Uncertainty Quantification for Probabilistic Stability Analysis and Uncertainty-Aware Control of Electric Power Systems

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Power system instabilities are unacceptable to society as they are not only extremely costly but also profoundly disrupt people's daily lives and the delivery of essential services (e.g., food, health care). The penetration of intermittent renewable energy sources, like wind and solar, introduces high uncertainty to power grids, which has been linked with severe power blackouts affecting millions of people and costing billions of dollars. Many real-life events (e.g., UK's power blackout in Aug. 2019 due to the failure of Hornsea offshore wind farm) demonstrate that it is essential and imperative to investigate systematically the impacts of uncertainty on power system stability and design effective measures to mitigate the adverse impacts of uncertainty. Leveraging on advanced metamodeling method, statistical learning and variance-based global sensitivity analysis, the proposed research aims to develop novel probabilistic stability assessment methods to quantify the impacts of uncertainty on power system transient and voltage stability, and design uncertainty-aware control measures to mitigate the negative impacts of uncertainty on the transient and voltage stability. Particularly, there are three short-term objectives: (1) design a probabilistic stability assessment method to quantify the impacts of uncertainty on power system transient stability accurately and efficiently; (2) design a probabilistic stability assessment method to quantify the impacts of uncertainty on power system voltage stability accurately and efficiently; (3) design uncertainty-aware control measures to mitigate the impacts of uncertainty on power system transient stability and voltage stability, respectively. To fulfill the three objectives, 2 PhD students and 2 Master's students will be trained to complete six tasks. The long-term goal of the proposed research is to develop a probabilistic paradigm, in which analytical methods and computational tools for the assessments of various types of stability (e.g., frequency stability, small-signal stability, converter-driven stability besides transient and voltage stability) are developed, and effective control measures for the mitigation of the negative impacts of uncertainty on various stability properties are designed. Canada has set a goal of increasing the share of zero-emitting sources from 80% to 90% by 2030 and to nearly 100% by 2050, by cleaning the supply, electrifying the transport, commercializing energy storage systems, etc. The outcomes of the proposed research will contribute to advancing Canada's energy transition by promoting the seamless integration of renewable energy sources and electric vehicles, managing uncertainty in the power industry, guiding the deployment of energy storage systems to deliver clean, reliable and affordable electricity to all Canadians, assisting Canada in moving towards nearly 100% renewable-based electricity by 2050.