and the original production scheme. The multi-objective problem at the lower level is solved by the augmented ε-constraint method to obtain the Pareto optimal solution set. The example results show that methods of tariff time period division and tariff pricing in peer-to-peer electricity trading can improve the profit of distributed energy power stations, reduce the cost of electricity for users, effectively mitigate wind and solar curtailment, and motivate the trading entities to participate in peer-to-peer electricity trading.
Design of user-side allocation mechanism for capacity compensation cost based on entropy weight method
Dong MO, Wenbin ZHENG, Mingyuan CHEN, Yufu LU, Youhui YANG, and Zikang DING
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.028
With the continuous and rapid development of new energy represented by wind power and photovoltaic power generation, conventional fossil energy units face difficulties in recovering fixed costs, and the fixed costs of conventional units need to be recovered through capacity prices. However, the current capacity price allocation process does not take load rate-related factors into account, and the allocation results cannot reasonably reflect the actual power supply costs of users with different load rates, leading to unfair allocation of capacity costs among users. To address this issue, we first outline the industry classification methods for electricity users and the commonly used electricity consumption behavior indicators in China. Next, appropriate customer labels are selected, and the fuzzy clustering analysis model is applied. A comprehensive user-side capacity compensation allocation method based on the entropy weight method is then proposed, along with an allocation scheme that considers both payment ability and deviation in electricity consumption. Finally, through practical examples, the specific calculation process and allocation results of the allocation mechanism are demonstrated, verifying the rationality, fairness, and feasibility of applying this allocation mechanism among different types of users.
Active power balance service product system design based on signal time-frequency domain analysis
Keyi YIN, Tianyao JI, NG Chao ZHA, Peng ZOU, Xiran HE, Zhaoxia JING, Xiaorui CUI, and Rui LI
Date posted: 2-1-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.06.029
In the context of the energy internet, traditional active power balance regulation products fail to meet the reliability, flexibility, and low-carbon demands of new power systems. To address the problems that domestic regulation products are single in configuration and ill-equipped to handle the increasing volatility of generation, the concept of balance demand is introduced, and an active power balance service product system based on signal time-frequency domain analysis is proposed. The method uses K-shape clustering to identify typical daily balance demands, applies ensemble empirical mode decomposition (EEMD) for multi-level decomposition of regulation signals, and standardizes product design through time-frequency domain analysis. Quantitative analysis is conducted using frequency regulation and reserve products as examples to demonstrate the effectiveness of the proposed method. The results show that this product system design approach facilitates optimal allocation of regulation resources and reduces market costs. Guidance is provided for the hierarchical classification of regulation capacity in regional power systems, the determination of product numbers and response time requirements, and the design of active power balance service product systems tailored to regional conditions.
