Natural gas and electric power systems have become the backbone of the U.S. energy infrastructure. This EArly-concept Grant for Exploratory Research (EAGER) project will investigate practical data-based approaches for producing simulated and synthetic datasets that faithfully represent the interdependence between the two critical infrastructure systems from mechanistic and human aspects. The project contributes to the grand national challenge of modernizing energy systems by laying the data foundation for future research in interdependent critical energy infrastructures. The research results will lead to publications as well as multi-disciplinary training opportunities that integrate data analysis and energy engineering for graduate and undergraduate students. By forging strategic alliances with the utilities in the Midwest and national laboratories, webinars on natural gas and power network data analysis will be given to a broad array of engineers and researchers on the results of this work.
The project will pioneer data-based approaches to understand and model the interdependence between natural gas and power networks. The interactive data generation method provides high-fidelity datasets with different spatial-temporal granularities and operation conditions. In particular, mechanistic principles and human impacts inherent in gas-electric systems are identified from practical data using graph-based and learning-based approaches. These generated datasets will be validated using practical data, and be available online through a project website, together with a list of use cases that leverage the data and existing modeling approaches to advance the understanding of gas/power network interdependence in terms of strong/weak coupling effects, economic operations, cascading outages, etc. The project promotes an interdisciplinary effort in science and technology from data analysis, graph theories, complex networks, as well as power and natural gas engineering to provide fundamental knowledge about synthetic data generation for critical interdependent infrastructures.
