ASCENT: From sensors to multiscale digital twin to autonomous operation of resilient electric power grids

通过整合多种可再生资源实现清洁能源电力系统的现代化，正在从根本上改变电网的动态。迫切需要了解新的现象和可能的故障机制，以解开对策的设计，使运营商能够使电网更具弹性。但所需的理解程度必须跟上发电和存储，传感和通信，优化和控制，电力电子，机器学习和数据科学等新技术的步伐。NSF的这个项目旨在开发一个统一的框架，从传感器到算法再到实时控制。该项目将通过利用控制、电力电子和机器学习的基本发展，并将其与可信的电力系统模型相融合，显著提高预测和控制电网动态的能力，从而带来变革性的变化。研究结果将通过建立一个大规模输电网的数字孪生模型进行验证。该项目的智力优势包括从第一原理开发的可再生综合电力系统模型与从数据中确定的模型之间的平衡解决方案，以及在其他分离的研究社区内开发的先进方法的融合。该项目更广泛的影响包括解决紧迫的研究问题，其解决方案将使更清洁的电网的构建成为可能。该项目还将吸引STEM中代表性不足的群体。阻碍可再生能源与电网整合步伐的一个核心问题是，在系统层面，对现有资产与大规模部署在输电网上的基于逆变器的资源（IBR）之间的动态相互作用了解不足。该项目将通过为大容量输电网创建一个统一的建模环境来解决这一挑战，该环境集成了数据驱动的IBR分析模型。由此产生的框架无缝地适合于那些与动力系统一起工作的人所熟悉的状态空间形式。因此，所提出的框架是包容性的，超越了传统的学科在电力系统建模。该奖项反映了NSF的法定使命，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。

The modernization of power systems for clean energy by integrating multiple renewable resources is changing the dynamics of power grids at a fundamental level. There is a dire need to understand new phenomena and possible failure mechanisms to unlock the design of countermeasures so that operators can make electric grids more resilient. But the required degree of understanding must keep up with the pace of new technologies in generation and storage, sensing and communications, optimization and control, power electronics, machine learning, and data science. This NSF project aims to develop a unified framework towards this goal, starting from sensors to algorithms to real-time control. The project will bring transformative change by leveraging fundamental developments in control, power electronics, and machine learning, and fusing them with trusted power system models, significantly enhancing the ability to predict and control grid dynamics with a high share of renewable energy resources. Results will be verified by building a digital twin of a large-scale transmission grid. The intellectual merits of the project include a balanced solution between models of renewable-integrated power systems developed from first principles and those identified from data, and the convergence of advanced methods under development within otherwise disconnected research communities. The broader impacts of the project include addressing pressing research questions whose solution will enable the building blocks of a cleaner power grid. The project will also engage underrepresented groups in STEM.A central problem hampering the pace at which one can integrate renewable energy sources into electric power grids is the insufficient understanding, at a systems level, of the dynamic interplay between existing assets and inverter-based resources (IBR) deployed at scale on a transmission grid of substantial size. This project will address this challenge by creating a unified modeling environment for bulk transmission grids that integrates data-driven yet analytical IBR models. The resulting framework lends itself seamlessly to a state-space form familiar to those working with dynamical systems. Thus, the proposed framework is inclusive beyond traditional disciplines in power systems modeling. The approach will be to leverage this inclusiveness by absorbing into a digital twin of a transmission grid the latest developments in tangential areas driving innovations in power systems.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.