Small Modular Reactors (SMRs) are expected to offer enhanced deployment flexibility and modular construction compared to traditional nuclear power plants (NPPs). However, during early design stages, plant-level availability and its economic implications are difficult to quantify due to limited component-specific reliability data and substantial parametric uncertainty.
This work develops a probabilistic framework that integrates conventional probabilistic risk assessment (PRA) modeling practices with Monte Carlo simulation to estimate plant availability under epistemic uncertainty in failure and repair parameters. Component reliability inputs are represented as lognormal distributions derived from generic data sources and associated error factors, enabling structured uncertainty characterization while maintaining consistency with established PRA methodologies.
Monte Carlo sampling propagates uncertainty from component-level parameters through system configurations to plant-level availability metrics and annual generation estimates. The framework further links availability outcomes to a simplified revenue model, enabling preliminary quantification of how reliability uncertainty may influence energy production and revenue variability in early-stage SMR deployment scenarios.
Results illustrate the sensitivity of plant-level availability and generation metrics to uncertainty assumptions, highlighting the potential magnitude of variability in economic performance attributable to reliability parameter dispersion. The proposed methodology provides a transparent approach for integrating PRA-based reliability modeling with generation and financial metrics, supporting risk-informed design and investment decisions for emerging reactor technologies.