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A multi-source data collection and information fusion method for distribution network based on IOT protocol

IOP Conference Series: Earth and Environmental Science 2021年第2期651卷

作者： Yongxiang Cai Xiaobing Xiao Hao Tian Yu Fu Peng Wu Hongliu He Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang China State Key Lab of Control and Simulation of Power Systems and Generation Equipments (Dept. of Electrical Engineering Tsinghua University) Beijing China Binjiang College of Nanjing University of Information Science and Technology Wuxi China

With the construction of Global Energy Internet, Intelligent distribution network has more and more characteristics of multi scene, multi service and changeable measurement and control objects. In order to realize data collection and information exchange among the intelligent terminal and energy, power grid, load, energy storage and other control resources in distribution network, we should research an architecture which can adapt to various data sources Based on dynamic self describing protocol.

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Measurement-driven AA-CAES Model for Distribution Network Dispatch in Coordination with Renewable generation

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IOP Conference Series: Earth and Environmental Science 2020年第1期529卷

作者： Yuguang Xie Jiayu Bai Dangwu Liu Tianwen Zheng Shengwei Mei State Grid Anhui Electric Power Research Institute Hefei 230601 China State Key Lab of Control and Simulation of Power Systems and Generation Equipments (Dept. of Electrical Engineering) Tsinghua University Beijing 100084 China State Grid Anhui Electric Power CO. LTD. Hefei 230022 China Sichuan Energy Internet Research Institute Tsinghua University Chengdu 610213 China

Advanced adiabatic compressed air energy storage (AA-CAES) could play an important role in power distribution system for mitigating the intermittency of distributed renewable generations and reducing the peak-valley difference of load demand, which requires to operate AA-CAES under off-design conditions. A power system dispatch oriented AA-CAES model was proposed based on the technically thermodynamic performance curves of compressor and expansion calibrated from experiments, highlighting the impact of AA-CAES part-lard features on charging/discharging efficiency and capacity. The proposed model is integrated with economic dispatch problem of power distribution systems, giving rise to a nonlinear program due to the state-dependent charging and discharging performances. The dispatch model is further linearized via piecewise linear approximation and special-ordered set of type 2, and its equivalent mixed integer linear formulation is presented. A modified IEEE 33-bus system is adopted to verify the effectiveness of proposed model for renewables accommodation.

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A software and hardware design of intelligent measurement and control terminal of distribution network based on a communication adaptive approach

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IOP Conference Series: Earth and Environmental Science 2021年第2期651卷

作者： Yujie Ding Xiaobing Xiao Hao Tian Chang Liu Guangze Ouyang Ruifeng Zhang Anshun Power Supply Bureau of Guizhou Power Grid Co. Ltd. Anshun China Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang China State Key Lab of Control and Simulation of Power Systems and Generation Equipments (Dept. of Electrical Engineering Tsinghua University) Beijing China Binjiang College of Nanjing University of Information Science and Technology Wuxi China

With the development of science and technology, the hardware and software design of transformer intelligent measurement and control terminal is very important. Based on the hardware and software appization of internet of things, the design scheme of hardware and software structure is given. Supported by high-performance operating system, the terminal device can be upgraded flexibly through APP application software. The intelligent terminal pair can realize full data collection, full control and full fusion, and real-time exchange of key operation data with the power distribution master station. The intelligent terminal can support 5G communication and realize full compatibility of communication conditions downward.

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Corrigendum to “Using fuzzy theory and information entropy for water quality assessment in three Gorges region, China” [Expert systems with Applications 37 (3) (2010) 2517–2521]

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Expert systems with Applications 2010年第6期37卷 4720-4720页

作者： Li Liu Jianzhong Zhou Xueli An Yongchuan Zhang Li Yang Dispatch & Communication Center Hunan Electric Power Company Hunan Province Changsha 410007 China College of Hydroelectric and Digitalization Engineering Huazhong University of Science and Technology Hubei Province 430074 Wuhan China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Thermal Engineering Tsinghua University Beijing 100084 China

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Carbon Monitor-power: near-real-time monitoring of global power generation on hourly to daily scales

arXiv

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arXiv 2022年

作者： Zhu, Biqing Song, Xuanren Deng, Zhu Zhao, Wenli Huo, Da Sun, Taochun Ke, Piyu Cui, Duo Lu, Chenxi Zhong, Haiwang Hong, Chaopeng Qiu, Jian Davis, Steven J. Gentine, Pierre Ciais, Philippe Liu, Zhu Department of Earth System Science Tsinghua University Beijing100084 China Laboratoire des Sciences du Climate et de l'Environnement LSCE Orme de Merisiers Gifsur-Yvette91191 France Product and Solution & Website Business Unit Alibaba Cloud Zhejiang Hangzhou311121 China Department of Earth and Environmental Engineering Columbia University New YorkNY United States Department of Civil & Mineral Engineering University of Toronto TorontoONM5S 1A1 Canada Department of Electrical Engineering The State Key Lab of Control and Simulation of Power Systems and Generation Equipment Institute for National Governance and Global Governance Tsinghua University Beijing China Institute of Environment and Ecology Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Earth System Science University of California Irvine 3232 Croul Hall IrvineCA92697-3100 United States

We constructed a frequently updated, near-real-time global power generation dataset: Carbon Monitor-power since January, 2016 at national levels with near-global coverage and hourly-to-daily time resolution. The data presented here are collected from 37 countries across all continents for eight source groups, including three types of fossil sources (coal, gas, and oil), nuclear energy and four groups of renewable energy sources (solar energy, wind energy, hydro energy and other renewables including biomass, geothermal, etc.). The global near-real-time power dataset shows the dynamics of the global power system, including its hourly, daily, weekly and seasonal patterns as influenced by daily periodical activities, weekends, seasonal cycles, regular and irregular events (i.e., holidays) and extreme events (i.e., the COVID-19 pandemic). The Carbon Monitor-power dataset reveals that the COVID-19 pandemic caused strong disruptions in some countries (i.e., China and India), leading to a temporary or long-lasting shift to low carbon intensity, while it had only little impact in some other countries (i.e., Australia). This dataset offers a large range of opportunities for power-related scientific research and policy-making. © 2022, CC BY.

关键词： Carbon

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Progress of the 4 K-class Vuilleumier type cryocooler and its further applications

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IOP Conference Series: Materials Science and Engineering 2020年第1期755卷

作者： J Wang C Z Pan T Zhang K Q Luo X T Xi L B Chen Y Zhou J J Wang CAS Key Laboratory of Cryogenics Technical Institute of Physics and Chemistry Beijing 100190 China University of Chinese Academy of Science Beijing 100049 China Laboratoire national de métrologie Et d'essais-Conservatoire national des arts et métiers (LNE-Cnam) F-93210 La Plaine France State Key Lab of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing 100084 China

Vuilleumier cycle was first patented in 1918. It was usually regard as the thermal-driven Stirling refrigeration cycle, which combined the low-frequency working pattern like Giffod-Mcmahon (GM) cryocooler and the compactness of Stirling-type cryocooler. With the continuous development in the past 100 years, the Vuilleumier-type (VM-type) cryocoolers have to been proved to generate cooling power from ambient temperature to liquid nitrogen temperature and even to liquid helium temperature. In recent decades, some efforts were made on traditional displacer-type VM cryocooler, Vuilleumier hybrid pulse tube cryocooler (VM-HPTC) and the VM-type pulse tube cryocooler (VM-type PTC) to obtain the liquid helium temperature. Successfully, not only the 4 K was obtained, but also the limit temperature of oscillating cryocooler by He4 was hit. This paper presents the progress of the 4 K-class VM-type cryocooler and analyzed the possible applications of 4 K class VM-type cryocooler.

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An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection

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Applied Energy 2022年 319卷

作者： Jiaqi Qu Zheng Qian Yan Pei Lu Wei Hamidreza Zareipour Qiang Sun School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China

Detecting PV system faults in a timely fashion is important to ensure the safe operation of equipment and reduce their impact on the economy of the PV systems. It is necessary to further improve the time-sensitive performance evaluation of the system. However, the hourly weather scenario segmentations are seldom considered during the hour-level online monitoring process. Therefore, a hybrid method based on unsupervised hourly weather status pattern recognition and blending fitting model is proposed for hourly fault detection to improve the performance evaluation of PV systems. The proposed solution includes three parts, firstly, in the data preprocessing stage, the measured power with the errors and noise under normal operation situation caused by the environment changes is corrected by monthly linear fitting. Secondly, an unsupervised hourly weather status pattern recognition method is constructed using the measured radiation data, including unsupervised clustering and the Multiclass-GBDT-LR classification process. Finally, after eliminating the anomalies and errors, the blending fitting model of the hourly sub-weather status is established. Through the analysis of power plants in Australia and China, the proposed solutions are validated and evaluated to be superior to existing data-driven solutions in terms of fitting accuracy, detection validity, and response time. Numerical results of case studies indicate that the developed methodology under sub-weather has improved the detection accuracy up to 97.71% and 99.29% compared to benchmark models.

关键词： Photovoltaic systems performance Hourly fault detection Unsupervised hourly weather status pattern recognition Blending fitting model

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Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic (vol 11, 5172, 2020)

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NATURE COMMUNICATIONS 2020年第1期11卷 5172页

作者： Liu, Zhu Ciais, Philippe Deng, Zhu Lei, Ruixue Davis, Steven J. Feng, Sha Zheng, Bo Cui, Duo Dou, Xinyu Zhu, Biqing Guo, Rui Ke, Piyu Sun, Taochun Lu, Chenxi He, Pan Wang, Yuan Yue, Xu Wang, Yilong Lei, Yadong Zhou, Hao Cai, Zhaonan Wu, Yuhui Guo, Runtao Han, Tingxuan Xue, Jinjun Boucher, Olivier Boucher, Eulalie Chevallier, Frederic Tanaka, Katsumasa Wei, Yiming Zhong, Haiwang Kang, Chongqing Zhang, Ning Chen, Bin Xi, Fengming Liu, Miaomiao Breon, Francois-Marie Lu, Yonglong Zhang, Qiang Guan, Dabo Gong, Peng Kammen, Daniel M. He, Kebin Schellnhuber, Hans Joachim Department of Earth System Science Tsinghua University Beijing China. zhuliu@***. Laboratoire des Sciences du Climat et de l'Environnement LSCE CEA CNRS UVSQ Centre d'Etudes Orme de Merisiers Gif-sur-Yvette France. Department of Earth System Science Tsinghua University Beijing China. Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA USA. Department of Earth System Science University of California Irvine 3232 Croul Hall Irvine CA USA. Division of Geological and Planetary Sciences California Institute of Technology Pasadena CA USA. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science & Technology Nanjing China. Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China. Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. School of Environment Tsinghua University Beijing China. School of Mathematical School Tsinghua University Beijing China. Department of Mathematical Sciences Tsinghua University Beijing China. Center of Hubei Cooperative Innovation for Emissions Trading System Wuhan China. Institut Pierre-Simon Laplace Sorbonne Université / CNRS Paris France. Université Paris Dauphine Place du Maréchal de Lattre de Tassigny 75016 Paris France. Center for Energy and Environmental Policy Research Beijing Institute of Technology Beijing China. Department of Electrical Engineering the State Key Lab of Control and Simulation of Power Systems and Generation Equipment Institute for National Governance and G

A Correction to this paper has been published: https://***/10.1038/s41467-020-20254-5

关键词： Climate sciences Atmospheric science Environmental sciences Environmental social sciences

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Author Correction: Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

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Nature communications 2020年第1期11卷 6292页

作者： Zhu Liu Philippe Ciais Zhu Deng Ruixue Lei Steven J Davis Sha Feng Bo Zheng Duo Cui Xinyu Dou Biqing Zhu Rui Guo Piyu Ke Taochun Sun Chenxi Lu Pan He Yuan Wang Xu Yue Yilong Wang Yadong Lei Hao Zhou Zhaonan Cai Yuhui Wu Runtao Guo Tingxuan Han Jinjun Xue Olivier Boucher Eulalie Boucher Frédéric Chevallier Katsumasa Tanaka Yiming Wei Haiwang Zhong Chongqing Kang Ning Zhang Bin Chen Fengming Xi Miaomiao Liu François-Marie Bréon Yonglong Lu Qiang Zhang Dabo Guan Peng Gong Daniel M Kammen Kebin He Hans Joachim Schellnhuber Department of Earth System Science Tsinghua University Beijing China. zhuliu@***. Laboratoire des Sciences du Climat et de l'Environnement LSCE CEA CNRS UVSQ Centre d'Etudes Orme de Merisiers Gif-sur-Yvette France. Department of Earth System Science Tsinghua University Beijing China. Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA USA. Department of Earth System Science University of California Irvine 3232Croul Hall Irvine CA USA. Division of Geological and Planetary Sciences California Institute of Technology Pasadena CA USA. Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science and Technology Nanjing China. Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China. Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. School of Environment Tsinghua University Beijing China. School of Mathematical School Tsinghua University Beijing China. Department of Mathematical Sciences Tsinghua University Beijing China. Center of Hubei Cooperative Innovation for Emissions Trading System Wuhan China. Faculty of Management and Economics Kunming University of Science and Technology 13 Kunming China. Economic Research Centre of Nagoya University Furo-cho Chikusa-ku Nagoya Japan. Institut Pierre-Simon Laplace Sorbonne Université/CNRS Paris France. Université Paris Dauphine Place du Maréchal de Lattre de Tassigny 75016 Paris France.

A Correction to this paper has been published: https://***/10.1038/s41467-020-20254-5.

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