Volume 41, Issue 1 (2026)
Smart grid
Aggregation optimization method for offshore wind-thermal-storage systems based on membership degree analysis
Da XIE, Zhou TIAN, Yu ZHANG, and Yuchuan WANG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.001
To address the collaborative optimization of offshore wind-thermal-storage systems, this paper proposes an aggregation optimization method based on membership degree analysis. The proposed method enables dynamic resource aggregation and flexible scheduling by modeling and optimizing offshore wind farms, thermal power plants, and energy storage systems. Specifically, the aggregation set is generated using the Cartesian product, and initial aggregation schemes are filtered by introducing constraint conditions. A comprehensive evaluation is then conducted using membership degree analysis across three dimensions: wind power accommodation rate, economic benefit, and coordinated frequency regulation capability of thermal power. Simulation results demonstrate that the optimized aggregation configuration significantly reduces system operating costs while enhancing the coordination of thermal power frequency regulation capability. A case study based on offshore wind-thermal-storage resources in Zhejiang Province, China, demonstrates the effectiveness of the proposed method and provides new theoretical support and technical pathways for large-scale new energy accommodation and optimized system operation.
Static voltage stability assessment of power systems based on MWMOTE and SSA-KELM
Songkai LIU, Jun CAO, Pan SU, Kun GAO, Yuheng WU, Ming WAN, and Di AI
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.002
The static voltage stability assessment method of a power system based on data drive usually has the problem of an unbalanced sample category in the initial data, which makes the performance of the data-driven assessment model greatly affected. Therefore, a static voltage stability assessment method of a power system is proposed based on the majority weighted minority oversampling technique (MWMOTE) and sparrow search algorithm-kernel extreme learning machine (SSA-KELM ) . First, MWMOTE is used to solve the problem of an unbalanced sample category and increase sample diversity. Then, the KELM model parameters are optimized by using SSA, and the static voltage stability assessment model of the power system based on SSA-KELM is constructed. Finally, the validation is carried out on a 10-machine 39-bus system of New England, and the test results show that the proposed method can effectively deal with the problem of unbalanced sample categories with good assessment accuracy and generalization ability.
Hybrid configuration method for grid-forming and grid-following energy storage systems considering transient stability and dynamic support
Baoyu ZHAI, Shuchao LIANG, Zh i XU, Huan JIANG, and Haiyang WU
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.003
With the significant increase in the proportion of renewable energy, power systems exhibit characteristics of low inertia and weak damping, posing challenges to transient stability. Traditional grid-following energy storage systems (GFL-ESS) lack the capability to autonomously support the operation of power systems, necessitating the configuration of grid-forming energy storage systems (GFM-ESS) to provide voltage support and enhance system stability. To address these issues, this paper investigates the proportion of GFM-ESS units required to effectively improve the transient stability and dynamic support capabilities of the system. First, a transient model of the hybrid grid-following (GFL) and grid-forming (GFM) system is developed to analyze the mechanism by which GFM-ESS integration affects the transient synchronization stability of the hybrid system, and evaluation indicators are proposed to characterize changes in transient stability. Second, based on the time-domain target characteristics of the transient model of the hybrid system, evaluation indicators are proposed to characterize dynamic support capabilities. Using the values of transient stability and dynamic support indicators as inputs and the configuration ratio as output, a GFL/GFM ratio configuration method based on an improved grey relational analysis (GRA) -VIKOR approach is proposed. Finally, the effectiveness of the proposed configuration method is verified through a case study of an actual power system in western China.
Prediction of transmission line icing in micrometeorological areas based on RF-GSWOA-SVRM
Wei ZHANG, Xingjie LIU, Rui HUANG, Yizhou RAO, Jianning LIU, and Dan CHEN
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.004
The transmission lines in the micrometeorological area are more prone to icing, so it is extremely destructive to the safe operation of the power grid system. In view of the characteristics that icing monitoring data in micrometeorological areas is scarce, and interference is strong, RF-GSWOA-SVRM, a prediction method for transmission line icing in micrometeorological areas based on random forest (RF), global search whale optimization algorithm (GSWOA), and support vector regression machine (SVRM), is proposed to improve the accuracy of icing prediction. Firstly, the RF algorithm is used to extract the correlation between transmission line icing and micrometeorological data, thereby reducing the overfitting phenomenon caused by a single meteorological factor and the superposition effect of multiple meteorological factors. Then, to address the issue that the SVRM algorithm is highly sensitive to the selection of the kernel function and the setting of the penalty factor, the traditional whale algorithm is optimized to obtain GSWOA, thereby avoiding the kernel function and penalty factor from falling into local optimal solutions. Furthermore, the two parameters of the SVRM algorithm are optimized via GSWOA, and a short-term icing prediction model based on RF-GSWOA-SVRM is established. Finally, by taking the online monitoring data of transmission lines in a single micrometeorological area of Henan power grid as an example, a comparative analysis is conducted to verify the effectiveness of the proposed method. This model is applied to the transmission line icing prediction in similar micrometeorological areas of a certain region, and high prediction accuracy is achieved, demonstrating that the model has certain general applicability.
Transmission line protec tion method based on time-frequency analysis matr ix and BP neural network
Jifeng FAN, Yongqiang WANG, Tianlong ZHANG, Peng CHEN, and Yunfeng YAN
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.005
To address the misoperation and failure issues of existing transient protection schemes for high-voltage transmission lines, this paper proposes a transmission line protection method that combines a back propagation (BP) neural network with the time-frequency matrix of transient voltage signals. Transient protection of transmission lines is implemented based on the time-frequency analysis matrix obtained through a one-dimensional continuous wavelet transform of fault-induced voltage traveling waves. According to the existing fault voltage traveling wave data or simulated fault voltage traveling wave data, the time-frequency matrix is obtained by time-frequency analysis. The part with obvious time-frequency characteristics is taken as the input of the BP neural network, and the fault condition is taken as the output. Through neural network learning, reliable discrimination of internal and external faults within the high-voltage transmission line protection zone is realized, and rapid protection of high-voltage transmission lines is achieved. Simulation results demonstrate that the proposed method comprehensively utilizes fault characteristics of transient waveforms in both the time and frequency domains while maintaining a high computational efficiency. It is expected to improve the reliability of transient protection of high-voltage transmission lines.
Phase selection capacity recovery strategy for full-scale new energy grid based on coordinated control and protection
Lin ZHANG, Hongbin WANG, Xun CHEN, Xiangning LIN, Zhengtian LI, and Fanrong WEI
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.006
The negative-sequence suppression control strategy and low-voltage ride-through control strategy are adopted when the inverter-based power supply fails, which leads to a serious inequality between the distribution coefficients of positive- and negative-sequence currents at the energy storage station side in a full-scale new energy system, resulting in incorrect phase selection of the phase current difference mutation element. Based on the fault sequence component network, the constraint condition for the correct phase selection of the phase current difference mutation element is derived. A phase selection performance recovery scheme based on fault component regulation is proposed, which can eliminate the degradation of phase selection performance at the energy storage side caused by unequal distribution coefficients of positive- and negative-sequence currents. Simulation results show that the proposed scheme restores the performance of the traditional current mutation phase selection element at the energy storage side in a full-scale new energy system, enabling existing current mutation phase selection schemes to remain applicable to high-penetration and even full-scale new energy grids.
Dynamic optimization method for provincial distribution network disaster recovery system resources based on adaptive feedback and grey wolf optimization
Tianlong WU, Longteng WU, Zhiwei CHEN, Manlu CHEN, Jianyong CHEN, Kaiyan PAN, and Wenyi HUANG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.007
The large-scale integration of renewable energy sources poses significant challenges to provincial distribution network disaster recovery systems, particularly in data processing and resource optimization. Therefore, an intelligent and dynamically optimized resource management method is urgently required to enhance resource utilization efficiency, reduce operational costs, and improve system stability. This paper proposes a dynamic resource optimization method for provincial distribution network disaster recovery systems by integrating an adaptive feedback mechanism with the grey wolf optimization (GWO) algorithm. At the data acquisition layer, a multi-level adaptive feedback mechanism is introduced to realize real-time feedback control. At the resource allocation and system operation layer, an adaptive adjustment mechanism is adopted to enable the system to dynamically perceive the operating status and adjust the optimization strategy according to the needs of different levels. In addition, a multi-objective optimization model is established, which comprehensively considers resource allocation efficiency, energy consumption, cost, and system reliability, and uses the GWO algorithm for global optimization and dynamic allocation of resources. Through simulation and actual application tests, the proposed method shows significant advantages in improving resource utilization efficiency, reducing operating costs, and enhancing system stability. This method can effectively optimize the resource allocation strategy of the provincial distribution network disaster recovery system, improve the robustness and flexibility of the power grid, and has high engineering application value.
Optimal load distribution method for substations based on inter-line correlations
Ziqi LIU, Yin ZHAI, Kuilin FU, Shuran LIU, Wei LI, Xinyu QIE, Shiguang ZHAN, and Lili CHEN
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.008
As the construction of new power systems advances, the spatio-temporal mismatch between the output of intermittent renewable energy and diversified load exacerbates the peak-valley difference in power grids, leading to an increased load rate of main transformers and a decline in load balance of each transformer within substations. This operational trend accelerates the risk of equipment aging and poses potential threats to the safe and stable operation of the power grid through hierarchical coupling effects. To address this issue, based on the hierarchical partition operation characteristics of substations and the spatio-temporal coupling relationship of outgoing line loads, a multidimensional evaluation index of inter-line correlation is proposed, which incorporates load similarity and complementarity. By comprehensively taking into account the load rate, load flatness, and load balance of the main transformers, a three-dimensional criterion for optimal load distribution of main transformers is proposed. Furthermore, a multi-objective optimization model considering load correlation characteristics is developed, and the solution methods of the enumeration method and genetic algorithm are employed to obtain the Pareto-optimal solution set under multiple constraints. Empirical validation analysis based on typical 110/35 kV substation cases in East China demonstrates that the proposed method reduces the standard deviation of the daily load rate of main transformers by 42. 7% and the peak-valley difference rate by 18. 3%, effectively enhancing equipment utilization efficiency and operational economy, which provides an innovative solution for the lean operation and maintenance of intelligent substations.
Improved Unet++-based approach for multi-state appliance load disaggregation
Gui GU, Jiangliang JIN, Liangliang HAO, and Qisheng HUANG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.009
Nowadays, non-intrusive load disaggregation techniques face two major challenges. First, it is difficult to effectively extract the power characteristics of multi-state appliances in low-power states. Second, the generalization capability of disaggregation models is insufficient. To address these two challenges, an improved Unet++-based approach for multi-state appliance load disaggregation is proposed. First, within the encoder-decoder framework, a parallel-structured encoder is adopted to enhance the parsing capability of complex power signals, while skip connections ensure that the decoder can accurately reconstruct the original signal, thus improving the refinement of the disaggregation. Second, a bidirectional long short-term memory (BiLSTM) module is introduced to capture long-term dependencies in time series, enhancing the learning and prediction capability of the model. Experimental results show that the proposed model accurately identifies and disaggregates multi-state appliances on both the UK domestic appliance-level electricity dataset (UK-DALE) and the reference energy disaggregation dataset (REDD). In terms of mean absolute error, the proposed model demonstrates superior performance, and results obtained from tests on publicly available datasets indicate that its performance is better than that of existing methods.
Charging optimization and regulation strategy for electric vehicles considering photovoltaic power prediction
Shuanglin SONG, Zhaoxing MA, Jing WANG, Ji LI, and Ruihua WANG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.010
The widespread adoption of electric vehicles and distributed power sources poses new challenges to power systems, as increased volatility and uncertainty in both load demand and power supply place greater pressure on distribution network operation. To address these challenges, an electric vehicle charging control strategy considering photovoltaic power accommodation is proposed. First, the Monte Carlo method is employed to simulate and analyze the electricity consumption characteristics and charging behaviors of electric vehicle users. Second, a photovoltaic power generation forecasting model is developed based on gated recurrent unit networks. Finally, an orderly charging control strategy for electric vehicles is proposed, with the objectives of maximum photovoltaic accommodation and minimum charging cost for users, and the improved dung beetle optimization algorithm is used to obtain the optimal charging strategy. The simulation results show that the proposed charging control strategy is effective and feasible in promoting the accommodation of photovoltaic power, and realizes the orderly charging of electric vehicles.
Bi-level low-carbon optimization operation method for electric vehicle grid integration considering peak load
Jinjin ZHAO, Junjie ZHONG, Jie DAI, and Ziling HUANG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.011
As an emerging green energy carrier, electric vehicles (EVs), with their long idle time and energy storage characteristics, can not only relieve load pressure of the electric power grid but also achieve coordinated interaction between power sources and loads through rational scheduling, thereby reducing carbon emissions. Therefore, how to fully leverage the flexibility of EVs and develop an optimization operation strategy that takes the objectives of electric power grid safety, economy, and low carbon into account has become an urgent and critical issue. A low-carbon optimization scheduling method for peak loads is proposed, focusing on the scheduling strategy of EVs as an energy storage resource of vehicle to grid (V2G). To incentivize EV owners' willingness to participate, a bi-level optimization scheduling method is introduced by considering the economic incentives of EV owners. In this method, the upper level represents the power system, aiming to develop the charging and discharging plans of V 2G energy storage by minimizing the comprehensive cost. The lower level represents the EV owners, aiming to minimize their economic expenditure while considering battery degradation. Simulation results demonstrate that the proposed bi-level optimization method not only enhances the power system's power supply reliability and low-carbon performance but also takes the economic interests of EV owners into account, thereby increasing the willingness and feasibility of EV owners to participate in V2G energy storage scheduling.
Adaptive phase-locked loop strategy based on improved second-order linear active disturbance rejection control
Hui LI, Jiaqi ZHAO, and Xinqiao FAN
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.012
To address the problems of traditional phase-locked loops (PLLs), including slow dynamic response, poor detection accuracy, and insufficient suppression capability under complex operating conditions such as grid voltage unbalance and sudden frequency changes in flexible DC transmission systems, an adaptive PLL control strategy based on improved second-order linear active disturbance rejection control (LADRC) is proposed. This strategy employs a cross-decoupled dual complex coefficient filter with a frequency feedback loop as a pre-filtering stage to extract and separate the positive- and negative-sequence components of the voltage. In addition, a first-order feedforward derivative stage is introduced at the input reference signal of the second-order LADRC, and an improved second-order LADRC controller is designed as the core module to accurately detect and lock the frequency and phase of the voltage positive-sequence component. A flexible DC grid simulation model is established on the PSCAD/EMTDC platform for simulation and verification. The results demonstrate that the proposed adaptive PLL control strategy exhibits fast dynamic response, strong anti-interference capability, and high tracking accuracy for grid frequency and phase, and can effectively eliminate the influence of voltage unbalance and harmonics on the PLL.
Clean energy and energy storage
Multiformer-TSA-based photovoltaic power forecasting method
Ziwen CAI, Zhukui TAN, Yun ZHAO, Yuelang ZHANG, Xipeng LIU, and Houyi ZHANG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.013
To address the limitations of current photo voltaic power forecasting methods, which rely heavily on meteorological monitoring and classification techniques and are unable to achieve accurate all-weather forecasting under large-scale complex data, a Multiformer-TSA method based on a two-stage attention (TSA) mechanism is proposed for photovoltaic power forecasting. First, a cross-scale embedding layer is constructed to generate staged sampling tokens and collect photovoltaic sequences at different scales, thus extracting cross-scale features. Then, point-based segments of different dimensions in multivariate time series are embedded to form new feature vectors, capturing cross-dimensional dependencies. Finally, cross-scale and cross-dimensional dependency information is fused through the TSA mechanism to achieve accurate all-weather photovoltaic power forecasting. Multi-scale forecasting comparison experiments and ablation experiments are conducted on a publicly available photovoltaic power dataset from Alice Springs, Australia. The experimental results demonstrate that the proposed method accurately captures cross-scale and cross-dimensional features of multivariate time series and improves the multi-scale forecasting accuracy of photovoltaic power generation. Compared with existing methods, the proposed method achieves the best performance in terms of root mean square error (RMSE) and mean absolute error (MAE).
Stability analysis of fractional-frequency offshore wind power system based on Lyapunov energy function
Yumeng JIANG, Peiyang HE, Ruanming HUANG, Wei XIE, Chen QIAN, Boyang ZHAO, and Yongqing MENG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.014
The fractional-frequency transmission method has become an important solution for large-scale long-distance offshore wind power collection and transmission due to its advantages in transmission losses and construction costs compared with power frequency and direct current transmission methods. However, the stability research of fractional-frequency offshore wind power systems, especially the analysis under the background of typical large disturbances in system operation, is lacking. A high-order nonlinear state space model of the fractional-frequency offshore wind power system is proposed, and the sector-based nonlinear method is used to deal with the high-order nonlinear part of the model; the Lyapunov energy function of the system is constructed, and the corresponding stable region is calculated. On this basis, based on Lyapunov stability theorem, the interactive stability problem between the permanent magnet direct drive fan and the onshore modular multilevel matrix converter (M3C) under various typical large disturbance conditions is analyzed, and based on the comparative analysis of the stable area, the influence rules of the main circuit and control parameters of the system on the stability under large disturbance are given, which is helpful for system parameter optimization and stability improvement. Finally, the correctness of the Lyapunov energy function and stable region is verified through MATLAB/Simulink simulation examples.
Frequency regulation method of wind-storage coordination considering differences in wind turbine state
Xilin ZHAO, Xiaoyu YE, and Jinxing LI
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.015
To address the issue of frequency stability in power systems under large-scale wind power grid integration, a frequency regulation control method of wind-storage coordination considering the differences in wind turbine states is proposed. Initially, a power system model incorporating wind power is constructed, with model predictive control (MPC) as the primary controller to optimize the output of the wind turbines. Then, on this basis, considering the differences in wind turbine state, the state consistency control (SCC) method is introduced to address the problem of online computational complexity arising from uniform control of all wind turbines via MPC, thereby reducing the order of the prediction model. Finally, in response to the deviation between the actual output power of the wind farm and the total predicted output power based on the dominant wind turbine in MPC, energy storage is added for compensation, forming a control mode for wind-storage coordination. The simulation results indicate that the proposed strategy not only mitigates the impact of internal differences within wind power but also reduces the online computational complexity of the system, ensuring the frequency stability of the power system.
Wide-band oscillation analysis method for grid-connected distributed photovoltaics with high penetration rate
Di ZHANG, Mingyang LIU, Junpeng CHEN, Ze GAO, Hua WANG, Haoyu TAN, and Shihong MIAO
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.016
With the continuous increase in the penetration rate of distributed photovoltaics (PV) in the distribution network, the impact of power electronic equipment represented by grid-connected inverters on the power grid is increasing, and the risk of wide-band oscillation in the power grid is intensifying. However, few existing studies have proposed wide-band oscillation stability criteria to meet the safe operation of grid-connected distributed PV with a high penetration rate, so that effective suppression measures cannot be taken. In view of this, an analysis method for wide-band oscillation under grid-connected distributed PV with a high penetration rate based on the equivalent model is proposed. Firstly, the distributed PV cluster division and aggregation modeling methods are studied, and the equivalent model of the distribution network is established. Secondly, the wide-band oscillation analysis is carried out based on the equivalent model, and the analysis method of wide-band oscillation under the grid-connected distributed PV with high penetration rate is proposed. Then, the impedance characteristics of the equivalent model are obtained by the frequency sweep method, and the wide-band oscillation analysis is carried out. Finally, the IEEE 33 detailed model and equivalent model are built based on Simulink, and the simulation research is carried out in the equivalent model of the actual 110 kV regional distribution network；the effectiveness of the wide-band oscillation analysis method proposed in this paper is verified by comparing the simulation results of the impedance characteristics of different models.
A distributed photovoltaic power anomaly perception method based on KPCA-OPTICS clustering
Sheng SU, Xiong LI, Zhiqiang LI, Changjiang WU, and Zhuo PENG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.017
In response to the lack of professional monitoring and difficulty in accurately locating abnormal sites in distributed photovoltaic power stations, a distributed photovoltaic power anomaly perception method is proposed based on kernel principal component analysis-ordering points to identify the clustering structure (KPCA-OPTICS) clustering, with the help of the similarity and correlation of nearby distributed photovoltaic power station output. Firstly, based on the output data of photovoltaic power stations, the OPTICS clustering algorithm is used to cluster multiple power stations. The KPCA is then employed to perform dimensionality reduction on the clustering data to lower the influence of high-dimensional data on the clustering accuracy of the OPTICS algorithm. Secondly, the anomaly perception processing is carried out with the divided clusters as the target. The output of the clusters under different weather conditions is weighted equally to gain the output curve characterizing the overall output of the clusters. The output interval of the clusters is fitted by quantile regression (QR) and serves as the anomaly perception basis for the distributed photovoltaic (DPV) clusters. At last, the distributed photovoltaic power data set in a certain city in southern China is applied as the actual verification data for the simulation experiment. The results show that the method can effectively perceive power anomalies in distributed photovoltaic systems, with a high detection rate and precision and a low false alarm rate, and has good model scalability for practical deployment.
State estimation of supercapacitor based on transient model
Shuang RONG, Xiaog uang CHEN, Wanlin GUAN, Wenbo HAO, Yuanting HU, Yanlong LIU, Jiandong DUAN, and Yuhan WANG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.018
The multi-branch model of a supercap acitor can accurately describe the charge and discharge characteristics of a supercapacitor, but it is difficult to obtain the exact parameters when it is used to estimate the state of the supercapacitor, resulting in large errors in the state estimation results. In order to improve the accuracy of supercapacitor parameter identification and state estimation in the transient process, a state estimation algorithm combining an extended Kalman filter (EKF) and an adaptive unscented Kalman filter (AUKF) based on a transient model is proposed. Firstly, the model simplification feasibility in the case of fast charge and discharge is analyzed, and the multi-branch model is simplified when the feasibility is established. Secondly, the model parameters are added into the state equation as extended states, and the EKF-AUKF algorithm is used to estimate the equivalent circuit parameters and states of the supercapacitor simultaneously. Finally, the accuracy of the EKF-AUKF algorithm is validated by simulation and experiment. The experimental results show that the EKF-AUKF algorithm based on the transient model can achieve accurate parameter identification and state estimation.
Microgrid and integrated energy
Coordinated optimization strategy of integrated energy system based on dynamic deviation IGDT
Huajun RAN, Daiqiang YANG, Youchun GAN, Can WANG, Mingchao WANG, Jun ZHENG, and Fang HUANG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.019
To improve the wind power accommodation rate and reduce the cost of hydrogen energy use and system-level carbon emissions, this paper proposes a coordinated optimization strategy for an integrated energy system (IES) based on information gap decision theory (IGDT) with dynamic deviation. First, a hybrid hydrogen production system and an oxy-fuel combustion capture system are introduced into the IES, and an IES model incorporating the hybrid hydrogen production and oxy-fuel combustion capture system is constructed. Second, a system scheduling model for deterministic scenarios is established with the objective of minimizing the total operating cost. In addition, an IGDT decision model based on dynamic deviation is proposed to address the issue that the traditional IGDT decision model is overly conservative when dealing with wind load uncertainty. Finally, the results verify that the proposed strategy effectively improves the overall economic performance of the system, achieving coordinated low-carbon economic operation of the system.
Optimized operation of integrated energy system with underwater compressed air energy storage based on power and heat storage
Shuai SHI, Xiaomeng LU, Yuanyuan LI, Chunyang GONG, Wei SONG, and Xiaoliang WANG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.020
At present, underwater compressed air energy storage (UW-CAES) is mainly used in isolated island power grids of islands or ports. Firstly, inland abandoned mine pit lakes are utilized to establish large-scale UW-CAES gas storage chambers, and an integrated energy system on land with UW-CAES is constructed. Secondly, a new heat supply mode is proposed, which realizes the power and heat storage integration of the energy storage system through the coordination of heat supply between the heat storage tank and the heat pump. Finally, the effects of operating cost, system benefit, wind power consumption, and low-carbon emissions of the integrated energy system in the pit lake area are considered comprehensively, and the model is optimized with multiple objectives. The test case simulation is carried out through the IEEE 6-node distribution network and 8-node heat supply network. The results show that the integrated energy system with UW-CAES is applied to inland grid-connected operation, solving the problem of low energy storage efficiency of traditional constant-volume compressed air energy storage caused by the compressor and turbine operating at off-rated variable ratios. At the same time, it effectively reduces the output of coal-fired units and the system ’s carbon emissions. The new heat supply mode converts excess wind power into heat energy through the heat pump and recycles it through the heat storage tank, improving the system ’s wind power consumption capacity and reducing the wind curtailment rate. The heat energy recovered in the heat storage tank is supplied to the heat load, reducing the frequency of the heat pump purchasing electricity from the power grid to supply the heat load, thereby reducing the power purchase cost of the energy station and increasing the net profit of the energy station, which has good economic performance.
Voltage balancing method for single energy storage system in bipolar DC microgrid
Laixin GUO, Zhi LI, Lei XIE, and Penghui HAN
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.021
The development of distributed energy resources brings significant advantages to bipolar DC microgrids. Due to load asymmetry, voltage imbalance may occur between the positive and negative poles of a bipolar DC microgrid. Voltage balancers have therefore been introduced and are generally classified into centralized and distributed configurations. A centralized voltage balancer cannot address voltage imbalance caused by long-distance power lines or terminal loads, while a distributed voltage balancer increases system cost due to the construction of additional facilities. To overcome these drawbacks, energy storage systems can be integrated into bipolar DC microgrids. However, this approach requires separate installation at each voltage pole. In this paper, a voltage balancing method is proposed that integrates the functions of a bidirectional converter and a voltage balancer into a single energy storage system. By controlling the charging and discharging of the energy storage battery, the proposed system is able to store and supply power while simultaneously mitigating the voltage imbalance between the positive and negative poles. Meanwhile, the number of power conversion stages is effectively reduced, thus improving the operational reliability of the bipolar DC microgrid. The effectiveness of the proposed method is verified through simulation studies and experiments conducted on a 4 kW rated laboratory prototype.
Electric power market
Evaluation of impact of high-energy-consuming industries on carbon emissions and carbon market based on Neural ODE-CGE model
Chengjun HUO, Xueting CHENG, Jinkui LIU, Peng ZOU, Jun WAN, and Jia WU
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.022
High-energy-consuming industries represent the primary source of carbon emissions in China, which makes the reduction of their carbon emissions a critical priority for the country's carbon emission reduction efforts. However, due to the lack of robust carbon emission constraint mechanisms targeting these industries, their motivation for emission reduction remains significantly insufficient. To address this issue, this paper proposes a simulation method that integrates neural ordinary differential equations (Neural ODE) with a computable general equilibrium (CGE) model. Using input-output data from 2022 at the national and provincial levels, this method constructs a baseline scenario and three emission reduction scenarios to assess the impact of high-energy-consuming industries participating in carbon trading on provincial carbon emissions and carbon market. The results indicate that, compared to the absence of additional policies, the inclusion of high-energy-consuming industries in carbon market trading effectively reduces energy consumption and total carbon emissions, increases total carbon market trading volume and prices, and facilitates the province ’s achievement of carbon peaking by 2028.
User prioritization for smart electricity services in retail market
Wei YANG, Jie QIN, Yuting XIE, Yuquan CHI, Yongjun ZHANG, and Runting CHENG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.023
To effectively select users for smart electricity services in the context of the retail market, a user prioritization method for smart electricity services is proposed. First, according to the physical characteristics of user load data and the behavioral characteristics of transaction data, the value index system of smart electricity services is proposed from two aspects of established value and potential value. Then, based on the G1 method and the improved entropy weight method, the comprehensive weight of the evaluation index is solved, and a user prioritization method for smart electricity services is proposed, which provides a new idea for improving the service efficiency of the operator. Finally, the simulation analysis is conducted on retail users ’ transaction and energy consumption data in a certain area, which verifies the effectiveness of the proposed method.
High voltage and insulation
Optimization of lightning impulse grounding performance of claw-shaped tower grounding device
Zhanglei CHEN, Tingfang YANG, Chao ZHUO, Gaojia LI, Minghao FENG, and Xiong LI
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.024
Transmission line towers are prone to lightning strikes. When the grounding resistance of a tower is too high, the fault rate of the line is increased. To effectively reduce the lightning impulse grounding resistance of typical grounding devices and guide the grounding design of transmission line towers, a composite grounding device consisting of a typical tower foundation combined with a claw-shaped grounding body is proposed. A model of the claw-shaped grounding device is established using CDEGS software, and its time-domain impulse response characteristics are analyzed while considering the soil spark effect. Quantitative comparisons of the impulse grounding resistance of the claw-shaped grounding device are conducted under different lightning current amplitudes, soil resistivities, claw-shaped grounding electrode connection locations, and grounding electrode burial depths. The results show that under the same environmental conditions and material lengths, the claw-shaped grounding device improves the lightning impulse grounding performance by 17.23% and 18.35% compared with vertical extended grounding and horizontal extended grounding, respectively. While further reducing the impulse grounding resistance of the grounding device, the installation difficulty of the grounding device is also reduced.
Small object defect detection of insulators based on YOLO-insulator model in complex background
Penglin DONG, Jiupeng CHEN, Sen WANG, Hongjun SAN, and Hongwei HU
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.025
Computer vision-based methods for insulator defect detection from aerial images are widely used in power inspection. To address missed and false detections caused by complex backgrounds and small target scales, a YOLO-insulator defect detection model is proposed to improve detection accuracy. First, the reparameterized convolution based on channel shuffle-one-shot aggregation (RCS-OSA) is introduced to replace the traditional two-dimensional convolution C2f, thus enhancing the network's feature extraction capability. In the neck network, the RCS-OSA module is used to replace some of the C2f convolutions, and the squeeze-and-excitation network (SENet) is introduced to enhance the model's ability to capture inter-channel relationships and express overall features. Finally, to address the difficulty in detecting multiple defect regions due to their small size, a small object detection layer method is proposed. This layer contains more detailed defect information and is more conducive to defect detection. Experimental results on a self-made insulator dataset demonstrate that, compared with the baseline YOLOv 8n, the YOLO-insulator model achieves higher precision, recall, and mean average precision, improving overall model performance.
Local electric field detection method for zero-value insulators based on quadcopter
Zhusheng QIAO, Sunton g GU, Dongdong ZHANG, Jicheng YU, Ao HAN, Yi ZHANG, and Hengdong SONG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.026
Insulators play the role of ensuring the insulation of conductors to the ground in the power system, which is an important piece of equipment for the external insulation of transmission and distribution, as well as a reliable guarantee for the safety and stability of the power system. Zero-value faults in insulators can cause serious problems in the operation of the power system. By detecting the local electric field by quadcopter, it is possible to determine whether the insulator is a zero-value unit. In order to figure out the reasonable detection distance and propose a rapid identification criterion, an insulator detection and device structure method based on a quadcopter is proposed, and the equivalent model of a 500 kV insulator string and quadcopter is designed. The detection results of the quadcopter at different detection distances are simulated and analyzed, so as to delineate a reasonable detection distance range. By analyzing the detected electric fields in different directions, an optimal detection method is proposed. By combining with the back propagation (BP) neural network algorithm, the measured discrete data are accurately fitted to the radial electric field distribution plot. Research results show that when the detection distance is 70 ~ 200 mm, and the minimum absolute value of the slope of the image curve is less than 0.015, the insulator can be determined as a zero-value unit. When the detection distance is less than 70 mm, and the variance of the electric field amplitude of the image is less than 55, the insulator is determined as a zero-value unit. The criterion accuracy of the method is more than 98.5%, and this achievement can provide technical support for the operation and maintenance of external insulation of transmission and distribution.
Grounding device with induced arc suppression function based on series resonant circuit
Yunhao SHU, Zhenghua SU, Shubin HOU, Yuhan QIAN, Zhenjia SHEN, and Zheng GONG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.027
In a strong electromagnetic induction environment, the outage line in the double-circuit line on the same tower will produce induced voltage and induced current. The arc generated when the ground wire is manually installed will seriously affect the safe operation of the equipment. Therefore, this paper proposes a grounding device with induced arc suppression function, which improves the ordinary grounding device by utilizing a series resonant circuit. Firstly, the calculation formula of induced electricity of the double-circuit line on the same tower is deduced theoretically. The working principle of the series resonant grounding device with arc suppression function is analyzed, and the action sequence of the device is introduced. Then, according to the topology, the function of each component and the parameter design method are analyzed. Finally, a simulation model of 220 kV double-circuit line on the same tower is built in ATP-EMTP, which verifies the effectiveness of the proposed series resonant grounding device in induced arc suppression.
Temperature distribution characteristics and heat identification method of cable intermediate joints with explosion-proof boxes
Dongdong ZHANG, Huijuan WAN, Yi ZHANG, Jicheng YU, Tonghui YE, Xinhan QIAO, Ting JIAO, Yinshan WANG, and Ruoxi WANG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.028
The three-core cable intermediate joint is a vulnerable component of the cable system. The addition of an explosion-proof box can effectively prevent joint failure from affecting the normal operation of other cables, and also affects the surface temperature distribution of the joint. In this paper, a three-dimensional full-scale model of a 35 kV cable intermediate joint with an explosion-proof box is developed, and a multiphysics coupled simulation model based on finite element theory is established. The influence of contact resistance on the temperature of the joint inside the explosion-proof box and the influence of the gel-filled explosion-proof box on the current-carrying capacity of the joint are studied. By simulating insulation aging and heating defect conditions, the surface temperature distribution characteristics of cable intermediate joints with explosion-proof boxes are obtained. The results show that, due to the thermal resistance effect, the conductor current-carrying capacity of the gel-filled explosion-proof box decreases by 12.77% compared with the bare joint condition, and insulation aging has little effect on the surface temperature rise. When a heating defect occurs at the splice tube, the presence of the gel-filled explosion-proof box causes the maximum temperature region on the joint surface to shift from the traditional end region to the outer surface corresponding to the splice tube. Single-phase and two-phase defects increase the temperature rise by 11.65 ℃ and 13.3 ℃, respectively. Heating defects of cable joints inside gel-filled explosion-proof boxes can be identified by monitoring the temperature rise amplitudes at six positions on the outer surface of the joint at the center of the splice tube. The results provide a theoretical basis for condition monitoring and reliability evaluation of cable intermediate joints and have reference value for optimizing condition-based maintenance procedures for smart grid equipment.
Anomaly sound detection of high-voltage shunt reactors based on soft-constrained latent regularized adversarial learning
Zhihong WANG, Fuqiang XIONG, Jiawen ZUO, Mingzhu TANG, Jianjun ZHANG, and Xingyu TANG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.029
With the widespread application of high-voltage shunt reactors in power systems, abnormal phenomena arising during their operation have attracted increasing attention. In existing latent regularization adversarial anomaly detection (LRAAD) methods, the hyperparameter M imposes a hard constraint on the upper bound of the KL divergence of generator-produced spectrograms, which hampers the model's ability to effectively distinguish normal from abnormal data in the latent space, leading to training instability and degraded anomaly detection performance. To address this issue, this paper proposes a soft-constrained latent regularization adversarial anomaly detection (Soft-LRAAD) method. The proposed method introduces a soft constraint loss to replace the hard constraint loss, and enhances the discrimination capability in the latent space and the training stability by approximating the upper bound of the KL divergence using a smooth function. Experimental results demonstrate that the proposed method effectively improves the accuracy and robustness of anomaly detection for high-voltage shunt reactors, providing a superior solution for power equipment fault diagnosis.
Research on detection and localization of defect discharge in switchgear based on fusion of acoustic array, ultra-high frequency, andtransient earth voltage
Yingjing CHEN, Shenjiong YAO, Jialuo CHAI, Tongtong LU, Bintong LI, and Zhousheng ZHANG
Date posted: 2-11-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.01.030
To achieve efficient classification and three-dimensional (3D) localization of partial discharge (PD) in switchgear, an integrated circular array sensor combining three types of signals, namely transient earth voltage (TEV), ultra-high frequency (UHF), and acoustic array (AA), is designed. A multimodal fusion classification model and an improved nonlinear crested porcupine optimizer (CPO) algorithm are proposed. First, a sensor system with multi-channel acquisition capability is constructed through structural integration and performance testing. Second, a neural network model is built based on nine-dimensional time-frequency domain features to achieve accurate classification of multiple types of discharge defects. Finally, a time delay extraction method combining ∣Δ∣-value time difference screening and a cross-correlation algorithm is proposed, and a consistency objective function with physical constraints is constructed. The CPO algorithm is improved to enhance the robustness and stability of localization. Experimental results indicate that the defect classification accuracy reaches 91.38%, and the localization error is controlled at the 10 mm level, validating the effectiveness of the method in terms of classification capability and localization performance. This study provides a new technical solution for the detection and localization of partial discharge in electrical equipment.
