EPCN: Learning power grids from limited measurements: fundamental limits and practical algorithms

该项目开发的方法使未来电网的管理更加高效、安全和可靠。为未来的能源系统开发智能基础和实用算法是该项目的一个重要挑战和目标。具体地说，网络拓扑和线路参数是电力系统管理的关键信息。尽管此类信息中的错误可能会显著影响系统运行，但识别它们的工作相对较少，特别是在测量在时间和空间上都有限的情况下。这一问题在中压和低压配电网中更为严重，这不仅是因为当今缺乏监控基础设施，还因为拓扑可能会随着负荷或太阳能发电的变化而更频繁地变化。我们的目标是开发一种用于拓扑和线路参数识别的理论和算法。我们将重点关注仅在有限的位置或/和在有限的时间段内可获得测量的情况。这些设置很困难，但很现实，因为，例如，今天的大多数配电系统都有来自配电网与大容量输电电网接口的变电站的测量，以及来自最终用户的智能电表的测量，但在两者之间没有太多的测量。未来的应用程序可能不得不基于有限数量的样本近乎实时地做出识别决策。这项拟议的研究包括三个突破口。当网络节点不是所有的节点都是可观测的，但在进行估计之前，可以随着时间的推移从可观测节点收集足够的样本时，推力1(利用有限的空间测量进行学习)侧重于理论和算法。推力2(学习与有限的时间测量)关注的情况下，所有的网络节点是可观测的，但只有有限数量的样本可用于识别。推力3(有限测量的综合学习)将推力1和推力2中开发的理论和算法集成到一个整体识别系统中，该系统适用于测量在空间和时间上都有限的情况。建议中提出的方法也适用于其他网络系统(社会、通信、交通、金融)。该项目将研究与教育相结合，包括课程开发和与创业的联系。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project develops methods to make the management of future power grids more efficient, secure, and robust. The development of an intellectual basis and practical algorithms for future energy systems is an important challenge and a goal of this project.Specifically, network topology and line parameters are critical information for the management of power systems. Even though errors in such information can significantly impact system operation, there is relatively little work in identifying them, especially when measurements are limited both temporally and spatially. This problem is even more severe in medium-voltage and low-voltage distribution grids, not only because the lack of monitoring infrastructure today, but also because topology may change more frequently in response to changes in load or solar generation. Our goal is to develop a theory and algorithms for topology and line parameter identification.We will focus on cases where measurements are available only at limited locations or/and for a limited time period. These settings are difficult but realistic, as, e.g., most distribution systems today have measurements from substations where a distribution grid interfaces with the bulk transmission grid, and from smart meters at end users, but not much measurements in between. Future applications may have to make identification decisions in near real-time based on limited numbers of samples. The proposed research consists of three thrusts. Thrust 1 (Learning with limited spatial measurements) focuses on the theory and algorithms when not all network nodes are observable, but sufficient samples can be collected over time from the observable nodes before an estimation has to be made. Thrust 2 (Learning with limited temporal measurements) focuses on the case where all network nodes are observable, but only a limited number of samples are available for identification. Thrust 3 (Integrated learning with limited measurements) integrates the theory and algorithms developed in Thrusts 1 and 2 into an overall identification system that is applicable where measurements are limited both spatially and temporally. The methods developed in the proposal are applicable to other network systems (social, communications, transportation, financial). The project integrates research with education including curriculum development and linking to entrepreneurship.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.