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

该研究计划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离线假设仿真研究和分层在线操作实践来确保电力可用性和质量。这种以模型为中心、层次化的现状在传统电力系统中表现良好，具有高可预测性和低不确定性。然而，未来的电力系统预计将可靠地供电，以应对（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，这两种情况都导致不确定和不可预测的行为，可能导致大范围停电。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）严重不准确的情况，传统的以模型为中心的工具可能不再足以保持系统范围的可靠性和效率。因此，存在开发变革性电力系统监测、操作和控制实践的动力，所述电力系统监测、操作和控制实践适应于操作点和网络拓扑结构中的（潜在快速的）变化，对模型误差有弹性，并且在计算上易于处理。实现这一目标的一个令人信服的策略是开发一个以数据为中心的框架，该框架由从系统获得的实时测量结果支持。为此，该计划在三个有凝聚力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）将网络优化与实时控制统一起来。

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.