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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 ...

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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...

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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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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...

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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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Modelling and Analysis of the Start-Up Phase of a Compressed Air Energy Storage system under Variable Operating Conditions 6

Modelling and Analysis of the Start-Up Phase of a Compressed...

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6th International Conference on Energy systems and Electrical power, ICESEP 2024

作者： Wang, Guohua Chen, Siyuan Chen, Laijun Zhang, Tong Mei, Shengwei Tsinghua University State Key Laboratory of Power System and Generation Equipment Department of Electrical Engineering Beijing100084 China China Salt Jintan Co. Ltd. Jiangsu Province Changzhou213200 China

ISBN: (纸本)9798350353563

Compressed air energy storage technology has outstanding advantages in integrating new energy. It is of great significance to model and study the start-up phase dynamic characteristics of the compression side. A dynamic simulation model for the compression side based on the principles of mass conservation, energy conservation, and momentum conservation has been proposed. To simulate the system's dynamic sliding pressure operation, speed control is applied to the final-stage compressor. Utilizing the simulation model, the dynamic response performance of pressure, mass flow rate, and temperature during the system's start-up process is investigated and analysed. The simulation results indicate that the dynamic model can capture the system's dynamic response performance and variable-condition characteristics during start-up. Due to the backpressure constraints from the storage reservoir and the variable-condition characteristics during start-up, the dynamic performance of the compression side differs from its steady-state performance. © 2024 IEEE.

关键词： Compressed air energy storage

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Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

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Energy Engineering 2024年第6期121卷 1671-1695页

作者： Guanfu Wang Yudie Sun Jinling Li Yu Jiang Chunhui Li Huanan Yu He Wang Shiqiang Li State Grid Liaoning Electric Power Co. Ltd.Liaoyang Power Supply CompanyLiaoyang111000China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin132012China Jilin Northeast Electric Power University Science and Technology Development Co. Ltd.Jilin132012China

Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network ***, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.

关键词： Self-adaptive the uncertainty of sources and load deep reinforcement learning dynamic economic scheduling

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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 Auxiliary Frequency control of Wind Turbine Generators and Coordination with Synchronous Generators

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CSEE Journal of power and Energy systems 2021年第1期7卷 78-85页

作者： Ming Sun Yong Min Lei Chen Kaiyuan Hou Deming Xia Hangyin Mao State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China with Northeast Branch of State Grid Corporation of China Shenyang 110180Liaoning ProvinceChina with State Grid Zhejiang Electric Power Co.LTD. Hangzhou 310007Zhejiang ProvinceChina

Auxiliary frequency control of a wind turbine generator(WTG) has been widely used to enhance the frequencysecurity of power systems with high penetration of renewableenergy. Previous studies recommend two types of control schemes,including frequency droop control and emulated inertia control,which simulate the response characteristics of the synchronousgenerator (SG). This paper plans to further explore the optimalauxiliary frequency control of the wind turbine based on previousresearch. First, it is determined that the virtual inertia control haslittle effect on the maximum rate of change of frequency (MaxROCOF)if the time delay of the control link of WTG is taken intoconsideration. Secondly, if a WTG operates in maximum powerpoint tracking (MPPT) mode and uses the rotor deceleration forfrequency modulation, its optimal auxiliary frequency control willcontain only droop control. Furthermore, if the droop control isproperly delayed, better system frequency response (SFR) willbe obtained. The reason is that coordination between the WTGand SG is important for SFR when the frequency modulationcapability of the WTG is limited. The frequency modulationcapability of the WTG is required to be released more ***, when designing optimal auxiliary frequency controlfor the WTG, a better control scheme is worth further study.

关键词： Frequency droop control power system frequency security rate of change of frequency(ROCOF) virtual inertia control wind turbine generator auxiliary frequency control

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Emergency control methods for power systems based on improved deep reinforcement learning 6

Emergency control methods for power systems based on improve...

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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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