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.

现代电网迅速发展朝着分布式和重新配置的系统以可再生能源为主导的系统，该系统由分布式生成，插入电动汽车，储能和需求响应资源代表。该项目的目的是开发计算工具，以解决在遇到异质灾害的分布式和可重构功率系统建模和控制方面产生的新挑战。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，从而利用机器学习，动态系统和控制理论的基本知识。该项目将有助于NSF的使命，即通过培训两名研究生和当前的发展，通过设计有关动态系统的网络物理微电网和机器学习主题的课程。该项目旨在设计复合数据驱动的建模，预测和控制方法，以确保分布式和可重新配置的可再生能源占主导地位的功率系统的过渡稳定性，这些功率系统本质上是非线性的，高维，部分观察到的，并受到异构不认真的不认真。该项目将阐明机器学习的进步，以开发可扩展和凝聚力的方法来解决系统操作中的基本挑战。具体而言，主要研究者（PIS）将（1）开发一种噪声复合双线性操作员理论方法，以识别可控制的可重新配置可再生能源系统瞬态动力学的控制模型； （2）通过整合严格的统计闭合公式和物理意识到的数据驱动的数据驱动的模型来为部分观察到的系统设计一个随机动力学模型； （3）将开发的模型与最佳控制算法集成在一起，以预测的方式提高分布式和可重新配置系统的瞬态稳定性，以实现实时自主操作能力。 PI预计，这些结果将第二次丰富和扩展有关大规模互连系统的动态建模和控制的当前研究，并支持这些技术的开发，以用于下一代分销网格的应用。该奖项反映了NSF的法规任务，并认为通过基金会的知识优点和广泛的cribia crietia criperia criperia crigitia criperia the Insportauction the Pocies take criped supportion。

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