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11th IET International Conference on Advances in power system control, Operation and Management, APSCOM 2018

作者： Wang, Jiazhen Shen, Xinwei Xu, Yinliang Guo, Qinglai Sun, Hongbin Chen, Yanbo Shenzhen Environmental Science and New Energy Technology Engineering Laboratory Tsinghua-Berkeley Shenzhen Insitute Tsinghua University Shenzhen China State Key Lab of Control and Simulation of Power System and Generation Equipments Department of Electrical Engineering Tsinghua University Beijing China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University China

ISBN: (纸本)9781839530043

With the increasing proportion of renewable energy generation into the power grid, more challenges are coming to the safe and stable operation of the power system. In order to keep the balance in energy market, system operator has to purchase much service in ancillary service market to guarantee the stable operation of the power transmission. Virtual power plant (VPP) has been extensively studied as a concept that aggregates multiple resource forms to serve the electricity market. Meanwhile, with the development of energy Internet (EI) Error! Reference source not found., more attention has been focused on the optimization and operation of Integrated energy system (IES), therefore, the introduction and exploration of Multi-energy Virtual power Plant (MVPP) provide possibilities for IES to participate in the Electricity Market and Ancillary Services Market. This paper extracts the flexible electric and heat load in the industrial production process, combining the CHP units and Electric Boiler (EB), which are main heat energy supply devices as well as energy Storage system (ESS) as a MVPP to provide Frequency Regulation (FR) service to the Auxiliary Service Market. Model Predictive control (MPC) method is applied to control the industrial load, the operation of CHP, EB and ESS are gonging to follow the AGC signal issued by the FR market effectively while avoiding impacts on the production process of the factory. Finally, case study validates the effectiveness of the model. © 2018 Institution of Engineering and technology. All rights reserved.

关键词： Model predictive control

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Research on the Grid-connected Photovoltaic power Scheduling Strategy Considering the Flexible Load 3

Research on the Grid-connected Photovoltaic Power Scheduling...

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3rd International Conference on energy Engineering and power systems, EEPS 2023

作者： Meng, Tao Li, Chenggang Li, Dexin Zhang, Jiajun State Grid Jilin Electric Power Co. Ltd Electric Power Research Institute China Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education School of Electrical Engineering Changchun China State Grid Jilin Electric Power Co. Ltd Electric Power Research Institute Changchun China

ISBN: (纸本)9798350313857

With the target of "peak carbon dioxide emission"and "carbon neutrality", the installed capacity of renewable energy power generation is increasing rapidly. To improve the absorption capacity of renewable energy, the frequency regulation ability of flexible load is considered. Diffusion strategy is an optimal strategy for the distributed multi-agent system. Considering the flexible load, the grid-connected photovoltaic power applies the diffusion strategy to optimize the scheduling strategy. The effectiveness of the proposed optimal algorithm is verified by a simulation example. The results show that the proposed algorithm can realize frequency regulation, and improve the installed capacity of photovoltaic power generation. © 2023 IEEE.

关键词： Multi agent systems

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AC Network-Constrained Local Electricity Market Mechanism in Low-voltage Distribution Networks

AC Network-Constrained Local Electricity Market Mechanism in...

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2022 IEEE power and energy Society General Meeting, PESGM 2022

作者： Yang, Jiajia Yan, Gangui Yang, Yulong Chen, Guo Li, Chaojie Wen, Fushuan Northeast Electric Power University Ministry of Education Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Jilin132012 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia School of Electrical Engineering Zhejiang University Hangzhou China Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Estonia

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

This paper proposes a fully decentralized local electricity market mechanism backboned by a modified version of the Alternating Direction Method of Multipliers (ADMM). Comparing with the state-of-the-art peer-to-peer (P2P) market in local distribution systems, the proposed electricity trading mechanism comprehensively incorporates power network constraints formulated by an Alternating Current (AC) power flow model. In the proposed method, nodal voltages are designed as the only public variables shared among participants. Privacy of each participant is thus sufficiently preserved without exposing the bid data to the others. Meanwhile, the proposed method can also ensure the global optimality of social welfare in electricity transactions. Comprehensive case studies on the IEEE 33-node distribution system are conducted to demonstrate the feasibility and effectiveness of the proposed electricity trading mechanism. © 2022 IEEE.

关键词： power markets

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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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Info-Hkelm Wind power Short-Term Prediction Based on Wind power Low Output Event Identification and Multi-Scenario Error Compensation

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SSRN 2023年

作者： Yang, Mao Guo, Yunfeng Huang, Yutong Gu, Dake Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin Province Jilin132012 China

The penetration of a high percentage of renewable energy poses a challenge to the safe and stable operation of the power system, frequent wind power low output events have seriously affected power dispatch and power supply reliability, it becomes necessary to anticipate the occurrence of wind power low output events in advance and to develop effective supply assurance measures. In this paper, we propose a method for identifying and accurately predicting wind power low output events, firstly, we define wind power low output events and identify low output event types based on CNN-BiGRU. Secondly, A weighted clustering comprehensive assessment indicator is proposed to evaluate the effect of clustering of low output events, hybrid kernel extreme learning machine based on weighted mean of vectors algorithm optimization (INFO-HKELM) is used for preliminary prediction of low output events in different scenarios, and a multi-scenario error compensation mechanism based on similar fragment matching is proposed for further correction of power prediction results. The proposed method was applied to a wind farm in Inner Mongolia Autonomous Region for validation. Compared with the rest of the methods, the NRMSE of the method in this paper decreases by 1.56%~1.7%, NMAE decreases by 0.6%~1.11%, and R2 increases by 2.50%~2.92%. The multi-scenario error compensation mechanism reduces the NRMSE and NMAE of the preliminary power prediction results by 1.3% and 1.4%, respectively, which further improves the prediction accuracy and proves the effectiveness of the proposed method in this paper. © 2023, The Authors. All rights reserved.

关键词： Wind power

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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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Visualization of CNN Transient Voltage Classification Based on Feature Recognition and Enhancement

Visualization of CNN Transient Voltage Classification Based ...

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Sustainable power and energy Conference (iSPEC)

作者： Zhendong Li Yutian Liu Jun An Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China

ISBN: (纸本)9781665414401

The deep learning algorithm represented by CNN has achieved remarkable results in complex feature extraction, classification and regression. Since the CNN adopts an end-to-end training process, the carrier of acquisition knowledge is a high-dimensional non-linear function, which is hard to understand by humans. Therefore, it is difficult to be widely used in online decision-making engineering scenarios such as the fast assessment of power system transient voltage stability. In this paper, the key input elements extracted by CNN are identified by Grad-CAM, and the key features captured by CNN in the assessment process are enhanced based on AM, so as to finally obtain the visual expression of key areas and variables of the original input data. Besides, a sample construction method based on single line diagram is proposed to improve the visualization effect of Grad-CAM and AM. simulation results and analyses of IEEE 39bus test system demonstrates the effectiveness of the proposed method.

关键词： Training Visualization simulation Data visualization Voltage power system stability Feature extraction

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Probabilistic prediction of short term wind power considering temporal and spatial dependence of prediction error

Probabilistic prediction of short term wind power considerin...

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作者： Yong Sun Zhenyuan Li Chaoyu Shi Baoju Li Mao Yang State Grid Jilin Electric Power Supply Company Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University)

The short-term probabilistic prediction of wind power has the characteristics of spatial dependence and time-series *** the two characteristics at the same time can improve the prediction level. In this paper, a probabilistic short-term wind power prediction model considering the temporal and spatial dependence of prediction error is proposed. Considering the coupling relationship between the two properties, the NWP(Numerical Weather Prediction) wind speed point prediction error in the historical period is hierarchical clustering, and the empirical distribution model is used to fit the probability distribution of the error under different wind conditions;the cumulative empirical distribution probability value corresponding to the NWP wind speed at the time to be predicted is bootstrap sampling;under the given confidence level, the possible wind speed at each time point to be predicted in the short term is calculated The power fluctuation range of the generator. The test results show that this method can ensure the statistical significance and fitting stability of the sub sample set at the same time, and improve the classification accuracy of the days to be predicted. Compared with considering a single property, the result of probability prediction performs better on multiple evaluation indexes.

关键词： Probabilistic prediction of short term wind power considering temporal and spatial dependence of prediction error

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Analysis of the Impact of Newly Built Grounding Grid on the Original Grounding Grid Based on the Method of Moments

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Procedia Computer Science 2023年 228卷 185-194页

作者： Letian Wang Zhen Liu Mengqing Song Xuan Zheng Chenglian Ma Shandong Electric Power Engineering Consulting Institute Co. Ltd. Jinan China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China

On the basis of the method of moments, the resistive coupling effect between the newly built grounding network. When the horizontal distance between two grounding networks of a substation changes, this original grounding network of a certain substation is analyzed. And establish corresponding soil models based on actual engineering soil resistivity data. Use the grounding grid analysis function of CDEGS software and powerstation software simulation software to simulate and calculate some parameters of the new and old grounding grids. The analysis parameters mainly include the grounding resistance, step voltage, and contact voltage of the grounding grid system. And the obtained parameter indicators are compared and analyzed with safety limits to determine the safety performance of the newly built grounding grid system in the substation. And the impact on the grounding performance of the original grounding network of the substation. Provide certain theoretical data support for the actual promotion of substation grounding network engineering.

关键词： Method of Moment Resistive Coupling Grounding Grid CDEGS powerstation

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Coupling Analysis and Protection Design of Substation Grounding Grid and Surrounding Metal Pipe Grid

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Procedia Computer Science 2023年 228卷 1262-1269页

作者： Tao Wang Haojie Yuan Xuan Zheng Mengqing Song Chenglian Ma Shandong Electric Power Engineering Consulting Institute Co. Ltd. Jinan China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology (Ministry of Education) Northeast Electric Power University Jilin China

When the substation grounding grid and part of the underground metal pipe grid are close to each other, the two produce resistive coupling, and the current of the transmission line will flow through the grounding body and through the soil to the buried pipe grid, which may break through the anti-corrosion layer of the pipe grid, and may burn the body of the pipe grid in serious cases; the underground metal pipe grid will also have an impact on the parameters of the grounding grid. To this end, this paper is based on the numerical calculation method of field-path coupling, combined with the actual situation of substation, using CDEGS and ETAP software to simulate and analyze the current dissipation process when a short-circuit fault occurs in the substation after the ground grid is put into operation under multi-layer soil environment, and to study the influence of resistive coupling between the grounding grid and the surrounding metal pipe grid during an industrial frequency grounding short circuit. Optimize the structure of the grounding system so that the contact voltage across and step voltage is lower than the safety limit to protect the personal safety in the station; at the same time, take certain protective measures to slow down the corrosion of the pipe grid.

关键词： Grounding Grid Metallic Pipe Grid Field Circuit Coupling Ground Potential Rise Resistive Coupling

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