Data-centric Real-time Power System Modelling, Monitoring, and Control

该研究项目将开发和集成一套全面的以数据为中心的应用程序，旨在实现对未来电力系统的实时建模、监测和控制。传统上，大容量输电系统通过重复的基于场景的离线模拟研究和分层在线操作实践来确保电力的可用性和质量。这种以模型为中心的分层现状在传统电力系统中表现良好，具有较高的可预测性和较低的不确定性。然而，未来的电力系统预计将可靠地供应电力，面对(i)可再生能源发电带来的更多不确定性，以及（ii）由于电力需求增长而增加的系统压力，这两者都会导致不确定和不可预测的行为，可能导致大范围的停电。面对这些可能导致先验模型（基于历史数据、维护记录和遥测获得）非常不准确的情况，传统的以模型为中心的工具可能不再足以维持系统范围的可靠性和效率。因此，有动力发展变革性的电力系统监测、运行和控制实践，这些实践适应（潜在的快速）运行点和网络拓扑的变化，对模型误差具有弹性，并且在计算上易于处理。实现这一目标的一个引人注目的策略是开发一个以数据为中心的框架，该框架通过从系统获得的实时测量来实现。为此，提出的计划在三个连贯的主题中实现其长期愿景：(i)估计降阶网络和动态模型，（ii）开发暂态稳定监测工具，以及（iii）统一网络优化与实时控制。拟议的研究计划将产生在加拿大和其他地区绝对必要的工具，以实现可再生资源的广泛整合，适应更高的电力需求，同时防止严重的系统故障。此外，通过该计划，研究生和本科生将具备(i)进行一流电力系统研究所需的知识和技能，以及（ii）实现大型电力系统功能和高效算法的能力。

This research program will develop and integrate a comprehensive suite of data-centric applications aimed at achieving real-time modelling, monitoring, and control of the future power system. Traditionally, the bulk transmission system ensures power availability and quality via repeated offline what-if scenario-based simulation studies and hierarchical online operational practices. This model-centric and hierarchical status quo performs well in the traditional power system with high predictability and low uncertainty. However, the future power system is expected to reliably supply electricity in the face of (i) more uncertainty arising from renewable electric power generation, and (ii) added system stress due to growing electricity demand, both of which lead to uncertain and unpredictable behaviour, potentially causing widespread blackouts. Confronted with these circumstances that may render an a priori model (obtained based on historical data, maintenance records, and telemetry) grossly inaccurate, traditional model-centric tools may no longer be sufficient to maintain system-wide reliability and efficiency. Thus, there exists an impetus to develop transformative power system monitoring, operation, and control practices that are adaptive to (potentially rapid) changes in operating point and network topology, resilient against model error, and computationally tractable. A compelling strategy to address this goal is to develop a data-centric framework that is enabled by real-time measurements obtained from the system. To this end, the proposed program realizes its long-term vision in three cohesive themes: (i) estimating reduced-order network and dynamic models, (ii) developing transient stability monitoring tools, and (iii) unifying network optimization with real-time control. The proposed research program will generate tools that are absolutely essential, in Canada and beyond, to enable widespread integration of renewable resources, accommodate much higher electricity demand, and simultaneously prevent severe system-wide failures. Furthermore, through this program, graduate and undergraduate students will be equipped with (i) the knowledge and skills required to conduct first-class research in power systems, and (ii) the ability to implement functional and efficient algorithms for large-scale power systems.