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检索条件"机构=Key Laboratory of Intelligent Dispatching and Control of Power Grid"

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Short- Term power Prediction Method for Photovoltaic power Generation Based on Elman Neural Network for Aspen Swarm Optimization

Short- Term Power Prediction Method for Photovoltaic Power G...

引用

Energy, Electrical and power Engineering (CEEPE), International Conference on

作者： Xinlei Cai Jinzhou Zhu Jiale Liu Zijie Meng Yuhang Huo Yang Yu Power Dispatching Control Center of Guangdong Power Grid Co. Ltd Guangzhou China State Key Laboratory of New Energy Power System North China Electric Power University Baoding China

The volatility and randomness of photovoltaic (PV) power generation lead to the difficulty of achieving ideal PV power prediction accuracyand improving the prediction accuracy of PV power generation is an effective way to suppress the adverse effects of PV grid connection. In order to improve the prediction accuracy of Elman neural network the weights and thresholds of output-implicit implicit-undertaking and implicit-output of Elman neural network are searched for optimality by using the aspen swarm search algorithm and a short-term power prediction method of PV power generation based on the aspen swarm search algorithm optimized Elman neural network (BSAS-Elman) is proposed. Firstly a model of Elman neural network is built in MATLAB; then the power of a PV plant is predicted under three weather conditions: sunnycloudy and rainy using the PV power data of a PV plant and meteorological data as inputs;finally the prediction results of BSAS-optimized Elman neural network model are compared with the prediction results of Elman neural network and the actual power output data of the PV plant are compared. The results show that the aspen swarm search algorithm can improve the prediction accuracy of the Elman neural network model and realize the effective prediction of PV power short-term prediction

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grid-forming control of VSC-HVDC Systems Connecting Asynchronous power grids

Grid-forming Control of VSC-HVDC Systems Connecting Asynchro...

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power System and Green Energy Conference (PSGEC)

作者： Mingqun Liu Guanming Zeng Xin He Liang Tu Peng He Yihua Zhu Electric Power Research Institute Yunnan Power Grid Co. Ltd. Kunming 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 CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798350365573

ISBN: (纸本)9798350365580

Interconnecting asynchronous power grids with the VSC-HVDC transmission is an important trend. To enhance the weak grid operation performance and grid frequency response capability of the VSC-HVDC system, a novel grid-forming without using the phase-locked-loop is applied in both ends of the VSC-HVDC system. The DC voltage-Active power droop-based grid-synchronization method is utilized. With the proposed method, the sending and the receiving end MMCs perform like a synchronous motor and a synchronous generator, respectively. When the frequency changes in either grid, the system can automatically response to it and change the active power transmission by VSC-HVDC to achieve primary frequency regulation and active frequency support. Finally, the validity of the dual-end grid-forming control is verified based on the simulations in PSCAD/EMTDC.

关键词： power transmission control systems Synchronous motors Market research power grids Synchronous generators Synchronization Phase locked loops Frequency control grid forming

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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 ...

引用

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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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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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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Energy Storage-Based Active Support Strategy for grid Frequency of grid-Configured Offshore Wind Farms

Energy Storage-Based Active Support Strategy for Grid Freque...

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

作者： Baoyi Zhang Xiangyu Kong Jiancun Liu Wen Sun Yue Yang Wenlong Yang Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin University of Technology Tianjin China Key Laboratory of Smart Grid Tianjin University Tianjin China Guangdong Power Grid Co. Ltd. Electric Power Research Institute Guangdong China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

With the new energy of large-scale grid-connected, the scale of offshore wind farms is also getting bigger and bigger. Offshore wind turbines are difficult to provide frequency support to the grid when they are fed directly via flex, and the weak grid operation capacity will be poor. Therefore, this paper proposes a grid-connected control method for offshore wind turbines based on virtual synchronous generator (VSG) technology. Enable offshore wind turbines with good frequency dynamic response capability and inertia support capability. At the same time, through the configuration of energy storage devices for wind turbines to provide the energy needed to support the grid, grid-configured offshore wind turbines smoothly realize active support to assist the grid-constructing type wind turbine in providing frequency support. Finally, the effectiveness of the proposed control method is verified by simulation using Matlab/Simulink software.

关键词： Renewable energy sources Time-frequency analysis Wind farms Synchronous generators Software Wind turbines Systems simulation Wind energy conversion Matlab Energy storage

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Multi-Factor Load Forecasting of Charging Stations

Multi-Factor Load Forecasting of Charging Stations

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Energy Systems and Electrical power (ICESEP), International Conference on

作者： Jianbin Lu Shuai Han Ning Wu Rui Lin Jing Xiao Electric Power Science Research Institute of Guangxi Power Grid Co. Ltd Nanning China Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Nanning China Guangxi Power Grid Co. Ltd Nanning China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

Under the background of carbon peaking and carbon neutrality goals, as electric vehicles (EVs) increasingly penetrate the market, large-scale charging chaos makes power grid operation more and more challenging. Accurate and effective load forecasting of charging stations is conducive to more accurate and effective interactive vehicle-grid service, thus ensuring stable power grids. In this paper, we dived into the potential influencing factors that impact the charging loads of charging stations, including natural influencing factors, charging methods, battery categories, and charging behaviors. Based on these charging behaviors, we established a function for driver mileage anxiety-based charging probability. Next, using the generative adversarial network (GAN), we trained the data generated using the charging probability function and multiple load-influencing factors, thus generating approximate data. Then, we introduced the long short-term memory (LSTM) model to learn and train the above data and simulated the loads of charging stations. Finally, we analyzed the forecasting results. What makes the paper innovative is that we not only considered the natural factors that influence the loads of charging stations but also took charging behaviors into account. Moreover, we introduced the LSTM model for neural networks and forecast loads of charging stations based on multiple influencing factors, which is much more accurate compared with traditional forecasting methods.

关键词： Analytical models Accuracy Load forecasting Predictive models Charging stations Generative adversarial networks Data models

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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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Research on Back-to-Back HVDC Additional Frequency Synchronous control Strategy Applied to Asynchronous Interconnected Systems

Research on Back-to-Back HVDC Additional Frequency Synchrono...

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Sustainable power and Energy Conference (iSPEC)

作者： Qinlei Chen Libin Huang Shuyong Li Xuehua Lin Wencong Wu Mingkang Wu Liang Tu Qi Guo CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China State Key Laboratory of HVDC China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China Southern Power Grid Guangzhou China

When regional power grid is asynchronized by high voltage direct current (HVDC) systems, the division of frequency regulation resources and reserve capacity weakens the system’s ability to resist load fluctuation and fault disturbance. With the continuous promotion of electricity market construction in China, the demand of building a unified frequency regulation auxiliary market for the whole power grid is increasingly frequent. In this paper, with the objectives of improving the overall frequency regulation performance of asynchronous interconnected systems and refers to the power transmission characteristics of AC transmission lines. The additional frequency synchronous control (AFSC) strategy of back-to-back HVDC was studied, which enables back-to-back HVDC to automatically adjust the transmission power based on the frequency difference on both sides. Then, taking Luxi back-to-back HVDC of China Southern power grid (CSG) as an example, the specific implementation of AFSC strategy is studied, and the effectiveness of the proposed strategy is verified by real-time simulation. Finally, the on-site practical application of this strategy was analyzed.

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Practical Research on On-Line Monitoring and control Function Module of Relay Protection in Smart Substation 18

Practical Research on On-Line Monitoring and Control Functio...

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18th International Conference on AC and DC power Transmission, ACDC 2022

作者： Huang, Zhida Wu, Wencong Cai, Haiqing Huang, Libin State Key Laboratory of HVDC Electric Power Research Institute CSG 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 Guangzhou510663 China

ISBN: (数字)9781839537615

ISBN: (纸本)9781839537615

At present, the power grid is carrying out the practical work of smart substation On-line monitoring and control function module of relay protection. Compared with traditional substations, the new-generation assurance system has been greatly improved in 7 functional modules, including fault archiving, special maintenance information patrol, fixed value checking, configuration checking, substation testing, intelligent alarm, and master station modeling. It can fully adapt to the functional requirements of smart substations and traditional substations. This paper analyzes the monitoring and analysis requirements of the assurance system based on the equipment configuration of the station control layer of the new generation of intelligent substations, and proposes a complete design scheme of seven functional modules from three aspects: functional requirements, implementation logic, and performance design. The practical application results in the power grid simulation laboratory show that the On-line monitoring and control function module of relay protection based on the guarantee system can be compatible with the traditional 103 substations and 61850 substations and achieve the expected goals and performance requirements. It has the value of promotion and application. © ACDC 2022. All rights reserved.

关键词： Relay protection

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