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

新兴的通信和计算能力有可能深刻改变和改善电力系统等基础设施。 近年来，现代电力系统的结构和组成发生了重大变化。这些包括新的能源，如燃气发电厂和热电联产设施，以及通过电力电子转换器连接并通过本地通信网络严格控制的新负载。新电源和负载的可编程特性提供了新的功能，但同时需要频繁重复的模型验证。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驱动的。这些模型在参数方面通常是非线性的。然而，可靠的参数识别测量是一个具有挑战性的问题，在很大程度上是未解决的情况下的非线性模型。该项目旨在部署新的模型验证工具，这些工具将联合收割机深厚的数学基础（微分几何和信息论）与现代计算算法相结合。该项目将对使用类似类型模型的其他工程分支产生直接影响。在能源系统中，该项目有可能通过实现未来电力市场的更精确操作以及实际发电厂和客户现场的控制来产生经济，环境和弹性效益。该项目建立在微分几何的计算进步基础上，并为能源系统的系统识别和模型简化中经常遇到的挑战提供了新的全球表征。这种方法的前提是，具有许多参数的模型是从参数空间到数据或预测空间的映射。 处理复杂系统模型的一个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，这意味着参数空间中的微小变化可能导致测量（数据）空间的急剧变化，而参数中的其他变化可能导致模型行为中的不可辨别的变化。本项目将使用日常操作中的事件记录（例如，从线路切换和负载变化之后的相量测量单元）来激励新的模型验证和选择算法。长期愿景是为非线性参数化能源组件和系统开发全球和半全球识别程序，建立相量测量单元传感器的性能限制，开发新的模型简化程序，并为大规模能源系统的识别奠定基础。具体目标包括：1）风能和太阳能发电厂的参数识别，包括更详细的流形图; 2）传统电源（同步发电机）和负载的参数识别;以及3）动态研究中通常采用的模型的重新参数化和简化。模拟将使用行业标准和定制软件以及硬件实验记录来量化进展。预期的结果将与微电网，虚拟实体（虚拟公用事业，能源枢纽），往往被认为是必不可少的智能电网的长期发展，以及未来的电力市场，可能会在较短的时间尺度上运行，因此取决于系统动态模型的保真度。

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.