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

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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arXiv

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arXiv 2020年

作者： Li, Jiashuo Zhou, Sili Wei, Wendong Qi, Jianchuan Li, Yumeng Chen, Bin Zhang, Ning Guan, Dabo Qian, Haoqi Wu, Xiaohui Miao, Jiawen Chen, Long Liang, Sai Feng, Kuishuang Institute of Blue and Green Development Shandong University Weihai264209 State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan430074 China School of International and Public Affairs Shanghai Jiao Tong University Shanghai200030 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Beijing Normal University Beijing100875 China Laboratory of Anthropogenic Systems Ecology College of Engineering Peking University Beijing100871 China Department of Earth System Science Tsinghua University Beijing100871 China Institute for Global Public Policy Fudan University Shanghai200433 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Geographic Information Science Ministry of Education School of Geographic Sciences East China Normal University Shanghai200241 China

China has implemented retrofitting measures in coal-fired power plants (CFPPs) to reduce air pollution through small unit shutdown (SUS), the installation of air pollution control devices (APCDs) and power generation efficiency (PGE) improvement. The reductions in highly toxic Hg emissions and their related health impacts by these measures have not been well studied. To refine mitigation options, we evaluated the health benefits of reduced Hg emissions via retrofitting measures during China's 12th Five-Year Plan by combining plant-level Hg emission inventories with the China Hg Risk Source-Tracking Model. We found that the measures reduced Hg emissions by 23.5 tons (approximately 1/5 of that from CFPPs in 2010), preventing 0.0021 points of per-foetus intelligence quotient (IQ) decrements and 114 deaths from fatal heart attacks. These benefits were dominated by CFPP shutdowns and APCD installations. Provincial health benefits were largely attributable to Hg reductions in other regions. We also demonstrated the necessity of considering human health impacts, rather than just Hg emission reductions, in selecting Hg control devices. This study also suggests that Hg control strategies should consider various factors, such as CFPP locations, population densities and trade-offs between reductions of total Hg (THg) and Hg2+. Copyright © 2020, The Authors. All rights reserved.

关键词： Fossil fuel power plants

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Effect of adoption of electric vehicles on public health and air pollution in China: a modelling study

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The Lancet Planetary Health 2021年 5卷 S8-S8页

作者： Daniel E Horton Jordan L Schnell Daniel R Peters David C Wong Xi Lu Hao Gao Hongliang Zhang Patrick L Kinney Department of Earth and Planetary Sciences and Institute for Sustainability and Energy Northwestern University Evanston IL USA Cooperative Institute for Research in Environmental Sciences at the University of Colorado Boulder National Oceanic and Atmospheric Administration Global Systems Laboratory Boulder CO USA Program in Environmental Sciences Northwestern University Evanston IL USA Environmental Defense Fund Austin TX USA US Environmental Protection Agency Research Triangle Park NC USA School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing China Beijing China Department of Earth System Science University of California Irvine Irvine CA USA Department of Environmental Science and Engineering Fudan University Shanghai China Department of Environmental Health Boston University School of Public Health Boston MA USA

Background Adoption of electric vehicles has the potential to reduce air pollutant and greenhouse gas emissions, hence China has implemented policies incentivising use of electric vehicles. However, much is unknown about the potential air quality and public health benefits of electric vehicles, including optimal vehicle type prioritisation and the vehicles' ability to reduce acute health impacts due to extreme air quality events. We aimed to assess the potential climate and acute public health benefits of use of electric vehicles during an extreme winter air pollution event. Methods In this modelling study, we used the Weather Research Forecast and Community Multiscale Air Quality Modeling system air quality model to simulate the interplay between weather and atmospheric chemistry. We used this model to examine potential co-benefits of electric vehicle adoption during an extreme pollution episode in China. We simulated heavy-duty and light-duty electric vehicle adoption scenarios, in which 40% of the population adopted a heavy-duty or light-duty passenger electric vehicle, re-mapped battery power needs to energy generation facilities, and characterised differences in public health outcomes using China-specific concentration-response functions. Findings We found that widespread adoption of heavy-duty electric vehicles would reduces nitric oxide and fine particulate matter resulting in 562 (95% CI 410–723) fewer premature acute deaths than the non-electrified baseline scenario. However, widespread adoption of heavy-duty electric vehicles does not reduce carbon dioxide emissions without the addition of emission-free electricity generation. By contrast, widespread adoption of light-duty electric vehicles robustly reduces greenhouse gas emissions, but results in lesser air quality improvements and fewer premature deaths avoided (145 [95% CI 38–333]) than the heavy-duty scenario. Economic effects of human health endpoints and carbon dioxide reductions for adoption of ligh

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The distributed co-simulation protocol for the integration of real-time systems and simulation environments 50

The distributed co-simulation protocol for the integration o...

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50th Summer Computer simulation Conference, SCSC 2018, Part of the 2018 Summer simulation Multi-Conference, SummerSim 2018

作者： Krammer, Martin Msc, Martin Benedikt Blochwitz, Torsten Alekeish, Khaled Amringer, Nicolas Kater, Christian Materne, Stefan Ruvalcaba, Roberto Schuch, Klaus Zehetner, Josef Damm-Norwig, Micha Schreiber, Viktor Nagarajan, Natarajan Corral, Isidro Sparber, Tommy Klein, Serge Andert, Jakob VIRTUAL VEHICLE Research Center Graz Austria Centre of Excellence for System Simulation ESI-ITI GmbH Dresden Germany dSPACE GmbH Paderborn Germany Leibniz Universität Hannover Hannover Germany Electrics and Electronics Department TWT GmbH Stuttgart Germany Advanced Simulation Technologies Department AVL List GmbH Graz Austria AVL List GmbH Graz Austria Ks.MicroNova GmbH Kassel Germany Automotive Engineering Group University of Ilmenau Ilmenau Germany ETAS GmbH Stuttgart Germany Robert Bosch GmbH Stuttgart Germany Spath Micro Electronic Design GmbH Graz Austria Institute of Mechatronic Systems for Combustion Engines RWTH Aachen University Aachen Germany

ISBN: (纸本)9781510860209

Virtual system development gets more and more important in many industrial domains. It is considered to reduce development times, lower computing costs, and shorten time-to-market. Co-simulation is a particularly promising approach for modular and interoperable development. In practice the integration and coupling of heterogeneous systems still require enormous efforts. The configuration and operation of distributed hardware-in-the-loop systems and simulations contribute to efficiency of testing. Currently no standardized interface or protocol specification is available, which allows the interaction of real-time and non-real-time systems of different vendors. This paper for the first time presents the Distributed Co-simulation Proto-col (DCP) which is subject to proposal as a standard for real-time and non-real-time system integration and simulation. The DCP consists of a data model, a finite state machine, and a communication protocol including a set of protocol data units. It is designed as a tool independent standard. It was developed in context of the ACOSAR project and is subject to standardization as a Modelica Association Project (MAP). It enables the definition, configuration and execution of a wide range of different simulations and test scenarios. It supports a master-slave architecture for simulation setup and control. The specification defines the design of a slave only, the design of a master is not in scope of the *** highlight the industrial applicability of the DCP, three examples from the automotive domain are shown. © 2018 Society for Modeling & simulation International (SCS).

关键词： Real time systems

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Monte-Carlo Tree Search with Tree Shape Control

Monte-Carlo Tree Search with Tree Shape Control

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IEEE Ukraine Conference on Electrical and Computer Engineering

作者： Oleksandr I. Marchenko Oleksii O. Marchenko Department of System Programming and Specialized Computer Systems National Technical University of Ukraine "Igor Sikorsky Kyiv polytechnic institute"

ISBN: (纸本)9781509030071

The paper, basing on analysis of the Monte-Carlo Tree Search (MCTS) method and specific features of its behavior for various cases of usage, proposes a new variant of the method, which was called as Monte-Carlo Tree Search with Tree Shape Control (MCTS-TSC) and which uses original Depth-Width Criteria (DWCs) for both tree shape estimation and control during search and for estimation and selection of potentially better options for search continuation. Proposed Tree Shape Control (TSC) technique can be used with some other tuning, pruning, and learning techniques. Besides, it can provide better scheduling of MCTS parallelization.

关键词： Monte-Carlo Tree Search (MCTS) Game tree MCTS improvement techniques Exploitation-exploration dilemma Tree shape control MCTS parallelization

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Preface

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CEUR Workshop Proceedings 2019年 2514卷 1-2页

作者： Avetisyan, Arutyun Ivar, Jacobson Pozin, Boris A. Petrenko, Alexander K. Pozin, Boris A. Petrenko, Alexander K. Cavalli, Ana Rosa Petrenko, Alexander K. Avetisyan, Arutyun Arlazarov, Vladimir Avdoshin, Sergey Batovrin, Victor K. Bershadsky, Alexander M. Boichenko, Alexander Juris, Borzovs Vasenin, Valery A. Schlingloff, Holger Kalyanov, Georgy Kantorovich, Georgy G. Korolev, Anton Kosolapov, Mikhail S. Kostogryzov, Andrey I. Koznov, Dmitry V. Kuznetsov, Sergey Prokhorov, Sergei Soyfer, Victor A. Starykh, Vladimir A. Stolyarov, Gennady K. Stupnikov, Sergey A. Telnov, Yury Ph. Shmid, Alexander Mira, Kajko-Mattssen Miroslawa Wentzl, Wilfried Mayer, Walter H. Yevtushenko, Nina Zakharov, Victor Zmeev, Oleg Cavalli, Ana Rosa Petrenko, Alexander K. Pozin, Boris A. RAS ISP Russia Ivar Jacobson International Switzerland NRU HSE «-leasing» Co Russia NRU HSE ISP RAS Russia Information and Analytical Systems «-leasing» Co Russia Department of Network Software TELECOM and Management SudParis France NRU HSE Russia Software Engineering of Ivannikov Institute for System Programming RAS Russia RAS System Analysis Institute of Federal Research Centre Informatics and Management Russia Russia Penza State University Russia Federal State Educational Centre of Higher Learning «Plekhanov Russian University of Economics» Russia CISA Academy of Sciences of Latvia State University of Latvia Latvia MSU Institut für Offene Kommunikationssysteme FOKUS Germany Verifikation und Testtheorie an der Humboldt-Universität zu Berlin Germany Institute of Control Sciences RAS Russia Institute of Sociology RAS NRU HSE Russia ISO IEEC Institute of Informatics Problems Federal Research Centre IM RAS Russia St. Petersburg State University Russia Institute for System Programming RAS Moscow State University Russia SRI Korolev Samara State Aerospace University Russia Belarus FRC IM RAS Russia FSEC «Plekhanov Russian University of Economics» Russia KTH School of Information and Communication Technology Sweden Governmental Agricultural Analyst Austria Austria Ivannikov Institute for System Programming RAS Russia Institute of Informatics Problems Federal Research Centre IM RAS Russia Tomsk State University Russia

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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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Unit commitment considering demand response behavior based on utility maximization

Unit commitment considering demand response behavior based o...

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2017 IEEE Conference on Energy Internet and Energy system Integration, EI2 2017

作者： Zhang, Yumin Han, Xueshan Xu, Bo Zhang, Li Sun, Donglei Miao, Xiaofeng Jiang, Jiayin Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Haidian District Beijing100084 China Economic and Technology Research Institute of State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Company Yantai Electric Power Company Yantai264001 China

ISBN: (纸本)9781538614273

The active participation of demand side resources in power grid operation not only benefits the individual participant but also benefits the whole system. The optimal goal can promote by excavating the active behaviors of demand side. The welfare maximization of market participant is the driving force of such active behaviors. Considering the independence of each user's behavior, the consumer's utility function is selected to depict consumer's preference and demand response behavior. In order to coordinate the consumers' maximum welfare with social welfare maximization, consumers' utility function and units' operation cost are integrated into Unit Commitment (UC) model. The objective function is to maximize social welfare, by subtracting units' operation cost from consumers' total utility, and this objective can be acquired. Considering the proposed model should be solved in a distributed manner, the idea of the centralized coordination and the decentralized decision-making is proposed. Based on the Lagrangian relaxation (LR) principle and distributed real-time pricing scheme, a closed-loop feedback mechanism is constructed to reflect the relationship among generator output, price level, and load demand. The ten generating units example can illustrate the effectiveness of the proposed method. © 2017 IEEE.

关键词： Power markets

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A system-of-systems architecture-driven modeling method for combat system effectiveness simulation 2

A system-of-systems architecture-driven modeling method for ...

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2nd Annual IEEE International Symposium on systems Engineering, ISSE 2016

作者： Li, Xiaobo Wang, Weiping Shu, Zhe Zhu, Ning He, Hua Liao, Tianjun College of Information Systems and Management National University of Defense Technology Changsha Hunan410073 China Department of Mathematics and Computer Science University of Antwerp Antwerp2020 Belgium State Key Laboratory of Complex System Simulation Beijing Institute of Systems Engineering Beijing China

ISBN: (纸本)9781509007936

Under the background of system-of-systems (SoS) counterwork, it is more apparent that the simulation modeling of the equipment should be domain-specific, formal, automatic and composable. Current equipment effectiveness simulation modeling approaches are mainly based on the methods and techniques from the modeling and simulation (M&S) community and software engineering community, and they are not capable to satisfy these four requirements systematically. Thus the systems engineering architecture model and domain knowledge should be used to support effectiveness simulation modeling more effectively. In this research, we propose an architecture-driven modeling method which conforms to the principle of architecture-driven development, uses ontology techniques to build equipment SoS architecture model and sub-domain ontology. This method performs architecture driven simulation modeling to realize the transformation from architecture models to simulation model frameworks;employs ontological metamodeling to design domain-specific modeling languages (DSML) based on the comprehensive usage of architecture models, sub-domain ontologies and M&S formalisms;integrates domain-specific simulation models from various domains using the model framework, and supports the composable development of simulation applications. © 2016 IEEE.

关键词： system of systems

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Preface

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CEUR Workshop Proceedings 2017年 1989卷 5-7页

作者： Pozin, Boris A. Cavalli, Ana Rosa Petrenko, Alexander K. Department of Information and Analytical Systems EC-leasing Co. Russia Department of Network Software TELECOM and Management SudParis France Department of System Engineering Institute of System Programming of RAS Russia

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