Objectives

The Dynamic Contingency Analysis Tool (DCAT) will bridge the gaps in cascading outage analysis in a single, unique tool that automatically simulates and analyzes cascading sequences in real systems. To do this it will

  1. Equip the power industry with the ability to simulate, understand, predict, and prevent consequences of major disturbances on the grid. These disturbances include cascading events that can lead to widespread power supply interruptions. The simulation component includes the modeling accuracy, speed of computations, and comprehensiveness considerations. These components are important because of the multitude of possible causes of cascades and multiple variants of cascade development. 
  2. Overcome the difficulties facing the power industry in implementing the North American Electric Reliability Corporation (NERC) Standard TPL-001-4, “Transmission System Planning Performance Requirements,” states “studies shall be performed to assess the impact of the extreme events.” Applying the open-platform, publicly available DCAT methodology can help power system planning engineers to assess the impact and likelihood of extreme contingencies and potential cascading events across their systems and interconnections. Outputs from the DCAT will help find mitigation solutions to reduce the risk of cascading outages in technically sound and effective ways. The current prototype DCAT implementation has been developed as a Python code that accesses the simulation functions of the Siemens PSS®E planning tool (PSS/E) and is being extended to other widely used commercial tools, such as Positive Sequence Load Flow (PSLF).
  3. Develop a data management system that enables future integration of DCAT with the industry grade big data analytical and interactive visualization platforms. This will help power system engineers to thoroughly understand and analyze system behavior under large amounts of scenarios and contingencies. To achieve this, big data will be properly handled at different layers.
  4. Create a visualization that can be easily used with various domains to help the user more easily discover and explore data.
  5. Develop a framework that enables running DCAT on cluster machines to utilize the power of high-performance computing (HPC) machines. This work leverages GridPACK’s task manager capability to obtain optimal speedup.