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International Conference on power system technology (powerCON)

作者： Cuiping Li Yang Liu Tianxun Zhang Zhiqiang Zhao Haojun Liu Xingxu Zhu Zhi Li Chenyu An Junhui Li Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China Beijing Technology and Business University Beijing China State Grid Hami Power Supply Company Hami City Xinjiang Uygur Autonomous Region China Hami China

With the rise of the application of sharing economy in various fields of power system, As a typical application of shared economy in the field of energy storage, the optimal allocation of shared energy storage on the source-network-load side has been a great topic. The problem dealt with in this paper is the configuration result of the source-grid-load energy storage system under the same control strategy. This paper designs an optimization method for the source-network-load side configuration of generalized shared energy storage in regional power grid: Firstly, according to the extensional usage scenario and demand of energy storage, the alternative set of shared energy storage configuration is constructed. Secondly, based on the profit model of shared energy storage, a power system source-network-load optimization scheduling strategy is proposed. Finally, a numerical example is carried out to prove the feasibility and efficacy of the suggested method.

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Evaluation of Regulating Capacity of power system Nodes and Transmission Lines

Evaluation of Regulating Capacity of Power System Nodes and ...

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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Xiuyu Yang Zekun Fan Almasbek Shakenbek Baoyu Di Chongbi Li Yi Wang Key Laboratory of Modern Power System Simulation Control and New Green Energy Technology of Ministry of Education Northeasst Electric Power University Jilin China State Grid Xinjiang Electric Power Co. Ltd. State Grid Xinjiang Electric Power Co. Ltd. Electric Power Research Institute Urumqi China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

The large-scale access of renewable energy to the power grid leads to a sharp increase in the demand for power system regulation capacity. In order to solve this problem, this paper explores the mechanism of supply and demand balance of power system regulation capacity, analyzes the transmission characteristics of regulation capacity, and proposes the adequacy index of node regulation capacity and the adequacy index of network transmission regulation capacity. The Garver-6 node system is used to verify the effectiveness and practicability of the proposed method. The example shows that the proposed evaluation method can accurately identify the weak links of regulation ability and clarify the causes of insufficient regulation ability.

关键词： renewable energy sources Supply and demand power transmission lines Evaluation models Regulation Smart grids Planning Artificial intelligence Load flow Capacity planning

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Probabilistic power Flow Calculation of AC/DC Hybrid system Based on Cumulant Method

arXiv

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arXiv 2022年

作者： Sun, Yinfeng Xia, Dapeng Gao, Zichun Wang, Zhenhao Li, Guoqing Lu, Weihua Wu, Xueguang Li, Yang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Global Energy Interconnection Research Institute Beijing102211 China Beijing Key Laboratory of DC Power Grid Technologies and Simulation Beijing102211 China

The operating conditions of the power system have become more complex and changeable. This paper proposes a probabilistic power flow calculation method based on the cumulant method for the voltage sourced converter high voltage direct current (VSC-HVDC) hybrid system containing photovoltaic grid-connected systems. Firstly, the corresponding control mode is set for the converter, including droop control and master-slave control. The unified iterative method is used to calculate the conventional AC/DC power flow. Secondly, on the basis of the probability model of load and photovoltaic output, based on the aforementioned power flow results, use correlation coefficient matrix of this paper will change the relevant sample into independent sample，the cumulants of the load and photovoltaic output are obtained;Then, the probability density function (PDF) and cumulative distribution function (CDF) of state variables are obtained by using Gram-Charlie series expansion method. Finally, the mean value and standard deviation of node voltage and line power are calculated on the modified IEEE 34-bus and IEEE 57-bus transmission systems. The algorithm can reflect the inherent uncertainty of new energy sources, and replace the complex convolution operation, greatly improving the calculation speed and the convergence. Copyright © 2022, The Authors. All rights reserved.

关键词： Electric load flow

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Application of deep reinforcement learning in power system scheduling strategy simulation

Application of deep reinforcement learning in power system s...

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energy, power and Electrical Engineering (EPEE), International Conference on

作者： Binjiang Hu Libin Huang Yufeng Hu Jie Zhang Haiping Guo Yuanhong Lu Zeqi Hong Jingyue Zhang Hui Luo Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

With the increasing scale and complexity of power system, the current scheduling strategy methods often have the problems of limited adaptability and unsatisfactory results. Therefore, a power system scheduling method using DQN (Deep Q Network) is introduced in this study. This method uses neural network model to learn and optimize scheduling strategy, and has strong adaptability and enhanced global optimization ability. Comparative evaluations with genetic algorithms and particle swarm algorithms on IEEE-118 and SG-126 datasets show that the proposed method has significant advantages in terms of cumulative reward and stable runtime.

关键词： Electrical engineering Runtime Neural networks power system stability Deep reinforcement learning Stability analysis Real-time systems Particle swarm optimization Optimization Genetic algorithms

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Technical Research on simulation and Predictive Analysis of Wind Farms in power systems

Technical Research on Simulation and Predictive Analysis of ...

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energy, power and Electrical Engineering (EPEE), International Conference on

作者： Tianyu Guo Libin Huang Yanjun Zhao Haiping Guo Yuanhong Lu Jie Zhang Hui Luo Binjiang Hu National Energy Power Grid Technology R&D Centre Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

To address the issue of insufficient accuracy in wind power output forecasting, this paper proposes a Support Vector Machine (VSM) model based on similar day matching and kernel entropy component analysis for wind power prediction. The model effectively extracts similar day features through the similar day approach and optimizes feature representation by combining it with kernel entropy component analysis (KECA), ultimately employing the SVM model for wind farm power forecasting. simulation results demonstrate that using this model for optimizing wind power generation predictions enhances the accuracy of wind power forecasts, surpassing the precision of single-method predictions. The Similar Day-KECA-SVM method leverages historical data from similar days and features extracted through KECA, with SVM performing the final prediction. This approach adeptly handles complex patterns and nonlinear relationships in wind power forecasting, thereby achieving superior predictive performance.

关键词： Support vector machines Analytical models Accuracy Wind speed Wind power generation Predictive models Feature extraction Entropy Forecasting Kernel

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Design and Implementation of power system simulation Platform Based on Cloud Computing

Design and Implementation of Power System Simulation Platfor...

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energy, power and Electrical Engineering (EPEE), International Conference on

作者： Yuanhong Lu Haiping Guo Yanjun Zhao Libin Huang Binjiang Hu Hui Luo Jingyue Zhang National Energy Power Grid Technology R&D Centre Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid 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

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

power system simulation involves simulating the operating states and dynamic processes of power systems using mathematical models and computer technology to analyze and predict system behavior. Traditional simulation methods struggle to meet the needs of large-scale, complex power grids due to significant demands for computational resources. Cloud computing, with its high scalability, resource pooling, and on-demand services, is ideally suited for the extensive simulation calculations required by modern power systems. Despite its advantages, the application of cloud computing in power system simulation poses challenges related to data security and privacy, particularly in collaborative simulations involving multiple power entities. This paper presents the design and implementation of a cloud computing-based power system simulation platform. The proposed platform leverages the distributed computing capabilities of cloud computing, integrates federated learning to ensure data privacy, and utilizes advanced power system simulation technologies. This approach significantly enhances simulation efficiency and accuracy while providing data security and privacy protection. The platform offers reliable decision support for power system planning, operation, and control.

关键词： Cloud computing Data privacy Accuracy Federated learning Data security power system simulation power system dynamics power system stability power grids Testing

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

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

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energy, power and Electrical Engineering (EPEE), International Conference on

作者： Jie Zhang Haiping Guo Yanjun Zhao Libin Huang Binjiang Hu Hui Luo Jingyue Zhang State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China 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 CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

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.

关键词： Accuracy Computational modeling power system stability Big Data Predictive models Stability analysis Data models Real-time systems Transient analysis Thermal stability

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Neural Network technology Applied in Fault Detection and simulation of power systems

Neural Network Technology Applied in Fault Detection and Sim...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Zeqi Hong Libin Huang Yufeng Hu Haiping Guo Yuanhong Lu Binjiang Hu Jingyue Zhang Hui Luo State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China 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 CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

With the escalating demand for power inspection and equipment maintenance, developing effective algorithms for power system fault detection is crucial for ensuring the safety and stability of power systems. This paper leveragesthe Trellis network, enhanced with an attention module, to significantly improve the Trellis network’s capability in recognizing power fault images, thus better addressing fault identification tasks within power systems. Through testing on a self-constructed dataset and comparative experiments with models such as DCNN, RCNN, and DenseNet, the proposed attention mechanismenhanced Trellis network algorithm has demonstrated its effectiveness and superiority in fault detection.

关键词： Fault diagnosis Accuracy Attention mechanisms Fault detection Neural networks power system stability Predictive models Feature extraction Electrical fault detection Maintenance

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Distributed power system simulation and Monitoring Supported by Cloud technology : A grid optimization scheduling method based on MADRL

Distributed Power System Simulation and Monitoring Supported...

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energy, power and Electrical Engineering (EPEE), International Conference on

作者： Hui Luo Jie Zhang Yufeng Hu Jingyue Zhang Haiping Guo Libin Huang China Southern Power Grid CSG Key Laboratory for Power System Simulation Electric Power Research Institute Guangzhou China China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

This study explores the optimization of Distributed Generation systems (DGS) within a Multi-Agent Deep Reinforcement Learning (MADRL) framework. The focus is on transforming the overall optimization goal of the system into a global reward function and designing local rewards adapted to heterogeneous agents. Using the IEEE 33 node system as a case study, the impact of power aggregation and load management from photovoltaic systems, energy storage systems, electric vehicle charging facilities, and HVAC systems is analyzed. The results demonstrate that the proposed edge-cloud collaborative framework optimizes resource allocation and scheduling. The cloud platform is capable of integrating data from diverse sources and managing it in a unified manner, thereby coordinating the operations of multiple heterogeneous agents. The scalability and flexibility of cloud technology allow for the dynamic adjustment of computational resources according to demand, ensuring global optimization and the judicious allocation of resources.

关键词： Photovoltaic systems HVAC Processor scheduling system performance Collaboration Deep reinforcement learning Smart grids Resource management Vehicle dynamics energy storage

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Capacity and power Allocation Strategy of energy Storage Participating in Grid Peak Shaving 5

Capacity and Power Allocation Strategy of Energy Storage Par...

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

作者： Qi, Tengyun Xu, Fei Wu, Qiren Li, Yajing Liu, Jianping Li, He Wang, Yiming Li, Cuiping Li, Junhui China Three Gorges Corporation Beijing China Haidian District Beijing China Co. Ltd Beijing101100 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)9781665434256

High penetration wind power grid with energy storage system can effectively improve peak load regulation pressure and increase wind power capacity. In this paper, a capacity allocation method of energy storage system under peak load regulation scenario is proposed. The upper model combines the investment cost, operation cost, arbitrage income, environmental income, and wind power grid benefits during the entire life cycle of the energy storage system, with the goal of maximizing the net income during the energy storage life cycle;the lower model optimizes the charging and discharging power of the energy storage system according to each energy storage configuration scheme in the upper modeling, and its objective is to minimize the standard deviation of system net load and waste wind power. Finally, under the conditions of local grid load and wind power data, verified the effectiveness of capacity allocation strategy. © 2021 IEEE

关键词： Economic analysis

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