Collaborative Research: Information Geometry for Model Verification in Energy Systems with Renewables

合作研究：可再生能源能源系统模型验证的信息几何

基本信息

批准号：
1710727
负责人：
Mark Transtrum
金额：
$ 21.88万
依托单位：
Brigham Young University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2021-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1710727&HistoricalAwards=false
关键词：
Collaborative Research Information Geometry Model

项目摘要

Emerging communication and computation capabilities have the potential to profoundly change and improve infrastructures such as electric power systems. The architecture and composition of modern power systems have been undergoing significant changes recently. These include new sources, such as gas-fired plants and co-generation facilities, and from new loads connected through power electronic converters and tightly controlled through local communication networks. The programmable nature of new sources and loads offers new capabilities, but at the same time necessitates frequently repeated model verification. Models preferred by energy engineers are often motivated by the physical properties of components and sub-systems. These models are typically nonlinear in terms of parameters. However, reliable identification of parameters from measurements is a challenging problem that is largely unsolved for the case of nonlinear models. This project aims to deploy new model verification tools that combine profound mathematical foundations (differential geometry and information theory) with modern computational algorithms. This project will have direct implications on other branches of engineering that use similar types of models. Within energy systems, this project has the potential to result in economic, environmental, and resilience benefits by enabling more precise operation of future electricity markets and control in actual power plants and customer sites.This project builds on computational advances in differential geometry, and offers a new, global characterization of challenges frequently encountered in system identification and model reduction of energy systems. The premise of this approach is that a model with many parameters is a mapping from a parameter space into a data or prediction space. A key difficulty in dealing with models of complex systems is the highly anisotropic nature of the mapping between the parameters and data spaces, meaning that small variations in parameter space may lead to dramatic changes in the measurement (data) space while other variations in parameters can lead to no discernable change in the in the model behavior. This project will use event recordings from daily operation (e.g., from phasor measurement units following line switchings and load variations) to motivate new model validation and selection algorithms. The long-term vision is to develop global and semi-global identification procedures for nonlinearly-parametrized energy components and systems, to establish limits of performance with phasor measurement unit sensors, to develop novel model reduction procedures, and to lay the groundwork for identification of large-scale energy systems. Specific goals include: 1) parameter identification for wind and solar plants, including more detailed manifold maps; 2) parameter identification for conventional sources (synchronous generators) and loads; and 3) re-parametrization and reduction for models that are typically employed in dynamic studies. Simulations will use industry-standard and custom software and recordings of hardware experiments to quantify progress. Anticipated results will be relevant for microgrids, virtual entities (virtual utilities, energy hubs) that are often considered essential in the long-term evolution of smart grids, and future electricity markets that will likely operate on shorter time-scales and thus depend on model fidelity of system dynamics.

新兴的沟通和计算功能有可能深刻改变和改善电力系统等基础设施。现代电力系统的结构和组成最近正在发生重大变化。其中包括新来源，例如燃气工厂和共同生成设施，以及通过电力电子转换器连接的新负载，并通过本地通信网络严格控制。新来源和负载的可编程性质提供了新功能，但同时需要经常重复进行模型验证。能源工程师首选的模型通常是由组件和子系统的物理特性激励的。这些模型通常在参数方面是非线性的。但是，从测量值中对参数的可靠识别是一个具有挑战性的问题，对于非线性模型而言，在很大程度上无法解决。该项目旨在部署将深刻的数学基础（差异几何学和信息理论）与现代计算算法相结合的新模型验证工具。该项目将对使用类似类型的模型的其他工程分支有直接影响。在能源系统中，该项目有可能通过在实际的发电厂和客户站点中更精确地运行未来的电力市场和控制，从而实现经济，环境和弹性利益。该项目以差异几何形状的计算进步为基础，并提供了一种新的全球全球挑战表征，这些挑战经常在系统识别和能源系统的模型中遇到挑战。这种方法的前提是，具有许多参数的模型是从参数空间映射到数据或预测空间。处理复杂系统模型的关键困难是参数和数据空间之间映射的高度各向异性性质，这意味着参数空间的较小变化可能会导致测量（数据）空间的巨大变化，而参数的其他变化可能会导致模型行为中的参数变化。该项目将使用日常操作的事件记录（例如，在线路开关和负载变化之后的相量测量单元中）来激发新的模型验证和选择算法。长期的视觉是为非线性投影能的能量组件和系统开发全球和半全球识别程序，以通过相位测量单元传感器建立性能的限制，以开发新的模型还原程序，并为识别大型能源系统奠定基础。具体目标包括：1）风电厂和太阳发电厂的参数识别，包括更详细的歧管图； 2）常规源（同步发电机）和负载的参数识别； 3）通常用于动态研究的模型的重新分配和还原。模拟将使用行业标准和自定义软件以及硬件实验的录制来量化进度。预期的结果将与通常认为在智能电网的长期演变以及未来可能在较短的时间尺度上运作的未来电力市场的微电网，虚拟实体（虚拟实体，能源枢纽）有关，因此取决于系统动力学的模型忠诚度。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Simultaneous Global Identification of Dynamic and Network Parameters in Transient Stability Studies

暂态稳定性研究中动态和网络参数的同步全局识别

DOI：
10.1109/pesgm.2018.8586586
发表时间：
2018
期刊：
2018 IEEE Power & Energy Society General Meeting (PESGM
影响因子：
0
作者：
Transtrum, Mark K.;Francis, Benjamin L.;Saric, Andrija T.;Stankovic, Aleksandar M.
通讯作者：
Stankovic, Aleksandar M.

State Estimation Model Reduction Through the Manifold Boundary Approximation Method

通过流形边界逼近法简化状态估计模型

DOI：
10.1109/tpwrs.2021.3091547
发表时间：
2022
期刊：
IEEE Transactions on Power Systems
影响因子：
6.6
作者：
Svenda, Vanja;Transtrum, Mark;Francis, Benjamin;Saric, Andrija;Stankovic, Aleksandar
通讯作者：
Stankovic, Aleksandar

Probabilistic Network Observability of a Hybrid Power System with Communication Irregularities

具有通信不规则性的混合电力系统的概率网络可观测性

DOI：
10.1109/naps46351.2019.8999986
发表时间：
2019
期刊：
2019 North American Power symposium
影响因子：
0
作者：
Svenda, Vanja G.;Stankovic, Alex M.;Saric, Andrija T.;Transtrum, Mark K.
通讯作者：
Transtrum, Mark K.

Integration of Physics- and Data-Driven Power System Models in Transient Analysis After Major Disturbances

DOI：
10.1109/jsyst.2022.3150237
发表时间：
2023-03
期刊：
IEEE Systems Journal
影响因子：
4.4
作者：
A. A. Sarić-A.;M. Transtrum;A. Sarić;A. Stanković
通讯作者：
A. A. Sarić-A.;M. Transtrum;A. Sarić;A. Stanković

Piecemeal Reduction of Models of Large Networks

大型网络模型的逐步缩减

DOI：
10.1109/cdc45484.2021.9683471
发表时间：
2021
期刊：
Conference on Decision and Control
影响因子：
0
作者：
Francis, Benjamin L.;Transtrum, Mark K.;Saric, Andrija T.;Stankovic, Aleksandar M.
通讯作者：
Stankovic, Aleksandar M.