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

协作研究：基于物理和数据驱动的深度集成方法，用于有效的电网弹性管理

• 批准号: 2000140
• 负责人: Roshanak Nateghi
• 金额: $ 25万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2000140&HistoricalAwards=false
• 关键词: Collaborative; Research; Deeply; Integrated; Physics

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.

这笔赠款将开发一种新的、深度集成的基于物理学的数据驱动方法，以评估和增强受极端气候影响的输电系统的弹性。美国的电力基础设施越来越容易受到气候风险的影响，导致大范围和持续的停电，每年造成数十亿美元的损失。虽然自然灾害引起的输电网故障会导致大规模和代价高昂的影响，但现有的输电扩展规划模型在很大程度上忽略了网络面临自然灾害的弹性考虑。然而，配电系统弹性分析中流行的纯数据驱动方法由于数据相对稀缺而不适用于输电系统。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，限制了它们扩展到网络级别的能力。NSF的这项资助旨在通过基于物理和数据驱动方法的深度整合来解决这一根本性差距。这项研究的结果，预计将有助于关键决策者的传输基础设施，以表征各种不确定的未来情景下的弹性，并确定最佳的适应或缓解战略。该研究计划与教育下一代学者在建模灾害和基础设施的弹性通过跨学科，研究综合教育计划，在学生培训和广泛传播的结果增加多样性的坚定承诺补充。研究方法是在大数据分析的最新发展，以及基于物理的结构故障分析接地。以物理为指导，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，并在气候不确定性下确定最佳投资决策。利用最先进的机器学习和计算机视觉，该项目将生成关于输电网络拓扑结构以及灾害影响的新的公开数据，以促进科学界对输电弹性规划的进一步研究。将建立输电网络故障量化的新极限状态函数，并开发结构系统不确定性量化的可扩展方法。多尺度方法来建模不确定的过程将有效地融合数据传输系统的基础设施的计算模型。开发的方法和数据将为输电系统在未来灾害情景下的脆弱性提供新的线索，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。