Probabilistic Risk Assessment (PRA) has been widely used to estimate the risk of engineering systems including nuclear power plants. Dynamic PRA represents a category of advanced methods for PRA and improves upon conventional PRA. Dynamic PRA methods generate scenarios using a computational process, typically simulation of scenarios, which allows the analysis to consider significantly more scenarios and complex dependencies. Existing dynamic PRA studies make use of many differing case studies to demonstrate their contributions, such as the integration of operator performance models, passive component modeling, and computational cost reduction. It is challenging to use the existing case studies in the literature to compare existing methods, and to clearly demonstrate the advantages of dynamic PRA. In this paper, a new case study for dynamic PRA studies is designed and proposed as a common test case.
Station blackout is selected as the initiating event for the case study because of its relatively large contribution to system risk as well as the strongly physics-dependent nature of the corresponding accident progression. This allows the results of the case study to clearly demonstrate the advantages of dynamic PRA on an important type of accident in a practical system. In addition to the initiating event, the case study includes a list of stochastic events to consider along with corresponding probabilistic models. The stochastic events can be categorized as failure on demand, time distributed failure to run, operator actions, and physics dependent failures. These categories are representative of the variety of possible events in a nuclear power plant risk assessment.