Cascading outages are complicated series of dependent failures that progressively weaken the large-scale electric power grid that transmits and distributes electric power to the nation. These cascading outages happen occasionally and can cause widespread blackouts, with up to tens of millions of people affected. The risk of cascading blackouts is present and could even increase because of the aging of power system infrastructure, severe weather events, increased variability due to the integration of renewable energy sources, and new threats such as cyber and physical attacks. Given the tremendous economic and societal impacts of blackouts, it is imperative to model and monitor the interactions and propagation of cascading outages, as well as develop effective mitigation techniques to reduce the risk of their occurrence. The causes of cascading outages are diverse and very complicated. However, the large amount of historical outage data collected by electric utilities provides an opportunity for new analysis of cascading outages. This project will use this data to understand the propagation and interactions of cascading outages, advance the monitoring of high-risk operating conditions, and provide engineering principles to reduce outage propagation. The research contributes to the grand national challenge of improving resilience of critical infrastructure systems by enhancing the resilience of power transmission and distribution grids against cascading blackouts. The project provides unique multi-disciplinary training opportunities for graduate and undergraduate students that combine research work and education.
The project will pioneer data-based approaches for extracting insights and actionable information from the considerable data already available to utilities. The outage data will be clustered using big data learning techniques to reveal patterns of similar outages. New models will characterize key aspects of complicated outage interactions. For example, how outages affect one another will be expressed as a network of interactions between power grid components. Data will be used to identify observable correlates of high-risk cascading conditions from the historical data, so that mitigation schemes for cascading events can be applied only when needed. Finally, the new models and data will be used to develop a framework for effective monitoring and mitigation. Since large transmission blackouts are high risk due to their catastrophic impact on society, and the smaller distribution blackouts are frequent, the research team will analyze outage dependencies and cascading using recorded data from both the transmission and distribution power grids. Prototype software will be developed for processing data, identifying model parameters and key metrics, and performing risk-based and data-driven analysis of cascading outages. The project integrates engineering and science approaches from data analysis, risk analysis, complex networks, and power systems engineering to mitigate blackout risk.
