Stephen M. Hess

Prioritizing Project Selection

We consider capital investments under uncertainty. A typical approach to this problem, when the problem parameters are assumed known, is via a multi-knapsack model. This model takes as input annual budgets as well as the cost streams and profit—i.e., net present value (NPV)—of each project. Its output is a portfolio of projects with the highest total NPV, observing yearly budget constraints. We argue that such a portfolio fails to hedge against uncertainties in the budgets, the cost streams, and the profits. As an alternative, we propose a model that forms an optimal priority list of projects, incorporating multiple scenarios for these input parameters. We apply our approach to two sets of example projects from the South Texas Project Nuclear Operating Company.

Pilot application of risk informed safety margin characterization to a total loss of feedwater event

In this paper we present the results of application of a risk-informed safety margin characterization (RISMC) approach to the analysis of a loss of feedwater (LOFW) event at a pressurized water reactor (PWR). This application considered a LOFW event with the failure of auxiliary feedwater (AFW) for which feed and bleed cooling would be required to prevent core damage. For this analysis the main parameters which impact core damage for the scenario were identified and probability distributions were constructed to represent the uncertainties associated with the parameter values. These distributions were sampled using a Latin Hypercube Sampling (LHS) technique to generate sets of sample cases to simulate using the MAAP4 code. Simulation results were evaluated to determine the safety margins relative to those obtained using typical probabilistic risk assessment (PRA) modeling (success criteria) assumptions.

Development of a dynamical systems model of plant programmatic performance on nuclear power plant safety risk

Application of probabilistic risk assessment (PRA) techniques to model nuclear power plant accident sequences has provided a significant contribution to understanding the potential initiating events, equipment failures and operator errors that can lead to core damage accidents. Application of the lessons learned from these analyses has resulted in significant improvements in plant operation and safety. However, this approach has not been nearly as successful in addressing the impact of plant processes and management effectiveness on the risks of plant operation. The research described in this paper presents an alternative approach to addressing this issue. In this paper we propose a dynamical systems model that describes the interaction of important plant processes on nuclear safety risk. We discuss development of the mathematical model including the identification and interpretation of significant inter-process interactions. Next, we review the techniques applicable to analysis of nonlinear dynamical systems that are utilized in the characterization of the model. This is followed by a preliminary analysis of the model that demonstrates that its dynamical evolution displays features that have been observed at commercially operating plants. From this analysis, several significant insights are presented with respect to the effective control of nuclear safety risk. As an important example, analysis of the model dynamics indicates that significant benefits in effectively managing risk are obtained by integrating the plant operation and work management processes such that decisions are made utilizing a multidisciplinary and collaborative approach. We note that although the model was developed specifically to be applicable to nuclear power plants, many of the insights and conclusions obtained are likely applicable to other process industries.

Application of risk informed safety margin characterization to extended power uprate analysis

In this paper we present some initial results of the application of a risk-informed safety margin characterization (RISMC) approach to the analysis of the impact of an extended power uprate (EPU) on plant safety for selected transient and accident sequences. These initial applications were conducted to demonstrate the feasibility and practicality of using the RISMC approach to analyze the safety impact of EPUs at both a pressurized water reactor (PWR) and a boiling water reactor (BWR). For the PWR application, the analysis focused on the loss of main feedwater (LOMFW) event with failure of auxiliary feedwater (AFW) where feed and bleed (FB the results obtained were then compared to those for the current nominal full power. The results obtained indicate, as expected, that safety margins may be reduced with increases in plant power level. However, for most power uprate levels, these safety margin reductions were found to be small. A limited study of margin recovery strategies was performed for the PWR case that indicated that minor to moderate changes in plant operation or design could be used to recover the safety margin reduction that would occur from the power uprate.

Optimizing Project Prioritization Under Budget Uncertainty

We consider a problem commonly faced in the nuclear power industry, involving annual selection of plant capital investments under the constraints of a limited and uncertain budget. When the budget is assumed known, a typical approach to such problems is built on a multi-dimensional knapsack model. This model takes as input the available budget in each year, the stream of liabilities induced by selecting each project, and the profit, i.e., net present value (NPV), of each project. The goal is to select the portfolio of projects with the highest total NPV, while observing the budget constraint for each year, as well as any additional constraints. We show that a portfolio selected in this manner can fail to hedge against uncertainties in the budget. While the budget may be known at the beginning of the planning period, external events can cause this to change as time unfolds, and hence the funds that will actually be allocated over time are typically uncertain. So, we propose a model that forms an optimal priority list of projects, incorporating multiple budget scenarios. The model is applied to example projects from the South Texas Project Nuclear Operating Company (STPNOC).

Analysis and Insights from a Dynamical Model of Nuclear Plant Safety Risk

In this paper, we expand upon previously reported results of a dynamical systems model for the impact of plant processes and programmatic performance on nuclear plant safety risk. We utilize both analytical techniques and numerical simulations typical of the analysis of nonlinear dynamical systems to obtain insights important for effective risk management. This includes use of bifurcation diagrams to show that period doubling bifurcations and regions of chaotic dynamics can occur. We also investigate the impact of risk mitigating functions (equipment reliability and loss prevention) on plant safety risk and demonstrate that these functions are capable of improving risk to levels that are better than those that are represented in a traditional risk assessment. Next, we analyze the system response to the presence of external noise and obtain some conclusions with respect to the allocation of resources to ensure that safety is maintained at optimal levels. In particular, we demonstrate that the model supports the importance of management and regulator attention to plants that have demonstrated poor performance by providing an external stimulus to obtain desired improvements. Equally important, the model suggests that excessive intervention, by either plant management or regulatory authorities, can have a deleterious impact on safety for plants that are operating with very effective programs and processes. Finally, we propose a modification to the model that accounts for the impact of plant risk culture on process performance and plant safety risk. We then use numerical simulations to demonstrate the important safety benefits of a strong risk culture.

Project Prioritization via Optimization

We consider a problem commonly faced in industry, involving annual selection of plant capital investments. A typical approach to such a problem uses a multi-knapsack formulation, which takes as input the available budget in each year, the stream of liabilities induced by selecting each project, and the profit, i.e., net present value, of each project. The goal is to select the portfolio of projects with the highest total net present value, while observing the budget constraint for each year, as well as any additional constraints. A portfolio selected in this manner can fail to hedge against uncertainties in the budget, the liability stream and the profit. So, we propose a model that forms an optimal priority list of projects, incorporating multiple scenarios for these input parameters. Our model is not a simplistic ranking scheme. Structural and stochastic dependencies among the projects are key to our approach. We apply our methods on a set of example projects from South Texas Project Nuclear Operating Company.Copyright

Risk-Informed, Technology-Neutral Design and Licensing Framework for New Nuclear Plants

The regulatory framework for the current generation of operating plants and advanced light water reactors (ALWRs) planned for near term construction has evolved over several decades to permit effective regulation of the light water reactor (LWR) designs. To address other reactor types, development of a framework that possesses the attributes of being technology neutral, risk-informed and performance-based with corresponding processes (regulations and guidance) is ongoing by several U.S. and international organizations. There are different visions for a revised design and licensing framework applicable to advanced (i.e. Generation III “Plus” and IV) reactors. The dominant visions are represented by activities underway at the US Nuclear Regulatory Commission (NRC), at two gas cooled reactor vendors and by the American Nuclear Society (ANS). To support development of a revised framework, the Electric Power Research Institute (EPRI) conducted research to identify and assess specific elements of possible technology neutral, risk-informed, performance based frameworks; to develop a preliminary integrated reference framework based on the results of this evaluation; and to provide recommendations in areas where additional development and testing would appear to be most beneficial. In the research discussed in this paper the key features of these various proposed frameworks were reviewed and combined into an integrated reference framework. This integrated reference framework includes elements that are intended to provide the roadmap for successfully designing and licensing an advanced reactor design if such an approach is used. The results of this research are being used to support Industry efforts to develop standards and guidance for advanced plants with safety characteristics which differ from existing and advanced LWRs.Copyright

Investigation of Nuclear Plant Safety Utilizing an Analytical Risk Management Model

In this paper we present use of risk management as an approach to control nuclear plant safety risk. This approach relies on processes currently embedded in the organizational structure and business practices of commercially operating plants; thus it can be implemented at a cost that is much lower than recent applications of risk-informed, performance-based regulatory initiatives. Additionally, a mathematical dynamical systems model that accounts for the interaction of those processes that provide significant impact on plant risk is presented. Some results from application of the model and insights obtained from its analysis are discussed.

Integration of Degradation Models Into Generation Risk Assessment: Challenges and Modeling Approaches

The main objective of generation risk assessment (GRA) is to assess the impact of equipment unavailability and failures on the ability of the plant to produce power over time. The system reliability models employed for this purpose are based on the standard fault tree/event tree approach, which assumes failure rates to be constant. However, this ignores the impact of aging degradation and results in static estimates of expected generation loss. Component and equipment degradation not only increases the probability of failure over time, but also contributes to generation risk through increased unavailability and costs arising from greater requirement for inspection and replacement of degraded components. This paper discusses some of the key challenges associated with integrating the results of component degradation models into GRA. Because many analytical and simulation methods are subject to limitations, the methodology and modeling approach proposed in this work builds on the current GRA practice using the fault tree approach. The modeling of component degradation can be done separately at the fault tree cut set level, assuming the cut sets are independent and the component unavailabilities are relatively small. In order to capture the joint contribution of equipment failure and unavailability to generation risk, new risk-based importance measures are also developed using the concept of net present value.