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power Electronics and power system Conference (PEPSC)

作者： Weike Zheng Haijiao Wang Bao Xie Meiqin Mao State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China Photovoltaic System Engineering Research Center of the Ministry of Education Hefei University of Technology Hefei China

Both the phase-locked loop (PLL) and the common coupling point (PCC) voltage feedforward may have impacts on the inverter system stability in weak grid. Therefore, a hybrid impedance reshaping control strategy is proposed in the paper. Firstly, the inverter impedance model is established. Next, the system stability is analyzed under weak grid, reflecting on the influence of PLL and the PCC voltage feedforward. Then, to improve the inverter system stability, the band-pass filter is added to the feedforward branch of PCC voltage, since it can attenuate the influence of unstable factors. Moreover, a new phase-locked loop structure containing a first-order decoupled complex filter is proposed, taking into account the fundamental positive voltage extraction characteristics of the first-order decoupled complex filter. The parameters of the hybrid impedance reshaping strategy are designed in detail. Finally, simulation is carried out to prove the theory analysis and hybrid impedance reshaping strategy in the paper.

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Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach

arXiv

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arXiv 2022年

作者： Li, Yang Bu, Fanjin Li, Yuanzheng Long, Chao Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China School of Artifcial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Water Energy and Environment Cranfield University CranfieldMK43 0AL United Kingdom

Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands. To address these challenges, a comprehensive scheduling framework is proposed by introducing a model-free deep reinforcement learning (DRL) approach based on modeling an island integrated energy system (IES). In response to the shortage of freshwater on islands, in addition to the introduction of seawater desalination systems, a transmission structure of "hydrothermal simultaneous transmission" (HST) is proposed. The essence of the IES scheduling problem is the optimal combination of each unit’s output, which is a typical timing control problem and conforms to the Markov decision-making solution framework of deep reinforcement learning. Deep reinforcement learning adapts to various changes and timely adjusts strategies through the interaction of agents and the environment, avoiding complicated modeling and prediction of multi-uncertainties. The simulation results show that the proposed scheduling framework properly handles multi-uncertainties from power sources and loads, achieves a stable demand supply for various resources, and has better performance than other real-time scheduling methods, especially in terms of computational efficiency. In addition, the HST model constitutes an active exploration to improve the utilization efficiency of island freshwater. © 2022, CC BY-NC-ND.

关键词： Desalination

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Deep Sequence to Sequence Attention Network Approach for Short-Term Electricity Price Forecasting

SSRN

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SSRN 2022年

作者： Chen, Haipeng Wu, Huayue Chen, Jian Kan, Tianyang Miao, Sen Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Chengde Power Supply Company State Grid Jibei Electric Power Co. Ltd. Chengde067000 China

With increasing renewable energy penetration and the extension of the power system scale, accurate electricity price prediction has long become a challenging task. The complexity of electricity market information, insufficient model training, and the lack of model generalization ability limit the prediction accuracy of the existing electricity price forecasting methods. This paper proposes a Sequence-to-Sequence Attention algorithm based on the Double Deep Q Network optimization method for short-term electricity price prediction. This algorithm first conducts the maximum information coefficient correlation analysis to select the input dataset for the prediction model of electricity price. Secondly, the Sequence-to-Sequence Attention model is proposed for point forecasting and probabilistic forecasting of short-term electricity prices. Finally, the hyperparameters of the model are optimized by the Double Deep Q Network method, which improves the prediction accuracy and the generalization ability of the prediction model of electricity price. simulations are carried out on Pennsylvania–New Jersey–Maryland market data and the New South Wales electricity market. The results show that the proposed method has superior performance than the existing prediction methods, which is appliable for the short-term electricity price prediction and provides a powerful decision-making basis for market participants and regulators. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Sharing Mobilized energy Storage for Temporal-Spatial Coordination of Transportation and power systems

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Journal of modern power systems and Clean energy 2024年第3期13卷 892-903页

作者： Yuhang Ding Xinjiang Chen Guangchun Ruan Gengyin Li Ming Zhou Jiang Dai Jianxiao Wang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China State Grid Hangzhou Lin'an District Power Supply Company Hangzhou China Department of Industrial Engineering and Management College of Engineering Peking University Beijing China Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Power Dispatching and Control Center Guizhou Power Grid Company Guiyang China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China

Mobilized energy storage (MES) can provide a variety of services for power systems, including peak shaving, frequency regulation, and congestion alleviation. In this paper, we develop an MES sharing approach based on temporal-spatial network (TSN) toward systemwide temporal-spatial flexibility enhancement, specifically in which the heavy-duty vehicles can exchange batteries at the energy storage stations connected with power grids. To achieve the temporal-spatial coordination of transportation and power systems, we propose a coordinated scheduling model. A decentralized algorithm based on the improved optimality condition decomposition (OCD) algorithm is proposed to address the information asymmetry between transportation and power systems while enhancing computational efficiency. Case studies based on IEEE 30-/118-bus and transportation systems demonstrate that MESs using the proposed approach can significantly improve the utilization of batteries while reducing operating costs by over 40% compared with stationary energy storages (SESs).

关键词： Transportation power systems Batteries Planning Costs power grids Uncertainty

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Optimal Scheduling Strategy of a Regional Integrated energy system Considering renewable energy Uncertainty and Heat Network Transmission Characteristics

SSRN

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SSRN 2022年

作者： Chen, Haipeng Gao, Lin Zhang, Yongling Zhao, Chang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Gongqing Institute of Science and Technology Gongqing City 332020 China National New Energy Group Jilin Jiangnan Thermal Power Limited Company Jilin132012 China

In order to overcome the curtailment of wind energy caused by "power determined by heat" of combined heat and power units, this study proposes an integrated demand response strategy and optimization method for heat-electric energy dispatching in a regional integrated energy system. Firstly, a scene analysis model based on Latin Hypercube Sampling (LHS) and Backward Reduction (BR) is constructed to analyze the probabilistic properties of wind power output in various scenes, and further provide a necessary reference for integrated energy scheduling. Secondly, the schedulable electrical power and thermal demand are evaluated with the consideration of the transmission characteristics of the heat supply network, then the integrated demand response of electric-thermal coordination is adopted to facilitate suppress the heat load fluctuation and wind power consumption. Finally, the optimal scheduling model of regional integrated energy system is established considering the source-load coordination, and the optimal output of each power unit is determined with the goal of minimizing overall cost during the scheduling period. The simulation results demonstrated that the proposed scheduling method reduces the operation cost of the integrated energy system and effectively promotes the wind energy consumption. Furthermore, it serves as a benchmark for the coordinated operation of wind turbines and power grids. © 2022, The Authors. All rights reserved.

关键词： Electric power utilization

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Research on hybrid real-time simulation interface of RTDS digital and physical model 9

Research on hybrid real-time simulation interface of RTDS di...

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9th China International Conference on Electricity Distribution, CICED 2021

作者： Cai, Haiqing Guo, Qi Su, Mingzhang Liu, Shiping Wu, Wencong Huang, Libin Electric Power Research Institute China Southern Power Grid State Key Laboratory of HVDC 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 Guangzhou510663 China Electric Power Research Institute of State Grid Jiangxi Electric Power Company Nanchang330096 China

ISBN: (纸本)9781728192291

Digital real-time simulation of physical mixing can connect renewable energy and new energy storage micro-grid physical systems to real-time simulation systems for research and testing. This paper selects ITM interface algorithm to implement digital physical hybrid real-time simulation of energy system. Through its digital-to-analog interface with external photovoltaic equipment to form a digital physical closed loop system. A hardware-in-the-loop simulation platform for photovoltaic grid-connected system is designed. In this paper, the characteristics of photovoltaic grid-connected systems are studied using a digital-physical hybrid hardware-in-the-loop simulation method. Multidimensional tests such as photovoltaic inverter function and dynamic performance tests and microgrid stability tests are performed, to verify the accuracy and stability of the digital-analog hybrid real-time simulation interface. The simulation provide an economical, stable and accurate test platform for future new energy grid-connected equipment testing. © 2021 IEEE.

关键词： Hardware-in-the-loop simulation

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Optimized functional design and simulation study of the fault line selection devices for non-solidly earthed network based on RTDS 9

Optimized functional design and simulation study of the faul...

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9th China International Conference on Electricity Distribution, CICED 2021

作者： Haiqing, Cai Qi, Guo Zhida, Huang Shiping, Liu Wencong, Wu Jiang, Yu Libin, Huang Zebing, Shi Shuyong, Li Electric Power Research Institute China Southern Power Grid State Key Laboratory of HVDC 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 Guangzhou510663 China Electric Power Research Institute of State Grid Jiangxi Electric Power Company Nanchang330096 China CSG Power Dispatch Control Center Guangzhou510663 China

ISBN: (纸本)9781728192291

The action accuracy of the fault line selection device is of great significance for ensuring the safe operation of the power system and personal safety. In view of the current imperfect function and the low accuracy of the small current grounding line selection device, the function of line selection of the device is further optimally designed and studied, and the post-acceleration and wheel-cutting functions are put forward and realized. The RTDS simulation model of non-solidly earthed network is established and connected with the actual grounding line selection device to form a closed-loop real-time simulation testing system. By setting up various test items, different types of ground fault test of non-solidly earthed net-work are carried out. The results of simulation show the accuracy of the line selection function, which provides important technical guidance for the implementation of the fault line selection device. © 2021 IEEE.

关键词： Electric grounding

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Research on load demand response behavior strategies in the steel industry guided by price signals

Research on load demand response behavior strategies in the ...

引用

4th energy Conversion and Economics Annual Forum (ECE Forum 2024)

作者： Junhui Li Jiong Zhang Yan Li Peng Yu Meng Yu Hengyu Liu Xingxu Zhu Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin People's Republic of China State Grid Electric Power Research Institute Co. Ltd State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Co. Ltd Hubei People's Republic of China State Grid Liaoning Electric Power Co. Ltd State Grid Liaoning Electric Power Company Limited Power Dispatch and Control Center Liaoning People's Republic of China State Grid Liaoning Electric Power Co. Ltd State Grid Liaoning Electric Power Company Limited Electric Power Research Institute Liaoning People's Republic of China

ISBN: (数字)9781837242924

The steel industry load has the advantages of high power consumption, high automation level, strong controllability, and strong demand response potential. In order to improve its enthusiasm for participating in the power grid and promote the consumption of renewable energy, the electricity response behavior and demand potential of each production process of the industrial load are first evaluated. The strategy of using price signals to guide the steel industry load in typical areas to participate in the demand response of the power system is proposed. Models are established for the power grid dispatch center and the industrial load response behavior respectively. In the process, the different interests and demands of the two parties, the power grid dispat ch and the industrial load, are comprehensively considered. A two-layer dispatch model is established to truly reflect the interactive game process between power supply and demand; To match the model solution with interaction, a hierarchical iterative algorithm is used to accelerate the convergence speed while ensuring the interaction between price signals and purchasing power between layers. Combining a certain industrial load area for case analysis, the results show that the proposed model not only improves the system's ability to absorb renewable energy, but also takes into account the interests and responsiveness of the steel industry load.

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Fuzzy control Based Virtual Synchronous Generator for Self-Adaptative control in Hybrid Microgrid

SSRN

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SSRN 2022年

作者： Lyu, Ling Wang, Xuesong Zhang, Liang Zhang, Zhe Koh, Leong Hai Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Hebei Electric Power Company Cangzhou Power Supply Company Cangzhou061000 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore National Metrology Centre 138635 Singapore

When there is a large penetration of renewable energy sources, maintaining the stability of a low-inertia microgrid becomes a major difficulty. However, in such systems, the virtual inertia values are often fixed constants, and the choice of their values will significantly affect the frequency and voltage stability of the microgrid. Higher frequency and voltage oscillations may occur due to improper selection of fixed virtual inertia values. Therefore, virtual inertia-based control has attracted a lot of attention. In this paper, an adaptive virtual inertia control system using a fuzzy system is proposed through the setting of fuzzy logic rules and affiliation functions to provide adaptive inertia control for the system to ensure the stability of frequency and voltage. In the proposed adaptive control strategy, the virtual inertia values are automatically adjusted according to the signal deviation and rate of change of the actual system, avoiding the selection of inappropriate inertia values and providing fast inertial response. simulation and experimental results show that the proposed adaptive control algorithm, by combining the advantages of large inertia and small inertia, enables effective improvement of the dynamic response of the system voltage and frequency in both rectifier and inverter modes. The effectiveness of the proposed control strategy is verified. © 2022, The Authors. All rights reserved.

关键词： Fuzzy control

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Unsupervised Domain Adaptation Using Attention Network and New Loss Function

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 704-718页

作者： Zhao, Li-Quan Zhou, Si-Ying Teng, Zi-Ming Jiang, Zi-Cong Jia, Yan-Fei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China College of information and communication Jilin University Changchun130012 China Graduate school of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan School of Electrical and Information Engineering Beihua University Jilin132013 China

An unsupervised domain adaptation method is proposed based on the step-wise adaptive feature norm method to improve transfer learning accuracy. The feature extraction network consists of channel attention and spatial attention modules, which en-hance feature extraction ability. To reduce the influence of dimension change on channel weight, a convolution layer with the same kernel size is used to replace the fully connected layer in the bottleneck layer used in the common channel attention module. In addition, a smooth loss function is also proposed. The curve of the proposed function is smoother than the L2 function that is the least square error function. The proposed function’s gradient changes more slowly than the L2 function. The proposed function is used as the activate function of classification prediction regression to reduce the risk of gradient explosion. Compared with a selection of existing methods (Resnet50, conditional domain adversarial networks method, deep adaptive network method, joint adaptation network method, domain adaptation neural network method, and stepwise adaptive feature norm method), the proposed transfer learning method has the highest accuracy than other methods on different datasets for all tasks. © 2022 Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Feature extraction

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