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Enhancing Autonomy Capability in Regional power Grids:A Strategic Planning Approach with Multiple Autonomous Evaluation Indexes

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energy Engineering 2024年第9期121卷 2449-2477页

作者： Jie Ma Tong Zhao Yuanzhao Hao Wenwen Qin Haozheng Yu Mingxuan Du Yuanhong Liu Liang Zhang Shixia Mu Cuiping Li Junhui Li Planning and Assessment Center Department State Grid Henan Economic Research InstituteZhengzhou450000China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China Development Planning Department State Grid Henan Electric Power CompanyZhengzhou450000China Innovation Development Research Center State Grid Shanghai Energy Internet Research Institute Co.Ltd.Shanghai201210China State Grid Simulation Center China Electric Power Research Institute Co.Ltd.Beijing100192China

After the integration of large-scale DistributedGeneration(DG)into the distribution network,the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and demand in the distribution network,exacerbating the phenomenon of wind and solar power *** a novel power system model,the fundamental concept of Regional Autonomous power Grids(RAPGs)is to achieve localized management and energy autonomy,thereby facilitating the effective consumption of ***,this paper proposes a distributed resource planning strategy that enhances the autonomy capabilities of regional power grids by considering multiple evaluation indexes for ***,a regional energy Storage(ES)configuration strategy is *** strategy can select a suitable reference value for the upper limit of ES configuration based on the regional load andDGoutput to maximize the elimination of source load deviations in the region as the upper limit constraint of ES ***,a control strategy for regional ES is proposed,the charging and discharging reference line of ES is set,and multiple autonomy and economic indexes are used as objective functions to select different proportions of ES to control the distributed resources of the regional power grid and establish evaluation indexes of the internal regional generation and load power ratio,the proportion of power supply matching hours,new energy consumption rate and tie line power imbalance outside the region to evaluate changes in the regional autonomy *** final simulation results showthat in the real regional grid example,the planning method in the planning year in the region of the overall power supply matching hour ratio and new energy consumption rate increased by 3.9%and 4.8%on average,and the power imbalance of the tie line decreased by 7.8%on *** proposed planning approach enables the maximization of regional autonomy while effectively smoothing the fluct

关键词： Regional autonomous power grid distributed generation distributed energy storage regional planning strategy evaluation index

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Fault Diagnosis Method for DC Distribution Networks Based on Semitensor Product Bayesian Networks

Fault Diagnosis Method for DC Distribution Networks Based on...

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International Youth Scientific and Technical Conference on Relay Protection and Automation (RPA)

作者： He Wang Shiqiang Li Huanan Yu Jing Bian Bo Wei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China

As distributed generators and new loads spread, grid operations have grown more complex, transitioning from AC to hybrid AC/DC and pure DC distribution grids [1] .

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Neural Network technology Applied in Fault Detection and simulation of power systems 8

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

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

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

ISBN: (纸本)9798331506230

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. © 2024 IEEE.

关键词： power distribution faults

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Optimization Method of Shared energy Storage Configuration on Source-Grid-Load Side of power system

Optimization Method of Shared Energy Storage Configuration o...

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2023 International Conference on power system technology, powerCon 2023

作者： Li, Cuiping Liu, Yang Zhang, Tianxun Zhao, Zhiqiang Liu, Haojun Zhu, Xingxu Li, Zhi An, Chenyu Li, Junhui Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Jilin132012 China Beijing Technology and Business University Beijing China State Grid Hami Power Supply Company Xinjiang Uygur Autonomous Region Hami Hami839000 China

ISBN: (纸本)9798350300222

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. © 2023 IEEE.

关键词： Profitability

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Fault Location Method for Dc Distribution Network Based on Compressed Sensing Group Sparse Representation

SSRN

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SSRN 2024年

作者： Wang, He Liu, Yiqi Li, Shiqiang Yu, Huanan Bian, Jing Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Northeast Electric Power University Ministry of Education Jilin Province Jilin132012 China

The common faults in a direct current (DC) distribution network consist of pole-to-pole faults and pole-to-ground faults. Under conditions of sparse configuration of measurement devices, existing fault location methods are unable to simultaneously locate pole-to-ground faults and pole-to-pole faults, and issues such as indistinct fault characteristics under the influence of transition resistance and noise are encountered. In response to these challenges, a fault location method for DC distribution networks is proposed based on compressed sensing group sparse representation. Firstly, the pole-to-mode transformation is utilized to convert the positive and negative pole electrical quantities of the DC distribution network into zero-mode and one-mode electrical quantities. Equivalent circuits for pole-to-ground faults and pole-to-pole faults are established separately, enhancing the fault characteristics corresponding to the two different short circuit faults. Secondly, the obtained impedance matrices for zero-mode and one-mode are constructed into a group sparse dictionary. By utilizing compressed sensing group sparse representation, the sparse current vector of high-frequency mode quantities is obtained, enabling the correct identification of fault types and accurate location of fault sections. Finally, a simulation model of the DC distribution network is built on the PSCAD platform to validate the effectiveness of the proposed fault location method. © 2024, The Authors. All rights reserved.

关键词： Electric power distribution

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Emergency Direct Current power Support for High-Percentage renewable energy systems Based on Deep Reinforcement Learning

SSRN

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SSRN 2024年

作者： Sun, Zhenglong Zhuang, Junzhi Chao, Pan Chao, Jiang Cai, Guowei The Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China

As a low-cost emergency frequency control method, emergency direct current power support (EPS) is crucial for frequency stabilization of alternating current (AC)–direct current (DC) hybrid systems. In the context of highly integrated renewable energy sources, the uncertainty of AC-DC hybrid systems increases and the inertia decreases during the transition process, and the existing EPS methods face prominent adaptation and mismatch problems in meeting the frequency restoration requirements. To fill this gap, this study proposes a deep reinforcement learning (DRL)-based EPS frequency control method. The EPS decision is transformed into a Markov decision process (MDP) and solved using the proximal policy optimization (PPO) algorithm. This paper designs a DRL reward function for EPS while taking into account the EPS line overload and phase change failure problems. In order to solve the contradiction between limited observation and global safety, this paper designs a data characterization method based on mechanism fusion. Meanwhile, this paper develops an open-source power system simulation platform with DRL control function by using Python and the power system simulation software DIgSILENT/powerFactory to realize the EPS frequency stabilization decision based on DRL control. Through a case study of the IEEE 39-bus system and a real power system in Northwest China, it is verified that the proposed DRL-based EPS agent is able to quickly, accurately, and securely restore the frequency and reduce the frequency oscillations while considering the security constraints. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Topology Optimization for Harmonic Excitation Structures with Minimum Length Scale control Using the Discrete Variable Method

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Computer Modeling in Engineering & Sciences 2023年第6期135卷 1941-1964页

作者： Hongliang Liu Peijin Wang Yuan Liang Kai Long Dixiong Yang Key Laboratory of Liaoning Province for Composite Structural Analysis of Aerocraft and Simulation College of Aerospace EngineeringShenyang Aerospace UniversityShenyang110136China State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian116023China State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing102206China

Continuumtopology optimization considering the vibration response is of great value in the engineering structure *** aimof this studyis toaddress the topological designoptimizationof harmonic excitationstructureswith minimumlength scale control to facilitate *** topology design based on discrete variables is proposed to avoid localized vibration modes,gray regions and fuzzy boundaries in harmonic excitation topology *** topological design model and sensitivity formulation are *** requirement of minimum size control is transformed into a geometric constraint using the discrete ***,thin bars,small holes,and sharp corners,which are not conducive to the manufacturing process,can be eliminated from the design *** present optimization design can efficiently achieve a 0–1 topology configuration with a significantly improved resonance frequency in a wide range of excitation ***,the optimal solution for harmonic excitation topology optimization is not necessarily symmetric when the load and support are symmetric,which is a distinct difference fromthe static optimization ***,one-half of the design domain cannot be selected according to the load and support *** examples are presented to demonstrate the effectiveness of the discrete variable design for excitation frequency topology optimization,and to improve the design manufacturability.

关键词： Discrete variable topology optimization harmonic excitation minimumlength scale control geometric constraint manufacturability

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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...

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

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

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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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