Risk-informed design: An unavailability allocation approach

Due to the technological complexity of nuclear power plants and the large number of components involved in their design, the objective space for balancing nuclear safety, availability performance, and cost in an optimization model becomes extremely vast. Consequently, this poses challenges in achieving operational optimization at the plant level, complicating the assurance of compliance with regulatory requirements. One solution is to partition the problem and analyze it at a system level to reduce the objective space. In this work, different options are analyzed to optimize cost and unavailability for systems that provide the means for meeting previously proposed unavailability targets. This ensures that licensing basis events (LBE) comply with risk targets and regulatory criteria. Additionally, the role of system unavailability due to maintenance activities and reliability issues related to unplanned component failures (e.g., random failures, common cause failures) in the optimization problem is analyzed. The various proposed optimization methods are implemented in the design of a pressurized water reactor (PWR) system. Evolutionary algorithms are used as optimization methods, with Genetic Algorithms for single-objective problems and Non-dominated Sorting Genetic Algorithm III (NSGA-III) for multi-objective problems. The main finding suggests that traditional models, which aim to minimize costs and unavailability, face challenges in adapting to the systems unavailability target. Therefore, optimization occurs by shifting the focus towards minimizing costs and distance to the unavailability target. This approach ensures that the results closely align with the unavailability target, thereby creating more efficiency in operating and maintenance costs while also ensuring acceptable design and operational regulatory compliance.