Abstract Given the rapid development of advanced information systems,microgrids(MGs)suffer from more potential attacks that affect their operational performance.Conventional distributed secondary control with a small, fixed sampling time period inevitably causes the wasteful use of communication resources.This paper proposes a self-triggered secondary control scheme under perturbations from false data injection(FDI)attacks.We designed a linear clock for each DG to trigger its controller at aperiodic and intermittent instants.Subsequently,a hash-based defense mechanism(HDM)is designed for detecting and eliminating malicious data infiltrated in the MGs.With the aid of HDM,a self-triggered control scheme achieves the secondary control objectives even in the presence of FDI attacks.Rigorous theoretical analyses and simulation results indicate that the introduced secondary control scheme significantly reduces communication costs and enhances the resilience of MGs under FDI attacks.©2025 Global Energy Interconnection Group Co.Ltd.Publishing services by Elsevier B.V.on behalf of KeAi Communications Co.Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Abstract During the transitional period of electricity market reforms in China,scheduling simulations of technical virtual power plants(TVPPs)are crucial owing to the lack of operational experience.This study proposes a model for TVPPs participating in the current multi-market;that is, TVPP coordinate bidding in the day-ahead energy and ramping ancillary market while purchasing unbalanced power and providing frequency regulation service in the real-time market.A multi-scenario optimization approach was employed in the day-ahead stage to manage uncertainty, and an improved Shapley value was utilized for revenue allocation.By employing linearization techniques, the model is transformed into a mixed-integer second-order cone-programming problem that can be efficiently solved using linear solvers.Numerical simulations based on actual provincial electricity market rules were conducted to validate the effectiveness of a TVPP coalition profitability.©2025 Global Energy Interconnection Group Co.Ltd.Publishing services by Elsevier B.V.on behalf of KeAi Communications Co.Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Abstract The development of wind power clusters has scaled in terms of both scale and coverage, and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent crossmapping algorithm.From the perspective of causality, key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is proposed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling method for a variety of different typical fluctuation processes is proposed, and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example analysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32 % higher than that of traditional methods.©2025 Global Energy Interconnection Group Co.Ltd.Publishing services by Elsevier B.V.on behalf of KeAi Communications Co.Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Abstract This study integrates the individual photovoltaic (PV) and thermoelectric generator (TEG)systems into a PV-TEG hybrid system to improve its overall power output by reutilizing the waste heat generated during PV power production to enhance its operational reliability.However, stochastic environmental conditions often result in partial shading conditions and nonuniform thermal distribution across the PV-TEG modules, which negatively affect the output characteristics of the system, thus presenting a significant challenge to maintaining their optimal performance.To address these challenges,a novel fitness-distance-balance-based beluga whale optimization(FDBBWO)strategy has been devised for maximizing the power output of the PV-TEG hybrid system under dynamic operation scenarios.A broader spectrum of complex and authentic operational contexts has been considered in case studies to examine the effectiveness and feasibility of FDBBWO.For this,real-world datasets collected from different seasons in Hong Kong have been used to validate the practical viability of the proposed strategy.Simulation results reveal that the FDBBWO based maximum power point tracking technique outperforms its competing methods by achieving the highest energy output, with a remarkable increase of up to 134.25%with minimal power fluctuations.For instance,the energy obtained by FDBBWO is 47.45%and 58.34%higher than BWO and perturb and observe methods, respectively, in the winter season.

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Abstract Hydrogen is emerging as a promising alternative to fossil fuels in the transportation sector.This study evaluated the feasibility of establishing hydrogen refueling stations in five cities in Oman, Duqm, Haima, Sur, Al Buraymi, and Salalah, using Hybrid Optimization of Multiple Electric Renewables (HOMER) software.Three hybrid energy systems, photovoltaic-wind turbine-battery, photovoltaicbattery, and wind turbine-battery were analyzed for each city.Results indicated that Duqm offers the lowest net present cost (NPC),levelized cost of energy, and levelized cost of hydrogen, making it the most cost-effective location.Additionally, Sensitivity analysis showed that as the life of electrolyzer increases during operation, the initial capital expenditure is distributed over a longer operational period, leading to a reduction in the NPC.More so, renewable energy systems produced no emissions which supports Oman’s mission target.This comprehensive analysis confirms the feasibility of establishing a hydrogen refueling station in Duqm, Oman,and highlights advanced optimization techniques’ superior capability in designing cost-effective, sustainable energy systems.©2025 Global Energy Interconnection Group Co.Ltd.Publishing services by Elsevier B.V.on behalf of KeAi Communications Co.Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Abstract Safety maintenance of power equipment is of great importance in power grids,in which image-processing-based defect recognition is supposed to classify abnormal conditions during daily inspection.However, owing to the blurred features of defect images, the current defect recognition algorithm has poor fine-grained recognition ability.Visual attention can achieve fine-grained recognition with its ability to model long-range dependencies while introducing extra computational complexity, especially for multi-head attention in vision transformer structures.Under these circumstances, this paper proposes a self-reduction multi-head attention module that can reduce computational complexity and be easily combined with a Convolutional Neural Network (CNN).In this manner, local and global features can be calculated simultaneously in our proposed structure,aiming to improve the defect recognition performance.Specifically,the proposed self-reduction multi-head attention can reduce redundant parameters, thereby solving the problem of limited computational resources.Experimental results were obtained based on the defect dataset collected from the substation.The results demonstrated the efficiency and superiority of the proposed method over other advanced algorithms.©2025 Global Energy Interconnection Group Co.Ltd.Publishing services by Elsevier B.V.on behalf of KeAi Communications Co.Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Abstract With the rapid development of large-scale regional interconnected power grids, the risk of cascading failures under extreme conditions, such as natural disasters and military strikes, has increased significantly.To enhance the response capability of power systems to extreme events, this study focuses on a method for generator coherency detection.To overcome the shortcomings of the traditional slow coherency method, this paper introduces a novel coherent group identification algorithm based on the theory of nonlinear dynamical systems.By analyzing the changing trend of the Euclidean norm of the state variable derivatives in the reduced system,the algorithm can accurately identify the magnitude of the disturbances.Based on the slow coherency methods, the algorithm can correctly recognize coherent generator groups by analyzing system characteristics under varying disturbance magnitudes.This improvement enhances the applicability and accuracy of the coherency detection algorithm under extreme conditions, providing support for emergency control and protection in the power system.Simulations and comparison analyses on IEEE 39-bus system are conducted to validate the accuracy and superiority of the proposed coherent generator group identification method under extreme conditions.©2025 Global Energy Interconnection Group Co.Ltd.Publishing services by Elsevier B.V.on behalf of KeAi Communications Co.Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Abstract Increasing interest has been directed toward the potential of heterogeneous flexible loads to mitigate the challenges associated with the increasing variability and uncertainty of renewable generation.Evaluating the aggregated flexible region of load clusters managed by load aggregators is the crucial basis of power system scheduling for the system operator.This is because the aggregation result affects the quality of the scheduling schemes.A stringent computation based on the Minkowski sum is NP-hard,whereas existing approximation methods that use a special type of polytope exhibit limited adaptability when aggregating heterogeneous loads.This study proposes a stringent internal approximation method based on the convex hull of multiple layers of maximum volume boxes and embeds it into a day-ahead scheduling optimization model.The numerical results indicate that the aggregation accuracy can be improved compared with methods based on one type of special polytope,including boxes,zonotopes,and homothets.Hence,the reliability and economy of the power system scheduling can be enhanced.©2025 Global Energy Interconnection Group Co.Ltd.Publishing services by Elsevier B.V.on behalf of KeAi Communications Co.Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Abstract Typhoons can cause large-area blackouts or partial outages of distribution networks.We define a partial outage state in the distribution network as a gray state and propose a gray-start strategy and two-stage distribution network emergency recovery framework.A phasespace reconstruction and stacked integrated model for predicting wind and photovoltaic generation during typhoon disasters is proposed in the first stage.This provides guidance for second-stage post-disaster emergency recovery scheduling.The emergency recovery scheduling model is established in the second stage,and this model is supported by a thermal power-generating unit,mobile emergency generators,and distributed generators.Distributed generation includes wind power generation,photovoltaics,fuel cells,etc.Simultaneously,we consider the gray-start based on the pumped storage unit to be an important first step in the emergency recovery strategy.This model is validated on the improved IEEE 33 node system,which utilizes data from the 2022 super typhoon‘‘Muifa”in Zhoushan,Zhejiang,China.Simulations indicate the superiority of a gray start with a pumped storage unit and the proposed emergency recovery strategy.©2025 Global Energy Interconnection Group Co.Ltd.Publishing services by Elsevier B.V.on behalf of KeAi Communications Co.Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Abstract The development of renewable energy power generation for carbon neutrality and energy transition has been increasing worldwide,leading to an increasing demand for high-power conversion.Compared with traditional interleaved paralleling,the integrated paralleling of three-level inverters can further reduce the output harmonics.Moreover,a well-designed switching sequence ensures that the average circulating current is zero,which provides a superior and feasible solution to satisfy the demands of high-power operations.However,a large instantaneous loop current exists between shunt converters,which leads to disadvantages such as higher switching device stress and loss.In this study,by utilizing the state-distribution redundancy provided by the integrated modulation process,a new design for switching sequences is suggested for the integrated modulation of shunt three-level converters.This design aims to reduce the circulating current while better preserving the same output current harmonics than traditional parallel methods.The proposal includes an in-depth analysis and explanation of the implementation process.Finally, the proposed method is validated through simulations and prototype experiments.The results indicate that compared with traditional methods, the adoption of the improved switching sequence presented in this study leads to an average reduction of 3.2%in the total harmonic distortion of the inverter’s output and an average decrease of 32%in the amplitude of the circulating current.Both the output harmonics and circulating currents are significantly suppressed across various modulation indices.©2025 Global Energy Interconnection Group Co.Ltd.Publishing services by Elsevier B.V.on behalf of KeAi Communications Co.Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Abstract The traditional transient stability assessment (TSA) model for power systems has three disadvantages: capturing critical information during faults is difficult, aperiodic and oscillatory unstable conditions are not distinguished, and poor generalizability is exhibited by systems with high renewable energy penetration.To address these issues, a novel ResGRU architecture for TSA is proposed in this study.First, a residual neural network (ResNet) is used for deep feature extraction of transient information.Second, a bidirectional gated recurrent unit combined with a multi-attention mechanism (BiGRU-Attention) is used to establish temporal feature dependencies.Their combination constitutes a TSA framework based on the ResGRU architecture.This method predicts three transient conditions: oscillatory instability, aperiodic instability, and stability.The model was trained offline using stochastic gradient descent with a thermal restart(SGDR) optimization algorithm in the offline training phase.This significantly improves the generalizability of the model.Finally, simulation tests on IEEE 145-bus and 39-bus systems confirmed that the proposed method has higher adaptability, accuracy, scalability, and rapidity than the conventional TSA approach.The proposed model also has superior robustness for PMU incomplete configurations, PMU noisy data, and packet loss.© 2025 Global Energy Interconnection Group Co.Ltd.Publishing services by Elsevier B.V.on behalf of KeAi Communications Co.Ltd.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Abstract This study proposes a method for analyzing the security distance of an Active Distribution Network (ADN) by incorporating the demand response of an Energy Hub (EH).Taking into account the impact of stochastic wind-solar power and flexible loads on the EH, an interactive power model was developed to represent the EH’s operation under these influences.Additionally, an ADN security distance model, integrating an EH with flexible loads, was constructed to evaluate the effect of flexible load variations on the ADN’s security distance.By considering scenarios such as air conditioning(AC)load reduction and base station(BS)load transfer,the security distances of phases A,B,and C increased by 17.1%,17.2%,and 17.7%,respectively.Furthermore,a multi-objective optimal power flow model was formulated and solved using the Forward-Backward Power Flow Algorithm,the NSGA-II multi-objective optimization algorithm, and the maximum satisfaction method.The simulation results of the IEEE33 node system example demonstrate that after optimization, the total energy cost for one day is reduced by 0.026 %, and the total security distance limit of the ADN’s three phases is improved by 0.1 MVA.This method effectively enhances the security distance, facilitates BS load transfer and AC load reduction,and contributes to the energy-saving, economical, and safe operation of the power system.©2025 Global Energy Interconnection Group Co.Ltd.Publishing services by Elsevier B.V.on behalf of KeAi Communications Co.Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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