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

作者： Mao Yang Yunfeng Guo Bo Wang Zhao Wang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China National Key Laboratory of Renewable Energy Grid-Integration China Electric Power Research Institute Beijing China

Load peak and valley periods as the key scheduling scenarios, the effectiveness of wind power prediction becomes extremely important, in order to minimize the impact of wind power on the power system due to prediction errors, a short-term prediction method of wind farm cluster power considering risk scenarios and improved loss function is proposed. Firstly, the coupled correlation mechanism between meteorological type and load demand is explored, and the load risk scenario and meteorological correlation are derived. Second, according to the risk scenarios of load demand, the traditional loss function is improved and used to train the power prediction model under the risk scenarios. Then, a dynamic division strategy of wind farm clusters considering the meteorological similarity of the previous day is proposed to obtain the sub-clusters of similar output. Finally, the model with improved weighted loss function is used to predict the wind power for different regional sub-clusters and extended to wind farm clusters. The above method is validated using the wind farm clusters in West Inner Mongolia, China. And the results show that in the low-risk load scenario, the accuracy of the model with improved loss is higher, and the accuracy decreases with the increase of the load risk level, but the overall risk indexes are significantly lower than the method with traditional loss function, which proves the validity of the proposed method.

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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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Optimised Configuration of Multi-Energy systems Considering the Adjusting Capacity of Communication Base Stations and Risk of Network Congestion

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SSRN 2024年

作者： Yang, Dongfeng Wang, Xi Sun, Yong Yang, Jingying Liu, Xiaojun Jiang, Chao Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Jilin Electric Power Co. Ltd Changchun130000 China

The high percentage of renewable energy sources presents unprecedented challenges to the flexibility of power systems, and planning for the system's flexibility resources has become a necessary research area. Thus, this study constructs a flexibility quota mechanism and a two-stage model for the optimal configuration of multi-energy system coupling equipment to satisfy the growing demand for flexibility in city-level electricity-gas-heat-storage coupling systems. First, it examines the relationship between supply and demand for system flexibility, leading to the design of a flexibility quota mechanism. Subsequently, the power supply method for communication base stations shifts from direct networking to a hydrogen fuel cell supply. This flexibility quota mechanism encourages communication operators to actively engage in flexibility quota trading. Simultaneously, the safety constraints of heterogeneous energy-[A1] flow subsystems are considered while conducting comprehensive capacity planning for the coupling equipment of a multi-energy system. Finally, an optimisation strategy is proposed under the established capacity planning scheme for determining the siting and capacity of energy storage plants to address the potential transmission blockage risk in the power network. The case analysis utilised the IEEE 14-node grid-7-node gas network-8-node heat network test system, demonstrating that the constructed model enhances the system economy and mitigates transmission blockage risk, while enhancing the flexibility adjustment capability of the city-level multi-energy coupled system. © 2024, The Authors. All rights reserved.

关键词： Electric power transmission networks

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Optimised Configuration of Multi-Energy systems Considering the Adjusting Capacity of Communication Base Stations and Risk of Network Congestion

SSRN

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SSRN 2024年

作者： Yang, Dongfeng Wang, Xi Sun, Yong Yang, Jingying Liu, Xiaojun Jiang, Chao Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Jilin Electric Power Co. Ltd Changchun130000 China

The high percentage of renewable energy sources presents unprecedented challenges to the flexibility of power systems, and planning for flexibility resources has become a necessary research area. Thus, this study constructs a flexibility quota mechanism and a two-stage model for the optimal configuration of multi-energy system coupling equipment to satisfy the growing demand for flexibility in city-level electricity-gas-heat-storage coupling systems. First, it examines the relationship between supply and demand for system flexibility, leading to the design of a flexibility quota mechanism. Subsequently, the power supply method for communication base stations shifts from networking to a hydrogen-fuel-cell supply. This mechanism encourages communication operators to actively engage in flexibility quota trading. Simultaneously, the safety constraints of heterogeneous energy-flow-subsystems are considered while conducting comprehensive capacity planning for the coupling equipment of a multi-energy system. Finally, an optimisation strategy is proposed under the established capacity planning scheme for determining the siting and capacity of energy-storage-plants to address the potential transmission blockage risk in the power network. The case analysis utilised the IEEE 14-node grid-7-node gas network-8-node heat network test system, demonstrating that the constructed model enhances the system economy and mitigates transmission blockage risk, while enhancing the flexibility adjustment capability of multi-energy coupled system. © 2024, The Authors. All rights reserved.

关键词： Electric power transmission networks

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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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The Function Mechanism of Virtual Impedance in Grid-forming Droop control and An Adaptive Parameter Tuning Method for Virtual Impedance Based on Network Equivalent Identification

The Function Mechanism of Virtual Impedance in Grid-forming ...

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Power Science and Technology (ICPST), IEEE International Conference on

作者： Chenhui Fan Xiaohui Qin Lei Qi Hongzhi Liu Baodi Ding Zhengji Meng State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University Beijing China Carbon Neutrality Research Center of Power System China Electric Power Research Institute Beijing China Hebei Key Laboratory of Energy Internet Simulation Modeling and Control State Grid Hebei Electric Power Research Institute China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

The penetration rate of renewable energy and other power electronic sources in the system continues to increase, the strength of the power grid is weakened, and the system security is challenged. Some converters urgently need to adopt grid-forming control, for example, the droop control. However, due to the different impedance characteristics of the power system environment and the varying electrical distances between the grid source and load, the grid-forming converter cannot adapt to traditional droop control in multi-point parallel operation, this condition can be improved by adding virtual impedance. This paper first derives the main function mechanism of virtual impedance taking droop control as an example, and proposes a virtual impedance adaptive parameter tuning method based on network equivalent identification, then verifies the method through MATLAB/Simulink simulation. The proposal of this method is of great significance for reflecting the operational status of the system, improving the adaptability of renewable energy equipment and stations, and enhancing their fault ride-through ability. It also has important value for improving other grid-forming technologies.

关键词： Renewable energy sources Adaptive systems Power system stability Stability analysis Impedance Security Proposals

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Research and Implementation of control Strategies for Multiple Transmission Modes in Yongren-Funing HVDC

Research and Implementation of Control Strategies for Multip...

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2022 IEEE International Conference on Power systems and Electrical Technology, PSET 2022

作者： Zhu, Yukun Huang, Libin Li, Shuyong Zhu, Yihua Jia, Xuantao Jia, Xuantao 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 China Southern Power Grid Guangzhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China Zhejiang Electric Power Co Ltd Ehv Branch Company of State Grid Hangzhou China Xj Electric Co Ltd Xuchang China

ISBN: (纸本)9781665489157

The ± 500 kV Yongren-Funing HVDC transmission project is the first HVDC whose sending and receiving ends are both located in the same province, its inverter station is required to transmit power to different AC asynchronous grids according to the different requirements of the grid operation. The present control and protection system of HVDC cannot adapt to and meet the technical requirements of multiple power transmission modes. In order to achieve this goal, the control system of HVDC must be capable of automatic discrimination and sequential control, selecting the correct strategy of reactive power control (RPC), which is essential for the stable operation of the HVDC system. Therefore, based on the operating conditions of the HVDC system and the switch configuration of the AC switch yard of the inverter station, the principle, process and interlocking conditions of automatic switching between different transmission modes are systematically analyzed. Effects on RPC, last breaker and the over voltage protection of AC busbar are researched. The control strategies for multiple transmission modes have great significance for the similar HVDC/UHVDC transmission projects in the future. © 2022 IEEE.

关键词： Reactive power

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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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