AMPS: Advanced Mathematical Algorithms for Model Reduction and Stochastic Modeling for the Emerging Power Grid

该项目将开发工具来分析和帮助设计强大和有弹性的现代电网系统。新兴电网与传统电网有很大的不同，传统电网的技术基础可以追溯到世纪初。随着新技术和监管政策的出现，在设计能够可靠输送电力的稳定电力系统方面出现了新的挑战。为了应对这些挑战，需要数学和计算工具来分析这些具有新功能的电网。该项目的目标是开发这些数学和计算工具。 具体来说，本项目将研究模型简化，不确定性/随机性和稳定性，这些都是现代网格模型中必须包含的一些新功能。稳定性将是这些主题中的连接主题，因为稳定性是确保电网弹性和降低风险所必需的。PI将研究（1）如何利用电网振荡的同步性来确定发电机和负载的相关集合，以实现精确的模型简化;（2）必须保留哪些相关结构以生成精确的简化模型;（3）如何为分散系统开发多级求解器;（4）随机性如何影响网格的同步性、相干性、模型简化和稳定性;（5）观测数据如何被同化到随机模型中。随机性将被纳入使用随机微分代数方程。这种随机性将使用最能描述系统组成部分的随机性的Orstein-Uhlenbeck过程来介绍。

This project will develop tools to analyze and help design robust and resilient modern power grid systems. The emergent power grid is very different from the traditional grid, which is based on technology dating back to the early 20'th century. With new technologies and regulatory policies, new challenges are arising in the design of stable power systems that can reliably deliver electric power. To tackle these challenges, mathematical and computational tools are needed to analyze these power grids with the new features. The goal of this project is to develop these mathematical and computational tools. Specifically, this project will examine model reduction, uncertainty/stochasticity, and stability, which are some of the new features that must be incorporated in the models of the modern grid. Stability will be the connecting theme in these topics since stability is required to ensure a resilient grid with reduced risks. The PI will examine (1) how synchrony in the oscillations of the grid can be used to determine coherent sets of generators and loads for accurate model reduction; (2) what are the relevant structures that must be reserved to produce accurate reduced models; (3) how to develop multilevel solvers for decentralized systems; (4) how stochasticity affects the synchrony, coherency, model reduction, and stability of the grid; and (5) how observed data can be assimilated into the stochastic models. The stochasticity will be incorporated using stochastic differential-algebraic equations. This stochasticity will be introduced using the Orstein-Uhlenbeck process that best describes the stochasticity in the components of the system.