CAREER: Overcoming Nonlinearities, Uncertainties, and Discreteness to Mitigate the Impacts of Extreme Events on Electric Power Systems

NSF的这一职业项目旨在开发算法，在野火、飓风、疏散等极端事件的背景下优化电力系统的规划和运行。该项目侧重于三个关键的计算挑战：与电网物理模型相关的非线性、风能和太阳能发电机的不确定性以及系统组件的故障，以及诸如在哪里升级基础设施等离散选择。该项目将通过为运营商提供准确建模重压电网所需的计算工具，带来变革性的变化。这将通过将新的机器学习技术与先进的非线性优化算法和新的电力系统建模方法相结合来实现。该项目的智力优势包括为极端事件期间电力系统中遇到的优化问题开发了新的求解算法。该项目的更广泛影响包括减轻气候变化的影响，以及教育努力开发侧重于电力系统弹性的视频游戏风格的模拟。本着公民科学的精神，参与者对这些模拟的解决方案将形成一个众包数据集，用于在该项目的研究工作中训练机器学习模型，关闭研究和教育之间的循环。该项目的目标是开发基本理论和算法，以解决极端事件期间电力系统固有的严重压力条件。精确地对这些重压条件进行建模会产生随机混合整数非线性优化问题，这些问题是现有理论和算法难以解决的。现有的方法使用不适用于极端事件固有的非典型条件的假设来应对这些挑战，从而导致无法充分减少极端事件的影响的大错误和复原力计划。该项目将开发新的算法，在不牺牲计算速度和可靠性的情况下，准确地对潮流的非线性、不确定性和离散决策进行建模。为了实现这一目标，该项目将改进和结合替代潮流模型、混合整数规划求解器、机器学习技术和非线性优化，以创建适用于弹性应用的定制理论和算法。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 develop algorithms for optimizing the planning and operation of electric power systems in the context of extreme events such as wildfires, hurricanes, evacuations, etc. The project focuses on three key computational challenges: nonlinearities associated with the physical models of electric grids, uncertainties from wind and solar generators and failures of system components, and discrete choices such as where to upgrade infrastructure. The project will bring transformative change by providing operators with the computational tools needed to accurately model heavily stressed power grids. This will be achieved by combining new machine learning techniques with advanced nonlinear optimization algorithms and novel power system modeling methods. The project’s intellectual merits include the development of new solution algorithms for the optimization problems encountered in power systems during extreme events. The broader impacts of the project include mitigating the impacts of climate change as well as educational efforts to develop video game style simulations focused on power system resiliency. In the spirit of citizen science, the players' solutions to these simulations will form a crowdsourced dataset that will be used to train the machine learning models in the project's research efforts, closing the loop between research and education.The goal of this project is to develop the fundamental theory and algorithms for addressing the heavily stressed conditions inherent to power systems during extreme events. Accurately modeling these heavily stressed conditions yields stochastic mixed-integer nonlinear optimization problems that are intractable with existing theory and algorithms. Existing approaches address these challenges using assumptions that are inapplicable for the atypical conditions inherent to extreme events, resulting in large errors and resiliency plans that fail to adequately reduce the impacts of extreme events. This project will develop new algorithms that can accurately model power flow nonlinearities, uncertainties, and discrete decisions without sacrificing computational speed and reliability. To accomplish this, the project will improve and combine alternative power flow models, mixed-integer programming solvers, machine learning techniques, and nonlinear optimization to create tailored theory and algorithms for resiliency applications.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.