Digital Twin Technologies for Enhanced Power Grid Resilience to Extreme Weather

As more renewable energy resources are integrated into the power grid and the increasing trends in the electrification of society, accurate weather forecasts become crucial for managing electricity supply and demand. Traditional planning tools are not well-equipped to handle the unpredictability and complexity of renewables, leading to inefficiencies such as generators burning extra fuel and overspending on backup power. A digital twin framework to dynamically couple power grid sensor data with high-resolution weather simulations may offer important advantages. The variability and uncertainty of weather conditions affecting solar and wind power production, for instance, require highly flexible management tools to switch between different energy sources and storage units. This challenge is increased during extreme weather events. Simulating historical high-impact weather events in detail and how the power grid responds to them—under present and projected levels of renewable energy penetration –allows us to identify the regions and zones most vulnerable to threats and to determine better protective measures. In this study, we developed use cases of extreme weather event simulations to determine whether and how one-day ahead and longer time forecasts of power energy generation can aid in the preparation, resistance, and power outage restoration. The intent is to demonstrate the benefit of an integrated Weather-Power Grid numerical system and AI methods for infrastructure management and upgrades for enhanced energy resiliency.