Collaborative Research: A Deeply Integrated Physics-Based and Data-Driven Approach for Effective Resilience Management of the Power Grid

这笔赠款将开发一种新的、深度集成的、以物理为基础的数据驱动的方法，以评估和增强受极端气候影响的电力传输系统的弹性。美国的电力基础设施越来越容易受到气候风险的影响，这种风险会导致大范围和持续的停电，每年造成数十亿美元的损失。然而，现有的输电网络扩展规划模型在很大程度上忽视了对面临自然灾害的电网的复原力考虑。然而，由于数据的相对稀缺性，在配电系统的弹性分析中流行的纯数据驱动的方法不适用于传输系统。单方面依赖基于物理的模型也是不可行的，因为它们的计算成本极高，限制了它们向上扩展到网络级别的能力。NSF的这笔拨款试图通过深度整合基于物理的方法和数据驱动的方法来解决这一根本差距。这项研究的结果有望帮助传输基础设施的主要决策者确定在各种不确定的未来情景下的复原力特征，并确定最佳的适应或缓解战略。该研究计划还通过跨学科、研究集成的教育计划，坚定地致力于增加学生培训的多样性并广泛传播结果，在建模危险和基础设施弹性方面教育下一代学者。该研究方法基于大数据分析的最新发展以及基于物理的结构故障分析。以物理为导向、以数据为中心的多尺度框架允许对网络弹性进行可扩展的评估，并确定在气候不确定性下的最佳投资决策。利用最先进的机器学习和计算机视觉，该项目将生成关于传输网络拓扑和危险影响的新的公开可访问数据，以促进科学界在传输弹性规划方面的进一步研究。将建立新的输电网络故障量化的极限状态函数，并将开发一种可扩展的结构系统不确定性量化方法。对不确定过程建模的多尺度方法将有效地将传输系统上的数据与基础设施的计算模型融合在一起。开发的方法和数据将揭示未来危险情景下输电系统的脆弱性，并能够评估投资决策对输电系统弹性的影响。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。

This grant will develop a novel, deeply integrated physics-based data-driven approach to assess and enhance the resilience of power transmission systems impacted by climatic extremes. The US electricity infrastructure is increasingly prone to climatic risks that cause wide-spread and sustained outages, costing billions of dollars annually. While natural hazard-induced failures in the transmission grid lead to large-scale and costly impacts, the existing transmission expansion planning models largely neglect resilience consideration of the network facing natural hazards. The purely data-driven approaches prevalent in resilience analytics of power distribution systems, however, are not applicable to transmission systems due to relative scarcity of data. A unilateral reliance on physics-based models is not feasible either due to their extreme computational costs, limiting their ability to scale up to the network level. This NSF grant seeks to address this fundamental gap via deep integration of physics-based and data driven methods. The outcome of this research is expected to help key decision-makers for the transmission infrastructure to characterize resilience under various uncertain future scenarios and identify optimal adaptation or mitigation strategies. The research program is complemented with educating the next generation of scholars in modeling hazards and infrastructure resilience through an interdisciplinary, research-integrated educational program, a strong commitment to increased diversity in student training and broad dissemination of the results.The research approach is grounded in the latest developments in big data analytics as well as physics-based analysis of structural failures. The physics-guided, data-centric and multiscale framework allows for scalable assessment of network resilience and identification of optimal investment decisions under climate uncertainty. Using the state-of-the-art machine learning and computer vision, the project will generate new publicly accessible data on transmission network topology as well as hazards’ impacts to facilitate further research in transmission resilience planning within the scientific community. Novel limit state functions for failure quantification of transmission networks will be established and a scalable approach to uncertainty quantification of structural systems will be developed. The multiscale approach to modeling uncertain processes will effectively fuse data on transmission systems with computational models of the infrastructure. The developed methodologies and data will shed new lights on the vulnerability of the transmission system under future hazard scenarios, and enable assessing the impact of investment decisions on transmission system resilience.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.