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

作者： Mingfei Bai Chunmiao Qiu Wangcheng Long Yanbo Chen Hua Geng State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 People's Republic of China Beijing TUSIES Technology Co. Ltd People's Republic of China China Electric Power Planning & Engineering Institute People's Republic of China Department of Automation Tsinghua University Beijing 10084 People's Republic of China

ISBN: (数字)9781837242924

The stable and economical operation of renewable-rich microgrids poses unprecedented challenges for the future. Effective energy storage planning is critical for addressing the inherent volatility of renewable energy. In this context, we propose a two-stage robust planning model for hybrid energy storage systems including thermal and battery energy. Our model demonstrates that an appropriately sized battery energy storage system can provide reserve power for grid-forming converters, thereby mitigating the derating of renewable energy output and enhancing the overall economic efficiency of the energy system. We construct the uncertain set using a novel data-based methodology and develop an unnested column-and-constraint generation (C&CG) approach to achieve accurate solutions. This approach enhances computational efficiency by circumventing the complexities associated with nested structures. Case studies indicate that the average solution time for the unnested C&CG algorithm is reduced by over 20% compared to traditional nested C&CG methods.

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Study on the whole life cycle energy management method of energy storage system with risk correction control 4

Study on the whole life cycle energy management method of en...

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4th IEEE Conference on Energy Internet and Energy system Integration, EI2 2020

作者： Yan, Ning Li, Shuai Yan, Tao Ma, Shao Hua University of Technology School of Electrical Engineering Shenyang Shenyang China China Electric Power Research Institute State Key Laboratory of Operation and Control of Renewable Energy Storage Systems China

ISBN: (纸本)9781728196060

With the rapid development of energy storage technology, more and more energy storage systems are connected to the large power grid. Therefore, how to improve the safety, reliability and stability of the grid-connected operation of energy storage power stations is the most important. Based on this background, this paper proposes the research on energy management and control methods of large-scale energy storage power station. First of all, the operating cost model of the energy storage system is established by comprehensively considering its life-cycle operation characteristics. Secondly, according to the spatial and temporal distribution characteristics of distributed power supply and load, the energy storage regulation technology based on dynamic safety margin is proposed. Finally, according to the set dynamic safety margin of energy storage system, the energy management method of energy storage power station based on risk correction control is proposed. An example is given to verify that the energy management method proposed in this paper has the following characteristics: it can effectively evaluate the system energy supply sufficiency and realize the accuracy of system regulation. © 2020 IEEE.

关键词： Energy storage

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Research of Virtual Impedance control Strategy of Hybrid Renewable Energy Microgrid 2020

Research of Virtual Impedance Control Strategy of Hybrid Ren...

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7th International Conference on Automation and Logistics, ICAL 2020

作者： Yu, Qingguang Jiang, Zhicheng Zhao, Mengchu State Key Laboratory of Power System Department of Electrical Engineering Tsinghua University Beijing China State Grid Beijing Urban District Power Supply Company Beijing China

ISBN: (纸本)9781450377263

The principle of virtual impedance is analyzed in detail, and the simulation of parallel connection of inverter with different capacity is compared and analyzed in detail. The principle of virtual impedance is analyzed, and its derivation and analysis are carried out in detail. The simulation results show that the reactive power of parallel inverters with different capacities without virtual impedance cannot be distributed proportionally, and there is a large reactive circulation between inverters, which seriously threatens the safe and stable operation of micro grid. After the introduction of virtual impedance, reactive power distribution can be effectively improved, and the distribution effect is better with the increase of virtual impedance, but it will cause voltage sag of AC bus. This paper introduced the virtual impedance into the traditional droop control, make the output impedance and line impedance of the inverter are mainly inductive, so the droop control algorithm is implemented. This paper presented a new method based on the virtual impedance to adjust the frequency and voltage of the micro-grid bus to realize the adjustment of the voltage of the micro grid. © 2020 ACM.

关键词： Electric inverters

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Investigation and Optimization of the Thermal Performances of Compressed Sco2 Energy Storage system Based on Dynamic Modeling and Transient Simulation

SSRN

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SSRN 2024年

作者： Jiang, Jiahui Zhang, Bin Chen, Wei Zhang, Xuelin Li, Dechun Li, Jian Li, Hengdong Xue, Xiaodai College of Electromechanical Engineering Qingdao University of Science & Technology Qingdao266061 China Shengli Petroleum Administrative Bureau Co. Ltd. Sinopec Group Exploration And Development Research Institute Shengli Oilfield Company Sinopec Group State Key Laboratory of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China

As a new type of energy storage, compressed CO2 energy storage system has the advantages of high energy storage density, negative carbon emission and compact structure. Grasping the system's dynamic operational characteristics is vital for its parameter optimization and control strategy formulation. Two dynamic compressed supercritical CO2 energy storage systems (SC-CCES) without additional cold and heat sources have been established. We simulate two systems under design conditions. It is found that the performance of single stage system is better than that of double stage system. The single stage system is simulated from design conditions to periodic dynamic operating conditions. The single stage compression system has been analyzed for energy and exergy analysis, and the established model conforms to the first and second laws of thermodynamics. The dynamic characteristics of the compressor, turbine, and storage tanks are analyzed and a sensitivity analysis of the key parameters of the system is performed. system parameters are optimized. The results show that the RTE of the optimized system is 63.35%, which is 3.09% higher than that before optimization, and the exergy loss decreases by 10.37%. © 2024, The Authors. All rights reserved.

关键词： Energy storage

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Accelerating exploitation and integration of global renewable energy

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Innovation 2025年

作者： Wang, Jianxiao Chen, Xinjiang Zhuang, Minghao Li, Yan Ruan, Ziwen Wang, Yuhan Zhang, Ning Song, Jie He, Kebin Lu, Xi National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Department of Industrial Engineering and Management College of Engineering Peking University Beijing100871 China Peking University Ordos Research Institute of Energy Ordos017000 China State Key Laboratory of Nutrient Use and Management College of Resources and Environmental Sciences China Agricultural University Beijing100193 China College of Environmental Science and Engineering Tongji University Shanghai200092 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing100084 China State Key Lab of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China

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Decision-Oriented Learning for Future power system Decision-Making under Uncertainty

arXiv

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arXiv 2024年

作者： Li, Ran Zhang, Haipeng Sun, Mingyang Wan, Can Pineda, Salvador Kariniotakis, Georges Xu, Wangkun Teng, Fei The Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai Non-Carbon Energy Conversion Utilization Institute Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China The College of Electrical Engineering Zhejiang University Hangzhou310027 China The OASYS Research Group University of Malaga Malaga29071 Spain MINES ParisTech PSL University Sophia-Antipolis06904 France The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

More accurate forecasts may not necessarily lead to better decision-making. To address this challenge, decision-focused learning (DFL) has been proposed as a new branch of machine learning that replaces traditional statistical loss with a decision loss to form an end-to-end model. Applications of DFL in power systems have been developed in recent years. For renewable-rich power systems, uncertainties propagate through sequential tasks, whereas traditional statistical-based approaches focus on minimizing statistical errors at intermediate stages but may fail to support optimal decisions at the final stage. This paper first elaborates on the mismatch between more accurate forecasts and more optimal decisions in the power system caused by statistical-based learning (SBL) and explains how DFL resolves this problem. Secondly, this paper extensively reviews DFL techniques and their applications in power systems while highlighting their pros and cons in relation to SBL. Finally, this paper identifies the challenges to adopting DFL in the energy sector and presents future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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Influences of temperature on Space Charge in Gas-to-Liquid Oil-Impregnated Paper 7

Influences of temperature on Space Charge in Gas-to-Liquid O...

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7th IEEE International Conference on High Voltage engineering and Application, ICHVE 2020

作者： Zhou, Yuanxiang Huang, Xin Zhang, Ling Zhang, Yunxiao Zhou, Zhongliu Teng, Chenyuan China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Department of Electrical Engineering Beijing China

ISBN: (纸本)9781728155111

Gas-to-liquid (GTL) transformer oil has been on trial in converter transformers. However, its influence on the space charge characteristic is not clear. This work aims at exploring space charge characteristics of GTL oil at different temperatures as well as comparing with commercial naphthenic oil (KI50X). Space charge distribution during the polarization and the dissipation process in the GTL oil-impregnated paper at different temperature was measured by the pulse electroacoustic method (PEA), which were contrasted with those of the KI50X oil-impregnated paper. Results illustrated that high temperature accelerated the injection and migration of charge carriers in the oil-impregnated paper. And the polarity of the accumulated charge in the vicinity of anode changed from positive to negative with the increase of temperature. It was perceived that oil-paper conductivity and carrier mobility grew with the increase of temperature, resulting in the easier migration of injected charge to another electrode. Furthermore, the space charge dissipation rate in the GTL oil-impregnated paper is lower than that in the naphthenic oil-impregnated paper at the same temperature. Thus, a longer time interval of tests or switch operation in the factory test and operation process may need if GTL oil is used rather than naphthenic oil. © 2020 IEEE.

关键词： Electric space charge

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power system Transient Voltage Vulnerability Assessment Based on Knowledge Visualization of Cnn

SSRN

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SSRN 2023年

作者： Li, Zhendong Yan, Jiongcheng Liu, Yutian Liu, Weipeng Li, Li Qu, Hanbing Key Laboratory of Power Grid Intelligent Dispatch and Control Ministry of Education Shandong University 17923 Jingshi Road Jinan250061 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States State Grid Shandong Electric Power Company Jinan Electric Power Supply Company Jinan250011 China

Because of the increasing proportion of grid-connected converters, the transient voltage problem has increased the risk of the secure and stable operation of power systems. As the extension of transient voltage stability assessment (TVSA), transient voltage vulnerability assessment is the key technique to detect the crucial elements which can cause the insecurity risk of power system transient voltage. Although deep learning-based TVSA models have demonstrated the high accuracy, it is more important to display the knowledge learnt by the models and utilize it to recognize the weakness of transient voltage. This paper presents a transient voltage vulnerability assessment method by knowledge visualization of convolutional neural network (CNN). First, the imageform sample construction method is proposed to characterize the power system operating condition, which prepares for training CNN-based TVSA model. Then, the knowledge in TVSA model is visualized based on Gradient-weighted Class Activation Mapping (Grad-CAM) and Activation Maximization (AM) algorithms, which is analyzed from the perspective of power system stability. Finally, a comprehensive vulnerability indicator is proposed for recognizing the weakness of transient voltage, which combines the knowledge inside the TVSA model and the indicators with clear physical meaning. Simulation results of the New England 39-bus test system and the real Shandong provincial power grid demonstrate that the visualization makes the model more reliable and the proposed method can accurately identify the vulnerable branches. © 2023, The Authors. All rights reserved.

关键词： Chemical activation

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Cogging Force Identification Based on Self-Adaptive Hybrid Self-Learning TLBO Trained RBF Neural Network for Linear Motors 13

Cogging Force Identification Based on Self-Adaptive Hybrid S...

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13th International Symposium on Linear Drives for Industry Applications, LDIA 2021

作者： Fu, Xuewei Ding, Chenyang Zanchetta, Pericle Yang, Xiaofeng Tang, Mi Liu, Yang State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Department of Electrical University of Pavia Pavia Italy Power Electronics Machine and Control Group University of Nottingham Nottingham United Kingdom Center of Ultra-Precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9781728172101

The cogging force arising due to the strong attraction forces between the iron core and the permanent magnets, is a common inherent property of the linear motors, which significantly affects the control performance. Therefore, significant research efforts have been devoted to the compensation of the cogging force. In this paper, an identification approach based on the radial basis function neural network (RBFNN) is proposed to obtain an accurate model of the cogging force. A self-adaptive hybrid self-learning teaching-learning-based optimization (SHSLTLBO) method is utilized to train the neural network. Finally, the experimental results confirm the effectiveness and the superiority of the proposed cogging force identification method. © 2021 IEEE

关键词： Radial basis function networks

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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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