Electric utilities are particularly vulnerable to the consequences of climate change as their operations rely on assets and infrastructure that are subject to failure due to weather events. Considering the increased frequency of extreme weather events induced by climate change, the goal of improving the resilience of the power grid has become a priority for electric transmission and distribution utilities. While the need of investing in hardening the power grid is evident, there is a gap in methodologies to effectively allocate resources for this purpose. Motivated by this, Rhizome proposes the use of machine learning and statistical models to assess and mitigate the climate risk of power grids. In particular, through the development of asset vulnerability models, we are capable of identifying strategies in power distribution systems that should be prioritized to enhance their resilience. As part of this work, we use data provided by a utility company in the Pacific Northwest to model extreme weather risk at a sub-circuit level on a diverse sample of distribution circuits and performing a cost benefit analysis analysis of mitigation actions.

Session Sponsored by Rhizome