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Analysis on carbon emission reduction intensity of fuel cell vehicles from a life-cycle perspective

Frontiers in Energy 2024年第1期18卷 16-27页

作者： Ziyuan TENG Chao TAN Peiyuan LIU Minfang HAN Fuel Cell and Energy Storage Center Department of Energy and Power EngineeringState Key Laboratory of Control and Simulation of Power Systems and Generation EquipmentTsinghua UniversityBeijing 100084China

The hydrogen fuel cell vehicle is rapidly developing in China for carbon reduction and *** paper evaluated the life-cycle cost and carbon emission of hydrogen energy via lots of field surveys,including hydrogen production and packing in chlor-alkali plants,transport by tube trailers,storage and refueling in hydrogen refueling stations(HRSs),and application for use in two different *** also conducted a comparative study for battery electric vehicles(BEVs)and internal combustion engine vehicles(ICEVs).The result indicates that hydrogen fuel cell vehicle(FCV)has the best environmental performance but the highest energy ***,a sufficient hydrogen supply can significantly reduce the carbon intensity and FCV energy cost of the current *** carbon emission for FCV application has the potential to decrease by 73.1%in City A and 43.8%in City *** only takes 11.0%–20.1%of the BEV emission and 8.2%–9.8%of the ICEV *** cost of FCV driving can be reduced by 39.1%in City *** improvement can be obtained with an economical and“greener”hydrogen production pathway.

关键词： hydrogen energy life-cycle assessment(LCA) fuel cell vehicle carbon emission energy cost

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Optimal Configuration Planning of Multi-energy Microgrid Based on Source-load-temperature Scenarios Deep Joint Generation

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CSEE Journal of power and Energy systems 2023年第3期9卷 1090-1102页

作者： Nantian Huang Wenting Wang Guowei Cai School of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Northeast Electric Power University)Jilin 132012China

An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy complementation and the reliability of energy supply in extreme ***,based on the historical meteorological data,the typical meteorological clusters and extreme temperature types are ***,to reflect the uncertainty of energy consumption and renewable energy output in different weather types,a deep joint generation model using a radiation-electric load-temperature scenario based on a denoising variational autoencoder is established for each weather *** the same time,to cover the potential high energy consumption scenarios with extreme temperatures,the extreme scenarios with fewer data samples are ***,the scenarios are reduced by clustering *** normal days of different typical scenarios and extreme temperature scenarios are determined,and the cooling and heating loads are determined by ***,the optimal configuration of a multi-energy microgrid system is carried *** show that the optimal configuration based on the extreme scenarios and typical scenarios can improve the power supply reliability of the *** proposed method can accurately capture the complementary potential of energy *** the economy of the system configuration is improved by 14.56%.

关键词： Deep joint generation denoising variational autoencoders multi-energy microgrid system optimal configuration planning

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Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

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Energy Engineering 2024年第6期121卷 1535-1555页

作者： Huanan Yu Hangyu Li He Wang Shiqiang Li Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization ***, a measurement point distribution optimization model is formulated, leveraging compressive *** model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

关键词： Optimal allocation improved particle swarm algorithm fault location compressed sensing DC distribution network

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An equivalent modeling method for integrated water-wind-solar systems based on sparrow search algorithm

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Energy Informatics 2025年第1期8卷 1-16页

作者： Lu, Yuanhong Zhang, Jie Zhang, Jingyue Huang, Libin Guo, Haiping Hu, Binjiang Guo, Tianyu State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou510663 China National Energy Power Grid Technology R & D Centre Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou510663 China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou510663 China

In the context of extensive integration of renewable energy sources into the electrical grid, the grid's fault transient behaviors have undergone significant changes. However, conventional single-unit equivalent models fail to accurately capture the fault transient responses of combined wind-solar-hydro power stations and often require substantial computational resources, leading to reduced simulation efficiency. This study proposes a cluster-based equivalent modeling approach for hybrid wind-solar-hydro power plants using the Sparrow Search Algorithm. key factors influencing fault characteristics, including the distance to the Point of Common Coupling, DC-side current-limiting measures, irradiance levels, water flow rates, wind speeds, and reactive power at the outlet, are identified and used to construct a transient model. Euclidean distances are computed for these factors, and initial clustering centers for wind turbines are determined using an improved max–min distance technique. These factors and clustering centers serve as the training dataset to establish the clustering equivalent model. simulation results, conducted on the MATLAB2022 platform, demonstrate that the SSA-based model outperforms the single-unit equivalent model by over 150 times in terms of accuracy. Additionally, the SSA-based model achieves a delay time, defined as the time required to compute the system's transient response after a fault, of less than 5 ms, which is less than one-twentieth of the delay time of the single-unit equivalent model. These improvements highlight the model's ability to accurately capture dynamic power responses under various disturbances, making it highly suitable for real-time applications in hybrid renewable energy systems. © The Author(s) 2025.

关键词： MATLAB

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The electromagnetic transient simulation acceleration algorithm based on delay mitigation of dynamic critical paths

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Energy Informatics 2025年第1期8卷 1-12页

作者： Guo, Qi Lu, Yuanhong Zhang, Jie Zhang, Jingyue Huang, Libin Guo, Haiping Guo, Tianyu Tu, Liang State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou510663 China National Energy Power Grid Technology R & D Centre Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou510663 China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou510663 China

The task scheduling problem based on directed acyclic graphs (DAGs) has been proven to be NP-complete in general cases or under certain restrictions. In this paper, building upon existing scheduling algorithms, we introduce a static task scheduling algorithm based on directed acyclic graphs. By incorporating the proportion of task transmission delay as a guiding metric in the optimization process, processors can be prioritized for tasks with high latency, thereby improving computational efficiency. We first validate the theoretical feasibility of the algorithm using a theoretical case study and illustrate the algorithmic effectiveness using two real case studies, direct current (DC) model and alternating current (AC) model respectively. The research indicates that the scheduling algorithm proposed in this paper achieves an average scheduling length improvement of over 1.2% compared to the Heterogeneous Earliest-Finish-Time algorithm (HEFT) in topologies with high latency tasks. Additionally, the experiments show that the HEFT algorithm consumes 39.85us and the EMT-DM algorithm consumes 38.29us during simulation using DC, and the HEFT algorithm consumes 31.23us and the EMT-DM algorithm consumes 26.51us during simulation using AC, both of which are improved compared to the HEFT algorithm. © The Author(s) 2025.

关键词： Flow graphs

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GD-YOLO:A Network with Gather and Distribution Mechanism for Infrared Image Detection of Electrical Equipment

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Computers, Materials & Continua 2025年第4期83卷 897-915页

作者： Junpeng Wu Xingfan Jiang Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China School of Electrical Engineering Northeast Electric Power UniversityJilin132012China

As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial in effective intelligent fault diagnosis of various electrical *** response to the increasing demand for sufficient feature fusion in current real-time detection and low detection accuracy in existing networks for Substation fault diagnosis,we introduce an innovative method known as Gather and Distribution Mechanism-You Only Look Once(GD-YOLO).Firstly,a partial convolution group is designed based on different convolution *** combine the partial convolution group with deep convolution to propose a new Grouped Channel-wise Spatial Convolution(GCSConv)that compensates for the information loss caused by spatial channel ***,the Gather and Distribute Mechanism,which addresses the fusion problem of different dimensional features,has been implemented by aligning and sharing information through aggregation and distribution ***,considering the limitations in current bounding box regression and the imbalance between complex and simple samples,Maximum Possible Distance Intersection over Union(MPDIoU)and Adaptive SlideLoss is incorporated into the loss function,allowing samples near the Intersection over Union(IoU)to receive more attention through the dynamic variation of the mean Intersection over *** GD-YOLO algorithm can surpass YOLOv5,YOLOv7,and YOLOv8 in infrared image detection for electrical equipment,achieving a mean Average Precision(mAP)of 88.9%,with accuracy improvements of 3.7%,4.3%,and 3.1%,***,the model delivers a frame rate of 48 FPS,which aligns with the precision and velocity criteria necessary for the detection of infrared images in power equipment.

关键词： Infrared image detection aggregation and distribution mechanism sample imbalance strategy lightweight structure

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Online Optimization to Suppress the Grid-Injected power Deviation of Wind Farms with Battery-Hydrogen Hybrid Energy Storage systems

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Energy Engineering 2025年第4期122卷 1403-1424页

作者： Min Liu Qiliang Wu Zhixin Li Bo Zhao Leiqi Zhang Junhui Li Xingxu Zhu State Grid Zhejiang Electric Power Co. Ltd.Electric Power Research InstituteHangzhou310014China Key Laboratory of Modern Power System Simulation Control and Green Power New Technology of the Ministry of Education Northeast Electric Power UniversityJilin132012China

To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is ***,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage ***,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and ***,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is *** study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable *** storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.

关键词： Battery-hydrogen hybrid energy storage systems grid-injected power deviations measurement feedback online optimization energy states

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Fault Diagnosis Method of Rolling Bearing Based on MSCNN-LSTM

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Computers, Materials & Continua 2024年第6期79卷 4395-4411页

作者： Chunming Wu Shupeng Zheng Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China Ministry of Education Northeast Electric Power UniversityJilin132012China

Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success *** address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios,a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network(MSCNN)and Long Short-Term Memory(LSTM)fused with attention mechanism is *** adaptively extract the essential spatial feature information of various sizes,the model creates a multi-scale feature extraction module using the convolutional neural network(CNN)learning *** learning capacity of LSTM for time information sequence is then used to extract the vibration signal’s temporal feature *** parallel large and small convolutional kernels teach the system spatial local *** gathers temporal global features to thoroughly and painstakingly mine the vibration signal’s characteristics,thus enhancing model ***,bearing fault diagnosis is accomplished by using the SoftMax *** experiment outcomes demonstrate that the model can derive fault properties entirely from the initial vibration *** can retain good diagnostic accuracy under variable load and noise interference and has strong generalization compared to other fault diagnosis models.

关键词： Bearing fault diagnosis convolutional neural network short-long-term memory network feature fusion

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Investigating Black-Box Model for Wind power Forecasting Using Local Interpretable Model-Agnostic Explanations Algorithm

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CSEE Journal of power and Energy systems 2025年第1期11卷 227-242页

作者： Mao Yang Chuanyu Xu Yuying Bai Miaomiao Ma Xin Su Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China Daqing Power Industry Bureau Daqing 163000China

Wind power forecasting(WPF)is important for safe,stable,and reliable integration of new energy technologies into power *** learning(ML)algorithms have recently attracted increasing attention in the field of ***,opaque decisions and lack of trustworthiness of black-box models for WPF could cause scheduling *** study develops a method for identifying risky models in practical applications and avoiding the ***,a local interpretable model-agnostic explanations algorithm is introduced and improved for WPF model *** that basis,a novel index is presented to quantify the level at which neural networks or other black-box models can trust features involved in ***,by revealing the operational mechanism for local samples,human interpretability of the black-box model is examined under different accuracies,time horizons,and *** interpretability provides a basis for several technical routes for WPF from the viewpoint of the forecasting ***,further improvements in accuracy of WPF are explored by evaluating possibilities of using interpretable ML models that use multi-horizons global trust modeling and multi-seasons interpretable feature selection *** results from a wind farm in China show that error can be robustly reduced.

关键词： Black-box model correlation analysis feature trust index local interpretability local interpretable modelagnostic explanations(LIME) wind power forecasting

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Monthly Reduced Time-Period Scheduling of Thermal Generators and Energy Storage Considering Daily Minimum Chargeable Energy of Energy Storage

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Energy Engineering 2025年第4期122卷 1469-1489页

作者： Xingxu Zhu Shiye Wang Gangui Yan Junhui Li Hongda Dong Chenggang Li Key Laboratory of Modern Power System Simulation Control and Green Power New Technology of the Ministry of Education Northeast Electric Power UniversityJilin132012China State Grid Jilin Electric Power Co. Ltd.Electric Power Science Research InstituteJilin130021China

To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage,a reduced time-period monthly scheduling model for thermal generators and energy storage,incorporating daily minimum chargeable energy constraints,was ***,considering the variations in the frequency of unit start-ups and shutdowns under different levels of net load fluctuation,a method was proposed to reduce decision time periods for unit start-up and shut-down *** approach,based on the characteristics of net load fluctuations,minimizes the decision variables of units,thereby simplifying the monthly ***,the relationship between energy storage charging and discharging power,net load,and the total maximum/minimum output of units was *** on this,daily minimum chargeable energy constraints were established to ensure the energy storage system meets charging requirements under extreme net load ***,taking into account the operational costs of thermal generators and energy storage,load loss costs,and operational constraints,the reduced time-period monthly schedulingmodel was *** studies demonstrate that the proposedmethod effectively generates economical monthly operation plans for thermal generators and energy storage,significantly reduces model solution time,and satisfies the charging requirements of energy storage under extreme net load conditions.

关键词： Monthly scheduling thermal generators energy storage daily minimum chargeable energy decision time-period reduction unit start-up and shut-down unit commitment renewable energy

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