AMPS: Renewable Energy Integration: Building Resiliency and Robustness through Data-Driven Modeling

The national power grid is undergoing a significant transformation as it increasingly incorporates renewable energy sources like solar panels, wind turbines, and energy storage systems. This shift is driven by the urgent need to move towards sustainable and environmentally friendly energy production. However, this simultaneously introduces new challenges related to maintaining the stability, resiliency, and robustness of the power supply. These issues are not only technical concerns but carry broader implications for society, affecting everything from daily household tasks to national security. Ensuring a reliable and efficient power grid amidst these changes is crucial for continued industrial and economic growth, and highlights the significance of this project in shaping a sustainable future. This project focuses on the following elements that are central to the future development of the power grid: renewable energy integration; building resiliency and robustness into the grid; and data-driven modeling. From a technical perspective, this project aims to tackle the complex mathematical and computational challenges posed by the integration of renewable energy into the power grid, focusing on the development of new mathematical frameworks that can manage and leverage the influx of high-volume real-time distributed data from phasor measurement units and wide-area monitoring systems. These frameworks will improve the modeling, monitoring, and analysis of power networks, facilitating rapid identification and response to potential threats to grid stability. By combining advanced computational strategies with modern system-theoretic methods, the project will enable more effective reallocation and rebalancing of power generation resources. This approach is critical for managing the inherent uncertainties and dynamic variations in renewable energy supply, thereby ensuring a more robust, resilient, and secure power grid operation. 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.