CPS: Medium: Collaborative Research: Demand Response & Workload Management for Data Centers with Increased Renewable Penetration
CPS: Medium: Collaborative Research: Demand Response & Workload Management for Data Centers with Increased Renewable Penetration
批准号:
1739355
负责人:
Steven Low
金额:
$25.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-01 至 2020-08-31
中文摘要
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英文摘要
The confluence of two powerful global trends, (1) the rapid growth of cloud computing and data centers with skyrocketing energy consumption, and (2) the accelerating penetration of renewable energy sources, is creating both severe challenges and tremendous opportunities. The fast growing renewable generation puts forth great operational challenges since they will cause large, frequent, and random fluctuations in supply. Data centers, on the other hand, offer large flexible loads in the grid. Leveraging this flexibility, this project will develop fundamental theories and algorithms for sustainable data centers with a dual goal of improving data center energy efficiency and accelerating the integration of renewables in the grid via data center demand response (DR) and workload management. Specifically, the research findings will shed light on data center demand response while maintaining their performance, which will help data centers to decide how to participate in power market programs. Further, the success of data center demand response will help increase renewable energy integration and reduce the carbon footprint of data centers, contributing to global sustainability. The PIs will leverage fruitful collaboration to eventually bring the research to bear on ongoing industry standardization and development efforts. The PIs teach courses spanning networks, games, smart grid and optimization, and are strongly committed to promoting diversity by providing research opportunities to underrepresented students. Built on the PIs expertise on data centers and the smart grid, this project takes an interdisciplinary approach to develop fundamental theories and algorithms for sustainable data centers. The research tasks are organized under two well-coordinated thrusts, namely agile data center DR and adaptive workload management. The strategies and decisions of data center DR will be made based on the workload management algorithms that balance quality of service and energy efficiency and determine the supply functions. The workload management algorithms will optimize quality of service under the electric load constraints imposed by DR accordingly. This project will make three unique contributions: (1) new market programs with strategic participation of data centers in DR, instead of passive price takers, (2) fundamental understanding of the impacts of power network constraints on data center DR and new distributed algorithms for solving optimal power flow with stochastic renewable supplies, and (3) high-performance dynamic server provisioning and load balancing algorithms for large scale data centers under time-varying and stochastic electric load constraints and on-site renewable generation.
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DOI:
--
发表时间:
2018
期刊:
The Power Systems Computation Conference (PSCC
影响因子:
--
作者:
[Guo, Linqi, Zhao, Changhong, Low, Steven H]
通讯作者:
Low, Steven H
DOI:
10.1016/j.epsr.2020.106695
发表时间:
2020-12
期刊:
Electric Power Systems Research
影响因子:
3.9
作者:
[Chenxi Sun;Tongxin Li;S. Low;V. Li]
通讯作者:
Chenxi Sun;Tongxin Li;S. Low;V. Li
Failure Localization in Power Systems via Tree Partitions
通过树分区进行电力系统故障定位
DOI:
10.1109/cdc.2018.8619562
发表时间:
2018
期刊:
December 2018
影响因子:
--
作者:
[Guo, Linqi, Liang, Chen, Zocca, Alessandro, Low, Steven H., Wierman, Adam]
通讯作者:
Wierman, Adam
DOI:
10.1109/tsg.2017.2711921
发表时间:
2017-06
期刊:
IEEE Transactions on Smart Grid
影响因子:
9.6
作者:
[Yujie Tang;S. Low]
通讯作者:
Yujie Tang;S. Low
Differential Privacy of Aggregated DC Optimal Power Flow Data
聚合直流最优潮流数据的差分隐私
DOI:
10.23919/acc.2019.8815257
发表时间:
2019
期刊:
2019 American Control Conference (ACC
影响因子:
--
作者:
[Zhou, Fengyu, Anderson, James, Low, Steven H.]
通讯作者:
Low, Steven H.
共 19 条
CPS: TTP Option: Small: Adaptive Charging Network Research Portal
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批准号:1932611
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项目类别:Standard Grant
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资助金额:$50.0万
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财政年份:2019
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负责人:Steven Low
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依托单位:
EPCN: Learning power grids from limited measurements: fundamental limits and practical algorithms
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批准号:1931662
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项目类别:Standard Grant
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资助金额:$38.0万
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财政年份:2019
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负责人:Steven Low
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依托单位:
AitF: Algorithmic challenges in smart grids: control, optimization & learning
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批准号:1637598
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项目类别:Standard Grant
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资助金额:$75.0万
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财政年份:2016
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负责人:Steven Low
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依托单位:
Design, Stability and Optimality of Cyber-networks for Frequency Regulation in the Smart Grid
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批准号:1619352
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项目类别:Standard Grant
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资助金额:$42.5万
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财政年份:2016
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负责人:Steven Low
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依托单位:
PFI:AIR - TT: Optimal adaptive charging system
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批准号:1602119
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项目类别:Standard Grant
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资助金额:$20.0万
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财政年份:2016
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负责人:Steven Low
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依托单位:
NetSE: Large: A theory of network architecture
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批准号:0911041
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项目类别:Standard Grant
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资助金额:$250.0万
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财政年份:2009
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负责人:Steven Low
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依托单位:
Collaborative Research: NeTS-NBD: Optimization and Games in Inter-domain Routing
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批准号:0520349
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项目类别:Standard Grant
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资助金额:$24.84万
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财政年份:2006
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负责人:Steven Low
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依托单位:
NeTS-NR: Counter-Intuitive Behavior in General Networks
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批准号:0435520
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项目类别:Continuing Grant
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资助金额:$45.0万
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财政年份:2005
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负责人:Steven Low
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依托单位:
CRCD/EI: Control and Optimization of Communication Systems
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批准号:0417607
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项目类别:Continuing Grant
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资助金额:$0.0万
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财政年份:2004
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负责人:Steven Low
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依托单位:
RI: Wide-Area-Network in a Laboratory
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批准号:0303620
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项目类别:Continuing Grant
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资助金额:$217.17万
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财政年份:2003
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负责人:Steven Low
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依托单位:
STI: Multi-Gbps TCP: Data Intensive Networks for Science & Engineering
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批准号:0230967
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项目类别:Continuing Grant
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资助金额:$150.0万
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财政年份:2002
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负责人:Steven Low
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依托单位:
ITR/SI(CISE):Optimal and Robust TCP Congestion Control
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批准号:0113425
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项目类别:Standard Grant
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资助金额:$44.38万
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财政年份:2001
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负责人:Steven Low
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依托单位:
海外基金