An HPC based real-time path rating calculation tool for congestion management with high penetration of renewable energy

Every year, transmission congestion costs billions of dollars for electricity customers. This clearly identifies the critical need for more transmission capacity and also poses big challenges for power grid reliability in stressed conditions due to heavy loading and in uncertain situations due to variable renewable resources and responsive smart loads. However, it becomes increasingly difficult to build new transmission lines, which typically involve both economic and environmental constraints. In this paper, advanced computing techniques are developed to enable a non-wire solution that realizes unused transfer capabilities of existing transmission facilities. An integrated software prototype powered by high-performance computing (HPC) is developed to calculate ratings of key transmission paths in real time for relieving transmission congestion and facilitating renewable integration, while complying with the North American Electric Reliability Corporation (NERC) standards on assessing total transfer capabilities. The innovative algorithms include: (1) massive contingency analysis enabled by dynamic load balancing, (2) parallel transient simulation to speed up single dynamic simulation, (3) a non-iterative method for calculating voltage security boundary and (4) an integrated package considering all NERC required limits. This tool has been tested on realistic power system models in the Western Interconnection of North America and demonstrates satisfactory computational speed using parallel computers. Various benefits of real-time path rating are investigated at Bonneville Power Administration using realtime EMS snapshots, demonstrating a significant increase in path limits. These technologies would change the traditional goals of path rating studies, fundamentally transforming how the grid is operated, and maximizing the utilization of national transmission assets, as well as facilitating integration of renewable energy and smart loads.