Collaborative Research: Power Systems Dynamics from Real-Time Data: Modeling, Inference, and Stability-Aware Optimization

电力能源领域灵活的分布式资源的快速脱碳和部署，正在迅速转变互联电网基础设施的实时运营模式。由于电网惯性能力的降低以及外部扰动和变化性的增加，这些变化导致人们对电力系统动态和稳定性的关注日益增加。与此同时，电力基础设施大大受益于正在进行的传感和网络资源的部署，这产生了在实时操作期间收集的大量高速率、高质量的数据和信息。由于丰富的数据可获得性，预计机器学习的进步将在解决电力系统动态和稳定性方面的挑战方面发挥越来越重要的作用。该项目旨在搭建特定领域机器学习工具的桥梁，以转换当前的网格动态建模、推理和稳定性增强解决方案。在社会层面上，预期的结果可以提高能源效率和安全，促进更高和更顺畅的可再生能源和无碳资源的普及。该项目将以先进的算法解决方案进一步惠及行业实践，并通过互动演示提供学生培训机会和接触大学预科学生，从而促进教育工作。该项目将开发针对电力系统动态的数据启用和物理信息建模、监控和优化算法解决方案。提出并探索了三个创造性、原创性和潜在变革性的想法：i)在两个任意网格位置收集的同步相量数据的关联可以有效地揭示相关线性时不变(LTI)系统在某些假设下的脉冲响应，这可以利用物理信息分析来省去；ii)高斯过程(GP)是推断LTI系统中发生的信号的强大工具，由于潜在的物理原理，GP可以唯一地适应于从不均匀的、有噪声的、空间和时间上不完整的和/或多速率同步相量读数中学习网格动态信号及其导数；Iii)建立良好的电网稳定性度量可以表示为稳态运行点的凸函数，并且稳定性感知OPF可以通过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操作的电网动力学，通过真实事件同步相量数据集和从现实电力系统生成的合成数据集进行评估，例如与ERCOT合作的德克萨斯州2000母线案例。研究成果还将纳入中等和高等教育水平的工程教育活动。除了标准的传播场所外，与电网运营商的密切合作将有助于展示项目发现在现实世界系统上的有效性，并导致快速采用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.