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2024 6th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2024

作者： Zhang, Jie Zhu, Yihua Liang, Zhuohang Ma, Qinfeng Zhang, Qingqing Liu, Mingshun An, Su Pu, Qingxin Dai, Jiang State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangdong Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangdong Guangzhou510663 China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangdong Guangzhou510663 China Power Dispatching and Control Center of Guizhou Power Grid Co. Ltd. Guizhou Guiyang550000 China

In order to achieve fast and accurate transient stability analysis and emergency control, this paper proposes a transient stability emergency control method based on improved deep reinforcement learning. In order to fully explore the temporal and spatial variation trend of transient response, a multi-dimensional feature containing information such as transient situation energy is constructed, and the deep reinforcement learning model is transformed based on the time-space graph neural network. On this basis, an emergency control model is constructed, and the power grid knowledge is integrated into the emergency control decision-making scheme to reduce the exploration of invalid decision-making and improve the performance of the model. The effectiveness of the proposed method is verified in the IEEE-39 system. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Deep reinforcement learning

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Fault Condition Testing of Photovoltaic Inverter controller Based on Active Support Technology using Hardware-In-the-Loop in RTDS 1

Fault Condition Testing of Photovoltaic Inverter Controller ...

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2024 1st International Conference on Smart Grids and power systems, ICSGPS 2024

作者： Li, Shengnan Hu, Yun He, Xin Zhu, Yihua He, Tingyi Tu, Liang Deng, Can Luo, Chao Su, Mingzhang Zeng, Guanming Yunnan Electric Power Research Institute Kunming China National Energy Power Grid Technology R&D Centre Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and 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

Photovoltaic power generation is an important component of sustainable power generation and has been widely utilized. Grid connected inverters are key equipment in photovoltaic integrated systems, which directly affect the stability of the system. In this paper, the transient model of photovoltaic inverter controller based on active support technology is derived. The transient mathematical model and corresponding self-closing loop control strategy are analyzed. Based on the RTDS, a hardware controller semi-physical simulation platform is established to simulate and test photovoltaic inverters under different fault and control methods. Finally, the simulation data obtained from the self-closing loop control method and the hardware control method are compared to verify the stability and reliability of the photovoltaic inverter hardware controller. © 2024 Published under licence by IOP Publishing Ltd.

关键词： controllers

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Research on the Application of Big Data in Transient Stability simulation of power systems 4

Research on the Application of Big Data in Transient Stabili...

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4th International Conference on Energy, power and Electrical Engineering, EPEE 2024

作者： Zhang, Jie Guo, Haiping Zhao, Yanjun Huang, Libin Hu, Binjiang Luo, Hui Zhang, Jingyue 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 Guangzhou5106633 China

ISBN: (纸本)9798331518066

With the rapid expansion of smart grids, there is a growing complexity in power system dynamics, which increases the availability of real-time operational data and underscores the need for advanced transient stability simulations. Traditional transient stability analysis methods are constrained by their computational demands and may compromise accuracy. Furthermore, deep learning-based methods often focus on the application of a single algorithm, lacking a systematic consideration of the entire simulation process. To address these challenges, this study proposes a novel approach that leverages big data to enhance transient stability simulations. By integrating deep learning and ensemble learning techniques, the model significantly improves the accuracy and efficiency of predicting transient instabilities. The methodology utilizes a multi-source data acquisition and fusion scheme to ensure data comprehensiveness and a distributed storage architecture to handle large-scale datasets effectively. Advanced visualization technologies are also employed to enhance the interpretability of the simulation results. This comprehensive framework aims to support the safe and stable operation of power systems amid increasing complexity and data availability. © 2024 IEEE.

关键词： Transient stability

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Optimal Scheduling of Multiple Energy Storage-Thermal power Units under Electricity Market 4

Optimal Scheduling of Multiple Energy Storage-Thermal Power ...

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4th IEEE New Energy and Energy Storage system control Summit Forum, NEESSC 2024

作者： Li, Baoju Xu, Shuguang Fu, Jiyue Fu, Xiaobiao Wang, Yao Zhu, Xingxu Li, Cuiping Li, Junhui Dispatching and Control Center State Grid Jilin Electric Power Co. Ltd Chang Chun Jilin130021 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China The Power Dispatching and Control Center State Grid Jilin Electric Power Co. Ltd Chang Chun Jilin130021 China

ISBN: (纸本)9798350389012

Aiming at the increasing burden of power system peaking caused by the integration of large-scale new energy generation into the grid and the imperfect participation mechanisms for peaking entities in the power market, this paper proposes an optimal dispatch method for multiple storage and thermal power units. Firstly, to reduce the impact of the volatility of new energy generation on system peaking, the planned market trading power of new energy is optimally allocated with the goal of minimizing the net load on the grid. Secondly, a multi-storage lease transaction mechanism and an energy storage lease declaration model based on the whole life cycle cost are designed, and the charging and discharging power of individual storage units is optimized with the objective of maximizing revenue from multi-storage leasing. Finally, typical daily data are selected and simulated in the improved IEEE 30-bus system. The results show that the proposed method reduces the curtailment rate of new energy and effectively improves the economic efficiency of peaking for power market entities. © 2024 IEEE.

关键词： Charge storage

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Day-Ahead Reporting Strategy of a Wind power Plant Based on CEEMDAN-IPSO-LSTM

Day-Ahead Reporting Strategy of a Wind Power Plant Based on ...

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2023 IEEE International Conference on power Science and Technology, ICPST 2023

作者： Lv, Dong Wang, Yuan Xu, Fei Zhang, Bing Wang, Hongye He, Li Zhu, Hui Li, Junhui Co. Ltd China Three Gorges Beijing China Tsinghua University State Key Laboratory of Power System and Power Generation Equipment Control and Simulation Beijing China

ISBN: (纸本)9798350311358

In the new power system, the output of new energy power stations has strong volatility, which leads to huge deviation assessment expenditure. Therefore, based on the improved data preprocessing algorithm and deep learning algorithm, a day-ahead reporting strategy of wind power plants considering error prediction characteristics is proposed. Firstly, considering the historical real output and prediction error, the historical data is decomposed based on the complete noise-assisted ensemble empirical mode decomposition algorithm (CEEMDAN). Then, the components are introduced into the long short-term memory (LSTM) optimized by the improved particle swarm optimization algorithm (IPSO), and the components are reconstructed to obtain the original prediction curve and the error prediction curve. Finally, the empirical distribution function is used to revise the prediction error curve, and the original prediction curve and the error prediction curve are superimposed to obtain the final day-ahead report curve. The correctness and feasibility of the reporting strategy are verified by simulation analysis. © 2023 IEEE.

关键词： Forecasting

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Pre-configuration Method and Scheduling Strategy of Distribution Network Mobile Energy Storage Vehicle Considering Intermittent Load of Rural Network

Pre-configuration Method and Scheduling Strategy of Distribu...

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2024 International Conference on Electronics and Devices, Computational Science, ICEDCS 2024

作者： Lu, Xiaoming Yao, Yiwen Xin, Yechen Wang, Dingheng Wang, Xinhong Xu, Xin Li, Hao State Grid Jilin Electric Power Co. Ltd Economic and Technological Research Institute 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 Co. Ltd Changchun China

ISBN: (纸本)9798331527624

Aiming at the problem of heavy overload in the distribution station area during the peak period of energy consumption caused by intermittent loads such as agricultural motor wells, the mobile energy storage technology is considered to meet the agricultural power demand, and a two-stage mobile energy storage vehicle pre configuration and dynamic adjustment strategy of "Rural busy power energization"and "rural idle distributed energy storage"is proposed, taking into account the guarantee of load power demand and the efficiency and economy of energy storage. Firstly, the mathematical model of agricultural motor well load considering the time shift characteristics and the mobile energy storage vehicle model considering the power supply time constraint are established;Secondly, a multi-stage mobile energy storage configuration and scheduling strategy is established. In the Rural busy power generation stage, the mobile energy storage vehicle capacity is configured with the minimum daily power supply cost as the goal, and the power supply path is optimized with the highest charge state consistency at the completion of power supply as the goal;At the stage of distributed energy storage in rural slack, the collaborative optimization of mobile energy storage vehicles participating in multi scenario power services is carried out with the goal of maximizing the annual net income. Finally, the proposed two-stage strategy has certain economic benefits under the premise of meeting the power demand of the load, which is verified by combining the power consumption data of wells in Jilin Province and IEEE 33 bus system simulation. © 2024 IEEE.

关键词： Charge storage

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Multi-Objective Hybrid Energy Storage Optimization Configuration Model for Reducing Thermal power Frequency Modulation Loss

Multi-Objective Hybrid Energy Storage Optimization Configura...

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2023 International Conference on power system Technology, powerCon 2023

作者： Li, JunHui Zhao, ZhenZhuo Zhao, ZhiQiang Liu, HaoJun Li, CuiPing Li, Zhi Jia, CaiQi Zhu, XingXu Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Jilin132012 China State Grid Hami Power Supply Company Xinjiang Uygur Autonomous Region HaMi Hami839000 China

ISBN: (纸本)9798350300222

The TPU (Thermal power Unit) equipped with HESS (Hybrid Energy Storage system) can effectively increase of FM (Frequency Modulation) performance of the unit and decrease the FM loss of the thermal power unit. The difficulty of rational allocation of ES (Energy Storage) is how to improve the FM performance of TPU and reduce the life cycle cost of ES. Therefore, the paper establishes a multi-objective hybrid energy storage optimization configuration model aimed at reducing the FM loss of thermal power. Firstly, a double-layer control policy of thermal power-hybrid energy storage is proposed. The first layer is the thermal power-hybrid energy storage AGC instruction allocation strategy based on EMD (Empirical Mode Decomposition). The second layer is a hybrid energy storage control strategy with flywheel ES priority action and lithium battery second response. Then, a multi-objective optimal configuration model considering the difference between life cycle cost and benefit and AGC comprehensive evaluation index Kp is established. It uses the multi-objective PSO (Particle Swarm Optimization) algorithm based on TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) to solve the model, which can avoid contingency of traditional multi-objective PSO in determining optimum solution in the Pareto solution set. Finally, based on the simulation analysis of power plant operation data in North China, the results verify the availability and feasibility of the established model. © 2023 IEEE.

关键词： Frequency modulation

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Optimized Strategies for Peak Shaving and BESS Efficiency Enhancement through Cycle-Based control and Cluster-Level power Allocation

arXiv

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arXiv 2025年

作者： Gan, Guo Junhui, Li Gang, Mu Gangui, Yan Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Battery Energy Storage systems (BESS) are essential for peak shaving, balancing power supply and demand while enhancing grid efficiency. This study proposes a cycle-based control strategy for charging and discharging, which optimizes capture rate (CR), release rate (RR), and capacity utilization rate (CUR), improving BESS performance. Compared to traditional day-ahead methods, the cycle-based approach enhances operational accuracy and reduces capacity waste, achieving a CUR increase from 75.1% to 79.9%. An innovative cluster-level power allocation method, leveraging an improved Particle Swarm Optimization (PSO) algorithm, is introduced. This strategy reduces daily energy loss by 174.21 kWh (3.7%) and increases BESS efficiency by 0.4%. Transient and steady-state energy loss components are analyzed, revealing that transient loss proportion decreases significantly as power depth increases, from 27.2% at 1 MW to 1.3% at 10 MW. simulations based on a detailed Simulink/Simscape model validate these methods, demonstrating enhanced peak shaving effectiveness and prolonged BESS lifespan by reducing equivalent cycles. The study provides a robust framework for optimizing BESS performance and efficiency in real-world applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Charge storage

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Aggregation Modeling of system Frequency Response Under Inertia control of Grid-following Direct-drive Wind Turbine

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Dianli Xitong Zidonghua/Automation of Electric power systems 2025年第10期49卷 39-48页

作者： Jiang, Chao Yang, Dongfeng Guo, Shujia Liu, Xiaojun Zhou, Shuyu Cao, Zhichong Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China School of Automation and Electrical Engineering Zhejiang University of Science and Technology Hangzhou310023 China State Grid Jilin Electric Power Research Institute Changchun130000 China

The inertia control of grid-following direct-drive wind turbines significantly influences the system frequency response pattern afrer disturbances. However, current research has not clearly identified the dominant response link of direct-drive wind turbines in frequency regulation, while the modeling is too complicated to be suitable for frequency characteristic analysis. Therefore, the linearized models under inertia control are derived for two typical types of direct-drive wind turbines according to the aerodynamic equations, generator models, power control link, and control circuit structure. The fast physical process of current control-boost circuit/stator voltage dynamic loop is clearly understood and reasonably ignored. It is revealed that the two types of direct-drive wind turbines have a common frequency response mechanism which can be described as the cascade effect of the speed regulator-wind rotor motion equation. Furthermore, combined with the frequency response model of synchronous unit, the aggregation model of system frequency response under the inertia control of grid-following direct-drive wind turbines is constructed, and the aggregation model is reduced to a second-order model by the balance truncation method for analytical calculation of frequency extremes. The accuracy and applicability of the aggregation model and its reduced-order model are verified by MATLAB/Simulink simulation, and the influence of control parameters and system states on frequency dynamic characteristics and stability margin is analyzed. © 2025 Automation of Electric power systems Press. All rights reserved.

关键词： Electric power system control

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Identifiability Analysis of Load Model Parameters by Estimating Confidential Intervals

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CSEE Journal of power and Energy systems 2023年第5期9卷 1666-1675页

作者： Xinran Zhang Chao Lu Ying Wang Qiantu Ruan Hongbo Ye Weihong Wang the School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China. the State Key Laboratory of Control and Simulation of Power System and Generation Equipment Tsinghua UniversityBeijing 100084China School of Technology Beijing Forestry UniversityBeijing 100083China the State Grid Shanghai Electric Power Company Shanghai 200122China

The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation,analysis and *** practical situations,the accuracy of the load model parameters identification results is impacted by data quality and measurement accuracy,which leads to the problem of *** this paper,an identifiability analysis methodology of load model parameters,by estimating the confidential intervals(CIs)of the parameters,is *** load model structure and the combined optimization and regression method to identify the parameters are first ***,the definition and analysis method of identifiability are *** CIs of the parameters are estimated through the profile likelihood method,based on which a practical identifiability index(PII)is defined to quantitatively evaluate ***,the effectiveness of the proposed analysis approach is validated by the case study results in a practical provincial power *** results show that the impact of various disturbance magnitudes,measurement errors and data length can all be reflected by the proposed ***,the proposed PII can provide guidance in data length selection in practical load model identification situations.

关键词： Confidential interval Load modeling parameter estimation practical identifiability

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