Modeling, Analysis and Control Design for Spatially Distributed Systems with Application to Wind Farms

这项工作将解决风电场建模和控制中出现的挑战，使电网运营商能够准确预测不同运行条件下的容量，并跟踪所需的功率输出曲线。与简单地最大化输出功率的控制器不同，这种增强的操作灵活性将允许风力发电场通过频率调节和功率斜坡等有价值的服务来支持公用事业。该项目的关键技术创新是一个降阶模型，该模型准确地捕捉了涡轮机之间的动态相互作用和涡轮机尾迹的影响，同时保持了足够的易处理能力，可以进行实时计算。模型和控制将使用高保真风电场模拟和实际操作员数据进行测试。将创建的工具将有助于提高风电作为电力系统核心组件的经济竞争力。该项目将为研究生提供跨学科的研究培训，重点是风电场流动物理和控制理论之间的相互作用，并包括国际研究机会。该项目包括与巴尔的摩高中的接触，以及约翰·霍普金斯大学针对高中生的STEM暑期项目。该项目将为多尺度系统创建模型、控制算法和仿真工具，在这些系统中，机械设备必须在无限(或高)维过程中执行协调行动，并具体应用于风力发电场。该项目的研究目标如下：(I)在计算上易于处理的方法，以捕捉涡轮机之间的复杂相互作用，并弥合过于简化的静态尾流模型与缓慢、昂贵的高维模型之间的差距；(Ii)基于模型的控制框架，超越最大功率提取的传统控制目标，促进更复杂的轨迹跟踪；(Iii)扩展现有的高保真仿真工具，以适用于风电场模型验证和控制算法测试和改进；(Iv)基于电网仿真系统的风电场建模和控制工具的联合开发，该电网仿真系统生成频率控制轨迹，并作为监督控制实体，将控制目标分配到多个风电场。该项目将提供一个基于大型涡流仿真(LES)的模型和控制验证平台，该平台可以促进风力涡轮机和尾流模型以及控制算法的模块化更改，从而可以有效地测试新的模型和流量控制策略。新平台将支持多个风电场参与频率调节服务的协调，使用互联的模拟电网作为监控系统。

This work will address challenges arising in the modeling and control of wind farms, enabling power grid operators both to accurately predict capacity under varying operating conditions, as well as to track a desired power output profile. In contrast to controllers that simply maximize output power, this enhanced operational flexibility will allow wind farms to support utilities with valuable services such as frequency regulation and power ramping. The key technical innovation of this project is a reduced-order model that accurately captures dynamic interaction between turbines and the effect of turbine wakes, while remaining tractable enough for real-time computation. The model and controls will be tested using high-fidelity wind farm simulations and actual operator data. The tools to be created will help improve the economic competitiveness of wind power as a core component of the electric power system. The project will provide interdisciplinary research training to graduate students, focusing on the interplay between wind farm flow physics and control theory, and including international research opportunities. The project includes outreach to Baltimore high schools and a STEM summer program for high school students at the Johns Hopkins University. This project will create models, control algorithms and simulation tools for multiscale systems in which mechanical devices must perform coordinated actions within an infinite (or high) dimensional process, with specific application to wind farms. The research goals of the project are as follows: (i) computationally tractable methods to capture the complex interactions between turbines, and to bridge the gap between overly simplified static wake models and slow, costly high-dimensional models; (ii) a model-based control framework to move beyond the traditional control goal of maximum power extraction, and facilitate more complex trajectory tracking; (iii) extension of current high-fidelity simulation tools to be suitable for wind farm model validation and control algorithm testing and refinement; (iv) combined wind farm modeling and control tool development based on a grid simulation system that generates frequency control trajectories and acts as a supervisory control entity to distribute the control objectives over multiple wind farms. The project will provide a large eddy simulation (LES) based model and control validation platform that can facilitate modular changes in wind turbine and wake models along with the control algorithms, so that new models and flow control strategies can be efficiently tested. The new platform will support the coordination of multiple wind farms participating in frequency regulation services, using an interconnected simulated power grid as a supervisory system.