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Typical Scenarios and Technical Requirements of China’s Power Grid Towards 2030 for Power System Transformation 认领 引用
1
作者 Qiang Zhao Yuqiong Zhang +3 位作者 Ziwei Chen Xiaoxin Zhou Jiameng Gao Honghua Yang 《Engineering》 SCIE EI CSCD 2026年第4期16-21,共6页
As the critical milestone for China’s Nationally Determined Contributions(NDCs)[1],2030 is a pivotal benchmark year for the transformation of China’s power system.From now to 2030,the rapid growth in installed capac... As the critical milestone for China’s Nationally Determined Contributions(NDCs)[1],2030 is a pivotal benchmark year for the transformation of China’s power system.From now to 2030,the rapid growth in installed capacity and power generation of wind and solar power will lead to profound changes in the stability mechanisms and balancing characteristics of power systems[[2],[3],[4],[5]],posing new challenges to system security and reliability. 展开更多
关键词 power system transformation power systems posing stability mechanisms balancing characteristics wind power system security power grid wind solar power solar power
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Fuzzy k-Means Clustering-Based Machine Learning Models for LFO Damping in Electric Power System Networks 认领 引用 被引量：1
2
作者 Md Shafiullah 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期803-830,共28页
Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous... Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions. 展开更多
关键词 Fuzzy k-means clustering grey wolf optimizer group method of data handling long short-term memory low-frequency oscillation power system stabilizer single machine infinite bus stability unified power flow controller
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Power system carbon emission flow analysis considering multiple operating conditions for power sources 认领 引用
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作者 Chen Yang Yaowang Li +4 位作者 Yuliang Liu Yuan Leng Zhilin Lu Rongfeng Deng Ning Zhang 《iEnergy》 EI 2026年第1期22-29,共8页
The real-time and accurate calculation of electricity indirect carbon emissions is not only the critical component for quantifying the carbon emission levels of the power system but also an effective mean to guide ele... The real-time and accurate calculation of electricity indirect carbon emissions is not only the critical component for quantifying the carbon emission levels of the power system but also an effective mean to guide electricity users in carbon reduction and promote power industry low-carbon transformation.Fundamentally,calculating indirect carbon emissions involves allocating direct carbon emission data from the power source side,indicating that accurate indirect emission results rely on the precise measurement of power source emissions.However,existing research on indirect carbon emissions in large-scale power systems rarely accounts for variations in carbon emission characteristics under different operating conditions of power sources,such as ratedon-rated operating conditions and ramping up/down conditions,making it difficult to reflect source-side and load-side carbon emission information variation during providing ancillary services.Quadratic and exponential functions are proposed to characterize the energy consumption profiles of coal-fired and gas-fired power generation,respectively,to construct a refined carbon emission model for power sources.By leveraging the theory of power system carbon flow,we analyze how variable operating conditions of power sources impact indirect carbon emissions.Case studies demonstrate that changes in power source emissions under variable conditions have a significant effect on the indirect carbon emissions of power grids. 展开更多
关键词 Power system carbon emission flow Gas turbine Coal-fired power plant Operating conditions Power source carbon emission Energy consumption model
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Researches on Low-Carbon Development Pathways for Provincial Power Systems from the Perspective of Carbon Emission Factor 认领 引用
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作者 Yang Li Xianfu Gong +3 位作者 Sifan Chen Yi Lei Donghui Zhang Yue Xing 《Energy Engineering》 EI 2026年第4期382-403,共22页
This paper develops an innovative computational model for assessing the Carbon Emission Factor(CEF)of provincial power systems that incorporates inter-provincial electricity transfers and hybrid generation portfolios ... This paper develops an innovative computational model for assessing the Carbon Emission Factor(CEF)of provincial power systems that incorporates inter-provincial electricity transfers and hybrid generation portfolios combining conventional and renewable sources.A key contribution lies in evaluating how deep regulation of thermal power plants influence the carbon intensity of coal-fired generation and coal-fired generation together with high penetration renewables.Furthermore,the study quantitatively analyzes the role of renewable energy consumption and the prospective application of Carbon Capture and Storage(CCS)in reducing system-wide CEF.Based on this framework,the paper proposes phased carbon emission targets for Guangdong’s power system for key milestone years(2030,2045,2060),along with targeted implementation strategies.Results demonstrate that in renewable-dominant systems,deep regulation of thermal units,load peak-shaving,and deployment of flexible resources such as energy storage are effective in cutting carbon intensity.To achieve the defined targets—0.367 kg/kWh by 2030,0.231 kg/kWh by 2045,and 0.032 kg/kWh by 2060—the following innovation-focused policy is recommended:in early stage,mainly on expansion of renewable capacity and inter-provincial transmission infrastructure along with energy storage deployment;in mid-term,mainly on enhancement of electricity market mechanisms to promote green power trading and demand-side flexibility;and in late-stage,mainly on systematic retirement of conventional coal assets coupled with large-scale CCS adoption and carbon sink mechanisms. 展开更多
关键词 Carbon emission factor power system renewable energy consumption thermal power flexibility CCS demand-side response
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Transient Voltage Control for AC-DC Hybrid Power System Based on ISAO-CNN-BiGRU 认领 引用
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作者 Xueting Cheng Rui Xu +3 位作者 Liming Bo Cheng Liu Huiping Zheng Zhichong Cao 《Energy Engineering》 EI 2026年第4期214-246,共33页
To address the issue of transient low-voltage instability in AC-DC hybrid power systems following large disturbances,conventional voltage assessment and control strategies typically adopt a sequential“assess-then-act... To address the issue of transient low-voltage instability in AC-DC hybrid power systems following large disturbances,conventional voltage assessment and control strategies typically adopt a sequential“assess-then-act”paradigm,which struggles to simultaneously meet the requirements for both high accuracy and rapid response.This paper proposes a transient voltage assessment and control method based on a hybrid neural network incorporated with an improved snow ablation optimization(ISAO)algorithm.The core innovation of the proposed method lies in constructing an intelligent“physics-informed and neural network-integrated”framework,which achieves the integration of stability assessment and control strategy generation.Firstly,to construct a highly correlated input set,response characteristics reflecting the system’s voltage stable/unstable states are screened.Simultaneously,the transient voltage severity index(TVSI)is introduced as a comprehensive metric to quantify the system’s post-disturbance transient voltage performance.Furthermore,the load bus voltage sensitivity index(LVSI)is defined as the ratio of the voltage change magnitude at a load node(or bus)to the change in the system-level TVSI,thereby pinpointing the response characteristics of critical load nodes.Secondly,both the transient voltage stability assessment result and its corresponding under-voltage load shedding(UVLS)control amount are jointly utilized as the outputs of the response-driven model.Subsequently,the snow ablation optimization(SAO)algorithm is enhanced using a good point set strategy and a Gaussian mutation strategy.This improved algorithm is then employed to optimize the key hyperparameters of the hybrid neural network.Finally,the superiority of the proposed method is validated on a modified CEPRI-36 system and an actual power grid case.Comparisons with various artificial intelligence methods demonstrate its significant advantages in model speed and accuracy.Additionally,when compared to traditional emergency control schemes and UVLS strategies,the proposed method exhibits exceptional rapidness and real-time capability in control decision-making. 展开更多
关键词 Transient voltage stability voltage control AC-DC hybrid power system improved snow ablation optimization hybrid neural network
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Flexible resource allocation for power systems to maximize diversified values in resilience,carbon reduction and renewable energy consumption 认领 引用
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作者 Zhaohong Bie Yiheng Bian Chenxing Yang 《iEnergy》 EI 2026年第1期43-54,共12页
The energy transition inspired by carbon neutrality targets and the increasing threat of extreme events raise multi-objective development requirements for power systems.This paper proposes a multi-objective resource a... The energy transition inspired by carbon neutrality targets and the increasing threat of extreme events raise multi-objective development requirements for power systems.This paper proposes a multi-objective resource allocation model to determine the type,number and location of flexible resources to increase the values of resilience,carbon reduction and renewable energy consumption.To evaluate the values of resilience,a restoration model for transmission systems is established that considers the coordination of fossil-fuel generators,energy storage systems(ESSs)and renewable energy generators in building restoration paths.The collaborative power-carbon-tradable green certificate(TGC)market model is then applied to evaluate the resource values in terms of carbon reduction and renewable energy consumption.Finally,the model is formulated as a mixed-integer linear programming(MILP)with a nonconvex feasible domain,and the normalized normal constraint(NNC)method is applied to obtain approximate Pareto frontiers for decision makers.Case studies validate the effectiveness of the proposed model in improving multi-factor values and analyze the impact of resource regulation capacity on values of restoration and carbon reduction. 展开更多
关键词 Multi-objective planning Power system Carbon reduction Resilience Flexible resource
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A Robust Hybrid WLS-EKF Algorithm for Power System State Estimation 认领 引用
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作者 Zahid Javid Kush Lohana +1 位作者 Danial Murtaza William Holderbaum 《Energy Engineering》 EI 2026年第7期525-547,共23页
This paper introduces a novel hybrid method for Power System State Estimation(PS-SE)that effectively integrates the strengths of Weighted Least Squares(WLS)and the Extended Kalman Filter(EKF)through an adaptive weight... This paper introduces a novel hybrid method for Power System State Estimation(PS-SE)that effectively integrates the strengths of Weighted Least Squares(WLS)and the Extended Kalman Filter(EKF)through an adaptive weighting mechanism.The proposed method addresses key challenges in modern PS-SE,including measurement uncertainties,bad data detection and handling,and convergence reliability.By incorporating an adaptive weighting mechanism,the hybrid approach dynamically adjusts estimation parameters based on the quality of the measurements,enabling it to maintain high accuracy for clean data while demonstrating exceptional resilience against outliers and noisy measurements.The performance of the proposed method is rigorously evaluated against established state estimation techniques,including WLS,EKF,Bayesian method,Huber-Adaptive Method(HAM)and Neural network-based Method.Simulations are performed on IEEE 14-bus,IEEE 34-bus and IEEE 342-bus test systems to assess estimation accuracy,convergence behavior,computational efficiency,and robustness in the presence of bad data.Results highlight the superior performance of the hybrid method,which achieves higher accuracy and robust convergence properties while requiring 40%fewer iterations than conventional WLS.Despite its enhanced capabilities,the computational burden remains comparable to traditional techniques,making it highly suitable for real-time applications.These findings underscore the proposed hybrid method as a significant advancement in power system state estimation,offering a reliable,efficient,and robust solution for modern power system monitoring and control.It represents a promising approach to address the increasing complexity and data uncertainties in contemporary power grids. 展开更多
关键词 Adaptive estimation hybrid estimator power system monitoring state estimation
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A Hybrid MZOA-PSO Optimized Cascaded PI(1+DD)-PI-PID Controller for Frequency Stability of Interconnected Power Systems with Renewable Energy and Electric Vehicles 认领 引用
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作者 AL-Wesabi Ibrahim Hassan M.Hussein Farh +3 位作者 Jiazhu Xu Mohamad A.Alawad Ahmed Alqurashi Abdullrahman A.Al-Shamma’a 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期650-697,共48页
Load frequency control(LFC)in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable e... Load frequency control(LFC)in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable energy sources and the impact of electric vehicles on the power system.Although various PI/PID and other advanced control strategies have been employed for LFC in power systems,the existing methods have shown some limitations in terms of dynamic flexibility and robustness in the presence of nonlinearities and couplings in the power systems.Moreover,the optimization methods employed for the tuning of the controllers have shown some limitations in terms of the balance between global and local search abilities of the optimization functions.To overcome the limitations of the existing methods and optimization functions,a hybrid Modified Zebra Optimization Algorithm-Particle Swarm Optimization(MZOA-PSO)is presented in this paper for the optimization of a cascaded PI(1+DD)-PI-PID controller for LFC in power systems.The MZOA enhances the original ZOA by chaotic initialization,adaptive parameter control,and Lévy-flight foraging to improve the global search ability,while PSO ensures efficient local search ability.The optimizer is first validated using four benchmark functions,achieving the global optimum for the Booth and Zakharov functions,a mean value of 2.13×10−28 with a 98%success rate for Rosenbrock,and 3.21×10−81 for Schwefel 2.22.Under a 1%step load perturbation,the proposed controller achieves a 13 s settling time,zero negative deviation in Area 2,a maximum positive excursion of 0.10 Hz,and tie-line undershoot limited to−0.10 p.u.Under random load variations,deviations remain within±0.03 Hz and±0.02 p.u.Under RES and EV integration,the peak frequency deviation is reduced to 0.46 Hz in Area 1.These results confirm that the proposed hybrid MZOA-PSO tuned cascaded controller provides improved damping,faster stabilization,and stronger robustness for modern interconnected LFC systems. 展开更多
关键词 Load frequency control cascaded PI(1+DD)-PI-PID controller hybrid MZOA-PSO algorithm interconnected power systems performance optimization
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Fault diagnosis of spacecraft electrical power system based on improved Newman community divisions method 认领 引用
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作者 Ziyang SONG Zhongcheng MU +2 位作者 Shufan WU Song JIN Jiyuan YI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期456-471,共16页
The Electrical Power System(EPS)is one of the spacecraft’s key subsystems,and its operational status directly affects the lifespan and performance of the entire spacecraft.The corresponding fault diagnosis has always... The Electrical Power System(EPS)is one of the spacecraft’s key subsystems,and its operational status directly affects the lifespan and performance of the entire spacecraft.The corresponding fault diagnosis has always been the discussion focus to ensure spacecraft reliability.In this paper,a few-shot unsupervised fault diagnosis method based on the improved Newman community division algorithm is proposed,to approach the scarcity of fault data samples and the inconspicuous characteristics of abnormal data.Firstly,aiming to capture the overall relevance of the fault dataset,a complex network model is built by adopting the K-Dynamic time warping distance Adjacent Nodes(KDAN)method.Based on the complex network model,the Newman community divisions algorithm is improved by using the Quantum-behaved Particle Swarm Optimization(QPSO).Subsequently,in order to evaluate the feasibility of the proposed method,experimental validation was conducted using an open-source dataset.The results indicate that the average accuracy can reach 96.43% for fault data diagnosis,and an F1_score of 97.76%with only 17.65%of the dataset used for training.The proposed method can accurately classify abnormal data by identifying the community structure in the data network,significantly improve the efficiency of the community divisions algorithm and reduce its complexity,and provide a new solution for fault diagnosis in large-scale complex systems. 展开更多
关键词 Community division Complex network Electrical power system Fault detection Quantum-behaved Particle Swarm Optimization Spacecraft
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Optimal Control-Based Small Signal Stability Analysis of Power System Incorporating Flexible AC Transmission System and Electric Vehicle Load 认领 引用
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作者 Naveen Guguloth Bishwajit Dey +2 位作者 Fausto Pedro García Márquez Prasenjit Dey Isaac Segovia Ramírez 《Energy Engineering》 EI 2026年第3期546-587,共42页
The increasing integration of electric vehicle(EV)loads into power systems necessitates understanding their impact on stability.Small-magnitude perturbations,if persistent,can cause low-frequency oscillations,leading ... The increasing integration of electric vehicle(EV)loads into power systems necessitates understanding their impact on stability.Small-magnitude perturbations,if persistent,can cause low-frequency oscillations,leading to synchronism loss and mechanical stress.This work analyzes the effect of voltage-dependent EV loads on this small-signal stability.The study models an EV load within a Single-Machine Infinite Bus(SMIB)system.It specifically evaluates the influence of EV charging through the DC link capacitor of a Unified Power Flow Controller(UPFC),a key device for damping oscillations.The system’s performance is compared to a modified version equipped with both a UPFC and a Linear Quadratic Regulator(LQR)controller.Results confirm the significant influence of EV charging on the power network.The analysis demonstrates that the best performance is achieved with the SMIB system utilizing the combined UPFC and LQR controller.This configuration effectively dampens low-frequency oscillations,yielding superior results by reducing the system’s rise time,settling time,and peak overshoot. 展开更多
关键词 Power system SMIB LQR EV small signal stability UPFC
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A Review of Optimization and Solution Methods for New Power Systems with Uncertainty 认领 引用
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作者 Zemin Liang Songyu Gao Qi Yao 《Energy Engineering》 EI 2026年第4期19-46,共28页
For mixed-integer programming(MIP)problems in new power systems with uncertainties,existing studies tend to address uncertainty modeling or MIP solution methods in isolation.They overlook core bottlenecks arising from... For mixed-integer programming(MIP)problems in new power systems with uncertainties,existing studies tend to address uncertainty modeling or MIP solution methods in isolation.They overlook core bottlenecks arising from their coupling,such as variable dimension explosion,disrupted constraint separability,and conflicts in solution logic.To address this gap,this paper focuses on the coupling effects between the two and systematically conducts three aspects of work:first,the paper summarizes the uncertainty optimization methods suitable for addressing uncertainty-related issues in power systems,along with their respective advantages and disadvantages.It also clarifies the specific forms and operational mechanisms through which these uncertainty optimization methods are integrated into MIP models.Meanwhile,based on the application scenarios of new power systems,the paper delineates the applicable boundaries of different optimization methods;second,the paper organizes three categories of solution methods,which are exact solution methods,decomposition-based methods,and meta-heuristic algorithms.It focuses on analyzing the improvement paths of various solution methods for resolving coupling bottlenecks,as well as their applicability in different types of power system optimization problems;finally,providing a summary and presenting an outlook on future directions:artificial intelligence-enabled optimization,development of dedicated solvers for extreme scenarios,and dynamic modeling of multi-source uncertainties.This study aims to help researchers in the field of new power systems quickly grasp uncertainty optimization methods and core solution methods,bridge existing research gaps,and promote the development of this field. 展开更多
关键词 Uncertainty new power system renewable energy optimal scheduling
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A Novel Digital Twin Platform for Accelerating Power Systems Restoration 认领 引用
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作者 Yiliang Fan Xiaolin Ding +6 位作者 Ray Zhang Yucong Zhao Nan Chen Xiao-Ping Zhang Yixin Li Chengyi Wu Wenyan Qian 《Energy Internet》 2026年第2期111-141,共31页
With the global energy structure transforming towards the goal of net zero emissions,the penetration of an inverter-based resource(IBR)in power systems is increasing.This transformation poses two critical challenges:t... With the global energy structure transforming towards the goal of net zero emissions,the penetration of an inverter-based resource(IBR)in power systems is increasing.This transformation poses two critical challenges:the system post blackout restoration process becomes extremely complicated,and this would lead to increased risks of protection maloperation caused by low inertia and short-circuit level(SCL).To address these challenges,this paper proposes a digital twin(DT)platform to facilitate the restoration of the power system.This platform deploys a real-time digital simulator(RTDS)to establish a DT power grid.By collecting real-time data from phasor measurement units(PMUs),the DT power grid can be synchronised with the physical system(emulated via RTDS).The key applications embedded in this DT platform are serving as a dynamic analysis tool to optimise and verify restoration plans(RPs),provide decision-making support and enable adaptive protection and control(P&C).Network resilience analysis metrics are proposed for assessing the effectiveness of network restoration acceleration.Detailed specifications of the DT platform are then provided for achieving all functionalities of accelerating restoration,securing an innovative and feasible technical path for enhancing the operational resilience of power systems in the context of future energy transformation. 展开更多
关键词 black start digital twin power system resilience restoration plan soft energisation
A unified robust learning framework for power system operation with noise-agnostic generalization 认领 引用
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作者 Congwei Zhang Huiqun Li +6 位作者 Pengfei Song Yanwei Zheng Yiqian Sun Pengpeng Kang Xiaoyu Tian Jiongcheng Yan Dongxiao Yu 《High-Confidence Computing》 EI CSCD 2026年第2期118-129,共12页
Power system security assessment is crucial for ensuring grid stability and reliability.Recent machine learning approaches have shown promise for accelerating this task,but their vulnerability to measurement noise and... Power system security assessment is crucial for ensuring grid stability and reliability.Recent machine learning approaches have shown promise for accelerating this task,but their vulnerability to measurement noise and communication disturbances limits practical deployment.This paper presents a unified robust training framework that addresses this challenge through multi-noise augmentation,boundary-aware sample selection,noise-invariant feature learning,and adaptive noise scheduling.We evaluate our approach on a provincial-level power system with 132 buses under various noise types including Gaussian,impulse,and adversarial perturbations.Results demonstrate that our framework achieves 96.1%accuracy on clean data while maintaining 91.4%average accuracy under noisy conditions—significantly outperforming standard training and existing robust methods.Notably,our approach reduces false negative rates from 28.4%to 9.5%under adversarial perturbations,addressing a critical concern for security assessment.The proposed framework enables more reliable application of machine learning for power system operations under real-world measurement uncertainties. 展开更多
关键词 Power system security assessment Robust training Measurement noise Adversarial robustness Neural networks Data augmentation
A Control Strategy Leveraging Adaptive Inertia to Enhance Transient Stability of Power Systems Integrated with Grid-Forming Wind Generation 认领 引用
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作者 Yuanxiang Luo Xinmeng Pan Xuyang Gao 《Energy Engineering》 EI 2026年第7期222-242,共21页
The integration of a high proportion of renewable energy sources via power electronic devices poses significant challenges to power systems.Their grid-connection characteristics differ considerably from those of synch... The integration of a high proportion of renewable energy sources via power electronic devices poses significant challenges to power systems.Their grid-connection characteristics differ considerably from those of synchronous generators,leading to a reduction in system inertia.Furthermore,the complex interactions between renewable energy units and the power grid substantially impact the transient stability of the system.Based on the virtual synchronous control characteristics of grid-forming wind turbines(GWT),this paper proposes an adaptive control method to enhance system transient stability.Firstly,a transient stability model for integrating GWT into conventional power systems is established,considering their control structure and typical control strategies.Subsequently,the power interaction mechanism between GWT and synchronous generators is analyzed,revealing the relationship between system stability and the active power control loop of GWT.An improved transient stability assessment index is introduced to quantify the influence of this control loop on system transient stability.Based on this,using the rate of change of virtual rotor speed and the depth of voltage dip as criteria,a flexible inertia control method for grid-forming wind turbines based on adaptive switching of virtual synchronous control is designed,achieving an enhancement in the power system's transient stability.Finally,a simulation model is built on the DIgSILENT/PowerFactory platform.The simulation results demonstrate that the proposed control method effectively suppresses rotor angle instability in synchronous generators and significantly improves the system's transient stability.The control system in this study is tuned using a hierarchical cascaded procedure.First,the inner current loop is tuned under the constraints imposed by the PWM and sampling frequencies to achieve the highest closed-loop bandwidth.Then,with the current loop closed,the outer voltage loop is tuned such that its bandwidth is significantly lower than that of the current loop.Finally,the VSG activeeactive power outer loops are tuned with the lowest bandwidth to ensure decoupling from the voltage loop.To avoid loop interaction,the crossover frequencies of adjacent loops are selected to differ by at least a factor of five. 展开更多
关键词 Grid-forming wind turbine power angle stability virtual inertia adaptive adjustment transient stability index
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Extraction of typical operating scenarios of new power system based on deep time series aggregation 认领 引用 被引量：2
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作者 Zhaoyang Qu Zhenming Zhang +5 位作者 Nan Qu Yuguang Zhou Yang Li Tao Jiang Min Li Chao Long 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第1期283-299,共17页
Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational s... Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy. 展开更多
关键词 convolutional neural networks deep time series aggregation high proportion of new energy new power system operation scenario image encoder power system operation mode
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Operation Modes and Development Prospects of Photovoltaic-Energy Storage Coupling Systems Under the New Power System 认领 引用
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作者 WANG Dashuai XIN Yufan 《外文科技期刊数据库（文摘版）工程技术》 2026年第4期055-060,共6页
Against the backdrop of the dual-carbon strategy and the development of a new power system, photovoltaic (PV) power generation has become a core component of the new energy system. However, the intermittency, volatili... Against the backdrop of the dual-carbon strategy and the development of a new power system, photovoltaic (PV) power generation has become a core component of the new energy system. However, the intermittency, volatility and randomness of PV power lead to severe peak regulation pressure, unstable power supply and prominent PV curtailment after large-scale grid integration, which restrict its large-scale application. Through the coordination of energy storage, the coupled PV-energy storage system can smooth power fluctuations, participate in peak and frequency regulation, and improve power accommodation capacity, serving as a key technical route for the new power system.Based on the characteristics of the new power system — source-grid-load-storage coordination, low carbon, high efficiency, safety and reliability — this paper elaborates on the technical mechanism and operation logic of the coupled PV-energy storage system. It further analyzes the operational characteristics and advantages of grid-connected, off-grid and distributed systems across different application scenarios. The study also summarizes existing challenges including collaborative control, economic efficiency, safe operation and maintenance, and adaptability to extreme environments. From four perspectives — technological upgrading, model innovation, policy improvement and industrial advancement — this paper puts forward optimization directions and development prospects. The research provides theoretical support for the engineering application, large-scale promotion and intelligent upgrading of PV-energy storage systems, and facilitates the safe and stable operation of the new power system as well as the high-quality development of the new energy industry. 展开更多
关键词 new power system photovoltaic power generation energy storage coupling operation mode source-grid-load-storage
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Research on Power System Design and Energy Efficiency Improvement of New Energy Agricultural Machinery 认领 引用
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作者 Yuxuan Mei 《Journal of Electronic Research and Application》 2026年第2期169-177,共9页
Driven by the dual objectives of agricultural green transition and carbon neutrality,new energy agricultural machinery has emerged as a pivotal solution to replace traditional fuel-powered equipment while enhancing en... Driven by the dual objectives of agricultural green transition and carbon neutrality,new energy agricultural machinery has emerged as a pivotal solution to replace traditional fuel-powered equipment while enhancing energy efficiency in farming.This study investigates powertrain systems for new energy agricultural machinery,analyzing design characteristics of mainstream technologies,including pure electric,hybrid,and clean fuel systems.Through critical design phases such as core component matching,drive configuration optimization,and energy management strategy formulation,the research proposes multidimensional energy efficiency enhancement methods tailored to operational conditions.Experimental validation confirms the feasibility and efficiency improvements of proposed designs.Results demonstrate that optimized powertrain systems with intelligent energy management can reduce energy consumption by 15-25%and increase operational efficiency by over 10%during typical tasks like plowing,rotary tilling,and transportation.Addressing existing challenges such as inadequate core technology compatibility and efficiency bottlenecks,the study offers technical breakthrough recommendations and industrial development strategies,providing theoretical references and practical pathways for advancing new energy agricultural machinery research and promotion. 展开更多
关键词 New energy agricultural machinery Power system design Energy efficiency improvement Energy management Drive configuration optimization
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Planning and Operating Power Systems to Proactively Respond to Climate Change 认领 引用
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作者 Jianbo Guo Weisheng Wang 《Engineering》 SCIE EI CSCD 2025年第8期3-4,共2页
The rapid growth in the proportion of renewable-energy gener-ation,such as wind and solar power,has significantly heightened the power system’s dependence on climate.Global climate change will profoundly impact vario... The rapid growth in the proportion of renewable-energy gener-ation,such as wind and solar power,has significantly heightened the power system’s dependence on climate.Global climate change will profoundly impact various aspects of the system,including renewable-energy resource potential,power-system planning and operation,and electricity markets.The Intergovernmental Panel on Climate Change(IPCC)has pointed out that as climate change accelerates,extreme weather events will continue to become more frequent and severe. 展开更多
关键词 weather events power system s power system extreme weather events solar powerhas renewable energy electricity markets climate change
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Bifunctional binder-free ZnCuSe2 nanostructures/carbon fabric-based triboelectric nanogenerator and supercapacitor for self-charging hybrid power system application 认领 引用 被引量：1
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作者 Punnarao Manchi Manchi Nagaraju +5 位作者 Mandar Vasant Paranjape Sontyana Adonijah Graham Anand Kurakula Venkata Siva Kavarthapu Jun Kyu Lee Jae Su Yu 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2025年第6期9-18,共10页
Herein,we report a simple self-charging hybrid power system(SCHPS)based on binder-free zinc copper selenide nanostructures(ZnCuSe2 NSs)deposited carbon fabric(CF)(i.e.,ZnCuSe2/CF),which is used as an active mate... Herein,we report a simple self-charging hybrid power system(SCHPS)based on binder-free zinc copper selenide nanostructures(ZnCuSe2 NSs)deposited carbon fabric(CF)(i.e.,ZnCuSe2/CF),which is used as an active material in the fabrication of supercapacitor(SC)and triboelectric nanogenerator(TENG).At first,a binder-free ZnCuSe2/CF was synthesized via a simple and facial hydrothermal synthesis approach,and the electrochemical properties of the obtained ZnCuSe2/CF were evaluated by fabricating a symmetric quasi-solid-state SC(SQSSC).The ZCS-2(Zn:Cu ratio of 2:1)material deposited CF-based SQSSC exhibited good electrochemical properties,and the obtained maximum energy and power densities were 7.5 Wh kg-1and 683.3 W kg-1,respectively with 97.6%capacitance retention after 30,000 cycles.Furthermore,the ZnCuSe2/CF was coated with silicone rubber elastomer using a doctor blade technique,which is used as a negative triboelectric material in the fabrication of the multiple TENG(M-TENG).The fabricated M-TENG exhibited excellent electrical output performance,and the robustness and mechanical stability of the device were studied systematically.The practicality and applicability of the proposed M-TENG and SQSSC were systematically investigated by powering various low-power portable electronic components.Finally,the SQSSC was combined with the M-TENG to construct a SCHPS.The fabricated SCHPS provides a feasible solution for sustainable power supply,and it shows great potential in self-powered portable electronic device applications. 展开更多
关键词 ZnCuSe2/carbon fabric Symmetric quasi-solid-state supercapacitors Multiple triboelectric nanogenerators Self-charging hybrid power system Energy harvesting and storage
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