Dynamic Model Identification for Inverter-Based Resources

从历史上看，同步发电机发明于19世纪80年代，是发电的主要主力。目前，用于电网动态分析的同步电机通用模型已经较为成熟，是电网动态分析不可或缺的一部分。当前的电网正在经历一个重大的转变。基于逆变器的资源（IBR），如风能、太阳能和电池的渗透率正在上升。例如，美国能源情报署的每小时电网监测显示，2020年5月2日，德克萨斯州电力可靠性委员会59%的电力来自风能。ibr显著改变了网格的动态特性。电网出现了前所未有的动态现象。例子包括2009年和2017年发生在德克萨斯州风电场的次同步振荡，2019年8月9日导致英国电力中断的海上风电场低频振荡，2017年和2018年在加利福尼亚州引发大规模太阳能光伏（PV）跳闸的次周期过电压动态，以及2020年7月在加利福尼亚州导致另一次大规模光伏跳闸事件的瞬时交流过流动态。ibr的高度普及导致了大容量电力系统行业建模实践的重大变化。网格动态评价的发展趋势是采用具有标准结构的通用模型。另一方面，原始设备制造商（oem）提供的模型是黑盒子。oem提供的实码模型通常采用动态链接库格式，指定输入和输出，而内部细节不公布。虽然这些实际代码模型已经通过硬件实验进行了基准测试，但详细的结构和参数是未知的。因此，有一个迫切的需要：如何根据我们所拥有的，本质上是黑盒的，找到通用模型的参数？本研究的目的是解决为ibr设计一个灰盒模型识别框架的迫切需要。本项目有两大创新：(1)基于第一性原理的IBR灰盒模型结构设计和线性时不变模型推导；（ii）基于黑箱模型识别和先进计算算法的基于测量的IBR模型结构参数估计。值得注意的创新是将来自两个不同领域的两项核心技术：电力电子和系统识别技术相结合。两个核心技术是：基于导纳的黑箱模型测量与识别和通过优化问题的制定与求解的灰箱模型识别。该方案采用计算机仿真和硬件实验两种方法进行验证。该项目解决了现实世界的IBR建模挑战，并可能对当前的电网行业产生重大影响。所解决的问题属于灰盒模型识别的范畴，本研究也将对许多使用灰盒模型识别方法进行模型构建和控制设计的其他领域（如汽车系统和飞机系统）产生参考价值。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Historically, synchronous generators, invented in 1880s, are the main work horse for electricity generation. To date, generic models of synchronous machines for power grid dynamic analysis are well developed and indispensable for power grid dynamic analysis. The current power grid is going through a significant transition. Penetration of inverter-based resources (IBR), e.g., wind, solar, and batteries, is going up. For example, the U.S. Energy Information Administration’s hourly electric grid monitor shows that on May 2, 2020, 59% of power was generated by wind energy in Electric Reliability Council of Texas. IBRs significantly change grid dynamic characteristics. Unprecedented dynamic phenomena appeared in power grids. Examples include subsynchronous oscillations that occurred in 2009 and 2017 in Texas wind farms, low-frequency oscillations in an offshore wind farm that caused Great Britain power disruption on 9 August 9 2019, subcycle overvoltage dynamics which triggered large-scale solar photovoltaic (PV) tripping in California in 2017 and 2018, and instantaneous ac overcurrent dynamics which caused another large-scale PV tripping event in California in July 2020. High penetration of IBRs leads to a significant change in the modeling practice of the bulk power system industry. It is expected that generic models with standard structures will be used for grid dynamic evaluation as a future trend. On the other hand, models provided by original equipment manufacturers (OEMs) are black boxes. Real-code models provided by OEMs are usually in dynamic-linked library format with input and output specified while internal details not released. Though these real-code models have been benchmarked by hardware experiments, detailed structures and parameters are unknown. Thus, there is an urgent need: How to find the generic model’s parameters based on what we have, which are essentially black boxes?The objective of the proposed research is to address the urgent need to design a gray-box model identification framework for IBRs. This project makes two significant innovations, including (i) first principle based IBR gray-box model structure design and linear time invariant model derivation; and (ii) measurement-based IBR model structure parameter estimation relying on black-box model identification and advanced computing algorithms. The notable innovation is the integration of the two core technologies from two different fields: Power Electronics and System Identification. The two core technologies are: admittance-based black-box model measurement and identification and gray-box model identification through optimization problem formulation and solving. The project employs both computer simulation and hardware experiments for validation. This project tackles real-world IBR modeling challenges and can generate significant impact to the current power grid industry. The tackled problem falls into the category of gray-box model identification and this research will also generate reference values to many other domains (e.g., automotive systems and airplane systems) that use gray-box model identification approaches for model building and control design.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.