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SSRN 2023年

作者： Sun, Yinfeng Guo, Yuhang Lu, Yuze Lu, Mingzhi Key Laboratory of Modern Power System Simulation and Control& Renewable Energy Technology Ministry of Education [Northeast Electric Power University Jilin132012 China State GridShijiazhuang Gaocheng District Electric Supply Company No. 76Changsheng SouthStreet Gaocheng District Shijiazhuang052160 China

Large wind farms connected to the grid through MMC-HVDC systems can cause overloading of the non-faulty converter station after a DC fault. This short communication proposes a system fault ride-through method by coordinating and controlling the energy storage system, the non-faulty converter station and the wind farm to jointly consume the unbalanced power during the fault. The model is created in the PSCAD simulation platform, and the correctness and effectiveness of the method are verified by simulation. © 2023, The Authors. All rights reserved.

关键词： Energy storage

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Dual-layer Economic Dispatch Method for Electric Fused Magnesia Loads Participating in Peak Shaving Guided by Price Compensation 4

Dual-layer Economic Dispatch Method for Electric Fused Magne...

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4th Energy Conversion and Economics Annual Forum, ECE Forum 2024

作者： Li, Junhui Fan, Tianzhen Xu, Jing Zheng, Wei Xiao, Chupeng Xu, Chaoyang Li, Cuiping Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China State Grid Electric Power Research Institute Co. State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Co. Ltd. Hubei China State Grid Liaoning Electric Power Supply Co. State Grid Fushun Electric Power Supply Co. Ltd. Liaoning China

ISBN: (纸本)9781837242627

To address the severe challenge of increasing fluctuations in supply and demand due to the expansion of new energy installations, this paper proposes a hierarchical optimization control strategy for electric fused magnesia loads participating in peak shaving, guided by a price compensation mechanism. In the upper-layer system control, a price mechanism is designed to enhance the flexibility of electric fused magnesia loads in participating in peak shaving, considering the overall benefit of the system. In the lower-layer enterprise net profit optimization model, the product revenue, demand response income, operation and maintenance costs, and carbon emission costs of the electric fused magnesia enterprise are balanced to ensure the maximization of the enterprise's net profit. Finally, numerical examples are used to verify the effectiveness of this method in improving the system's peak shaving capability. © The Institution of Engineering & Technology 2024.

关键词： electric fused magnesia load hierarchical optimization control price compensation

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Ultra-Short-Term Multi-Step Prediction of Wind Power Based on ModernTCN and Multi-Task Learning

Ultra-Short-Term Multi-Step Prediction of Wind Power Based o...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Mao Yang Xiangyu Li Zifen Han Yong Sun Xiqiang Chang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China State Grid Gansu Electric Power Company Gansu China State Grid Jilinsheng Economic And Technological Research Institute Jilin China State Grid Xinjiang Electric Power CO. LTD Xinjiang China

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

The intermittent and fluctuating nature of wind energy makes it difficult to accurately predict wind power, leading to low penetration in the wind power grid. To address this problem, this paper designs ultra-short-term multi-step prediction of wind power based on ModernTCN and multi-task learning to improve the prediction accuracy. Firstly, ModernTCN's ability to have a large receptive field and capture cross-variate timing dependencies is exploited to obtain multivariate timing feature information and establish a multi-step mapping of input features to power. Then it is inputted into the MMoE multi-task learning method, and the prediction results are corrected to obtain the ultra-short-term prediction of wind power using multiple expert networks and gating units. The experimental results show that the method proposed in this paper can effectively improve the ultra-short-term prediction accuracy of wind power, and can provide a favourable reference for the scheduling centre to adjust the power generation plan in real-time response to the power demand.

关键词： Renewable energy sources Accuracy Wind energy Wind power generation Predictive models Multitasking Feature extraction Real-time systems Timing Wind forecasting

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Visualization of CNN Transient Voltage Classification Based on Feature Recognition and Enhancement

Visualization of CNN Transient Voltage Classification Based ...

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Sustainable Power and Energy Conference (iSPEC)

作者： Zhendong Li Yutian Liu Jun An Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China

ISBN: (纸本)9781665414401

The deep learning algorithm represented by CNN has achieved remarkable results in complex feature extraction, classification and regression. Since the CNN adopts an end-to-end training process, the carrier of acquisition knowledge is a high-dimensional non-linear function, which is hard to understand by humans. Therefore, it is difficult to be widely used in online decision-making engineering scenarios such as the fast assessment of power system transient voltage stability. In this paper, the key input elements extracted by CNN are identified by Grad-CAM, and the key features captured by CNN in the assessment process are enhanced based on AM, so as to finally obtain the visual expression of key areas and variables of the original input data. Besides, a sample construction method based on single line diagram is proposed to improve the visualization effect of Grad-CAM and AM. simulation results and analyses of IEEE 39bus test system demonstrates the effectiveness of the proposed method.

关键词： Training Visualization simulation Data visualization Voltage Power system stability Feature extraction

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Physical Passivation of Deep-Level Defect States in Polymer Dielectrics for Outstanding Dielectric Properties

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SSRN 2023年

作者： Li, Xiao-Fen Wang, Tian-Yu Yin, Si-Jia Zhang, Gui-Xin Liu, Jian-Bo Liu, Bai-Xin School of Materials Science and Engineering Tsinghua University Beijing100084 China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China College of Art and Design Visual Communication Design SuZhou University Suzhou234111 China

The limited dielectric strength of polymer dielectrics hampers their reliable use in highly integrated electrical systems and high-energy storage film capacitors. This constraint is closely associated with the presence of deep-level defect states in polymer dielectrics. Here, we present an efficient and stable approach for the physical passivation of deep-level defect states aimed at enhancing the dielectric properties of polymer dielectrics. Employing inductively coupled plasma technology with argon as the carrier gas, the polymer undergoes substantial decreased deep-level defect states, as evidenced by nano-micro-region defect measurements. Consequently, the localized charge accumulation in the treated polymer dielectric is significantly reduced, resulting in a remarkable 62% increase in flashover voltage. The breakdown strength is also enhanced, and the treated material exhibits long-term stability. Moreover, materials featuring passivated deep-level defect states can boost discharge energy density without compromising efficiency. For instance, PTFE with passivated deep-level defect states maintains a discharge energy density of 1.18 J/cm3 and a charge-discharge efficiency of 95.06% at 200 °C and 300 MV/m. By focusing on deep-level defect states passivation, we introduce a strategy for receiving polymers with exceptional performance and stability. © 2023, The Authors. All rights reserved.

关键词： Passivation

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Refined power modeling method of ultra-short-term wind power cluster based on power reverse deduction 8

Refined power modeling method of ultra-short-term wind power...

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8th IEEE Conference on Energy Internet and Energy system Integration, EI2 2024

作者： Sun, Yupeng Yang, Mao Ma, Chenglian Jia, Yunpeng Fang, Guozhong Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable EnergyTechnology Ministry of Education Jilin China Northeast Electric Power University Jilin China Jilin Power Supply Company State Grid Jilin Electric Power Co.Ltd. Jilin China State Grid Northeast China Branch Shenyang China

ISBN: (纸本)9798331523527

The accurate prediction of wind power provides a guarantee for the safety and stability of the power system and the power market trading. The existing wind power prediction methods mainly focus on the depth of the model, and improve the accuracy of the prediction by constantly improving the model and deepening the model, but they ignore the limitations of the model itself. In this paper, a fine modeling method for wind power is proposed, and the prediction accuracy is improved by horizontal expansion of the model. Firstly, the power output class and its NWP characteristics are determined by power reverse deduction, and the power output class is divided by the degraded and simplified U-net model. Then, Non-stationary Transformer (NSTransformer) is introduced to model each output level separately to get the predicted power. Finally, the proposed method is applied to a wind farm cluster in China. The accuracy of the proposed method is higher than other methods in ultra-short-term prediction, which proves the reliability and effectiveness of the proposed method. © 2024 IEEE.

关键词： backward deduction non-stationary transformer refine model wind power prediction

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Operational Optimization of Photovoltaic-Storage system Considering Demand Response 8

Operational Optimization of Photovoltaic-Storage System Cons...

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8th IEEE Conference on Energy Internet and Energy system Integration, EI2 2024

作者： Gao, Xuefeng Qin, Wenwen Wang, He Yao, Yiwen Li, Cuiping Peng, Hao Zhu, Xingxu Li, Junhui Economic and Technical Research Institute State Grid Jilin Electric Power Company Limited Changchun China Ministry of Education Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Jilin China State Grid Jilin Electric Power Company Limited Songyuan Power Supply Company Songyuan China

ISBN: (纸本)9798331523527

Aiming to address the stability challenges in power systems caused by the volatility and uncertainty of photovoltaic (PV) power generation, an optimization strategy for PV-storage systems incorporating demand response (DR) is proposed. Which is based on PV generation characteristics, Time-of-Use Pricing (TUP), and demand side flexibility. A multi-objective optimization model is established to balance the operational and environmental costs of PV-storage configuration. Building on dynamic adjustments at the demand side, a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with dynamic inertia weighting is introduced to enhance global search capability for optimized energy storage (ES) configuration. In case study, two scenarios are designed to validate the effectiveness of the proposed model in reducing costs and improving PV utilization rates. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Refined power modeling method of ultra-short-term wind power cluster based on power reverse deduction

Refined power modeling method of ultra-short-term wind power...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Yupeng Sun Mao Yang Chenglian Ma Yunpeng Jia Guozhong Fang Key Laboratory of Modern Power System Simulation and Control & Renewable EnergyTechnology Ministry of Education Northeast Electric Power University Jilin China Northeast Electric Power University Jilin China Jilin Power Supply Company of State Grid Jilin Electric Power Co. Ltd. Jilin China State Grid Northeast China Branch Shenyang China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

关键词： Accuracy system integration Predictive models Wind power generation Power system stability Wind farms Transformers Stability analysis Safety Thermal stability

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Research on Energy Storage Capacity Configuration Method and Performance Evaluation in New Energy Base

Research on Energy Storage Capacity Configuration Method and...

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Power and Electrical Technology Conference (APET), Asia

作者： Ruoyu Zhang Liangyou Wang Changping Sun Bo Le Mian Wang Kangsheng Cui Institute of Science and Technology China Three Gorges Corporation Beijing P.R. China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Tsinghua University Beijing P.R. China

In order to improve the power output stability and frequency stability when large-scale new energy is integrated into the grid, large-scale new energy base must consider the configuration of energy storage systems with a certain capacity. Facing the demand under the background of new energy development, this paper analyzes the positive impact of energy storage to new energy base. The configuration method of energy storage capacity is proposed, and furthermore, the proposed method is used to calculate the capacity of the energy storage system required to be configured in a typical new energy base, and the power suppression and stability improvement effects brought by the energy storage are evaluated. The case analysis results show that the required energy storage capacity of a new energy base is about 10% of its total wind power and photovoltaic capacity. This configuration ratio can basically achieve the stability of the new energy output fluctuation, and at the same time provides the same frequency regulation capability with conventional generator set of equal capacity.

关键词： Performance evaluation Technical requirements Photovoltaic systems Fluctuations Power system stability Wind power generation Stability analysis

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Vehicle driving behavior predicting and judging using LSTM and statistics methods 4

Vehicle driving behavior predicting and judging using LSTM a...

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4th CAA International Conference on Vehicular control and Intelligence, CVCI 2020

作者： Zhang, Chao Che, Guangxu Gao, Bingzhao Qingdao Legee Transmission System Technology Co. Ltd Qingdao China State Key Laboratory of Automotive Simulation and Control Jilin University Changchun China

ISBN: (纸本)9781728184968

Autonomous driving is one of the three major innovations in automotive industry. Autonomous vehicles promise to revolutionize human mobility and vehicle safety. This promise can't be realized without the ability to constantly make the right decisions even in the complex situations. This paper proposed a new decision-making system including a new way using the long short-term memory neural network to predict the states of the vehicles nearby in the short future using the history of which got from the cognitive ability. Based on the future states predicted by the neural network, this paper also proposed some statistics methods to give a classification criterion to judge a vehicle is dangerous, attentive or safe. © 2020 IEEE.

关键词： Autonomous vehicles

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