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

ASCENT：从传感器到多尺度数字孪生，再到弹性电网的自主运行

• 批准号: 2328241
• 负责人: Marcos Netto
• 金额: $ 150万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328241&HistoricalAwards=false
• 关键词: ASCENT; sensors; multiscale; digital; twin

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.

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理，以解锁对策的设计，以便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。该NSF项目旨在为这一目标开发一个统一的框架，从传感器到算法再到实时控制。该项目将利用控制、电力电子和机器学习方面的基本发展，并将它们与可信的电力系统模型相融合，从而带来革命性的变化，显著增强预测和控制电网动态的能力，并利用大量可再生能源资源。结果将通过建立一个大规模输电网的数字孪生模型来验证。该项目的学术价值包括根据基本原则开发的可再生集成电力系统模型与根据数据确定的模型之间的平衡解决方案，以及在其他方面互不相连的研究界内正在开发的先进方法的汇聚。该项目的更广泛影响包括解决紧迫的研究问题，这些问题的解决将使构建更清洁的电网成为可能。该项目还将使代表不足的群体参与STEM。阻碍人们将可再生能源纳入电网的步伐的一个核心问题是，在系统一级对现有资产与大规模部署在相当大规模的输电网上的逆变器资源之间的动态相互作用认识不足。该项目将通过为大容量输电电网创建一个统一的建模环境来应对这一挑战，该环境集成了数据驱动但分析性的IBR模型。由此产生的框架将自己无缝地提供给那些使用动态系统的人所熟悉的状态空间形式。因此，所提出的框架超越了电力系统建模的传统学科。其方法将是通过将推动电力系统创新的切线领域的最新发展吸收到传输电网的数字孪生兄弟中来利用这种包容性。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。