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IEEE Energy Conversion Congress and Exposition

作者： Chu Sun Syed Qaseem Ali Geza Joos Francois Bouffard Department of Electrical and Computer Engineering McGill University Montreal Canada Application eXpertise and Electrical Simulation Division (AXES) OPAL-RT TECHNOLOGIES Inc Montreal Canada

The reduced inertia due to integration of power-electronic converters brings about large frequency deviation and rate of change of frequency (ROCOF) in power system which may trigger frequency protection or increase the tears and wears of generators. The existing synthetic inertia and fast frequency response based on renewable energy resource is limited by the unidirectional power reserve, while the emerging virtual synchronous machine (VSM) control is limited by the energy storage needed on DC side. In this paper, an enhanced VSM control is proposed, considering the limitation of energy storage in response speed and energy capacity. The fast-acting energy storage system (FAESS), usually having small energy capacity, is used for emulating inertia and damping. An energy recovery control is proposed so that the energy will be automatically recovered after disturbance, thus reducing the energy capacity needed. The slow-acting energy storage system (SAESS) is controlled like governor control of synchronous machine, which adjusts power based on frequency change, thus taking advantage of its high energy capacity. DC and AC coupling of governor-like control and inertia-damping emulation is proposed, which is unanimous under the scheme of grid-forming and grid-following control. Comprehensive simulation results demonstrated the effectiveness of the proposed control strategy.

关键词： Frequency control Energy storage Synchronous generators Inverters Power systems

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Power Characterization of Memory Intensive Applications: Analysis and Implications 1st

Power Characterization of Memory Intensive Applications: Ana...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Qiu, Yeliang Jiang, Congfeng Fan, Tiantian Wang, Yumei Zhang, Liangbin Wan, Jian Shi, Weisong Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou310037 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310037 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science Wayne State University DetroitMI48202 United States

ISBN: (纸本)9783030328122

DRAM is a significant source of server power consumption especially when the server runs memory intensive applications. Current power aware scheduling assumes that DRAM is as energy proportional as other components. However, the non-energy proportionality of DRAM significantly affects the power and energy consumption of the whole server system when running memory intensive applications. Thus good knowledge of server power characterization under memory intensive workloads can help better workload placement with power reduction. In this paper, we investigate the power characteristics of memory intensive applications on real rack servers of different generations. Through comprehensive analysis we find that (1) Server power consumption changes with workload intensity and concurrent execution threads. However, fully utilized memory systems are not the most energy efficient. (2) Powered memory modules of installed memory capacity, i.e. the memory capacity per processor core has significant impact on the application’s performance and server power consumption even if the memory system is not fully utilized. (3) Memory utilization is not always a good indicator for server power consumption when it is running memory intensive applications. Our experiments show that hardware configuration, workload types, as well as concurrently running threads have significant impact on a server’s energy efficiency when running memory intensive applications. Our findings presented in this paper provide useful insights and guidance to system designers, as well as data center operators for energy efficiency aware job scheduling and power reductions. © 2019, Springer Nature Switzerland AG.

关键词： Energy efficiency

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Scalability Evaluation of Big Data Processing Services in Clouds 1st

Scalability Evaluation of Big Data Processing Services in Cl...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Zhou, Xin Jiang, Congfeng Qiu, Yeliang Fan, Tiantian Wang, Yumei Zhang, Liangbin Wan, Jian Shi, Weisong Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou310037 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310037 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science Wayne State University DetroitMI48202 United States

ISBN: (纸本)9783030328122

Currently, many cloud providers deploy their big data processing systems as cloud services, which helps users conveniently manage and process their data in clouds. Among different service providers’ big data processing services, how to evaluate and compare their scalability is an interesting and challenging work. Most traditional benchmark tools focus on performance evaluation of big data processing systems, such as aggregated throughput and IOPS, but fail to conduct a quantitative analysis of their scalability. In this paper, we propose a measurement methodology to quantify the scalability of big data processing services, which makes the cloud services scalability comparable. We conduct a group of comparative experiments on AliCloud E-MapReduce and Baidu MRS, and collect their respective scalability characteristics under Hadoop and Spark workloads. The scalability characteristics observed in our work could help cloud users choose the best cloud service platform to set up an optimized big data processing system to achieve their specific goals more successfully. © 2019, Springer Nature Switzerland AG.

关键词： Big data

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Improved VSG Control for Type-IV Wind Turbine Generator Considering Operation Limitations

Improved VSG Control for Type-IV Wind Turbine Generator Cons...

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IEEE Energy Conversion Congress and Exposition

The gradually reduced inertia in powers system due to proliferation of renewable energy resource may lead to larger frequency deviation and change rate and thus fault protection triggering. Type-IV wind turbine generator (WTG) can provide inertia and damping via releasing the rotational energy by adopting virtual synchronous generator (VSG) control. However, the energy stored for inertia emulation depends on its machine side inertia and wind speed. To investigate its impact on inertia and damping emulation, linearized model of VSG-controlled type-IV WTG is established in the present research. It is revealed that lower machine-side inertia and higher wind speed will result in worse frequency response than the ideal VSG. An inverse control block is introduced to mitigate this effect. Supplementary control blocks are also added to limit the power and rotor speed of WTG and help damp the oscillation between synchronous generator and WTG. Comprehensive case studies demonstrated the effectiveness of the proposed control strategy.

关键词： Rotors Wind speed Wind turbines Damping Synchronous generators Frequency control

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Analyzing Emergency Evacuation Strategies For Large Buildings Using Crowd simulation Framework

Analyzing Emergency Evacuation Strategies For Large Building...

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simulation Winter Conference

作者： Imran Mahmood Talha Nadeem Faiza Bibi Xiaolin Hu Center for Research in Modeling Simulation (Crimson) School of Electrical Engineering and Computer Science National University of Sciences & Technology Islamabad PAKISTAN Department of Computer Science Georgia State University Atlanta GA USA

ISBN: (数字)9781728132839

ISBN: (纸本)9781728120522

The occurrence of natural or man-made emergencies can be quite complex and demand flawless preparedness, through tested strategies, in order to ensure the safety of the individuals. For large-scale infrastructures, whether commercial or residential, having a reliable evacuation strategy is crucial. Formulation and evaluation of these evacuation strategies is however a daunting challenge. In this paper, we propose a Crowd simulation and Analysis framework for the formulation and evaluation of effective evacuation strategies in large buildings, using real-scale building structures and agent based approach. We further propose an algorithm to devise an evacuation strategy. We first demonstrate the functionality of our algorithm using a simplistic example and then apply the algorithm in a campus evacuation case study using three scenarios. The main goal of this research is to assist regulatory authorities in developing effective disaster management plans through the use of M&S methods and tools.

关键词： Buildings Analytical models Solid modeling Safety Computational modeling Tools Vehicle dynamics

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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Outcomes from a Workshop on a National Center for Quantum Education

arXiv

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arXiv 2024年

作者： Barnes, Edwin Bennett, Michael B. Boltasseva, Alexandra Borish, Victoria Brown, Bennett Carr, Lincoln D. Ceballos, Russell R. Dukes, Faith Easton, Emily W. Economou, Sophia E. Edwards, E.E. Finkelstein, Noah D. Fracchiolla, C. Franklin, Diana Freericks, J.K. Goss, Valerie Hannum, Mark Holincheck, Nancy Kelly, Angela M. Lanes, Olivia Lewandowski, H.J. Matsler, Karen Jo Mercurio, Emily Montaño, Inès Murdock, Maajida Peltz, Kiera Perron, Justin K. Richardson, Christopher J.K. Rosenberg, Jessica L. Ross, Richard S. Ryu, Minjung Samuel, Raymond E. Schrode, Nicole Schwamberger, Susan Searles, Thomas A. Singh, Chandralekha Tingle, Alexandra Zwickl, Benjamin M. Department of Physics Virginia Tech Center for Quantum Information Science and Engineering Virginia Tech 850 West Campus Drive BlacksburgVA24061 United States Q-SEnSE NSF QLCI University of Colorado 440 UCB BoulderCO80301 United States Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University 610 Purdue Mall West LafayetteIN47907 United States Quantum Science Center A National Quantum Information Science Research Center of The U.S. Department of Energy Oak Ridge National Laboratory 1 Bethel Valley Road Oak RidgeTN37830 United States JILA National Institute of Standards and Technology the University of Colorado BoulderCO80309 United States Department of Physics University of Colorado 390 UCB BoulderCO80309 United States QuSTEAM 510 Ronalds St. Iowa CityIA52245 United States Quantum Engineering Program Department of Physics Colorado School of Mines 1523 Illinois St. GoldenCO80401 United States QuSTEAM Non-Profit Wilbur Wright College City Colleges of Chicago 4300 N Narragansett Ave ChicagoIL60634 United States Government and Community Relations Office - K-12 Programs Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA United States Chicago Quantum Exchange 5235 S. Harper Court ChicagoIL60615 United States Department of Physics Virginia Tech 850 West Campus Drive BlacksburgVA24060 United States Department of Electrical and Computer Engineering Duke Quantum Center Duke University Duke University 130 Hudson Hall DurhamNC27708 United States American Physical Society 555 12th St. NW WashingtonDC20004 United States Computer Science University of Chicago 5730 S. Ellis Ave. ChicagoIL60637 United States Department of Physics Georgetown University 552 Reiss Science Building WashingtonDC20057 United States Chemistry Physics and Engineering Studies Chicago State University 9501 S. King Drive WSC 309 ChicagoIL606

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science. © 2024, CC BY.

关键词： Students

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Near-real-time global gridded daily CO2 emissions 2021

arXiv

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arXiv 2022年

作者： Dou, Xinyu Hong, Jinpyo Ciais, Philippe Chevallier, Frédéric Yan, Feifan Yu, Ying Hu, Yifan Huo, Da Sun, Yun Wang, Yilong Davis, Steven J. Crippa, Monica Janssens-Maenhout, Greet Guizzardi, Diego Solazzo, Efisio Lin, Xiaojuan Song, Xuanren Zhu, Biqing Cui, Duo Ke, Piyu Wang, Hengqi Zhou, Wenwen Huang, Xia Deng, Zhu Liu, Zhu Department of Earth System Science Tsinghua University Beijing100084 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Laboratoire des Sciences du Climat et de l'Environnement LSCE/IPSL CEA-CNRS-UVSQ UniversitéParis-Saclay Gif-sur-Yvette France Key Laboratory of Marine Environment and Ecology Frontiers Science Center for Deep Ocean Multispheres and Earth System Ministry of Education Ocean University of China Qingdao266100 China Environmental Sciences and Engineering University of North Carolina at Chapel Hill Chapel Hill United States Key Laboratory of Sustainable Forest Ecosystem Management Northeast Forestry University Harbin150040 China School of Environmental Science and Engineering Tianjin University Tianjin300072 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Department of Earth System Science University of California IrvineCA United States Ispra Italy Product and Solution & Website Business Unit Alibaba Cloud Hangzhou311121 China

We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1°×0.1°spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from a near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies’ effectiveness and make adjustments quickly. © 2022, CC BY.

关键词： Uncertainty analysis

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Islanding of a Microgrid Using a Distributed Multi-agent Control System

Islanding of a Microgrid Using a Distributed Multi-agent Con...

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IEEE Energy Conversion Congress and Exposition

作者： Mohamad Fares Al Jajeh Syed Qaseem Ali Geza Joos Ilja Novickij Department of Electrical and Computer Engineering McGill University Montreal Canada Application eXpertise and Electrical Simulation Division (AXES) OPAL-RT TECHNOLOGIES Inc. Montreal Canada

The use of distributed control systems for microgrid control has gained traction in recent years for their reliability and modularity. As part of microgrid control core functions, islanding is implemented to enhance power supply reliability during outages. This paper proposes a distributed multi-agent control system architecture for the management of microgrid assets. Specifically, it addresses the islanding problem, whereas three agent categories are designed to control distributed energy resources, loads and other assets. The proposed control approach is unique as it centrally sources critical commands while keeping the commands execution local and based on distributed coordination. Post-islanding transients are minimized and system stability is retained by implementing two distributed load shedding and load curtailment algorithms for unplanned and planned islanding events, respectively. A controller hardware-in-the loop real-time simulation on a modified CIGRE North American medium voltage distribution benchmark has been developed and used to demonstrate the effectiveness of the approach.

关键词： microgrid distributed control system multi-agent system islanding controller hardware-in-the-loop

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CrossSimON: A novel probabilistic approach to cross-platform online social network simulation 17

CrossSimON: A novel probabilistic approach to cross-platform...

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17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019

作者： Liu, Jinwei Chung, Wingyan Huang, Yifan Toraman, Cagri School of Modeling Simulation and Training University of Central Florida OrlandoFL32826 United States Department of Computer Science Faculty of Engineering University of Hong Kong Pokfulam Hong Kong Hong Kong Dept. of Decision Sciences and Managerial Econ. School of Business Admin. Chinese University of Hong Kong Shatin Hong Kong

ISBN: (纸本)9781728125046

The increasing popularity and diversity of online social networks (OSNs) have attracted more and more people to participate in multiple OSNs. Learning users' behavior and information diffusion across platforms is critical for cyber threat detection, but it is still a challenge due to the surge of users participating in multiple social platforms. Existing research on profile matching requires user identity information to be available, which may not be realistic. Little prior research payed attention to mapping behavioral patterns across platforms. We designed and implemented an efficient two-level probabilistic approach called CrossSimON to mapping user-group behavior across platforms. CrossSimON considers the activity level and network position at both individual user level and group level to correlate activities across social platforms. To evaluate the effectiveness of CrossSimON in modeling social activity across platforms, we conducted experiments on three online social platforms: GitHub, Reddit and Twitter. Our experimental results show that CrossSimON outperformed the Benchmark in 3 out of 5 simulation metrics. CrossSimON achieved better performance in user activity prediction. The research provides new strategy for cross-platform online social network simulation, and new findings on simulating OSNs and predictive analytics for understanding online social network behavior. © 2019 IEEE.

关键词： Mapping

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