Methods and Tools to Assess Robustness of Nuclear Plant Outages

Refueling outages are one of the most challenging phases in a nuclear power plant (NPP) operating cycle. They are extremely costly for an NPP due to the large amount of required resources and lost revenue from the plant being off the grid. Outage durations have steadily decreased across the industry over the last few decades primarily due to improved planning and coordination, but there are still many plants that struggle to meet the performance metrics accomplished by other utilities. Schedule resilience is one of the issues. NPP outages require scheduling thousands of activities within an average of 30 days. Despite detailed planning, once the outage starts, numerous emergent issues typically appear along with schedule delays, requiring continuous replannig and adjustment. When a schedule disruption occurs during an outage, plant staff make urgent efforts to recover but are often not able to maintain the planned outage duration. These outage delays can cost a utility several millions of dollars per day. Tools that could help outage schedulers create a more resilient schedule and allow them to optimally reschedule emergent work could significantiy reduce outage delays. One key aspect of creating a resilient schedule is to have accurate estimates of activity duration. Another important outage scheduling capability is the ability to schedule emergent work with minimal disruption. This paper focuses on developing tools and methods to support NPPs with outage schedule optimization and describes the initial development of tools to support outage management that leverage computational and machine learning methods.