Learning-Enabled Modeling, Monitoring, and Decision Making for Distribution Grids

配电网的学习建模、监控和决策

基本信息

  • 批准号:
    2130706
  • 负责人:
  • 金额:
    $ 35万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-09-01 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

This NSF project aims to propel the zero-carbon emission transition of the electric grid infrastructure by develop a holistic framework for integrating renewable and flexible resources at grid edge. The project will bring transformative changes to the real-time monitoring and coordination of these grid-edge resources in support of the efficiency and safety of their connected distribution grids. This will be achieved by synthesizing machine learning advances into the algorithmic developments that can recognize the governing physics of the underlying systems and address the limitations in cyber infrastructure in distribution grids. The intellectual merits of the project include a suite of machine learning enabled solutions to attain an efficient and safe operation of grid-edge resources under the information constraints due to limited model knowledge and low observability. The broader impacts of the project include the acceleration of integrating renewable energy and low-carbon resources into the electricity infrastructure, and a comprehensive education plan consisting of updating power engineering curriculum and designing hands-on demos for pre-college students. The overarching goal of this proposal is to establish a learning-enabled framework for operating distributed energy resources (DERs) with efficiency, adaptivity, and robustness. To address the status quo of limited sensing and communications in power distribution grids, we advocate to incorporate the unique features of the underlying feeder models and data profiles. Our proposed research consists of three cohesive thrusts: T1) Designing data-driven distribution modeling approaches under partial observability; T2) Developing monitoring algorithms of grid-edge resources from heterogeneous data sources; and T3) Developing scalable and safe DER policies using graph-based and risk-aware learning. These three tasks will be further integrated to support each other into a holistic framework as validated by real-world feeder systems and datasets. In a nutshell, our research agenda will fulfill the dual objectives of enabling distribution system operations by fully embracing a multitude of data sources, while attaining timely and safe DER actions to address the information-limited and resource-constrained scenarios.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
该NSF项目旨在通过开发一个整合电网边缘可再生和灵活资源的整体框架,推动电网基础设施的零碳排放转型。该项目将为这些电网边缘资源的实时监控和协调带来革命性的变化,以支持其连接的配电网的效率和安全性。这将通过将机器学习的进步综合到算法的发展中来实现,这些算法可以识别底层系统的控制物理,并解决配电网中网络基础设施的限制。该项目的智力优势包括一套机器学习解决方案,可以在模型知识有限和低可观察性的信息约束下实现网格边缘资源的高效安全运行。该项目的更广泛影响包括加速将可再生能源和低碳资源整合到电力基础设施中,以及一项全面的教育计划,包括更新电力工程课程和为大学预科学生设计动手演示。本提案的总体目标是建立一个具有效率、适应性和鲁棒性的分布式能源(DERs)运营学习支持框架。为了解决配电网中传感和通信有限的现状,我们主张将底层馈线模型和数据配置文件的独特功能结合起来。本文提出的研究方向包括三个方面:T1)设计部分可观测条件下数据驱动的分布建模方法;T2)开发异构数据源网格边缘资源监控算法;T3)利用基于图表和风险意识的学习,制定可扩展和安全的DER政策。这三项任务将进一步整合,以相互支持,形成一个整体框架,并得到实际馈线系统和数据集的验证。简而言之,我们的研究议程将实现双重目标,即通过充分利用大量数据源使配电系统运行,同时获得及时和安全的DER行动,以解决信息有限和资源受限的情况。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(9)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
On the Sample Complexity of Decentralized Linear Quadratic Regulator With Partially Nested Information Structure
  • DOI:
    10.1109/tac.2022.3215940
  • 发表时间:
    2021-10
  • 期刊:
  • 影响因子:
    6.8
  • 作者:
    Lintao Ye;Haoqi Zhu;V. Gupta
  • 通讯作者:
    Lintao Ye;Haoqi Zhu;V. Gupta
Risk-aware learning for scalable voltage optimization in distribution grids
配电网可扩展电压优化的风险意识学习
  • DOI:
    10.1016/j.epsr.2022.108605
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Lin, Shanny;Liu, Shaohui;Zhu, Hao
  • 通讯作者:
    Zhu, Hao
Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network
Data-driven Modeling for Distribution Grids Under Partial Observability
Scalable Learning for Optimal Load Shedding Under Power Grid Emergency Operations
电网应急操作下最佳减载的可扩展学习
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Hao Zhu其他文献

Li-Yorke chaos induced by A-coupled-expansion for time-varying discrete systems
时变离散系统 A 耦合展开引起的 Li-Yorke 混沌
Molecular cloning, characterization, and expression patterns of the hatching enzyme genes during embryonic development of pikeperch (Sander lucioperca)
梭鲈 (Sander lucioperca) 胚胎发育过程中孵化酶基因的分子克隆、表征和表达模式
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    2.9
  • 作者:
    Chenglong Pan;Lingling Li;Hao Zhu;Wenjia Mao;T. Han;Xuqian Zhao;Caijuan Li;Qufei Ling
  • 通讯作者:
    Qufei Ling
Association between Pericoronary Fat Attenuation Index Values and Plaque Composition Volume Fraction Measured by Coronary Computed Tomography Angiography.
冠状动脉计算机断层扫描血管造影测量的冠状动脉周围脂肪衰减指数值与斑块成分体积分数之间的关联。
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    4.8
  • 作者:
    M. Jing;H. Xi;Yuanyuan Wang;Hao Zhu;Qiu Sun;Yuting Zhang;Wei Ren;Zheng Xu;L. Deng;Bin Zhang;T. Han;Junlin Zhou
  • 通讯作者:
    Junlin Zhou
Epitaxial Crystallization of Precisely Methyl-Substituted Polyethylene Induced by Carbon Nanotubes and Graphene
碳纳米管和石墨烯诱导精确甲基取代聚乙烯的外延结晶
  • DOI:
    10.3390/cryst8040168
  • 发表时间:
    2018-04
  • 期刊:
  • 影响因子:
    2.7
  • 作者:
    Weijun Miao;Yiguo Li;Libin Jiang;Feng Wu;Hao Zhu;Hongbing Chen;Zongbao Wang
  • 通讯作者:
    Zongbao Wang
ROS conditional proteomics (2): identification of H2O2-rich subcellular compartments
ROS条件蛋白质组学(二):富含H2O2的亚细胞区室的鉴定
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Fumitaka Hashiya;Kaoru Onda;Kohei Nomura;Gao Yiuno;Hirotaka Murase;Kosuke Nakamoto;Masahito Inagaki;Haruka Hiraoka;Naoko Abe;Yasuaki Kimura;Natsuhisa Oka;Goro Terai;Kiyoshi Asai;Hiroshi Abe;橋谷 文貴・恩田 馨・野村 浩平・Gao Yiuno・村瀬 裕貴・中本 航介・稲垣 雅仁・平岡 陽花・阿部 奈保子・木村 康明・岡 夏央・寺井 悟朗・浅井 潔・阿部 洋;Hao Zhu;Hao Zhu;Hao Zhu;Hao Zhu
  • 通讯作者:
    Hao Zhu

Hao Zhu的其他文献

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{{ truncateString('Hao Zhu', 18)}}的其他基金

Collaborative Research: III: Medium: New Machine Learning Empowered Nanoinformatics System for Advancing Nanomaterial Design
合作研究:III:媒介:新的机器学习赋能纳米信息学系统,促进纳米材料设计
  • 批准号:
    2402311
  • 财政年份:
    2023
  • 资助金额:
    $ 35万
  • 项目类别:
    Standard Grant
Collaborative Research: III: Medium: New Machine Learning Empowered Nanoinformatics System for Advancing Nanomaterial Design
合作研究:III:媒介:新的机器学习赋能纳米信息学系统,促进纳米材料设计
  • 批准号:
    2245158
  • 财政年份:
    2022
  • 资助金额:
    $ 35万
  • 项目类别:
    Standard Grant
Collaborative Research: Power Systems Dynamics from Real-Time Data: Modeling, Inference, and Stability-Aware Optimization
协作研究:实时数据的电力系统动力学:建模、推理和稳定性感知优化
  • 批准号:
    2150571
  • 财政年份:
    2022
  • 资助金额:
    $ 35万
  • 项目类别:
    Standard Grant
Collaborative Research: III: Medium: New Machine Learning Empowered Nanoinformatics System for Advancing Nanomaterial Design
合作研究:III:媒介:新的机器学习赋能纳米信息学系统,促进纳米材料设计
  • 批准号:
    2211489
  • 财政年份:
    2022
  • 资助金额:
    $ 35万
  • 项目类别:
    Standard Grant
SCC-PG: ECET: Empowering Community-centric Electrified Transportation
SCC-PG:ECET:增强以社区为中心的电气化交通
  • 批准号:
    1952193
  • 财政年份:
    2020
  • 资助金额:
    $ 35万
  • 项目类别:
    Standard Grant
CAREER: Cyber-Physical Situational Awareness for the Power Grid Infrastructures
职业:电网基础设施的网络物理态势感知
  • 批准号:
    1653706
  • 财政年份:
    2017
  • 资助金额:
    $ 35万
  • 项目类别:
    Standard Grant
Collaborative Research: Towards Communication-Cognizant Voltage Regulation and Energy Management for Power Distribution Systems
合作研究:面向配电系统的通信认知电压调节和能源管理
  • 批准号:
    1807097
  • 财政年份:
    2017
  • 资助金额:
    $ 35万
  • 项目类别:
    Standard Grant
CAREER: Cyber-Physical Situational Awareness for the Power Grid Infrastructures
职业:电网基础设施的网络物理态势感知
  • 批准号:
    1802319
  • 财政年份:
    2017
  • 资助金额:
    $ 35万
  • 项目类别:
    Standard Grant
Collaborative Research: Towards Communication-Cognizant Voltage Regulation and Energy Management for Power Distribution Systems
合作研究:面向配电系统的通信认知电压调节和能源管理
  • 批准号:
    1610732
  • 财政年份:
    2016
  • 资助金额:
    $ 35万
  • 项目类别:
    Standard Grant
SBIR Phase I: Electromagnetic Pulse Sensors Based on Magnetic Nanowire Arrays
SBIR 第一阶段:基于磁性纳米线阵列的电磁脉冲传感器
  • 批准号:
    1013468
  • 财政年份:
    2010
  • 资助金额:
    $ 35万
  • 项目类别:
    Standard Grant

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