SBIR Phase II: Cloud-Based Electric Power Grid Simulation Software With Equivalent Circuit Methods

Award ID: 1951083

The broader impact of this Small Business Innovation Research (SBIR) Phase II project will result from the ability to enable a safer, more reliable, and more resilient electric power system. A reliable and efficient grid is the cornerstone of the nations economy. With the rapid adoption of distributed energy resources (DERs), a transition from conventional generation plants to utility-scale wind and solar farms, and the threat of cyber- and/or physical attacks on the bulk electric system, ensuring the reliability and resilience of the grid has become ever more challenging. The software being developed in this project will help grid planning engineers and operators cope with these modern challenges. For the first time, engineers from organizations across the utility industry will be able to collaboratively plan the grid on a secure, scalable, cloud-based software platform that provides the capabilities needed to fully assess the impact of renewables and DERs. The softwares industry-leading simulation robustness will allow users to analyze extreme contingencies, cascading events, and weak grid scenarios to develop preventive and corrective actions that secure the grid. The software is designed to support an annual $50B+ in US power infrastructure investment and an annual $400B+ in power market transactions. This Small Business Innovation Research Phase II project will develop software for electric power transmission and distribution (T&D) planning. Technical tasks are to: 1) Make cloud architecture responsive to demand with autoscaling and queueing services; 2) Architect results generation for minimized data storage requirements; 3) Manage data handling for reduced re-loading of datasets; 4) Implement a file parser for industry-standard three-phase unbalanced distribution system models; 5) Implement models of common distribution system components; and 6) Extend the cloud platform to support integrated T&D analysis. Integration of these optimized components will lead to an improved system engineered for usage at scale. 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.

NSF Program Director: Peter Atherton