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4th International Conference on Electrical Engineering and control Technologies, CEECT 2022

作者： Yu, Jiawei Zhu, Yihua Li, Chengxiang Luo, Chao State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid China National Energy Power Grid Technology R&d Center Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China Csg Key Laboratory for Power Sytem Simiulation ELectic Power Reasearch Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9781665498999

With the development of power electronics technology, the power system is now evolving into a new-generation power system dominated by renewable energy. Due to the physical limitations of power electronics, controller saturation is widely adopted in wind turbines and photovoltaics. In this paper, a nonlinear system model considering output saturation is proposed in the view of general dynamic systems. By defining saturation-activation vector L, all the saturations of the system are uniquely represented. Then, the proposed modeling method is applied to a voltage source converter (VSC) grid-connected system. Based on RTDS simulation, this paper revealed that a class of oscillations at a specific frequency are caused by the activation of the phase-locked loop (PLL) saturation. The frequency of the oscillation depends on the top and bottom boundaries of PLL saturation. © 2022 IEEE.

关键词： Chemical activation

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Building a Large-Scale Intrinsically-Safe Energy Storage system by Using Retired EV Batteries

Building a Large-Scale Intrinsically-Safe Energy Storage Sys...

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International Conference on power Electronics systems and Applications (ICPESA)

作者： Congjia Zhang Qi Yu Yanglin Zhou Yuxin Li Baochang Liu Ende Lin Song Ci Department of Electrical Engineering Tsinghua University Beijing China China Three Gorges Corporation Wuhan China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China

ISBN: (数字)9798350365801

ISBN: (纸本)9798350365818

Utilizing retired batteries in energy storage systems (ESSs) poses significant challenges due to their inconsistency and safety issues. The implementation of dynamic reconfigurable battery networks (DRBNs) is promising in maintaining the reliability and safety of battery energy storage systems (BESSs). Recently, large-scale BESSs based on DRBN have been deployed with the use of retired batteries, but the operational performance of these systems in real-world working conditions still remains uncertain. To bridge the research gaps, a case analysis is conducted based on the operational data from a real-world large-scale retired BESS. First, the framework of the retired BESS and the structure of the DRBN are illustrated in detail. Then, the core principle of DRBN is comprehensively demonstrated in terms of energy digitization and time-oriented battery management mode. Finally, the operational results indicate that the ESS can effectively improve the voltage and capacity consistency of battery modules and realize efficient thermal management to improve systematic safety of the retired BESS.

关键词： Thermal management of electronics systematics Battery management systems Voltage Thermal management Thermal conductivity Batteries

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Renewable Energy Penetration Improving Method in Distribution Network based on Dynamic Reconfigurable Battery Network

Renewable Energy Penetration Improving Method in Distributio...

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Smart Grid and Energy Internet (SGEI), International Conference on

作者： Ziyi Xiong Yi Yang Yanglin Zhou Qi Zhou Song Ci Department of Electrical Engineering Tsinghua University Beijing China State Grid Jiangsu Electric Power Co. Ltd. Electric Power Research Institute Nanjing China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China

ISBN: (数字)9798350368314

ISBN: (纸本)9798350368321

High proportion of renewable energy brings great challenges to the stable operation of the distribution network. Distributed energy storage (DES), as an interactive Lithium-ion battery energy storage system, can effectively improve the grid connection and serve as a backup power source. Aiming at the DES system supporting the flexibility of distribution network, this paper proposes a new type of dynamic reconfigurable battery (DRBN) system and its efficient collaborative optimization control method with the power converter system, clarifies the operation control mechanism of the DRBN system of the CPS architecture, proposes a multi-objective optimization control strategy for the DRBN system based on the CPS architecture, and formulates a demonstration application and verification plan.

关键词： Lithium-ion batteries Renewable energy sources Energy Internet Collaboration Distribution networks Smart grids Optimization power generation Information exchange

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Cooperative Prediction Approach for the Small-Signal Stability Margin of Renewable power system Based on Vertical Federated Learning

Cooperative Prediction Approach for the Small-Signal Stabili...

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power systems and Electrical Technology (PSET), IEEE International Conference on

作者： Xiang Lai Zhihao Chen Yun Liu Tianchen Dai Haiqing Cai Haohan Gu Wei Chen School of Electric Power Engineering South China University of Technology Guangzhou China State Key Laboratory of HVDC CSG Electric Power Research Institute Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

Since sub-synchronous oscillations have been frequently witnessed in large-scale wind farms under weak grid conditions, it is significant to predict the critical stability situations in short term so that timely preventative actions can be taken to avoid potential small-signal stability (SS) risks. To this end, this paper proposes a SS prediction approach based on the grid strength metric of generalized short-circuit ratio (gSCR) under the mechanism of vertical federated learning. Specifically, the improved ridge regression prediction model is employed and its corresponding distributed training and the distributed predicting are derived by utilizing the separable characteristic of gradient vector calculation in the context of federated learning. On this basis, the original largescale predicting problem is decomposed into a large number of small-scale subproblems, and each subproblem is calculated locally by the corresponding wind farm. The feature data information of adjacent wind farms can be effectively used to improve the accuracy of the prediction of the stability margin while ensuring the data privacy and security of all participants. Finally, simulation results illustrate the effectiveness and efficiency of the proposed approach under various scenarios.

关键词： Training Accuracy Federated learning Simulation power system stability Wind farms Predictive models Stability analysis Vectors Security

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On Fast-Converging Deep Reinforcement Learning for Optimal Dispatch of Large-Scale power systems under Transient Security Constraints

arXiv

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arXiv 2023年

作者： Xiao, Tannan Chen, Ying Diao, Han Huang, Shaowei Shen, Chen State Key Laboratory of Power System Operation and Control Tsinghua University Beijing10084 China Department of Electrical Engineering Tsinghua University Beijing100084 China

power system optimal dispatch with transient security constraints is commonly represented as Transient Security-Constrained Optimal power Flow (TSC-OPF). Deep Reinforcement Learning (DRL)-based TSC-OPF trains efficient decision-making agents that are adaptable to various scenarios and provide solution results quickly. However, due to the high dimensionality of the state space and action spaces, as well as the non-smoothness of dynamic constraints, existing DRL-based TSC-OPF solution methods face a significant challenge of the sparse reward problem. To address this issue, a fast-converging DRL method for TSC-OPF is proposed in this paper. The Markov Decision Process (MDP) modeling of TSC-OPF is improved by reducing the observation space and smoothing the reward design, thus facilitating agent training. An improved Deep Deterministic Policy Gradient algorithm with Curriculum learning, Parallel exploration, and Ensemble decision-making (DDPG-CPEn) is introduced to drastically enhance the efficiency of agent training and the accuracy of decision-making. The effectiveness, efficiency, and accuracy of the proposed method are demonstrated through experiments in the IEEE 39-bus system and a practical 710-bus regional power grid. The source code of the proposed method is made public on GitHub. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Transient Voltage Stability Assessment Method based on gcForest

Transient Voltage Stability Assessment Method based on gcFor...

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2021 International Conference on Electrical, Electronics and Computing Technology, EECT 2021

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

Aiming at the fact that the traditional method cannot quickly and accurately judge the power system transient voltage stability, and the deep neural network method needs many sample data sets and has a large number of hyperparameters, a power system transient voltage stability assessment method based on deep forest (gcForest) is proposed. gcForest effectively extracts features through ensemble learning, multi-layer feature transformation, and dynamically adjusting the number of model layers. Finally, this method is applied to transient voltage stability assessment of an electrolytic aluminium load-intensive area in Guangxi power Grid. The results show that the method has high accuracy, the feasibility and effectiveness of this method are verified. This method can assist dispatchers to judge the transient voltage stability of the power grid. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Research on operation Error Detection Model of Metering Device

Research on Operation Error Detection Model of Metering Devi...

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IEEE/IAS Industrial and Commercial power system Asia (I&CPS Asia)

作者： Ying Liu Qingsheng Zhao Dingkang Liang Yuhuan Han Yi Zhang Hehong Guo Shanxi Key Laboratory of Power System Operation and Control College of Taiyuan University of Technology Taiyuan China State Grid Jincheng Power Supply Company Jincheng China State Grid Power Supply Company Taiyuan China

In order to reduce the large cost waste caused by the problems of heavy workload, low efficiency, complex operation site and limited experience level of operators in the daily measurement error verification and operation maintenance management of electric energy meters. Based on the data collected by electricity information, this paper establishes a big data analysis model, and proposes a data-driven calculation method for the operation error of electric energy meter. Based on the collected file data of a station area, the power supply data of the assessment table and the power consumption data of all user tables, the equation is established according to the energy conservation, and the linear equations are established by using multi-metering cycle data. Combined with deep learning, the short-term abnormal data of the electric energy meter are located and eliminated, and the algorithm of base tracking and Moore-Penrose generalized inverse is used to solve the operation error of the intelligent electric energy meter. As an important reference for the evaluation of the operation state of the electric energy meter, it provides effective support for line loss management, operation and maintenance of the electric energy meter and accurate replacement, which can effectively reduce the number of on-site operation and maintenance personnel and improve the accuracy of fault analysis and location. The accuracy of the method is verified by the actual station data, and the trial operation results are good.

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Value-at-Risk Measurement and Case Analysis of Electricity Consumption

Value-at-Risk Measurement and Case Analysis of Electricity C...

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IEEE/IAS Industrial and Commercial power system Asia (I&CPS Asia)

作者： Yuesheng Yang Qingsheng Zhao Dingkang Liang Zhiqin Qin Yi Zhang Hehong Guo Shanxi Key Laboratory of Power System Operation Control College of Taiyuan University of Technology Taiyuan China State Grid Jincheng Power Supply Company Jincheng China State Grid Power Supply Company Taiyuan China

Accurate and timely electricity consumption prediction results can not only provide calculation basis for power project investment, but also provide data support for optimal operation decision of power grid. power demand shows different trends of change and development at different time stages, with complex changes, random occurrence and other characteristics. In order to predict the change of electricity consumption at different times, this paper uses VaR (Value-at-Risk) method to evaluate the electricity consumption at different times. VaR method is an important risk management method, which provides people with a comprehensive measurement of market risk and has been widely used. This paper first gives the relevant introduction of VaR; Then the Historical Simulation (HS) method and Extreme Value Theory (EVT) method are introduced; The VaR value of electricity consumption is calculated by these two methods, and the results are visualized and compared, and finally the effect of the two methods is evaluated.

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A multi-dimensional quantitative evaluation framework for resources complexity of new energy stations based on grey correlation analysis

A multi-dimensional quantitative evaluation framework for re...

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

作者： Dong Lv Yun Wang Fei Xu Li He Yuzhi Liu Wenjing Yang Mengjiao Chen Nan Li Haonan Dai Fei Wang China Three Gorges Renewables(Group) Co. Ltd Beijing China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China

In recent years, new energy has developed rapidly around the world and its role in promoting energy transformation is obvious to all, but it also poses a serious challenge to the safety and stability of the power system caused by the random fluctuation of output. New energy forecasting is currently considered to be the key technique to improve the operation level of the new power system. However, influenced by geographical location, installed capacity, forecasting scale, and other factors of the forecasting object, the power forecasting accuracy of each station varies significantly, which hinders the power generation group from delicacy management of owned new energy stations. Resource complexity evaluation is the key to solving the above challenges, but there are still gaps in related research currently. In this paper, a multi-dimensional quantitative evaluation framework for the resource complexity of new energy stations is proposed, aiming to realize the real-time dynamic evaluation of the complexity of new energy station resources. Firstly, wholeness and locality are defined as two dimensions to comprehensively evaluate the resources’ complexity. Secondly, complexity evaluation indicators such as overall range, frequency, and sampling loss area are designed under these two dimensions. Lastly, all indicators are assembled to obtain a comprehensive score based on the gray correlation analysis. The data from two real wind farms and two real photovoltaic power stations are adopted for simulation calculation. The results indicate that the proposed approach can accurately quantify the complexity of resources of a new power station, thus providing a reference for the establishment of the assessment basis of the power forecasting accuracy of the station.

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Research on Operating system Runtime Jitter control Method for Electromagnetic Transient Real time Simulation Calculation

Research on Operating System Runtime Jitter Control Method f...

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Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Qi Guo Tianyu Guo Yuanhong Lu Haiping Guo Libin Huang Liang Tu State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

Real-time electromagnetic transient simulation can quickly analyze and solve the problems in complex large-scale power systems, realize accurate prediction and diagnosis of faults, and is a necessary means to promote the implementation of new power systems. However, the real-time simulation of electromagnetic transient puts forward strict requirements for the real-time performance of the underlying operating system. In this paper, the test method of system runtime jitter on Linux platform is designed, and the causes of jitter are analyzed based on the test results, and the control method of runtime jitter is studied. The experimental results show that the jitter of the system can be controlled in nanosecond level after using the methods of interrupt shutdown, CPU core binding and configuration of NOHZ_FULL, which can meet the needs of real-time simulation of electromagnetic transient.

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