Methodology to evaluate the monetary benefit of Probabilistic Risk Assessment by modeling the net value of Risk-Informed Applications at nuclear power plants

Justin Pence, Marzieh Abolhelm, Zahra Mohaghegh, Seyed Reihani, Mehmet Ertem and Ernie Kee

Reliability Engineering and System Safety, 2018, vol. 175, issue C, 171-182

Abstract: Probabilistic Risk Assessment (PRA) used in Nuclear Power Plants serves as a pillar of the U.S. Nuclear Regulatory Commission's Risk-Informed Regulatory framework, and is required for new reactor licenses to satisfy regulatory safety compliance. The benefits of PRA are not only experienced in terms of safety, but also from the monetary value derived from Risk-Informed Performance-Based Applications (RIPBAs), where risk estimated from PRA is utilized in decision making to expand the safe operational envelope of plants, leading to either an increase in profits or a reduction in costs. This paper introduces a methodology to evaluate this monetary value by the systematic causal modeling of the net value of RIPBAs and demonstrates the methodology for one of the RIPBAs, called Risk-Managed Technical Specifications (RMTS). The key steps of this methodology are: (i) Cost-Benefit Analysis to formulate the net value of PRA based on the net value of RIPBAs, (ii) Causal modeling to systematically model the operational scenarios leading to costs and benefits associated with RIPBAs, (iii) Uncertainty analysis, and (iv) Sensitivity analysis and validation. The results of this research could help decision makers with evaluating investment strategies in PRA that go ‘beyond-compliance’ to maximize industry profit while maintaining regulatory safety goals.

Keywords: Probabilistic Risk Assessment; Risk-Informed Performance-Based Applications; Causal Modeling; Decision Tree; Cost-Benefit Analysis; Uncertainty Analysis; Global Sensitivity Analysis; Risk-Managed Technical Specifications; Risk-Informed Decision Making (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:175:y:2018:i:c:p:171-182

DOI: 10.1016/j.ress.2018.03.002

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