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Optimizing Energy Management in Microgrids with Datacenters: An Integrated Approach

优化数据中心微电网的能源管理：综合方法

基本信息

批准号：
1610471
负责人：
Shaolei Ren
金额：
$ 30万
依托单位：
University of California-Riverside
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-15 至 2020-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1610471&HistoricalAwards=false
关键词：
Optimizing Energy Management Microgrids Datacenters

项目摘要

Microgrids are localized grids that have become an integral component of future smart grids. Meanwhile, datacenters have been expanding enormously to support the exploding digital economy worldwide, emerging as significant energy consumers that are commonly co-located with a diverse set of non-datacenter loads in microgrids. Nonetheless, prior research on datacenter energy management, although encouraging, has traditionally viewed datacenters as isolated stand-alone facilities and rarely considered their physical interconnection with other loads. On the other hand, the rich literature on microgrid energy management has primarily focused on non-datacenter loads (e.g., thermostatically controlled loads), while treating energy-intensive datacenters as miscellaneous loads and ignoring their unique global-routing capabilities. This lack of integration and coordination between datacenter and non-datacenter loads poses a series of challenges to microgrid management, such as high peak demand, waste of on-site renewables, and even potential threats to the stability of main grids. This project seeks to address the challenges and optimize microgrid energy management for minimizing the energy cost and facilitating demand responses to better protect the main grid. Towards this end, this project takes a transformative shift from the current view that treats datacenter and non-datacenter loads separately, to an integrated approach that holistically coordinates both datacenter and non-datacenter loads in microgrids. Specifically, this project investigates two complementary research thrusts. First, when the microgrid operator can directly schedule the loads, this project investigates novel control algorithms to holistically manage both datacenter and non-datacenter loads, while taking into account datacenters spatial-routing and heterogeneous communications capabilities. Second, when the loads are managed by self-interested entities, this project studies market mechanisms to coordinate both datacenter and non-datacenter loads for microgrid-level efficiency.This project advances the existing research on microgrid energy management by transforming datacenters' role in microgrids from miscellaneous loads into a valuable asset with high scheduling flexibilities. It can catalyze a shift in the way that microgrids evolve, bearing great economic, environmental and societal impacts. This project will also incorporate the research into existing courses and provide abundant opportunities to nurture and attract students, especially those from under-represented groups, to engage in research careers.

微电网是局部化电网，已成为未来智能电网的重要组成部分。与此同时，数据中心一直在大力扩张，以支持全球爆炸式的数字经济，成为重要的能源消费者，通常与微电网中的各种非数据中心负载共存。尽管如此，以前对数据中心能源管理的研究虽然令人鼓舞，但传统上将数据中心视为孤立的独立设施，很少考虑它们与其他负载的物理互连。另一方面，关于微电网能量管理的丰富文献主要集中在非数据中心负载（例如，恒温控制的负载），同时将能量密集型的路由器视为杂项负载，并忽略其独特的全局路由能力。数据中心和非数据中心负载之间缺乏集成和协调，这对微电网管理提出了一系列挑战，例如高峰需求，现场可再生能源的浪费，甚至对主电网稳定性的潜在威胁。该项目旨在应对挑战并优化微电网能源管理，以最大限度地降低能源成本并促进需求响应，从而更好地保护主电网。为此，该项目需要从目前的观点，分别对待数据中心和非数据中心负载的转型转变，以整体协调微电网中的数据中心和非数据中心负载的集成方法。具体而言，该项目调查了两个互补的研究重点。首先，当微电网运营商可以直接调度负载时，该项目研究新型控制算法来全面管理数据中心和非数据中心负载，同时考虑数据中心空间路由和异构通信能力。第二，在由自利实体管理负荷的情况下，研究通过市场机制协调数据中心负荷和非数据中心负荷，以实现微电网级的效率。本项目通过将微电网中的调度中心从各种负荷转变为具有高度调度灵活性的宝贵资产，推进了微电网能量管理的现有研究。它可以催化微电网发展方式的转变，产生巨大的经济，环境和社会影响。这项计划亦会把研究纳入现有课程，并提供大量机会，培育和吸引学生，特别是来自代表性不足的群体的学生，从事研究事业。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Increasing the Trustworthiness of Deep Neural Networks via Accuracy Monitoring

DOI：
发表时间：
2020-07
期刊：
ArXiv
影响因子：
0
作者：
Zhihui Shao;Jianyi Yang;Shaolei Ren
通讯作者：
Zhihui Shao;Jianyi Yang;Shaolei Ren

On the Latency Variability of Deep Neural Networks for Mobile Inference

关于移动推理深度神经网络的延迟可变性

DOI：
发表时间：
2020
期刊：
Workshop on Hot Topics in Edge Computing (1-page Poster
影响因子：
0
作者：
Yang, Luting;Lu, Bingqian;Ren, Shaolei
通讯作者：
Ren, Shaolei

Multi-Feedback Bandit Learning with Probabilistic Contexts

DOI：
10.24963/ijcai.2020/427
发表时间：
2020-07
期刊：
影响因子：
0
作者：
Da Yu;Huishuai Zhang;Wei Chen;Tie-Yan Liu;Jian Yin
通讯作者：
Da Yu;Huishuai Zhang;Wei Chen;Tie-Yan Liu;Jian Yin

DeepPM: Efficient Power Management in Edge Data Centers using Energy Storage

DOI：
10.1109/cloud49709.2020.00058
发表时间：
2020-10
期刊：
2020 IEEE 13th International Conference on Cloud Computing (CLOUD)
影响因子：
0
作者：
Zhihui Shao;Mohammad A. Islam;Shaolei Ren
通讯作者：
Zhihui Shao;Mohammad A. Islam;Shaolei Ren

Automating Deep Neural Network Model Selection for Edge Inference

DOI：
10.1109/cogmi48466.2019.00035
发表时间：
2019-12
期刊：
2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)
影响因子：
0
作者：
Bingqian Lu;Jianyi Yang;L. Chen;Shaolei Ren
通讯作者：
Bingqian Lu;Jianyi Yang;L. Chen;Shaolei Ren