AMPS: Compositional Data-Driven Modeling, Prediction and Control for Reconfigurable Renewable Energy Systems

现代电网正迅速向以分布式发电、插电式电动汽车、储能和需求响应资源为代表的可再生能源资源为主的分布式、可重构系统发展。该项目的目标是开发计算工具，以解决异构干扰下的分布式和可重构电力系统的建模和控制中出现的新挑战。这一目标将通过开发新的数学算法和理论来实现，这些算法和理论将部署在电力系统应用中，利用机器学习，动力系统和控制理论的基础知识。该项目将通过培训两名研究生和通过设计关于网络物理微电网和动力系统机器学习主题的课程开发课程，促进科学、技术、工程和数学的国家科学基金会使命。该项目旨在设计组合数据驱动的建模，预测和控制方法，以确保分布式和可重构的可再生能源占主导地位的电力系统的暂态稳定性，这是固有的非线性，高维，部分观察，并受到异构的不确定性。该项目将阐明机器学习在开发可扩展和内聚方法方面的进展，以解决系统运行中的根本挑战。具体而言，主要研究人员（PI）将（1）开发一种抗噪声的组合双线性算子理论方法，以确定可重构可再生能源系统瞬态动力学的可控制模型;（2）通过整合严格的统计闭合公式和物理信息拓扑感知数据驱动模型，为部分观测系统设计随机动力学模型;以及（3）将所开发的模型与最优控制算法相结合，以预测的方式提高分布式和可重构系统的暂态稳定性，以实现实时自主操作能力。PI预计，这些成果将大大丰富和扩大目前的研究动态建模和控制的大规模互联系统，并支持这些技术的发展，应用于下一代的配电grids.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

The modern power grid is rapidly evolving towards a distributed and reconfigurable system dominated by renewable energy resources, represented by distributed generation, plug-in electric vehicles, energy storage, and demand-response resources. The goal of this project is to develop computational tools to address new challenges arising in modeling and control of the distributed and reconfigurable power systems subject to heterogeneous disturbances. This objective will be addressed by the development of new mathematical algorithms and theory that will be deployed in power system applications, leveraging the fundamental knowledge from machine learning, dynamical systems, and control theory. This project will contribute to the NSF mission of advancing STEM through the training of two graduate students and curricular development through the design of courses on the topics of cyber-physical microgrids and machine learning for dynamical systems. This project aims to devise compositional data-driven modeling, prediction, and control methods to ensure the transient stability of the distributed and reconfigurable renewable-energy-dominant power systems, which are inherently nonlinear, high dimensional, partially observed, and subject to heterogeneous uncertainties. This project will illuminate the machine learning advances for developing scalable and cohesive approaches to solve the fundamental challenge of in system’s operation. Specifically, the principal investigators (PIs) will (1) develop a noise-resilient compositional bilinear operator theoretic method to identify a control-amenable model for the transient dynamics of reconfigurable renewable energy systems; (2) devise a stochastic dynamics model for the partially-observed system by integrating a rigorous statistical closure formulation and a physics-informed topology-aware data-driven model; and (3) integrate the developed models with the optimal control algorithms to improve the transient stability of the distributed and reconfigurable system in a predictive manner towards a real-time autonomous operation capability. The PIs anticipate that these outcomes will substantially enrich and expand the current research on dynamic modeling and control of large-scale interconnected systems and support the development of these techniques for applications of the next-generation distribution grids.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.