CAREER: Modeling and Quantification of the Interdependent Power Grid Uncertainties

该NSF职业项目旨在应对电网不确定性增加带来的挑战。预计间歇性可持续能源的增加将给电网带来不可避免的不确定性。与此同时，近年来的恶劣天气模式显著增加了电网停电和元件故障。迫切需要新的技术来对这些高度相互依赖的不确定性进行建模和量化。这些技术将更好地为未来电网做好准备，以应对即将到来的不确定性激增，并促进电网的可靠性。该项目将为实时工具带来变革性的变化，这些工具对相互依赖的电网不确定性的影响进行建模和量化，即可再生发电和停电。该项目的学术价值包括引入了关于电网不确定性及其对电网运营的因果影响的建模和量化的新知识。该项目的更广泛影响包括在不确定因素下更高效、更可靠的电网运营，解决整合可持续能源的障碍，以及应对气候变化的挑战。该项目的综合研究和教育目标将培养来自不同群体的学生，包括本科生和高中生。教育和推广活动通过与行业专家、公共图书馆和当地学校的合作，促进未被充分代表的群体在工程领域的参与和留住。该项目将开发高效和自适应的技术，对相互依赖的电网不确定性进行建模和量化，同时考虑到它们的快速发展性质。由于分析电力系统的非线性动态和大规模历史数据的复杂性，特别是在近实时的情况下，现有的电网不确定性量化的努力一直是有限的。该项目将融合级联故障建模、机器学习和统计的工具，以开发可扩展的高效量化框架，以了解发电和停电不确定性之间的相互依赖关系，并允许进行接近实时的分析。为了更真实地量化电网的不确定性，数据驱动模型和物理潮流方程将相互作用，产生混合随机模型。在这些模型的基础上，将开发新的因果模型，以展示个人不确定性如何影响电网运营。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 NSF CAREER project aims to address the challenges introduced by the increasing power grid uncertainties. The expected increase in intermittent sustainable energy resources will introduce inevitable uncertainties to the power grids. At the same time, the severe weather patterns in recent years have significantly increased the grid outages and component failures. There is a critical need for new techniques that model and quantify these highly interdependent uncertainties. Such techniques will better prepare future grids for the impending surge in uncertainties and facilitate grid reliability. This project will bring transformative changes to real-time tools that model and quantify the impacts of interdependent grid uncertainties, namely, renewable generation and outages. The intellectual merits of the project include the introduction of new knowledge on modeling and quantification of power grid uncertainties and their causal impacts on grid operations. The broader impacts of the project include more efficient and reliable grid operations under uncertainties, tackling barriers for the integration of sustainable energy resources, and addressing the challenges of climate change. The integrated research and education objectives of the project will train students from diverse groups, including undergraduate and high school students. The education and outreach activities promote the participation and retention of the underrepresented groups in engineering through collaborations with industry experts, public libraries, and local schools.The project will develop efficient and adaptive techniques to model and quantify interdependent grid uncertainties while considering their fast-evolving nature. The existing efforts on grid uncertainty quantification have been limited due to the complexity of analyzing the nonlinear dynamics of power systems and large-scale historical data, particularly in near real-time. This project will fuse tools from cascading failure modeling, machine learning, and statistics to develop a scalable and efficient quantification framework to understand the interdependencies among generation and outage uncertainties and allow for near real-time analysis. For a more realistic quantification of the grid uncertainties, the data-driven models and physical power flow equations will inform each other to yield hybrid stochastic models. Building upon these models, new causal models will be developed to demonstrate how individual uncertainties impact grid operations.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.