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

EPCN：从有限的测量中学习电网：基本限制和实用算法

基本信息

批准号：
1931662
负责人：
Steven Low
金额：
$ 38万
依托单位：
California Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931662&HistoricalAwards=false
关键词：
EPCN Learning power grids limited

项目摘要

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.

该项目开发了使未来电网管理更加高效、安全和稳健的方法。开发未来能源系统的智能基础和实用算法是本项目的一个重要挑战和目标。具体来说，网络拓扑和线路参数是电力系统管理的关键信息。尽管这些信息中的错误会严重影响系统操作，但识别它们的工作相对较少，特别是当测量在时间和空间上都受到限制时。这个问题在中压和低压配电网中更为严重，不仅因为目前缺乏监测基础设施，而且还因为拓扑结构可能会随着负载或太阳能发电的变化而更频繁地变化。我们的目标是发展拓扑和线路参数识别的理论和算法。我们将重点关注只能在有限地点或/和有限时间内进行测量的情况。这些设置是困难的，但却是现实的，例如，今天大多数配电系统都有来自配电网与大输电网接口的变电站和最终用户的智能电表的测量，但两者之间没有太多的测量。未来的应用可能需要基于有限的样本数量在接近实时的情况下做出识别决策。拟议的研究包括三个重点。推力1（有限空间测量的学习）侧重于理论和算法，当不是所有的网络节点都是可观察的，但随着时间的推移，在必须进行估计之前，可以从可观察节点收集足够的样本。推力2（有限时间测量的学习）关注的是所有网络节点都是可观察的，但只有有限数量的样本可用于识别的情况。推力3（有限测量的集成学习）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。提案中开发的方法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。