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

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Application of deep reinforcement learning in power system scheduling strategy simulation 4

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

引用

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

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

ISBN: (纸本)9798331518066

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. © 2024 IEEE.

关键词： Deep reinforcement learning

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Technical Research on Simulation and Predictive Analysis of Wind Farms in power Systems 4

Technical Research on Simulation and Predictive Analysis of ...

引用

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

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

ISBN: (纸本)9798331518066

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. © 2024 IEEE.

关键词： Wind forecasting

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Design and Implementation of power System Simulation Platform Based on Cloud Computing 4

Design and Implementation of Power System Simulation Platfor...

引用

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

作者： Lu, Yuanhong Guo, Haiping Zhao, Yanjun Huang, Libin Hu, Binjiang Luo, Hui Zhang, Jingyue 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: (纸本)9798331518066

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. © 2024 IEEE.

关键词： Cloud platforms

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Energy Storage-Based Active Support Strategy for grid Frequency of grid-Configured Offshore Wind Farms 25

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

引用

25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

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

ISBN: (纸本)9798350366600

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. © 2024 IEEE.

关键词： Offshore wind farms

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The Development of Simulation Mirror System and its Application in HVDC 6

The Development of Simulation Mirror System and its Applicat...

引用

6th International Conference on HVDC, HVDC 2024

作者： Ou, Kaijian Wu, Wencong Cai, Haiqing State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid National Energy Power Grid Technology R&d Centre. Guangzhou China Guangdong Prov. 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: (纸本)9798350387759

The testing of HVDC new control function and training of HVDC operation personnel require the integration of power system simulation and dispatching automation. This paper presents and builds AC&DC power system simulation mirror system, which is the closed-loop of real-time digital simulation and dispatching automation. Moreover, the applications of simulation mirror system on HVDC new control function testing and HVDC simulation training cabinet are also introduced in this paper. © 2024 IEEE.

关键词： automatic generation control (A GC) automatic HVDC control (ADC) dispatching automation high voltage direct curren t(HVD C) real-time digital simulator(RTDS)

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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...

引用

2023 IEEE Sustainable power and Energy Conference, iSPEC 2023

作者： Chen, Qinlei Huang, Libin Li, Shuyong Lin, Xuehua Wu, Wencong Wu, Mingkang Tu, Liang Guo, Qi 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

ISBN: (纸本)9798350322699

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. © 2023 IEEE.

关键词： additional frequency synchronous control (AFSC) Asynchronous interconnected systems back-to-back HVDC

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Distributed power System Simulation and Monitoring Supported by Cloud Technology : A grid optimization scheduling method based on MADRL 4

Distributed Power System Simulation and Monitoring Supported...

引用

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

作者： Luo, Hui Zhang, Jie Hu, Yufeng Zhang, Jingyue Guo, Haiping Huang, Libin 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 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: (纸本)9798331518066

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. © 2024 IEEE.

关键词： Cloud platforms

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Fast Transient Voltage Stability Assessment for Complex Receiving-End power grid 7

Fast Transient Voltage Stability Assessment for Complex Rece...

引用

7th International Conference on power and Energy Applications, ICPEA 2024

作者： Liu, Xiyang Liu, Tianhao Liu, Yutian Li, Zhendong Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China Laiwu Power Supply Company State Grid Shandong Electric Power Company Laiwu China

ISBN: (纸本)9798350356113

The continuous increase of new energy generation and AC-DC interconnection make the transient voltage stability of the receiving-end power grid more and more complicated. In order to realize the rapid assessment of transient voltage stability, a one-dimensional Convolutional Neutral Network (CNN) for transient voltage stability assessment (TVSA) is constructed by selecting the features reasonably on the basis of using the Agglomerative Hierarchical Clustering (AHC) algorithm to partition the receiving-end grid. In order to enhance the assessment work, the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm is utilized to find out the concerns of the model to features, and the model knowledge of the neural network is analyzed on the basis of surveying on a large number of samples. Based on the transient voltage stability mechanism and the concerns of deep learning model, the factors that cause transient voltage instability are analyzed. The effectiveness of the proposed method is demonstrated by the case study of a provincial grid in eastern China. © 2024 IEEE.

关键词： Transient stability

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Short- Term power Prediction Method for Photovoltaic power Generation Based on Elman Neural Network for Aspen Swarm Optimization 6

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

引用

6th International Conference on Energy, Electrical and power Engineering, CEEPE 2023

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

ISBN: (纸本)9798350348279

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 © 2023 IEEE.

关键词： Elman neural networks

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Propagation characteristics simulation of weak signals of electrical and optical state parameters inside GIS equipment 8

Propagation characteristics simulation of weak signals of el...

引用

8th International Conference on Energy System, Electricity, and power, ESEP 2023

作者： Lu, Yufeng Liu, Yanbin Su, Yi Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid Co. Ltd. road 6-2 Minzhu 530012 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

Partial discharge of GIS equipment refers to various discharge phenomena caused by breakdown of local areas within the switching equipment. By monitoring GIS Partial discharge, insulation accidents can be effectively prevented. Currently, GIS equipment detection methods lack multi parameter information fusion state diagnosis methods for GIS equipment, and equipment state assessment techniques and operation and maintenance strategies based on GIS equipment multi basic parameters have not been formed. Setting up a simulation platform for the propagation characteristics of weak signals of electrical and optical state parameters inside GIS equipment, and mining the mathematical relationship between multi-source information and the type and deterioration degree of insulation defects inside GIS equipment by data analysis is helpful to evaluate the condition of GIS equipment, and then formulate reasonable operation and inspection strategies to ensure reliable operation of power grid. © 2024 SPIE.

关键词： Deterioration

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