Volume 41, Issue 3 (2026)
Smart grid
Power flow calculation method and application of low-frequency alternating current transmission system for offshore wind power based on DRU
Hao Yu, Yuwei Yao, Minjia Zheng, Honglin Chen, Zhengmin Zuo, Chenxin Yu, and Haishun Sun
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.001
The low-frequency alternating current transmission system for offshore wind power based on a diode rectifier unit (DRU) is a novel technical scheme with application prospects for the grid connection of deep-sea offshore wind power. Due to the uncontrollability of the DRU, the operation state of the low-frequency alternating current system is controlled by wind turbines using a grid-forming amplitude-phase control mode. To analyze the relationship between system operation state and wind power control characteristics, a power flow calculation model of the DRU-based low-frequency alternating current transmission system for offshore wind power was established. Definite conditions and equations to be solved were determined according to the system operation control mode, which accurately reflects the steady-state operation condition of the system under given wind power control. Based on the established power flow calculation model, the sensitivity of the amplitude-phase control mode and the system operation state under different output levels was analyzed, and the system power flow under different reactive power control strategies was compared. The analysis results provide a good reference for engineering planning and system control design.
Preventive control method for transient stability of power system with wind power based on hybrid improved grey wolf algorithm
Songkai Liu, Yifan Zhao, Lei Zhang, Wenpei Ding, Yukun Ai, Yuheng Wu, and Biqing Ye
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.002
To address the influence of large-scale wind power generation on power system stability, a new preventive control method for the transient stability of power systems was proposed. Firstly, the expression of wind power output was established based on the Weibull distribution. Secondly, in a system containing wind power, energy storage, and flexible load, taking the lowest adjustment cost as the objective function, a transient stability estimator based on a stacked ensemble deep belief network was used as the transient stability constraint to construct a preventive control model. Finally, to solve the problems of insufficient population diversity and easy trapping into local optima in the traditional grey wolf optimization algorithm, a hybrid improved grey wolf algorithm combining artificial fish swarm foraging behavior and a quadratic interpolation method was constructed. The simulation results on the IEEE 39-bus and IEEE 68-bus systems show that the proposed model and method can reduce the preventive control cost of wind power grid-connected systems and provide a guarantee for transient stability operation.
Two-stage optimal scheduling of wind-storage combined power generation system based on improved beetle swarm algorithm
Shuai Xiao, Zhiyong Lin, G Chuyu ZHEN, Zhiguo Ouyang, Lirou He, and Xue Yu
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.003
To solve the problem that wind farms equipped with energy storage systems are difficult to balance the grid peak shaving demand and their own operational efficiency under the condition of limited local information, a two-stage optimal scheduling model for wind-storage combined systems with localized information is proposed. In the day-ahead optimal scheduling stage, based on the easily obtainable total system load curve, the charging and discharging power of the energy storage is actively optimized with the goal of minimizing the peak-valley difference of the net load at the grid connection point; under the premise of not relying on global information, the anti-peak shaving characteristics of wind power are improved to indirectly assist the grid in peak shaving. In the intraday rolling optimal scheduling stage, based on ultra-short-term prediction data, the power adjustment is rollingly optimized to smooth the wind power fluctuation with the goals of minimizing the day-ahead plan deviation and the wind curtailment, so as to realize the time-sharing reuse of energy storage on two time scales of macro peak shaving and micro suppression. In addition, an improved beetle swarm algorithm (IBSO) integrating chaotic opposition-based learning and Lévy flight is proposed to solve the difficulty caused by the high-dimensional multi-period coupling constraints of the model.
An adaptive multi-objective reactive power optimization control strategy for wind-integrated power systems based on swarming genetic algorithm
Haoyu Liu, Yuan Zhao, Pengfei Dai, Xin Tang, and Chongru Liu
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.004
An adaptive multi-objective reactive power optimization control strategy considering the scenario partitioning of wind turbines is proposed to address the issues of voltage fluctuation and reactive power imbalance in new power systems caused by the strong randomness and intermittency of wind power. A multi-scenario collaborative optimization model is constructed using the Weibull wind speed probability distribution. A comprehensive reactive power index based on scenario partitioning is proposed to quantify the impact of wind power output uncertainty, and a dynamic weighting mechanism is designed to adaptively balance the objectives of voltage security and network loss economy. A swarming genetic algorithm, which integrates the global search mechanism of genetic algorithms and the fast convergence characteristics of particle swarm optimization, is developed to synchronously coordinate the reactive power output of wind turbines, the switching of discrete capacitor banks, and the continuous regulation of SVG, achieving dynamic optimization across multiple time scales. Simulation results demonstrate that the collaborative optimization of power system security and economy under multi-scenario wind power grid connection is achieved by the proposed algorithm, and its comprehensive regulation advantages in complex power systems are validated.
Multi-objective interval optimal dispatch method for regional power grid considering hydrodynamics
Xiaofeng Yu, Xuan Sheng, Baohua Zhong, Rixin Luo, and Shunjiang Lin
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.005
The uncertainties of the power output of renewable energy stations such as wind power and photovoltaic and the hydrodynamic characteristics of the river channel connecting various run-of-river hydropower stations pose a great challenge to the generation dispatch scheduling of the regional power grid containing wind power-photovoltaic-run-of-river hydropower. The Saint-Venant equations are used to describe the dynamic relationship between flow rate and water level in the river channel. The numerical solution of the equations is obtained by the Preissmann difference method, and the convexified equations are further obtained through McCormick envelope and convex envelope relaxation methods. Interval numbers are used to describe the uncertain fluctuation characteristics of multiple uncertain quantities, including light intensity, wind speed, and upstream water flow, and a multi-objective interval optimal dispatch model for the regional power grid aiming at minimizing the central value and radius value of the total operation cost is proposed. The interval possibility degree method and extreme value theorem are used to deal with the constraints and objective functions containing interval numbers. Through the method of directly solving the compromise optimal solution of the multi-objective optimization problem, the original optimization problem is finally transformed into a directly solvable single-objective mixed-integer quadratically constrained programming problem. In addition, a convex relaxation tightening strategy is proposed to reduce the relaxation gap generated by the convex envelope relaxation of the quadratic equality constraints and improve the computational accuracy. Finally, the correctness and effectiveness of the proposed model and solution method are verified through the simulation analysis of a real regional power grid case containing wind power-photovoltaic-run-of-river hydropower.
A novel fault location method for renewable energy transmission lines based on time-domain transient voltage fitting
Li Zhang, Shengnan Li, Shiyao Wang, Shoudong Xu, Ligen Xie, and Jinrui Tang
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.006
Affected by the control strategy and parameters of renewable energy units, once a fault occurs in a renewable energy transmission line, the short-circuit current on the renewable energy side exhibits characteristics such as controlled amplitude, sudden phase change, and a high proportion of non-power-frequency components, which leads to the failure of traditional double-end and single-end fault location methods based on power-frequency components. To address this problem, higher-order fault transient differential equations of the renewable energy transmission line before and after the fault point were provided. A joint equation system for solving fault distance was constructed based on the time-domain sampled voltages and sampled currents at both ends of the line. The time-domain transient voltage fitting was realized by integrating the Newton-Raphson method and the least squares method. The solution of the higher-order nonlinear differential joint equation system was achieved by using the transient multi-sampling data within the time window, and the accurate fault location of the transmission line was ultimately accomplished. The results indicate that the proposed location method only requires the instantaneous sampling values of three-phase voltages and currents at both ends of the renewable energy transmission line, and is not affected by the converters of power electronic equipment. The location error is within 5%. The installation of expensive traveling-wave location devices can be avoided, and its engineering implementation is easy.
Adaptive reclosing strategy for active distribution networks considering secondary arc
Zhenxing Li, Xiaorong Zhang, Cong Hu, Yi Zhu, Ruofan Huang, Hanli Weng, and Qiujie Wang
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.007
In view of the problem that the current adaptive reclosing scheme for active distribution networks recloses after an inherent delay upon identifying the disappearance of faults, without considering whether the accompanying arc is completely extinguished, an adaptive reclosing strategy for active distribution networks considering secondary arc was proposed. First, measurement points were set at the circuit breaker of the faulted line and the new energy outlet, and the reclosing and new energy off-grid operations were realized without interconnection communication according to the in-situ measured quantities of the two locations. Second, a secondary arc model of the distribution network was established, and arc characteristics were analyzed in depth. According to the time-varying nature of arc resistance, variation rules of measured impedance phase angle and total harmonic distortion (THD) during the period from secondary arc to recovery voltage were obtained, and action strategies of circuit breakers at different measurement points were derived by comprehensively synthesizing dual criteria. Simulation results prove that this criterion can reliably perform the reclosing operation according to the arc-extinguishing moment, and is not affected by factors such as fault type, fault location, transition resistance, and noise.
Optimal power flow solving and feasibility restoration method for distribution networks based on deep learning
Ji Chen, Huihuang Cai, Weidong Zhong, Minghua Chu, Huan Long, and Ziqing Shi
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.008
As the core of optimal dispatching decision-making for distribution networks, optimal power flow (OPF) urgently requires fast calculation methods under large-scale grid frameworks. A deep learning-based OPF solving method oriented toward feasibility restoration was proposed. First, an OPF solving architecture based on state-control variable decomposition was constructed, and an OPF state variable solving model was built based on a deep neural network. Second, to address the problem that OPF results based on deep neural networks fail to satisfy control variable constraints, samples with inequality constraint violations were screened to construct a correction sample set; considering actual physical constraints and supply-demand balance relationships, joint correction constraint conditions based on overall control variables were proposed to establish correction intervals. Finally, the Sinkhorn algorithm based on multi-marginal distributions was used to adjust control variable solutions, and constraint-violating variables were iteratively projected into correction intervals to meet actual physical constraint conditions. The proposed method is verified based on an improved IEEE 123-node distribution network case. Experimental results show that the proposed method can effectively achieve feasibility restoration of control variables, balance the mean absolute error of each control variable, and improve solution accuracy.
High-impedance ground fault identification method for distribution networks based on data-knowledge joint driving
Ming Chen, Tianle Li, Yilin Chen, Yang Li, and Feng Deng
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.009
When a high-impedance ground fault occurs in a distribution network, traditional knowledge-driven methods suffer from low accuracy in threshold selection, while data-driven methods have poor interpretability due to the lack of mechanism support. To address this problem, a data-knowledge joint-driven approach was proposed for identifying high-impedance ground faults in distribution networks. First, wavelet packet time-frequency entropy was used to quantitatively analyze the panoramic characteristics of high-impedance ground faults and normal disturbance conditions, thus revealing significant differences in their time-frequency distributions. Then, by qualitatively analyzing the characteristics of time-frequency energy spectrum matrices at different fault points, the knowledge-driven identification method based on the time-frequency energy spectrum matrix and the Transformer-based data-driven identification method were established. Guided by the characteristics of the time-frequency energy spectrum matrix, a data-knowledge joint-driven model based on a series mechanism was constructed. Finally, the simulation results of the IEEE 33-node system established in PSCAD simulation software show that the accuracy of the proposed method reaches 97.8%, and that it can accurately and sensitively detect a high-impedance ground fault with a resistance of 10 kΩ in a distribution network.
Multi-dimensional feature fusion identification of tree-line grounding faults
Lei Han and Chun Chen
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.010
High-resistance grounding faults induced by tree-line contact are characterized by weak signal features and difficult fault localization, and are one of the significant hidden risks in power grid operations. Existing studies mostly focus on the feature extraction, monitoring, and early warning of faults, but there are still deficiencies in the precise identification and classification of tree-line grounding faults. To address this issue, a tree-line grounding fault identification method combining Bayesian optimization-extreme gradient boosting (Bayes-XGBoost) was proposed. First, a tree-line grounding fault simulation model was established to simulate the zero-sequence current characteristics of different tree species under high-resistance grounding faults, and the signal envelope was extracted combining the Hilbert transform. Second, by analyzing the feature differences of tree-line grounding faults, multi-feature parameters such as shape entropy, waveform smoothness, and root mean square variation rate were designed to comprehensively characterize the global and local characteristics of fault signals. Finally, the Bayes-XGBoost model was adopted for multi-feature fusion classification, and Bayesian optimization was utilized to automatically adjust the model hyperparameters. Experimental results indicate that this model performs excellently in the task of distinguishing tree-line grounding faults from other single-phase high-resistance grounding faults.
Early warning and fault diagnosis of abnormal states in key components of power maintenance equipment under complex disturbance of power grid lines
Zhuang Liu, Huanqing Cai, Guiwei Shao, Houxuan Liu, Zhike Wen, and Bo Zhang
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.011
Transmission line maintenance is an important way to ensure highly reliable power supply. The adoption of intelligent power maintenance equipment to replace manual labor has become a trend. Transmission line maintenance equipment operates in the environment of high-altitude strong wind disturbance and strong electric interference for a long time, and its key components are easily damaged, which leads to failures. Therefore, it is significant to accurately diagnose the faults of intelligent power maintenance equipment for ensuring the safe and stable operation of the power grid. In this paper, spatial-temporal graphs are created from the measured signals by short-time Fourier transform, and the fault diagnosis features of intelligent power maintenance equipment are extracted. The deep reinforcement learning framework is used to enhance the diagnosis process, and ChebyGCN is used to classify the fault features in different fault states and working conditions. This is the first time that DRL and ChebyGCN are jointly used for fault diagnosis of intelligent power maintenance equipment. Comprehensive experiments are carried out on a self-built dataset and a public dataset to verify the effectiveness of the proposed method, in which its diagnostic accuracy is 99.51% and 100%, respectively. Compared with common prediction models, the proposed method can achieve higher prediction accuracy.
Generalized load modeling based on SOM-GWO-TCN integrated algorithm
Qikai Zhao, Ying Wang, Jia min Lyu, Wenqian Fang, Yi Ru, and Di Zheng
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.012
To address the complication of load characteristics caused by the extensive integration of distributed generation into distribution networks, a high-precision generalized load modeling method is proposed. Firstly, the self-organizing map (SOM) neural network is utilized to perform feature extraction and dimensionality reduction on grid node load data, and nodes with similar dynamic characteristics are divided into typical subnetwork systems through unsupervised clustering. Secondly, the grey wolf optimization (GWO) algorithm is adopted to perform global optimization on the hyperparameters of the temporal convolutional network (TCN), establishing a generalized load model for the subnetwork systems. Finally, simulation experiments based on the IEEE 33-node distribution system show that the proposed method outperforms comparison methods in clustering metrics (DBI and silhouette coefficient) and modeling metrics (MAE, RRMSE, MAPE, and R2). Specifically, DBI is reduced by an average of 28.2%; the silhouette coefficient is increased by an average of 56.2%; MAE is reduced by an average of 32.6%; RRMSE is reduced by an average of 37.1%; MAPE is reduced by an average of 33.1%, and R2 is increased by an average of 3.1%. The generalized load modeling method based on the SOM-GWO-TCN integrated algorithm can effectively reduce model complexity and improve the accuracy of the established model.
Dynamic interleaved charging and discharging strategy for electric vehicles coupling travel chains and regional characteristics
Qin Yan, Jinxin Wang, Jun Wu, Qisheng Wang, and Daoyi Gu
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.013
In view of the increasingly significant differences in the spatiotemporal distribution of regional loads caused by the large-scale development of electric vehicles, an optimization strategy for regional dynamic interleaved electricity prices based on travel chains was proposed, and a three-dimensional coupling mechanism of “user travel characteristics, regional load characteristics, and dynamic time-series response of electricity prices” was constructed to meet the differentiated electricity demand in different regions. Firstly, based on the travel chain theory, the time characteristics of cross-regional movements of electric vehicles were analyzed, and the temporal correlation of electricity price strategies in each region was dynamically adjusted: in the residential region, the discharge compensation price is implemented during the high load period at night to reduce the peak load, and the encouraged charging price is adopted during the low load period to achieve load balance; in the working region, combined with the characteristic that the charging demand is concentrated in the high load period, the discharge compensation price and peak-valley deviation price are designed to smooth load fluctuations; in the commercial region, the power adjustment price is used to smooth the power distribution during the peak period, and the peak-valley price is utilized to improve the efficiency of the power grid during other periods. The optimization model is targeted at minimizing the user charging and discharging costs and the mean square deviation of peak-valley load fluctuations of the power grid. The simulation results show that the strategy significantly optimizes the regional load distribution, reduces power grid fluctuations and user charging costs, and provides a new idea for the design and scheduling optimization of electricity price strategies.
Layered energy management strategy for extended-range electric vehicles
Jiayi Wang, Ci Tang, Min Luo, Shihui Zhou, and Zhenzhong Wang
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.014
To improve the overall fuel economy of extended-range electric vehicles and optimize battery usage, a hierarchical energy management strategy combining the advantages of model predictive control and fuzzy control was proposed to address the trade-off between real-time control and optimal performance in electric vehicle energy management. An energy flow model for an extended-range electric vehicle, consisting of a generator set, a hybrid energy storage system, and an electric motor, was developed. To optimize equivalent fuel consumption, optimal power allocation between the range extender and the hybrid energy storage system was determined through online rolling optimization using a model predictive controller. For the hybrid energy storage system, a fuzzy control strategy integrating variable speed intention was proposed to enhance battery protection. Simulation results show that, under urban driving cycle conditions, compared with traditional rule-based energy management strategies, the proposed hierarchical energy management strategy improves fuel economy by 11.23% and reduces battery power fluctuations by 44.4%.
Microgrid and integrated energy
Optimized configuration of power sources for distributed photovoltaic power stations based on bi-level programming
Xing He, Shuai Yang, Cong Wu, Rui Huang, and Mouhai Liu
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.015
The large-scale integration of distributed photovoltaics (DPV) poses significant threats to the safe and stable operation of distribution networks, creating an urgent need to optimize the configuration of power resources. A bi-level programming model that considers the dynamic clustering of DPV was proposed; it uses an improved adaptive genetic algorithm for dynamic DPV cluster partitioning. Based on the proposed bi-level programming model, the dynamic optimized configuration of power resources was achieved, node voltages were maintained, and power losses were reduced. The results show that, compared with the voltage optimization scheme, the comprehensive optimization approach reduces the total cost by 4.68%. Compared with the pre-adjustment state and the initial cluster partitioning, the system power consumption after dynamic cluster adjustment and voltage adjustment decreases by 26.51% and 13.30%, respectively. The photovoltaic-storage system incorporated into the planning model effectively reduces active power losses in the distribution network, and the effect of reducing active power loss is more obvious when solar radiation is relatively weak.
Research on resonance detection and suppression methods for wind farms with SVG
Jikai Chen, Ziming Mu, Shuangshuang Yao, Qianxin Li, Jiayang Zhang, Zhuang Chu, and Haoru Li
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.016
To address the resonance problem of wind farms with grid-following (GFL) static var generator (SVG), a resonance detection model based on the fuzzy cognitive map (FCM) algorithm was proposed. Granular computing combined with Fourier transform was used to preprocess the operation data of the constructed wind farm. Based on the processed data, the model was trained, and the detection accuracy test was completed. By using the overall sequence impedance model of the constructed wind farm combined with the impedance analysis method, the influence of SVG using GFL and grid-forming (GFM) control on the overall stability of the wind farm system was analyzed, and a wind farm resonance suppression method based on SVG control mode switching was proposed. The electromagnetic simulation model of the wind farm system with SVG was established by using StarSim-HIL, and the simulation results prove that the proposed resonance detection and suppression methods can quickly and accurately realize the detection and suppression of resonance.
Collaborative planning of distributed energy storage in industrial parks considering stepped carbon trading and demand-side response
Shuai Huang, Jing Gao, Jinya Zhou, Keteng Jiang, Jian Xiong, Caiqian Wang, and Qian Hu
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.017
With the aggravation of global climate change and the energy crisis, distributed energy storage systems play an important role in reducing users’ energy consumption costs and carbon emissions. However, few studies fully consider the synergy of various demand-side responses and flexible resources when planning distributed energy storage systems. To solve this problem, first, a stepped carbon trading calculation method considering carbon quotas and a characterization method for demand-side responses were proposed, and the elasticity matrix was used to characterize the transfer-type and reduction-type responses. Then, a collaborative planning method for distributed energy storage in industrial parks considering stepped carbon trading and demand-side responses was proposed, and the rapid solution of the optimization model was achieved through the twin delayed deep deterministic policy gradient algorithm. Finally, taking a certain industrial park as a case, it is verified that the proposed collaborative planning model can effectively integrate various demand-side responses and multiple flexible resources. The reduced system operation, maintenance, and carbon emission costs of the planning scheme can offset the investment cost, significantly improving the economy of the park operation, and proving the necessity and economic feasibility of flexible resource planning.
Optimization method for selection and capacity configuration of electrochemical energy storage batteries based on EW-AHP
Zhouping Shan, Xingwei Zhang, Yu ming Zhang, Luyu Liu, and Hongming Yang
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.018
With the large-scale application of battery energy storage in diverse power system scenarios, the matching of energy storage battery selection with scenario requirements becomes an important issue in power system energy storage planning. Therefore, an optimization selection and capacity configuration method for electrochemical energy storage batteries combining the entropy weight (EW) method and the analytic hierarchy process (AHP), named EW-AHP, was proposed. Firstly, the differentiated requirements of two typical scenarios, peak shaving and frequency regulation, for energy storage batteries were analyzed, and the selection evaluation system and hierarchical structure of the battery energy storage system were established. Secondly, the subjective and objective weights of each decision indicator were calculated based on AHP and EW, respectively, and they were fused to form comprehensive fixed weights. Thirdly, the division of frequency regulation and peak shaving scenarios was realized by calculating the average fluctuation of grid-connected power and the peak-valley difference rate of load, and the fixed weights formed by EW-AHP were modified based on the indicator correction coefficients under the corresponding scenarios. Finally, through case analysis, the influence of the energy storage configuration capacity interval on battery selection was discussed, and optimization selection under different application scenarios and specific configuration capacity intervals was realized. Furthermore, the evaluation results of the technique for order preference by similarity to ideal solution (TOPSIS) method under fixed weights were compared, which verifies that the proposed method can dynamically adjust the decision weights of key indicators according to scenario requirements and improve the accuracy and rationality of energy storage battery selection.
Secondary frequency regulation control and economic evaluation method for energy storage stations considering active state-of-charge recovery
Cuomu Yixi, Zhuangxi Tan, Lamu Jiayang, Yuzhou Chen, Li He, and Keke Yan
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.019
A secondary frequency regulation control strategy and an economic evaluation method that accounts for active state-of-charge (SOC) recovery were proposed for energy storage stations to improve their frequency regulation performance and cost-effectiveness in secondary frequency regulation. First, the control principles of large-scale energy storage stations in secondary frequency regulation were described, and their power support requirements were analyzed. A frequency support strategy that combines area control error (ACE) and area regulation requirement (ARR) was proposed for energy storage stations. Second, the decoupling characteristics between system frequency support requirements and active SOC recovery requirements were analyzed. Based on active SOC recovery scenarios for energy storage, an additional control strategy for active SOC recovery was proposed, in which the recovery process is made consistent with system frequency regulation requirements to reduce the uncertainty introduced into the system. Third, in order to comprehensively and quantitatively analyze the frequency regulation contribution of energy storage itself and the impact of frequency regulation losses, while taking into account the secondary frequency regulation support energy of energy storage, an energy storage operation life and economic evaluation model was constructed. Through quantitative analysis, the effects of the proposed method on the cycle life and calendar life of energy storage systems were evaluated, thus addressing limitations in existing research. Finally, simulation studies were conducted under step disturbances, typical-day continuous fluctuations, and different initial SOC values. The results show that the proposed method improves the secondary frequency regulation performance of energy storage stations, reduces the risk of SOC exceeding its limits, makes the frequency support capability more stable, and enhances the economic benefits of energy storage stations in secondary frequency regulation.
Fuzzy robust low-carbon optimal scheduling of island park multi-microgrid considering green hydrogen trading and offshore wind power–based hydrogen production
Xiaodan Wang, Huanhuan Han, M ingge Li, Sixing Ji, and Boyang Zheng
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.020
Research on offshore wind power-based hydrogen production technology provides an effective way for meeting the energy supply-demand of remote island users and promoting the realization of the goals of “carbon peaking and carbon neutrality.” Therefore, a fuzzy robust optimal operation strategy for an island park multi-microgrid system considering offshore wind power-based hydrogen production and green hydrogen trading was proposed. Firstly, the operation mechanism of offshore wind power-based hydrogen production was explored, and an offshore wind power-based hydrogen production system integrating seawater desalination, offshore wind power-based hydrogen production, hydrogen compression, and hydrogen transportation was constructed. Secondly, to improve green hydrogen consumption, green hydrogen certificates were introduced, and a green hydrogen certificate trading mechanism was proposed to promote the coordinated operation of green hydrogen and green electricity. Finally, considering the multiple uncertainties of sources and loads in the system, the uncertainties of sources and loads in the multi-microgrid were modeled based on fuzzy theory and robust theory. A fuzzy robust low-carbon economic scheduling model for the island park multi-microgrid system was constructed, and the model was efficiently solved based on the equivalent deterministic transformation idea and adaptive harmonic aliasing composite differential algorithm. The case study results show that the proposed model can effectively promote the consumption of offshore wind power and green hydrogen, and take into account system robustness, low-carbon performance, and economy.
Source-load extreme scenario generation method for integrated energy systems based on WGAN-GP
Zhengxing Zhong, Tong Xing, Chaoyu Xiong, Dechang Yang, and Lijun Zhang
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.021
To enhance the operational security of integrated energy systems (IES) under extreme weather conditions, it is essential to fully consider the impact of extreme scenarios in production scheduling. However, because historical samples of extreme scenarios are scarce, IES face challenges in operational scenario modeling and scheduling, making effective methods for generating extreme scenarios highly significant. To address this issue, a method based on a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) was proposed for generating source-load extreme scenarios in IES. By introducing the Wasserstein distance and gradient penalty, the proposed method improves model training stability, increases the diversity of generated samples, and expands the original scenarios. Subsequently, a two-stage source-load screening strategy was designed. Thresholds were set based on multiples of the standard deviation to identify samples located at the extremes of the distributions of renewable energy output and multi-energy load data, respectively, thus constructing source-load extreme scenarios. Case study results show that the scenario set generated by the proposed method is highly consistent with historical data in terms of probability distribution, temporal characteristics, and load correlation, while also exhibiting good diversity. Finally, two typical types of extreme scenarios were identified, validating the effectiveness of the proposed method.
Low-carbon optimization in master-slave game of integrated energy system considering demand response guided by carbon quota
Yan Zhang, Ruifang Li, Jian Zhao, Ying Wang, and Chuang Song
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.022
To build a green and low-carbon energy operation mode, it is of great significance to study the coordination between carbon quota and demand response, as well as the low-carbon and clean characteristics of hydrogen energy. Therefore, a low-carbon coordinated optimization strategy for the master–slave game of electricity–heat–gas–hydrogen integrated energy system (IES) based on comprehensive demand response guided by carbon quota is proposed. First, given the low-carbon and clean characteristics of hydrogen energy, a hydrogen energy utilization system composed of an electrolytic cell, a methane reactor, and a hydrogen fuel cell is introduced, and an electricity–heat–gas–hydrogen IES coupling model is constructed. Second, to fully release the low-carbon regulation potential on the demand side, a dual incentive strategy for comprehensive demand response guided by a carbon quota is introduced. A ladder-type carbon trading mechanism is embedded into the game model to construct a low-carbon interaction model between IES operators and users. Finally, a bi-level solution algorithm based on the chaotic particle swarm optimization algorithm and the Gurobi solver is adopted to solve the proposed game model. In the case simulation, different scenarios are set for comparative analysis to respectively verify the effectiveness of the proposed dual incentive strategy for comprehensive demand response guided by carbon quota and the hydrogen energy coupling model, and to fully exert the emission reduction potential and response capability on the demand side.
Low-carbon economic dispatch for multi-park integrated energy systems considering electricity-carbon coupling and differentiated demand response
Liwei Yang, Fei Jiang, Xinhe Zhang, Zhenlan Dou, and Xiaojia Zhang
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.023
In order to reduce the carbon emissions of park integrated energy systems and to explore the demand response potential of different types of loads, a low-carbon economic dispatch method for multi-park integrated energy systems considering electricity–carbon coupling and differentiated demand response is proposed. Considering the electricity–carbon coupling among different parks and the characteristics of the external carbon trading market, a multi-park carbon trading model with a stepped external carbon trading mechanism is constructed; in view of the differences in energy demands and demand response capabilities of users in different parks, a differentiated demand response model including flexible and adjustable electrical and thermal loads is established. With the joint park operator as the leader, and user load aggregators and energy storage providers as followers, a multi-agent master–slave game mechanism and the decision-making models of each agent are established, and an improved particle swarm parameter optimization algorithm is used to solve the master–slave game model. Based on the example simulation of a multi-park integrated energy system in Hunan Province, the results show that compared with traditional optimal dispatch methods, the proposed optimization strategy reduces the total carbon emissions of the system by 9.31% and improves the total revenue by 21.32%.
Power electronics
High-frequency direct current converter based on gallium nitride devices and planar magnetic technology
Zhiyuan Ma, Zhong Xu, and Yiping Cui
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.024
With the rapid development of electric vehicles and the energy storage industry, the production scale of high-rate batteries such as lithium batteries expands continuously, and the efficiency and power density of direct current converters in the battery formation process become research hot spots in the industry. In view of the shortcomings of traditional high-gain direct current converters, such as the high voltage stress of high-voltage side devices, the large current stress of low-voltage side devices, and the low efficiency and power density, a high-gain direct current converter topology suitable for the intermediate stage of the battery formation power supply system is proposed. A circuit structure in which two symmetrical half-bridge LLC resonant converters based on gallium nitride devices are connected in series on the high-voltage side and in parallel on the low-voltage side is adopted, which realizes automatic voltage sharing on the high-voltage side and automatic current sharing on the low-voltage side and reduces device stress. Furthermore, the height of the converter is reduced, and the power density is improved through the interleaved complementary modulation strategy and planar magnetic technology. Finally, a finite element simulation model and a 2 kW experimental platform are built to complete the simulation and experimental verification.
A novel fault-tolerant control strategy under submodule faults of modular multilevel converters
Yu Guo, Xiangyang Xia, Runwu Li, and Rui Yi
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.025
To address the problems of asymmetric arm operation and fundamental and second-harmonic circulating currents caused by submodule faults in a modular multilevel converter (MMC) under hot standby in a flexible direct current system, a fault-tolerant control strategy coordinated by an improved PIR circulating current control and an inner-loop current sliding mode control is proposed. By introducing a three-parameter notch filter to precisely separate the circulating current components, the improved PIR control achieves the coordinated suppression of fundamental and second-harmonic harmonics. Simultaneously, the traditional PI inner-loop control is replaced by the sliding mode control to significantly improve the dynamic response speed and robustness of the system. Simulations based on Matlab/Simulink and RT-LAB hardware-in-the-loop experiments show that the proposed strategy can effectively suppress the circulating current harmonic components, rapidly stabilize the capacitor voltage and direct current, and reduce the grid-side alternating current fluctuation. Furthermore, it achieves stable system recovery with superior regulation performance after a submodule fault, verifying its multi-frequency harmonic suppression capability and dynamic control advantages.
Modeling and stability improvement methods for grid-connected systems of heterogeneous distributed photovoltaic interconnected inverters
Diyang Gong, Xiangjin Wang, Junhao Li, Jiayuan Gao, and Shuai Huang
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.026
After grid-following and grid-forming heterogeneous interconnected photovoltaic inverters are connected to the power grid, interactive influences easily occur, resulting in the instability of grid-connected inverters and threatening the safe and stable operation of the new energy grid-connected system. Therefore, considering the dynamic influence of photovoltaic direct current side voltage, small-signal impedance models of grid-following, grid-forming, and their interconnected heterogeneous distributed photovoltaic inverters connected to the grid were constructed, respectively, and the adaptability of heterogeneous grid-connected inverters to grid impedance was analyzed. Meanwhile, to expand the adaptation range of heterogeneous interconnected systems to grid impedance and promote the consumption of new energy, starting from the frequency characteristics of the eigenvalues of the return ratio matrices of grid-following and grid-forming inverters, a hybrid control strategy involving asymmetric compensation for grid-following inverters and active amplitude phase angle compensation for grid-forming inverters was proposed, and the control parameter design was completed to enhance the adaptability of heterogeneous interconnected systems to the grid. The correctness of the related theoretical analysis is verified by simulation and experiment.
LQR control based on GA optimization for suppressing power oscillations in fractional-order VSG
Jun Zhu, Zhengbin Chen, Jiantang Chen, Haozhuo Wei, Penghui Liu, and Ming Yang
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.027
A linear quadratic regulator (LQR) optimized based on the genetic algorithm (GA) was designed to suppress power oscillations in a virtual synchronous generator (VSG) under external disturbances. A state-feedback LQR controller was designed by establishing a small-signal model of the VSG power loop. To optimize the control performance, the GA was used to perform offline optimization of the weight-matrix parameters of LQR. The weight-optimized LQR control law was applied to the fractional-order VSG. The simulation results show that, compared with traditional VSG control, the proposed control strategy can effectively suppress power oscillations, reducing the oscillation overshoots of active and reactive power by 19.1% and 28.3%, respectively, during dynamic operation, and shortening the oscillation process by 2.1 s.
High voltage and insulation
Diagnosis method for GIS metal particle discharge and mechanical faults based on ultrasonic and vibration signals
Jialuo Chai, Shenjiong Yao, Tongtong Lu, Yingjing Chen, and Zhousheng Zhang
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.028
Metal particle discharge and mechanical faults inside gas insulated switchgear (GIS) are key factors affecting its safety performance. These faults can be detected and identified using the ultrasonic and vibration signals generated by them. Simulated experiments of four types of particle discharge, normal operation, and flange bolt loosening were designed on a GIS platform, and the time-frequency domain characteristics of the signals were analyzed. It is found that, in the ultrasonic signals, the frequency bands of linear, spherical, and lump particle discharges are mainly concentrated in 50–60 kHz, while that of flaky particle discharge is concentrated in 15–30 kHz; in the time domain, the linear particle fluctuation frequency reaches nearly 200 times at the fastest, and the lump particle signal decays by 68.23%, showing the highest decay degree; in the vibration signals, the main frequency of normal operation is 100 Hz, and the amplitude of the 100 Hz frequency significantly decreases when the bolt is loosened. Based on the signal characteristics, the positive/negative half-cycle peak ratio, ringing times, attenuation coefficient, as well as the centroid frequency, frequency amplitude ratio, and total harmonic distortion were extracted. Combining the characteristics of ultrasonic and vibration signals and the random forest algorithm, effective identification of six operating states is successfully achieved, and the recognition accuracy is improved.
Design method for end shape of explosion-proof high-voltage cable joints based on virtual objective optimization
Xiang Gao, Xin Yang, Honghua Hu, Yafei Huang, Wei Qiu, Yangning Chen, and Rui Qin
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.029
For the explosion-proof weak points at the end of the intermediate joint of high-voltage cables, an optimization design method for the end of the explosion-proof copper shell was proposed. By using a finite element calculation method coupling the temperature field, displacement field, and flow field, the stress concentration factor K at the copper shell end of the joint under different deflection angles α was calculated; then, under the deflection angle with the minimum stress concentration factor, a circularization and copper shell thickness coordination design was conducted. Orthogonal experimental design is conducted on the two arc curvature radii and the thickness of the explosion-proof copper shell of the cable joint, and virtual objective optimization is performed taking the maximum stress borne by the copper shell and the copper shell mass in the end region, which represent safety and economy, as indicators. Taking the 220 kV explosion-proof high-voltage cable joint as an example, the optimal deflection angle α = 123°, optimal radii of curvature R1 = 130 mm and R2 = 40 mm, and copper shell thickness h = 3.0 mm are obtained. By comparing with the copper shell end with deflection angle α = 123°, it is obtained that the maximum stress borne by the optimized end decreases to 28%, and the average stress decreases to 44%. The circularization design has a significant effect in reducing stress, greatly improves the explosion-proof performance and safety margin of the intermediate joint of high-voltage cables, and can provide a basis and design method for the end design of explosion-proof high-voltage cable joints.
Multi-factor influence analysis on temperature rise of tulip contacts in handcart circuit breakers
Honglei Deng, Yu Chen, Feiyang Han, and Ziheng Gao
Date posted: 7-2-2026
DOI: https://doi.org/10.19781/j.issn.1673-9140.2026.03.030
Tulip contacts are widely used in power systems due to their high reliability. However, with the increase of operation years and the cyclic variation of boundary loads, their contact parts are prone to degradation, leading to overheating faults. Temperature rise, as a key parameter characterizing the evolution of thermal faults, is commonly used to monitor the contact state of switchgear. The GC 5-630 tulip contact is taken as the research object. A three-dimensional electro-thermal-mechanical multiphysics coupling model is established based on the finite element analysis method to analyze the influence laws of different factors on the temperature rise of the contact parts, and a total of 180 sets of relationship data between contact temperature rise and multiple factors are obtained. Based on the simulation data, an empirical formula for predicting the contact temperature rise is fitted, and the effectiveness of the formula is verified through experiments. The research results indicate that the temperature rise has a power function relationship with current, roughness, and contact pressure, and the exponents are 1.97, 0.315, and −0.46, respectively. When the docking angle is 2°, contact failure occurs in the contact, and the temperature rise of contact fingers increases significantly under poor contact conditions. This research provides a theoretical basis for the condition monitoring and fault early warning of tulip contacts.
