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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

基本信息

批准号：
2150596
负责人：
Vassilis Kekatos
金额：
$ 28万
依托单位：
Virginia Polytechnic Institute and State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-15 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2150596&HistoricalAwards=false
关键词：
Collaborative Research Power Systems Dynamics

项目摘要

Rapid decarbonization and deployment of flexible, distributed resources in the electricity energy sector are quickly transforming the real-time operation paradigms of the interconnected power grid infrastructure. These changes have led to growing concerns over power system dynamics and stability, due to the reduced capability of grid inertia and increasing levels of external disturbances and variability. Meanwhile, the electricity infrastructure has benefited significantly from the ongoing deployment of sensing and cyber resources, which give rise to a huge amount of high-rate, high-quality data and information collected during real-time operations. Thanks to the enriched data availability, machine learning advances are envisioned to play an increasingly important role to address the challenges in power system dynamics and stability. This project aims to bridge domain-specific machine learning tools to transform the current grid dynamic modeling, inference, and stability-enforcing solutions. At a societal level, the anticipated outcomes can improve energy efficiency and security, and facilitate higher and smoother penetration of renewables and carbon-free resources. This project will further benefit industry practices with advanced algorithmic solutions, as well as education efforts by providing student training opportunities and reaching out to pre-college students via interactive demos. This project will develop data-enabled and physics-informed modeling, monitoring, and optimization algorithmic solutions targeting power system dynamics. The proposed activities put forth and explore three creative, original, and potentially transformative ideas: i) Correlating synchrophasor data collected at two arbitrary grid locations can efficiently unveil the impulse response of the associated linear time-invariant (LTI) system under certain assumptions, which can be waived leveraging physics-informed analysis; ii) Gaussian processes (GPs) constitute a powerful tool for inferring signals occurring in LTI systems, and thanks to the underlying physics, GPs can be uniquely adapted to learn grid dynamic signals and their derivatives from heterogeneous, noisy, spatially and temporally incomplete, and/or multirate synchrophasor readings; iii) Well-established grid stability metrics can be expressed as convex functions of the steady-state operating point, and stability-aware OPFs can be handled via a semidefinite program relaxation. The outcome will be a comprehensive suite of computational tools dealing with grid dynamics from learning to power system operations, evaluated by both real-event synchrophasor datasets, and synthetic datasets generated from realistic power systems such as a Texas 2000-bus case in collaboration with ERCOT. The research results will also be integrated into engineering educational activities at the secondary and higher education levels. In addition to standard dissemination venues, close collaboration with grid operators will assist in showcasing the effectiveness of the project findings on real-world systems and lead to quick adoption.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.

电力能源部门的快速脱碳和灵活分布式资源的部署正在迅速改变互联电网基础设施的实时运行模式。这些变化导致了越来越多的关注电力系统的动态和稳定性，由于电网惯性能力的降低和增加的外部干扰和变化的水平。与此同时，电力基础设施从传感和网络资源的持续部署中受益匪浅，这些资源在实时操作中收集了大量高速率，高质量的数据和信息。由于丰富的数据可用性，机器学习的进步被认为在解决电力系统动态和稳定性方面的挑战方面发挥着越来越重要的作用。该项目旨在桥接特定领域的机器学习工具，以转换当前的网格动态建模，推理和稳定性实施解决方案。在社会层面，预期成果可以提高能源效率和安全性，并促进可再生能源和无碳资源的更高和更顺利的渗透。该项目将通过先进的算法解决方案进一步造福行业实践，并通过提供学生培训机会和通过互动演示接触大学预科学生来促进教育工作。该项目将开发基于数据和物理信息的建模、监控和优化算法解决方案，以电力系统动态为目标。拟议的活动提出并探索了三个创造性、原创性和潜在变革性的想法：i）将在两个任意网格位置收集的同步相量数据进行关联，可以在某些假设下有效地揭示相关线性时不变系统的脉冲响应，而这些假设可以通过物理学分析来免除; ii）高斯过程（GP）构成了用于推断LTI系统中发生的信号的强大工具，并且由于基础物理学，GP可以独特地适于从异构的、有噪声的、空间和时间不完整，和/或多速率同步相量读数; iii）完善的电网稳定性度量可以表示为稳态操作点的凸函数，并且稳定性感知OPF可以经由半定程序松弛来处理。其结果将是一套全面的计算工具，处理从学习到电力系统运行的电网动态，通过实时同步相量数据集和从现实电力系统（如与ERCOT合作的德克萨斯州2000总线案例）生成的合成数据集进行评估。研究成果还将纳入中等和高等教育阶段的工程教育活动。除了标准的传播场所，与电网运营商的密切合作将有助于展示项目结果在现实世界系统中的有效性，并导致快速采用。该奖项反映了NSF的法定使命，并被认为值得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来支持。