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state Estimation for Integrated Energy systems: Motivations, Advances, and Future Work

IEEE Transactions on Power systems 2024年

作者： Zhao, Junbo Srivastava, Ankur Guo, Ye Cetenovic, Dragan Lin, Yuzhang Levi, Victor Yin, Guanxiong Huang, Manyun Zhang, Tingting Li, Zhigang Chen, Yanbo Exposito, Antonio G. Zhang, Wen Zheng, Weiye Chakrabarti, Saikat Li, Yang Sharma, Ankush Kamwa, Innocent Guo, Qinglai Terzija, Vladimir Mili, Lamine University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States Tsinghua University Tsinghua-Berkeley Shenzhen Institute Shenzhen518055 China The University of Manchester Department of Eee ManchesterM13 9PL United Kingdom University of Kragujevac Faculty of Technical Sciences Čačak Čačak32000 Serbia New York University Department of Electrical and Computer Engineering NY11201 United States Tsinghua University Department of Electrical Engineering Beijing100084 China Hohai University College of Energy and Electrical Engineering Nanjing210098 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China The Chinese University of Hong Kong-Shenzhen School of Science and Engineering Shenzhen518172 China North China Electric Power University School of Electrical and Electronic Engineering Beijing102206 China University of Sevilla Spain Sevilla41092 Spain South China University of Technology School of Electrical Power Engineering Guangzhou510641 China Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Northeast Electric Power University School of Electrical Engineering Jilin132012 China Laval University Department of Electrical and Computer Engineering QuebecQCG1V 0A6 Canada Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering Falls ChurchVA22043 United States

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on state Estimation (SE) for Integrated Energy systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensive discussion on Electric Power systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scale dynamics. An extensive comparative analysis between Integrated Energy systems-state Estimation (IES-SE) and more established Electric Power system-state Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE. © 1969-2012 IEEE.

关键词： Power system analysis

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The China groundwater crisis: A mechanistic analysis with implications for global sustainability

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Sustainable Horizons 2022年 4卷

作者： Lancia, Michele Yao, Yingying Andrews, Charles B. Wang, Xiaoli Kuang, Xingxing Ni, Jinren Gorelick, Steven M. Scanlon, Bridget R. Wang, Yanxin Zheng, Chunmiao State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China EIT Institute for Advanced Study Ningbo China Department of Earth and Environmental Sciences School of Human Settlements and Civil Engineering Xi'an Jiaotong University Xi'an China S.S. Papadopulos & Associates Inc. RockvilleMD United States Department of Civil Engineering The University of Hong Kong Hong Kong Hong Kong College of Environmental Sciences and Engineering Peking University Beijing China Department of Earth System Science Stanford University StanfordCA United States Bureau of Economic Geology Jackson School of Geosciences University of Texas at Austin AustinTX United States China University of Geosciences Wuhan China

Over the past several decades, China has rapidly depleted groundwater storage, and has lagged in managing and protecting this resource. As a result, dried up rivers, land subsidence, saltwater intrusion, and wetland losses are extensive. To assist with managing this vital groundwater resource, we constructed the first national-scale numerical groundwater flow model of China to provide a comprehensive overview and understanding of the subsurface flow dynamics. The groundwater model is three-dimensional with a 10 km by 10 km horizontal resolution and five discretized vertical layers (up to 1700 m below the land surface). The calibrated model matched calculated to observed water levels and sufficiently reproduced water budgets. The model was used to reconstruct groundwater depletion by retracing the different stages of Chinese economic development. From 1945 to 2020, the northern provinces are characterized by an average groundwater depletion of ∼16.7 km3/yr (equivalent to 30% of the Yellow River's annual flow) while southern China has experienced no substantial groundwater depletion. Groundwater depletion is consistent with a large observed reduction in groundwater discharge as baseflow to the Yellow River (-23%) relative to negligible variation in baseflow in the Yangtze River. Our study provides a holistic tool to support development of nationwide water management strategies and temper the effects of a future water crisis that China's growing economy could propagate at the global scale. © 2022

关键词： Groundwater resources

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Zn Doped NiMn-Layered Double Hydroxide for High Performance Ni–Zn Battery

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Journal of The Electrochemical Society 2020年第16期167卷

作者： Shiyuan Wang Xinxuan Duan Tengfei Gao Zhaolei Wang Daojin Zhou Kai Sun Zhicheng Shang Yun Kuang Shubo Tian Xuejin Li Wen Liu Xiaoming Sun State Key Laboratory of Chemical Resource Engineering Beijing Advanced Innovation Centre for Soft Matter Science and Engineering College of Chemistry Beijing University of Chemical Technology Beijing 100029 People's Republic of China Beijing Key Laboratory of Power Generation System Functional Material Guodian New Energy Technology Research Institute Beijing 102209 People's Republic of China Songshan Lake Materials Laboratory Dong Guan Guang Dong 523808 People's Republic of China

Rechargeable Ni–Zn battery with aqueous alkaline electrolyte represents a low-cost, safe and environmentally benign battery technology but limited in energy density and inferior cycling stability. Herein, Zn doped NiMn-layered double hydroxides have been proposed to enhance the specific capacity and cycle stability of Ni–Zn battery, which featuring lower Ni valence state and flower-like micro-structure. As a result, NiMn-LDH with 5 at% Zinc doping in can achieve a high mass-specific capacity of 294 mAh g−1 at 1 A g−1 and enhanced stability, manifesting one of the best cathode materials for Ni–Zn batteries. Moreover, the corresponding Ni–Zn battery behaves a high capacity of 180 mAh g−1 at 1 A g−1 and 100% retention after 1000 cycles. This work demonstrates the superiority of materials design in both elemental composition and hierarchical structure to achieve high performance in energy storage, which can be also adapted to other battery systems.

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Global daily CO2 emissions for the year 2020

arXiv

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arXiv 2021年

作者： Liu, Zhu Deng, Zhu Ciais, Philippe Tan, Jianguang Zhu, Biqing Davis, Steven J. Andrew, Robbie Boucher, Olivier Arous, Simon Ben Canadell, Pep Dou, Xinyu Friedlingstein, Pierre Gentine, Pierre Guo, Rui Hong, Chaopeng Jackson, Robert B. Kammen, Daniel M. Ke, Piyu Le Quere, Corinne Monica, Crippa Janssens-Maenhout, Greet Peters, Glen Tanaka, Katsumasa Wang, Yilong Zheng, Bo Zhong, Haiwang Sun, Taochun Schellnhuber, Hans Joachim Department of Earth System Science Tsinghua University Beijing100084 China Laboratoire des Sciences du Climate et de l'Environnement LSCE Orme de Merisiers Gif-sur-Yvette91191 France Department of Earth System Science University of California Irvine 3232 Croul Hall IrvineCA92697-3100 United States CICERO Center for International Climate Research Oslo Norway Institute Pierre-Simon Laplace Sorbonne Université/CNRS Paris France Kayrros 33 Rue La Fayette ParisFR 75009 France Global Carbon Project CSIRO Oceans and Atmosphere Australia Capital Territory Australia College of Engineering Mathematics and Physical Sciences University of Exeter Exeter United Kingdom Department of Earth & Environment Sciences Columbia University United States Earth System Science Department Woods Institute for the Environment Precourt Institute for Energy Stanford University StanfordCA United States Energy and Resources Group Goldman School of Public Policy University of California BerkeleyCA United States School of Environmental Science The Tyndall Centre for Climate Change Research University of East Anglia Norwich United Kingdom Ispra Italy Center for Global Environmental Research National Institute for Environmental Studies Tsukuba Japan Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzheng China Department of Electrical Engineering Tsinghua University Beijing100084 China Potsdam Institute for Climate Impact Research Potsdam14412 Germany

The diurnal cycle CO2 emissions from fossil fuel combustion and cement production reflect seasonality, weather conditions, working days, and more recently the impact of the COVID-19 pandemic. Here, for the first time we provide a daily CO2 emission dataset for the whole year of 2020 calculated from inventory and near-real-time activity data (called Carbon Monitor project: https://***) for power generation (for 29 countries), industry (for 73 countries), road transportation (for 406 cities), aviation and maritime transportation and residential fuel use sectors (which we estimate for 206 countries). It was previously suggested from preliminary estimates that did not cover the entire year of 2020 that the pandemics may have caused more than 8% annual decline of global CO2 emissions. Here we show from detailed estimates of the full year data that the global reduction was only 5.4% (-1,901 MtCO2, ±7.2% for 2-sigma range). This decrease is 5 times larger than the annual emission drop at the peak of the 2008 Global Financial Crisis. However, global CO2 emissions gradually recovered towards 2019 levels from late April with global partial re-opening. More importantly, global CO2 emissions even increased slightly by +0.9% in December 2020 compared with 2019, indicating the trends of rebound of global emissions. Later waves of COVID-19 infections in late 2020 and corresponding lockdowns have caused further CO2 emissions reductions particularly in western countries, but to a much smaller extent than the declines in the first wave. Despite partial mobility restrictions in European countries and in some US states that have been affected by the second and third waves of the pandemics, emission reductions observed in Autumn 2020 were much smaller than those of Spring 2020. That even substantial world-wide lockdowns of activity led to a one-time decline in global CO2 emissions of only 5.4% in one year highlights the significant challenges for climate change mitigation

关键词： Fossil fuels

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Publisher Correction: Regional disparities in warm season rainfall changes over arid eastern-central Asia

引用

Scientific reports 2018年第1期8卷 17413页

作者： Wenhao Dong Yanluan Lin Jonathon S Wright Yuanyu Xie Yi Ming Han Zhang Rensheng Chen Yaning Chen Fanghua Xu Namei Lin Chaoqing Yu Bin Zhang Shuang Jin Kun Yang Zhongqin Li Jianping Guo Lei Wang Guanghui Lin Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science and Joint Center for Global Change Studies (JCGCS) Tsinghua University Beijing 100084 China. Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science and Joint Center for Global Change Studies (JCGCS) Tsinghua University Beijing 100084 China. yanluan@***. Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science and Joint Center for Global Change Studies (JCGCS) Tsinghua University Beijing 100084 China. jswright@***. Geophysical Fluid Dynamics Laboratory Princeton/NOAA Princeton New Jersey 08540-6649 USA. Qilian Alpine Ecology and Hydrology Research Station Key Laboratory of Inland River Ecohydrology Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou 730000 China. State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi 830011 China. chenyn@***. State Key Laboratory of Cryospheric Sciences/Tian Shan Glaciological Station Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences Lanzhou 730000 China. State Key Laboratory of Severe Weather and Key Laboratory of Atmospheric Chemistry of CMA Chinese Academy of Meteorological Sciences Beijing 100081 China. Key Laboratory of Tibetan Environment Changes and Land Surface Processes Institute of Tibetan Plateau Research Chinese Academy of Sciences (CAS) and the CAS Center for Excellence in Tibetan Plateau Earth Sciences Beijing 100101 China.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

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The Phenology and Water Level Time-Series Mangrove Index for Improved Mangrove Monitoring

SSRN

引用

SSRN 2024年

作者： Sun, Weiwei Huang, Ke Yang, Gang Fu, Bolin Chen, Chao Meng, Xiangchao Feng, Tian Ge, Yong Department of Geography and Spatial Information Techniques Ningbo University Ningbo315211 China Zhejiang Collaborative Innovation Center Ningbo Universities Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences & Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Geomatics and Geoinformation Guilin University of Technology Guilin541006 China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou215009 China

Mangroves are declining and degrading under the influence of human activities and natural factors, making their accurate mapping and dynamic monitoring essential. However, most of the existing mangrove indices based on the spectral characteristics of multispectral images have some limitations in recognition accuracy and universality. Therefore, this study aimed to develop a robust and efficient Phenology and Water level Time-series Mangrove Index (PWTMI) for mangrove monitoring. PWTMI is constructed by combining spectral and temporal characteristics from dense time-series multispectral data, wherein phenology and water level time-series characteristics are extracted from NDVI and MNDWI time series. The results show that PWTMI outperforms existing multispectral-based mangrove indices and has an accuracy similar to a hyperspectral-based mangrove index, with overall accuracy ranging from 91.49% to 98.83% and F1 score ranging from 0.91 to 0.98 in four typical areas in China, indicating great potential for long time-series and large-scale mangrove monitoring. © 2024, The Authors. All rights reserved.

关键词： Water levels

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Photo-Based Monitoring of Particulate Matter in the Campus: A New Strategy for Student Health

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IOP Conference Series: Earth and Environmental Science 2020年第1期555卷

作者： Yonghui Zhang Ke Gu Zhifang Xia Junfei Qiao Faculty of Information Technology Beijing University of Technology 100124 Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education 100124 Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System 100124 Beijing China Beijing Artificial Intelligence Institute 100124 Beijing China State Information Center 100045 Beijing China

It is imperative for the students' future health to ensure the students in good physical levels. Recent years have witnessed the increasingly serious harm to student health caused by the continually growing concentration of Particulate Matters (PMs). Consequently, the task of preventing and controlling PM concentrations in the campus is eagerly required. A well-designed model for the monitoring of PM (as the basis for PM prevention and control) has posed a big challenge. Prior works have revealed that photo-based methods are available for the monitoring of PM. Towards validating the effectiveness of existing methods for PM monitoring in the campus, we construct a novel dataset that involves 1,500 photos collected in the beijing University of Technology. Results confirm that stated-of-the-art methods are far from ideal for the monitoring of PM in the campus. To solve the aforesaid issue, this paper further proposes a novel photo-based PM monitoring model by using the weighted average method solved by LASSO regression to fuse the above methods' outputs tested to infer the PM values. Results demonstrate the superiority of our proposed model as compared to state-of-the-art methods on the large-scale AQPDBJUT dataset.

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Author Correction: Historical impacts of grazing on carbon stocks and climate mitigation opportunities

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Nature Climate Change 2024年 883页

作者： Shuai Ren Juan Li Shanshan Yang Dan Liu César Terrer Yingfang Cao State Key Laboratory of Tibetan Plateau Earth System Environment and Resources Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing China Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge MA USA University of Chinese Academy of Sciences Beijing China Key Laboratory of Cold Regions Restoration Ecology Northwest Institute of Plateau Biology Chinese Academy of Sciences Xining China

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Photonic real time video image signal processor at 17Tb/s based on a Kerr microcomb

arXiv

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arXiv 2024年

作者： Tan, Mengxi Xu, Xingyuan Boes, Andreas Corcoran, Bill Nguyen, Thach G. Chu, Sai T. Little, Brent E. Morandotti, Roberto Wu, Jiayang Mitchell, Arnan Moss, David J. School of Electronic and Information Engineering Beihang University Beijing100191 China Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia School of Engineering RMIT University MelbourneVIC3001 Australia State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China School of Electrical and Electronic Engineering University of Adelaide Adelaide5005 SA Australia Department of Electrical and Computer System Engineering Monash University ClaytonVIC3168 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Xi’an Institute of Optics and Precision Mechanics CAS Xi’an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada

Signal processing has become central to many fields, from coherent optical telecommunications, where it is used to compensate signal impairments, to video image processing. Image processing is particularly important for observational astronomy, medical diagnosis, autonomous driving, big data and artificial intelligence. For these applications, signal processing traditionally has mainly been performed electronically. However these, as well as new applications, particularly those involving real time video image processing, are creating unprecedented demand for ultrahigh performance, including high bandwidth and reduced energy consumption. Here, we demonstrate a photonic signal processor operating at 17 Terabits/s and use it to process video image signals in real-time. The system processes 400,000 video signals concurrently, performing 34 functions simultaneously that are key to object edge detection, edge enhancement and motion blur. As compared with spatial-light devices used for image processing, our system is not only ultra-high speed but highly reconfigurable and programable, able to perform many different functions without any change to the physical hardware. Our approach is based on an integrated Kerr soliton crystal microcomb, and opens up new avenues for ultrafast robotic vision and machine learning. © 2024, CC BY.

关键词： Energy utilization

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Correction to: Temporal and spatial evolution patterns of drought in China over the past 500 years

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Arabian Journal of Geosciences 2021年第23期14卷 1-1页

作者： Wei, Cuixia Guo, Bing Zang, Wenqian Yang, Fei Lu, Yuefeng Zhang, Dafu Wu, Hongwei Zhen, Xiaoyan Xue, Haorun Meng, Chao Chen, Shuting Yang, Xiao Zhang, Hailing Huang, Xiangzhi Zhang, Rui School of Civil Architectural Engineering Shandong University of Technology Zibo China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research of Chinese Academy of Sciences Beijing China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Meteorology and Ecological Environment of Hebei Province Shijiazhuang China International School Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Land use MNR China Land Survey and Planning Institute Beijing China Land Satellite Remote Sensing Application Center Ministry of Natural Resources Beijing China

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