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2022 2nd International Conference on power Grid systems and Green Energy, PGSGE 2022

作者： Zhang, Tieyan Yang, Junbao Zhao, Tao Li, Min Zhang, Dawei Wang, Yuri Shenyang Ligong University China Shenyang Institute of Engineering China Key Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province China GD Hefeng Wind Power Development Co. Ltd. China Liaoning Shengfang Electric Power Technology Co. Ltd. China Benxi High-Tech Industrial Development Zone Power Supply Branch State Grid Liaoning Electric Power Co. Ltd. China

In order to effectively evaluate the impact of wind power connection on the operation of the colding-heating-electricity interconnection system, a probabilistic energy flow calculation method based on the semi-invariant method and improved K-means is proposed. First, a colding-heating-electricity joint system model was established. Secondly, based on the semi-invariant method, an improved K-means clustering technique was proposed to solve the problems caused by different input random sample types. Finally, the efficiency of the proposed method is verified by simulation. © Published under licence by IOP Publishing Ltd.

关键词： Wind power

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A Classification and Recognition Model of Distributed Resources Based on Feature Extraction

A Classification and Recognition Model of Distributed Resour...

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

作者： Yu Zhu Le Yang Xizai Yang Jiaxin Lu Yuqing Wang Fei Wang Information and Communication Technology R & D Center State Grid Shaanxi Electric Power Co. Ltd. Information and Communication Company Xi’an China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China

The extensive integration of distributed resources such as distributed photovoltaic system and electric vehicles enhances the uncertainty of the load side greatly. Corresponding demand response strategies can be developed for different distributed resource types and characteristics of households’ load, which is of great significance for new energy consumption and stable operation of the power system. However, only the net load power of households can be obtained through smart meters instead of the distributed resource types of households. Based on this, a distributed resource classification and identification model based on feature extraction is proposed in this paper, and effectively solves the problems of low accuracy and strong data dependence of existing recognition methods. Firstly, a generalized weather class generation algorithm based on clustering and voting is established, which is used to determine the weather class of each day to provide a basis for the following feature extraction. Then, based on the typical net load profiles under different generalized weather classes, a two-stage feature extraction and a classification identification model based on integrated learning algorithms are established to identify whether a user contains DPVS and EV, respectively. Finally, based on the category labels of each user for each stage, the final classification of the category to which the user belongs is carried out. simulation experiments show that the recognition model built using the typical features extracted in the paper has good recognition accuracy.

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The Regional Distributed PV Ultra-short-term power Forecasting Based on Static-dynamic Spatiotemporal Correlation Modeling

The Regional Distributed PV Ultra-short-term Power Forecasti...

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

作者： Yu Zhu Le Yang Xizai Yang Fei Xu Zinuo Su Zhao Zhen Information and Communication Technology R & D Center State Grid Shaanxi Electric Power Co. Ltd. Information and Communication Company Xi’an China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China

Accurate prediction of distributed photovoltaic power can help the arrange the scheduling plan reasonably to Maintain the safe and stable operation of the distribution network. However, distributed photovoltaic power generation exists lack of meteorological data and real-time monitoring data, the distribution range is large and dispersed, the installed capacity is relatively small and so on, the centralized photovoltaic power prediction method is not applicable to distributed photovoltaic power prediction, so it is necessary to carry out a regional power prediction study for distributed photovoltaic. Distributed PV is located in close proximity, in a similar external environment, and the power output between stations has strong spatial and temporal correlation, which can be captured to improve the accuracy of power forecasting. To this end, a regional distributed PV power prediction model based on static-dynamic spatiotemporal correlation modeling is proposed. The k-means is firstly used to cluster the regional distributed photovoltaic sites into several sub-regions, according to the geographical location and output characteristics that reflect the long-term static correlation between power stations. Then, graph convolutional neural network are applied to capture the dynamic correlation between subregions according to the power. In this way, the static and dynamic correlation are effectively considered in power prediction modeling, and the prediction accuracy has been improved. The superiority of the proposed method is verified by a real world dataset in China.

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Integrated Fuzzy control Strategy for Primary Frequency Regulation of Energy Storage system

Integrated Fuzzy Control Strategy for Primary Frequency Regu...

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

作者： YongHui Yang Ying Qiao Zongxiang Lu Yu Li Wei Ma State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments of Electrical Engineering Tsinghua University Beijing China State Grid Xinjiang Electric Power Company Limited Xinjiang China

Aiming at the problem that the proportion of new energy is gradually increasing and the frequency quality is getting worse, a comprehensive control strategy for primary frequency regulation of energy storage system based on fuzzy control is proposed. Firstly, the influence of energy storage virtual droop, virtual inertia and virtual negative inertia control on the frequency response of the system is analyzed, and the fuzzy control is used to realize the smooth switching and complementary advantages between the control modes; Then, the feedback control strategy of SOC is designed by using logistics function, and the self-recovery control strategy of energy storage SOC is introduced to make the energy storage SOC reach the ideal state and improve the frequency modulation ability of energy storage. Finally, the simulation results show the effectiveness of the proposed method.

关键词： Fuzzy control Time-frequency analysis Frequency modulation Switches system integration Maintenance engineering control systems

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Complementary benefit mechanism of wind-photovoltaic-thermal-storage in large-scale energy bases

Complementary benefit mechanism of wind-photovoltaic-thermal...

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4th Energy Conversion and Economics Annual Forum (ECE Forum 2024)

作者： Xu Xie Wenjia Chu Yinxiao Li Yanbin Li Xue Liu Fan Shi Hongye Guo North China Branch of State Grid Corporation of China Beijing People's Republic of China State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing People's Republic of China

ISBN: (数字)9781837242924

With the rapid development of renewable energy in large-scale energy bases, the uncertainty and volatility of renewable energy power pose significant challenges to power transmission. To address this, the paper proposes a complementary benefit mechanism that incentivizes thermal units and co-located energy storage systems (ESSs) to provide flexible regulation services. When renewable energy output is excessive, renewable energy units can purchase deep peak regulation services from thermal power units or sell the excess electricity to other co-located ESSs. With the goal of maximizing social welfare, a clearing model for the complementary benefit mechanism is established to achieve optimal benefits for all stakeholders involved. The results of the case study demonstrate that the proposed mechanism effectively promotes renewable energy accommodation and increases the profits of all stakeholders. The mechanism fosters market competition and encourages participants to bid based on their true costs, contributing to the healthy development of the complementary benefit mechanism.

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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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Prototype Guided Personalized Federated Intrusion Detection system

Prototype Guided Personalized Federated Intrusion Detection ...

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Smart World Congress (SWC), IEEE

作者： Long Cheng Huiru Yan Hanlin Zhou Ying Wang Haichuan Tang Fang Fang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China AI Lab of CRRC Academy Beijing China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Preventing network attacks and protecting user privacy are consistently hot research topics in the Internet of Things (IoT) and edge computing fields. Recent advancements in Federated Learning (FL) have shown promise in addressing these challenges. FL allows various clients to collaboratively build an intrusion detection system (IDS) without sharing their private data. However, most existing methods train a single intrusion detection model for all clients and assume that the training and test data distribution are identical, leading to unsatisfactory detection accuracy and generalization abilities in practice. To overcome these challenges, this paper introduces a prototype-guided personalized FL approach named PG-FedIDS. We propose two novel mechanisms within this method. Firstly, we utilize class prototypes as auxiliary information carriers to generate personalized models for each client, rather than generating a single global model as in previous works. Secondly, we propose a prototype-guided ensemble learning strategy, which can leverage the global knowledge in prototypes to enhance detection accuracy and generalization abilities for each client. We conduct extensive experiments on two benchmark datasets with different evaluation test settings. The results demonstrate that our PG-FedIDS achieves promising detection accuracy and consistently outperforms other FL baselines.

关键词： Training Privacy Accuracy Federated learning Image edge detection Prototypes Intrusion detection Data models Ensemble learning Internet of Things

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A battery model parameter identification method considering topology reconfiguration

A battery model parameter identification method considering ...

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20th International Conference on AC and DC power Transmission 2024 (ACDC 2024)

作者： Aizuo Chen Yue Quan Yanglin Zhou Ertao Lei Junkun Zhang Song Ci Department of Electrical Engineering Tsinghua University Beijing 100084 People's Republic of China Electric Power Research Institute of Guangdong Power Grid Co. Ltd Guangdong 510080 People's Republic of China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing 100084 People's Republic of China

ISBN: (数字)9781837241552

In the field of battery circuit model parameter estimation, the combination of bilinear transformation with the least squares method has garnered widespread attention due to its excellent performance under continuous current conditions. However, in dynamic reconfigurable battery network (DRBN), the network's topological structure is constantly changing, and the battery current consists of non-periodic pulse signal sequences, making it challenging for traditional methods to accurately estimate the battery model parameters within such networks. To address this issue, this paper proposes a lithium-ion battery circuit model parameter estimation method that takes into account network topology reconfiguration. This method, based on the operational modes and sampling methods of DRBN, derives a new discrete transformation formula for the transfer function and constructs a least squares model parameter identification model using this formula. By establishing an experimental platform for DRBN, the effectiveness of the proposed method is validated in this paper.

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A Novel DC Fault Ride-Through Coordinated control Strategy for DC Grid with Hybrid MMC

SSRN

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SSRN 2022年

作者： Jiang, Shouqi Li, Guoqing Wang, Xiaoxue Xin, Yechun Wang, Weiru Wang, Yanxu Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132000 China

DC grid based on hybrid modular multilevel converter (MMC) is an effective means to achieve large-scale renewable energy integration, multi-energy complementarity and friendly grid-connection. Aiming at the problem that the traditional DC fault ride-through (FRT) scheme will cause the interruption of power transmission in the whole network, a novel DC FRT coordinated control strategy is proposed in this paper, which can quickly interrupt the fault current and avoid the MMC exiting operation. Based on the analysis of fault current composition and key influencing factors, for fault line MMC (FLMMC), a negative voltage control strategy adaptive to the change of fault current is proposed, which can improve the current attenuation speed and ensure reliable fault isolation. For non-fault line MMC (NFLMMC), considering the overvoltage and overcurrent risks of FLMMC, the coordinated current-limiting control method and parameter selection principle of NFLMMC is proposed, where the current-limiting performance can be adaptive to the voltage change of DC bus, so as to realize the rapid recovery of DC power grid while restraining fault current. Finally, a four-terminal DC grid real-time digital simulation model is built to verify the validity of the proposed FRT coordinated control strategies. © 2022, The Authors. All rights reserved.

关键词： Voltage control

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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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