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

AMPS：可重构可再生能源系统的组合数据驱动建模、预测和控制

• 批准号: 2229435
• 负责人: Yan Li
• 金额: $ 42.92万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229435&HistoricalAwards=false
• 关键词: AMPS; Compositional; Data; Driven; Modeling

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.

现代电网正在迅速向以可再生能源为主的分布式可重构系统发展，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。该项目的目标是开发计算工具来实现这一目标。解决受异构干扰影响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解决，这些算法和理论将部署在电力系统应用中，并利用机器学习的基础知识。 , 动态该项目将通过设计网络物理微电网和动力系统机器学习主题的课程来培训两名研究生并开发课程，从而为 NSF 的使命做出贡献。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、可再生能源为主的电力系统的暂态稳定性，这些电力系统本质上是非线性的、高维的、部分可观测的，并且受到异构不确定性的影响。该项目将阐明机器学习的进展，以开发可扩展和内聚的方法来解决系统运行的基本挑战。具体来说，主要研究人员 (PI) 将 (1) 开发一种抗噪声组合双线性算子理论方法来识别控制。 -可重构可再生能源系统瞬态动力学的适用模型；（2）通过整合严格的统计闭包公式和物理信息，为部分观测系统设计随机动力学模型；拓扑感知数据驱动模型；(3) 将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可重构系统的瞬态稳定性，以实现实时自主操作能力。成果将大大丰富和扩展当前关于大规模互连系统动态建模和控制的研究，并支持这些技术在下一代配电网应用中的开发。该奖项反映了 NSF 的法定使命，并被认为值得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。

项目成果

A data-driven statistical-stochastic surrogate modeling strategy for complex nonlinear non-stationary dynamics

复杂非线性非平稳动力学的数据驱动统计随机代理建模策略

• DOI: 10.1016/j.jcp.2023.112085
• 发表时间: 2023
• 期刊: Journal of Computational Physics
• 影响因子: 4.1
• 作者: Qi, Di;Harlim, John
• 通讯作者: Harlim, John

Data-Driven Modeling of Microgrid Transient Dynamics Through Modularized Sparse Identification

• DOI: 10.1109/tste.2023.3273127
• 发表时间: 2024-01-01
• 期刊: IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
• 影响因子: 8.8
• 作者: Nandakumar，Apoorva;Li，Yan;Chen，Bo
• 通讯作者: Chen，Bo