MRI: Acquisition of a Hybrid Real-Time Simulator for Real-Time Power Grid Simulations

MRI：获取用于实时电网仿真的混合实时模拟器

• 批准号: 1828066
• 负责人: Dongliang Duan
• 金额: $ 103.34万
• 依托单位: University of Wyoming
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1828066&HistoricalAwards=false
• 关键词: MRI; Acquisition; Hybrid; Real; Time

This MRI proposal requests funds for the acquisition of a hybrid real-time digital simulator, which is a combination of specialized high-performance computing (HPC) hardware and software tools capable of performing real-time power grid and hardware-in-loop simulations with various external devices. The equipment will be located at the University of Wyoming (UWyo) and made accessible online for open-access use by investigators at UWyo, Montana Tech, other academic institutions and power companies worldwide. The acquisition of the requested hybrid real-time power system digital simulator will promote interdisciplinary collaboration efforts to build our next-generation smart power grid. In addition, the curricula in the power engineering area will be updated with better demonstrations of power system operations to students and therefore attract and provide competent workforce for the challenges of our next-generation power system. The open accessibility of the equipment will also promote collaborations among researchers. Meanwhile, the integration of research and education will help attract undergraduate students for higher education. Furthermore, given that UWyo is the only provider of baccalaureate and graduate education and research in Wyoming, acquisition of the proposed instrument is expected to have a tremendous impact on the training of future scientists at the high school, community college, and the undergraduate level. UWyo actively promotes college preparedness, access, and success among students traditionally underrepresented in STEM fields by focusing special programs on first-generation, female, low-income, and ethnic minority students.The acquisition of the hybrid real-time digital simulator will provide a great platform for research efforts in many different areas including data mining, statistical signal processing, high-performance computing and wind energy towards power grid modernization. Specifically, the capability of the proposed instrumentation to perform both real-time power grid and hardware-in-loop simulations is vital for a variety of projects currently underway in several departments at the UWyo and Montana Tech, including 1) the development of machine learning and statistical signal processing algorithms using synchrophasor measurements for reliability analysis and dynamic wide-area situational awareness; 2) the invention of advanced measurement and analyzing techniques based on point-on-wave measurements for both transient and steady-state analysis; 3) the application of modern high-performance computational technologies for real-time analysis of the grid; 4) the integration of improved system models including renewable generation sources for different time scale power system stability studies; and 5) other large system simulation studies. The research team at UWyo and Monata Tech has an excellent record of contributions to power system stability analysis and high-performance computation over decades. However, their past research has mostly been based on actual field measured data and on smaller power grid models implemented in software. This new hybrid simulator will provide them a powerful tool to take this research to a more in-depth level in multiple ways. It will enable them to simulate larger power grids in finer detail and more complex scenarios. A hybrid simulator also enables them to interface with actual hardware devices such as PMUs used in grid monitoring. Furthermore, the hybrid simulator has the capability to interface with other real-time digital simulators as a network simulator to conduct smart grid communication co-simulations. All these add tremendous new dimensions to their research, putting them in a position to continue to make high impact contributions to the US power grid.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MRI提案要求提供资金用于购买混合实时数字模拟器，该模拟器是专用高性能计算（HPC）硬件和软件工具的组合，能够使用各种外部设备执行实时电网和硬件在环模拟。这些设备将位于怀俄明州大学（UWyo），并可供UWyo、蒙大拿理工大学、其他学术机构和全球电力公司的研究人员在线开放使用。所要求的混合实时电力系统数字模拟器的收购将促进跨学科的合作努力，以建立我们的下一代智能电网。此外，电力工程领域的课程将更新，为学生提供更好的电力系统操作演示，从而吸引和提供合格的劳动力，以应对下一代电力系统的挑战。设备的开放访问也将促进研究人员之间的合作。同时，研究与教育的融合将有助于吸引本科生接受高等教育。此外，鉴于UWyo是怀俄明州唯一的学士学位和研究生教育和研究提供者，预计收购拟议的仪器将对高中，社区学院和本科层次的未来科学家的培训产生巨大影响。UWyo积极促进大学的准备，访问，并通过专注于第一代，女性，低收入和少数民族学生的特殊计划，在STEM领域传统上代表性不足的学生中取得成功。混合实时数字模拟器的收购将为许多不同领域的研究工作提供一个很好的平台，包括数据挖掘，统计信号处理，高性能计算和风能，以实现电网现代化。具体而言，所提出的仪器执行实时电网和硬件在环仿真的能力对于UWyo和Montana Tech的几个部门目前正在进行的各种项目至关重要，包括1）使用同步相量测量进行机器学习和统计信号处理算法的开发，用于可靠性分析和动态广域态势感知; 2）基于波上点测量的先进测量和分析技术的发明，用于瞬态和稳态分析; 3）现代高性能计算技术的应用，用于电网的实时分析; 4）改进的系统模型的集成，包括用于不同时间尺度的电力系统稳定性研究的可再生发电源;（5）其他大系统仿真研究。UWyo和Monata Tech的研究团队数十年来在电力系统稳定性分析和高性能计算方面做出了卓越的贡献。然而，他们过去的研究主要是基于实际的现场测量数据和软件中实现的较小的电网模型。这种新的混合模拟器将为他们提供一个强大的工具，以多种方式将这项研究带到更深入的水平。它将使他们能够以更精细的细节和更复杂的场景模拟更大的电网。混合仿真器还使它们能够与实际硬件设备（如电网监控中使用的PMU）进行接口。此外，混合仿真器具有与其他实时数字仿真器接口的能力，作为网络仿真器进行智能电网通信协同仿真。所有这些都为他们的研究增加了巨大的新维度，使他们能够继续为美国电网做出高影响力的贡献。该奖项反映了NSF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力审查标准进行评估，被认为值得支持。

项目成果

Detection of Small Changes in Power Systems with Hardware-in-Loop Testing

通过硬件在环测试检测电力系统的微小变化

• DOI: 10.1109/pesgm46819.2021.9637887
• 发表时间: 2021
• 期刊: 2021 IEEE Power & Energy Society General Meeting
• 作者: Hosur, Sanjay;Duan, Dongliang
• 通讯作者: Duan, Dongliang

Performance Analysis of Accelerator Architectures and Programming Models for Parareal Algorithm Solutions of Ordinary Differential Equations

常微分方程拟实数算法解的加速器架构和编程模型性能分析

• DOI: 10.4236/jcc.2021.92003
• 发表时间: 2021
• 期刊: Journal of Computer and Communications
• 作者: Lakshmiranganatha, Sumathi;Muknahallipatna, Suresh S.
• 通讯作者: Muknahallipatna, Suresh S.

Graphical Processing Unit Based Time-Parallel Numerical Method for Ordinary Differential Equations

• DOI: 10.4236/jcc.2020.82004
• 发表时间: 2020-02
• 期刊: Journal of Computer and Communications
• 作者: Sumathi Lakshmiranganatha;S. Muknahallipatna

Modified Parareal Algorithm for Solving Time-Dependent Differential Equations

求解时态微分方程的改进拟实数算法

• DOI: 10.11159/eee21.110
• 发表时间: 2021
• 期刊: Proceedings of the 7th World Congress on Electrical Engineering and Computer Systems and Sciences (EECSS’21
• 作者: Lakshmiranganatha, S.;Muknahallipatna, S.
• 通讯作者: Muknahallipatna, S.

Decomposed Iterative Optimal Power Flow with Automatic Regionalization

自动分区的分解迭代最优潮流

• DOI: 10.3390/en13184987
• 发表时间: 2020
• 期刊: Energies
• 影响因子: 3.2
• 作者: Zheng, Xinhu;Duan, Dongliang;Yang, Liuqing;Wang, Haonan
• 通讯作者: Wang, Haonan