Elements: Open Access Data Generation Engine for Bulk Power System under Extreme Windstorms

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The electric power grid is a critical infrastructure that is regularly disrupted by natural disasters. Currently, there is a lack of correlated data on windstorms and the physical and cyber power system infrastructure. This project addresses that gap in knowledge in dealing with high wind disaster events for preventative and restorative resiliency of electric power networks. By integrating multiple data sources into a robust simulation tool, researchers can design new methods to mitigate the impact of hurricanes and other extreme wind events on power system operations. The outcomes of the research would positively impact reliability, resiliency, and delivery of electric power to US population centers.The framework is unique and will enable inter-disciplinary research between atmospheric sciences, civil engineering, and electric power engineering. A preliminary result is provided for the Texas power system (using a digital surrogate model of the physical infrastructure and the Texas synthetic power network) under the impacts of Hurricane Harvey. The calculated damage using the model results in very similar structural damage and power systems impact as the real disaster (the ground truth model). In addition to three deliverable test cases (Texas, New York, and Florida), the project will design automated tools to create new test cases in the future for other researchers. The power system digital surrogate, correlation of datasets, and overall data generation engine will enhance the national cyberinfrastructure ecosystem. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) and the Division of Electrical, Communications and Cyber Systems (ECCS) within the NSF Directorate for Engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.