The reliability of energy systems is strongly influenced by the prevailing climate conditions. With the increasing prevalence of renewable energy sources, the interdependence between energy and climate systems has become even stronger. This study examines the impact of different spatial resolutions in climate modeling on energy grid reliability assessment, with the Texas interconnection between 2033 and 2043 serving as a pilot case study. Our preliminary findings indicate that while low-resolution climate simulations can provide a rough estimate of system reliability, high-resolution simulations can provide more informative assessment of low-adequacy extreme events. Furthermore, both high- and low-resolution assessments suggest the need to prepare for severe blackout events in winter due to extremely low temperatures.
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Substantial changes are occurring in electric distribution systems due to ambitious targets towards carbon-neutrality in many regions around the world. One of the key challenges is how to analyze the interactions of massive amount of energy end-users with the electric distribution grid operator. In this paper, we introduce a comprehensive simulation platform, AI4Dist, that is capable to perform a wide collection of distribution system studies that capture multiple timescales ranging from market planning to transient event analysis. AI4Dist is designed to effortlessly integrate with off-the-shelf machine learning packages and algorithm implementations. We envision that AI4Dist will serve as a platform to empower researchers with different expertise to contribute to the development of low carbon electricity sector.