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power Science and Technology (ICPST), IEEE International Conference on

作者： Jin Liu Pengbo Han Zeqi Hong Hui Luo Siyang An Shujun Yao School of Electrical and Electronic Engineering North China Electric Power University Beijing China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid National Energy Power Grid Technology R&D Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Generally, detailed model of power electronics converter is used in electromagnetic transient simulations. It will use large sources of hardware in FPGA and the speed of simulation is slow. This paper is focus on the modeling method of real-time simulation, and proposed a new model of converter which based on the semi-implicit latency decoupling and paralleling (SILDP) technology. According to the proposed model, the SILDP model of voltage source converter(VSC) is build up. Base on the comparative between the SILDP VSC model and the simulation result waveform of PSCAD model, give the verification that the accuracy and effectiveness of the proposed SILDP VSC model. Finally, the SILDP VSC model is finished the verification in the FPGA experiment platform.

关键词： Accuracy Computational modeling Simulation Voltage Mathematical models Real-time systems Numerical models

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Research on Early Warning of Continuous Commutation Failure of HVDC Transmission Line Based on Machine Learning Methods 10

Research on Early Warning of Continuous Commutation Failure ...

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10th Renewable power Generation Conference, RPG 2021

作者： Wang, Bin Sheng, Yibo Liu, Chao Li, Yan State Key Laboratory of Operation and Control of Renewable Energy and Storage System Beijing China Dept. of Electrical Engineering Tsinghua University Beijing China

ISBN: (纸本)9781839536069

High-voltage direct current (HVDC) transmission systems have developed rapidly in recent years. Continuous commutation failure is a serious malfunction in HVDC transmission systems, which results in the blockage of the converter station. Though commutation failure is inevitable when serious AC side faults occur, continuous commutation failure can be prevented by control methods based on a sensitive and accurate forecast. However, the HVDC transmission systems are complex and nonlinear when commutation failure occurs. Thus, analytical methods are limited. Hence, a new forecasting method based on machine learning is introduced. First, this paper analyses the physical process of commutation failures. Commutation voltage, direct current, and advancing firing angle are the main factors that affect the commutation failure of HVDC. Second, this paper realizes the training and evaluation of the continuous commutation failure classification and probabilistic forecasting model. Data processing and feature engineering methods have significantly improved the model performance. Finally, the proposed model is tested on the CIGRE benchmark system. The proposed model can predict whether a continuous commutation failure will occur within tens of milliseconds after the first commutation failure, where the risk of continuous commutation failure is greatly reduced. © The Institution of engineering and Technology.

关键词： Data handling

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ROBUST OPTIMAL SCHEDULING FOR DISTRIBUTED ENERGY STORAGE system CONSIDERING UNCERTAIN FREQUENCY CHANGE

ROBUST OPTIMAL SCHEDULING FOR DISTRIBUTED ENERGY STORAGE SYS...

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CIRED 2022 Shanghai Workshop

作者： Cao, Yongji Zhang, Hengxu Zhang, Xiaoning Li, Changgang Qin, Hao Gao, Zhimin Centre for Electric Power and Energy Department of Electrical Engineering Technical University of Denmark Kgs. Lyngby Denmark Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781839538391

With the rapid development of distributed renewable energies, the number and severity of the active power disturbances that affect frequency stability have increased. The distributed energy storage system (DESS) characterized by high flexibility and dispatchability can provide distribution network with frequency regulation services after disturbances. This paper proposes a robust optimal scheduling approach for DESSs considering uncertain frequency changes. The concept of the probabilistic transient frequency deviation security margin (PTFDSM) is presented to quantify the power deficit that the system can tolerate. And an extended system frequency response model is employed to obtain the PTFDSM. Then, a robust optimization model is established to minimize the network loss and maintain the operating point of DESSs within the PTFDSM for frequency stability. The AC power flow constraints are introduced into the optimization model. And the second-order cone relaxation method is used to deal with the nonlinear constraints through relaxation variables. Based on the column-and-constraint generation method, the relaxed second-order cone optimization model is solved, and scheduled DESS power is attained. A case study is carried out to verify the effectiveness of the proposed approach. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Energy storage

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Basic reason for the accumulation of charge on the surface of polymer dielectrics

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Science China Materials 2022年第10期65卷 2884-2888页

作者： Tianyu Wang Xiaofen Li Boya Zhang Dayu Li Jianbo Liu Guixin Zhang State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Key Laboratory of Advanced Materials(MOE) School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong UniversityShaanxi 710049China

Due to their outstanding electrical insulation performance,low energy loss and high reliability,polymer dielectrics are widely used in flexible electronics,capacitors and large-scale power transmission equipment[1–5].However,polymer dielectrics accumulate large amounts of charge on their surface during use,which causes local electric field distortion and induces flashover,explosion,and equipment damage[6–9].Although researchers have attempted to develop methods to reduce the accumulation of surface charge through material modification,such as plasma treatment[10],direct fluorination[11],and coating[7,9,12],each of these methods has drawbacks;thus,the bottleneck has yet to be overcome.

关键词：电荷量第一性原理计算聚合物表面电介质电场畸变深能级缺陷电荷测量电荷特性

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Dynamic Phasor Estimation Method Considering False Data Injection Attack 5

Dynamic Phasor Estimation Method Considering False Data Inje...

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5th IEEE Conference on Energy Internet and Energy system Integration, EI2 2021

作者： Zhao, Qingsheng Liu, Yifeng Zhao, Dakai Liang, Dingkang Guo, Zun Shanxi Key Laboratory of Power System Operation and Control College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China State Grid Economic and Technological Research Institute Co. Ltd Beijing China

ISBN: (纸本)9781665434256

When the smart grid data acquisition system is attacked by false data injection, because the measured data is tampered, the wrong state estimation will lead to the wrong action of the smart grid controller. This paper proposes a dynamic phasor estimation method considering false data injection attack. Firstly, the electrical signal model suitable for dynamic conditions is established by Taylor series expansion of the electrical signals which include harmonic, noise and other conditions;Then, the method which based on the double suboptimal-scaling factor strong tracking Kalman filter algorithm (DSTKF) and Euclidean distance is used to detect, track and estimate the phasor;Finally, the proposed estimation method is used to simulate the electrical signal with noise and false data attack. Besides, the signal of IEEE 39-bus system is also estimated with this method. The results show that this proposed method can not only detect the false data attack and mutation in electrical signal, but also has high estimation accuracy and tracking ability for dynamic phasor. © 2021 IEEE

关键词： Kalman filters

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Spatial-Temporal Attention Mechanism and Graph Convolutional Networks Based Distributed PV Ultra-short-term power Forecasting

Spatial-Temporal Attention Mechanism and Graph Convolutional...

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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Xinyu Liu Xu Xie Qin Fang Fei Xu Chen Huang Zhao Zhen Fei Wang State Grid Chongqing Electric Power Company Chongqing China State Grid Chongqing Electric Power Company Shinan Power Supply Branch Chongqing China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China Department of Electrical Engineering North China Electric Power University Baoding China

To ensure the efficient operation and healthy development of photovoltaic power generation systems, reliable distributed photovoltaic power prediction is significant. However, the lack of modeling ability of existing distributed photovoltaic power forecasting methods for dynamic spatiotemporal correlation of distributed photovoltaic power data limits the further improvement of prediction accuracy. Therefore, a distributed photovoltaic ultra-short-term power forecasting method based on spatial-temporal attention mechanism and graph convolutional networks is proposed in this paper. Firstly, distributed photovoltaic cluster is divided into sub-regions and graph structured data can be generated with each sub-region being a node on the graph. Then, spatial-temporal attention mechanism is used to adaptively capture spatiotemporal correlation information and dynamically update adjacency matrix. Finally, spatial-temporal convolution is used to extract deep spatiotemporal features of distributed photovoltaic power data and achieve regional power prediction. The validity and progressiveness of the model have been verified in real world data sets.

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Unit Equivalent Distribution Network Pricing for Electricity Retail Market

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第2期2卷 276-288页

作者： Wang, Shuangyuan Li, Ran Ma, Qiuyang Gu, Chenghong Li, Furong University of Shanghai for Science and Technology Department of Mechanical Engineering Shanghai200093 China Shanghai Jiao Tong University Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai Non-Carbon Energy Conversion and Utilization Institute Shanghai200240 China State Grid Energy Research Institute Beijing102211 China University of Bath Department of Electronic and Electrical Engineering BathBA2 7AY United Kingdom

Most existing network pricing methodologies are designed for retailers and large customers. With the development of responsive technologies, domestic customers may have very different impacts on networks cost, thus calling for a cost-reflective network pricing method for mass customers in the retail market. The naive volumetric and marginal pricing methods may cause issues of inequality and mis-signaling. Inspired by the Passenger Car Equivalent (PCE) in transportation economics, this paper proposes a Unit Home Equivalent (UHE) pricing to reflect the compatibility between electricity networks and a certain type of users. The method is validated against the Distribution Use of system (DUoS) charging methodology in the U.K. by using real network and household data. The results show the proposed pricing can encourage existing customers to adjust energy usage behaviours and guide new customers and EVs to the right locations. © 2023 IEEE.

关键词： power markets

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Transient voltage stability margin prediction method based on lightgbm 4

Transient voltage stability margin prediction method based o...

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4th International Conference on Advanced Electronic Materials, Computers and Software engineering, AEMCSE 2021

作者： Jie, Zhang Binjiang, Hu Qi, Guo Yihua, Zhu Zhiyuan, Sun Mingpo, Li Yan, Sun Electric Power Research Institute CSG State Key Laboratory of HVDC China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China CSG Key Laboratory of Power System Simulation Guangzhou China Guangxi Power Grid Electrical Power Research Institute Nanning China Guangxi Power Grid Dispatching Control Center Nanning China

ISBN: (纸本)9781665415965

Traditional time-domain simulation method has long calculation time on transient voltage stability margin prediction problem, the direct method is difficult to adapt to the complex power grid model, and the deep neural network method requires a large data set. In this paper, we propose the method based on LightGBM, which has the advantages of appropriate model complexity and less training time demand. Based on a regional subsystem of Guangxi power Grid with more electrolytic aluminum load, and a power system transient voltage stability margin prediction model based on LightGBM is constructed. To solve the long tail distribution learning problem, target clipping and log transformation techniques are proposed to enhance the model. The effectiveness of them is verified by experiment results. And we propose a new metric, partial RMSE, to evaluate the performance of the model fitting the instability data. The experiment results also show that it has achieved better performance and training time than other machine learning models. © 2021 IEEE.

关键词： Forecasting

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Optimal power system Scheduling Considering Uncertainty-Induced Generation Unit Output Curtailment

Optimal Power System Scheduling Considering Uncertainty-Indu...

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system Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Wei Hu Ning Mi Hailiang Zhong Weihan Chen Kun Ma Tianxiang Geng Yun Wang Bo Su Long Yang State Key Laboratory of Power System and Generation Equipment(Department of Electrical Engineering) Tsinghua University Beijing China Dispatching Control Center State Grid Ningxia Electric Power Co. Ltd. Yinchuan China Department of Electrical Engineering and Applied Electronic Technology Tsinghua University Beijing China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

Under the call of the national “dual carbon” goals, the growth of the proportion of new energy generation has become an inevitable trend in energy structure adjustment. The “static parameter” model, which traditional thermal power units rely on, can no longer accurately reflect their output uncertainty, urgently requiring a new optimization scheduling strategy to cope with this change. This study proposes an optimized scheduling model for power generation systems that considers the output curtailment and unplanned outages of thermal power units. It employs non-parametric kernel density estimation techniques to model the uncertainty of thermal power unit output and uses chance constraints to define the fluctuation range of generation output and the system’s reliability. The model adopts a scenario generation method based on Copula theory to construct output scenarios and further employs reduction techniques to optimize the number of scenarios. Moreover, this paper introduces Conditional Value at Risk as a key indicator to measure the cost of system uncertainty risk, thereby providing a more comprehensive perspective in risk assessment. Simulation experiments on the IEEE 30-node standard system demonstrate that compared to traditional optimization scheduling models relying on static parameters, the proposed model can more effectively balance the output fluctuations of different thermal power units, significantly reducing the risk of curtailment due to supply shortages. This study offers a new theoretical basis and practical guidance for optimizing the scheduling of power systems under the context of large-scale integration of new energy and economic fluctuations.

关键词： Uncertainty Fluctuations Costs Estimation Thermal management systems simulation Scenario generation Kernel Optimization Standards

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Combining Physical Mechanisms with Data-Driven Approaches for Short-Term Photovoltaic power Forecasting

Combining Physical Mechanisms with Data-Driven Approaches fo...

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

作者： Xuan Yu Yue Xia Zheng Wang College of Information and Electrical Engineering China Agricultural University Beijing China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Accurate photovoltaic (PV) power forecasting is critical for the reliable and stable operation of the power grid. Most existing studies directly use weather conditions as data-driven inputs without incorporating PV physics models to improve the quality of input weather condition features. Therefore, a novel PV power forecasting method that combines physical models and data-driven approaches is proposed. First, a physical model of the PV system is established to convert solar irradiance data into the instantaneous observed irradiance received by the PV system. Then, this newly obtained irradiance data, along with other weather features, is used to reconstruct the data-driven inputs, achieving short-term power forecasting by combining physical models and data-driven approaches. Finally, simulation analysis is conducted using a real PV system dataset. The results show a significant improvement in forecasting accuracy, validating the effectiveness of the proposed method. The results indicate that this article combines physical mechanisms with data-driven approaches, fully considering the advantages and disadvantages of two commonly used models, greatly improving prediction accuracy.

关键词： Photovoltaic systems Solar irradiance Accuracy system integration Predictive models Prediction algorithms Data models Reliability Forecasting Meteorology

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