Development of performance goals for SSCs

During the design process, the initial set of SSC performance goals will be derived from actuarial data and expert judgment. These goals will be further refined as the design matures, and will be finalized at the time that procurement specifications are issued. The PSA and RAM analyses will be used to confirm that these SSC performance goals are consistent with the overall pant safety and economic goals. One overall approach to the development of plant level performance goals is shown in Fig. 5-3.

FIG. 5-3. Development of plant level goals.

5.4.1. Performance goals for generation 5.4.1.1. Plant level goals (generation)

Plant level goals are defined from a broad economic analysis and assurance that they comply with specific requirements which have been imposed by either the regulatory authorities or the owner. A description of one process which can be used to define these goals is provided below. However, as a practical matter, NSSS vendors and architect engineers generally provide a pre-certified or pre-licensed nuclear plant design package which implicitly, or explicitly, defines performance which is consistent with the generally accepted levels identified by the EPRI Utility Requirements Documents, the EUR or their equivalents used by other Member States.

In this particular situation, the options open to the plant owner may be limited to the selection, sizing and control philosophy at the component or major equipment level. The approach suggested and described in these guidelines does not presuppose any restrictions on the freedom of the plant owner to make choices, so that the full process can be described “in context”.

The issues which have an important influence on the definition of high level plant performance goals lie both outside, and within, the scope of the basic plant design. Hardware design parameters (redundancy, diversity and operability) influence its reliability and capacity factor, but the generation mix, system size and system reliability criteria determine its economic worth. This means that to define the optimum plant reliability and capacity factor goals, it is necessary to be able to compare their incremental worth for each feasible option with the costs of achieving them. This is done by, first, examining the normally operating generation systems and defining the feasible plant configurations:

(a) NSSS size, type (BWR, PWR, number of loops), manufacturer and whether evolutionary or innovative;

(b) Nuclear power control philosophy (solid or liquid absorbers) and ability to quickly change power and run-back on loss of load;

(c) Main steam and energy conversion systems:

- Turbine generator configuration, e.g. manufacturer, fast valving options, type of reheat, number of LP units,

- Turbine bypass capability and the plant’s ability to run in a self-sustaining mode following a load rejection or loss of electrical grid without SCRAM;

(d) The type of heat sink, e.g. run-of-river or cooling towers;

(e) Condensate and feedwater system, specifically:

- Deaerator and system control margin which accommodate condensate and feedwater transients without feed-pump trip,

- Heater drains and drain cooler configurations,

- Number and types of condensate, condensate booster and main feedwater pumps (variable speed electric, turbine driven, etc.) and control philosophy (auto run-back, etc.),

- Steam generator manufacture, design and type.

The expected reliability and capacity factor will be predicted for each basic design configuration, either by quantifying specially developed RAM models, or, by using a combination of expert opinion and historical information from similar plants.

An electrical grid system simulation model will provide the basis for a series of sensitivity analyses to predict the likely differences between annual and life cycle system operation.

Because the system simulation uses the expected mix of generation, awaiting plant seasonal availability and efficiency and the predicted seasonal system demands it can provide good estimates of the relative benefits expected from each specific plant configuration. Since the costs of installing each option are generally well characterized, the optimal reliability and capacity factor can be determined from a comparison between averted outage costs and the costs of increased system reliability or availability.

Results from quantification of the RAM models for the selected design option become the candidate plant level performance goals.

The remaining issue is whether uncertainty in the point estimate values from the RAM analysis should influence selection of appropriate goals. Because it is generally appropriate to set goals which are challenging, provided they are feasible, it may be appropriate to select the final goal at the “one-sigma” level, i.e. one standard deviation above the mean value, because it should be feasible, and not far enough away from the mean to make it economically unjustified. The ultimate decision must be made by the owner, although the approach provided above will quickly move the decision into an area in which the technical and economic bases are well defined.

5.4.1.2. System and major component goals

The RAM model previously developed and quantified for the selected design option is used as the basis for a series of sensitivity analyses, in which each feasible change to plant systems and train is superimposed on the model to calculate its corresponding effect on plant reliability and capacity factor.

The costs associated with each change are compared to the benefits they return and an optimization algorithm is used to identify the least cost configuration which meets the overall plant level goals. This model becomes part of the preliminary design basis and the levels of performance assumed by the model become the basis for the system and sub-system performance goals. The uncertainty in each sub-system goal is used to guide their modification to make them feasible, yet challenging.

Figure 5-3 depicts one process which could be used to define the preliminary set of economic and safety goals for the ALWR.

5.4.2. Performance goals for safety 5.4.2.1. Plant level goals (safety)

The approach to selecting high level safety goals is similar to that used for performance goals, except that the level 1, 2 or 3 reliability models replace RAM models as the basic computational tool used in the goal setting process. The selected plant configuration, with the

baseline safety system configuration provided by the NSSS vendor and Architect engineer, is used to construct the initial PSA (probably a level 1 or 1/2, defined to the subsystem level).

Following prediction of the baseline estimates of core damage frequency and confirmation that it is consistent with the plant’s ability to meet applicable safety goals, the design optimization process begins. Within this optimization process, the design engineer uses the PSA to perform a series of high level sensitivity studies by modifying the reliability models to reflect various system configurations and success criteria and calculating the corresponding benefits, measured as changes in the predicted CDF, CCFP or large early release frequency (LERF).

When the magnitudes of the risk reductions which are predicted to occur as a result of various proposed changes to the plant baseline configurations are compared to their individual costs, an optimization strategy is used to identify the best overall configuration, i.e. the configuration which produces the greatest risk reduction from the baseline, at the minimum cost. This configuration becomes the basis for the new reference design after a detailed solution of this model has provided assurance that the plant is expected to comply with each prescribed safety goal.

5.4.2.2. System and major component goals (safety)

The PSA is used to develop system level goals in much the same way that the RAM models were used for generation system goal setting, although in this case there is not only a desire to meet the prescribed goals but also try to define a design in which the individual contributors to risk are of the same magnitude, i.e. there are no dominant classes of contributors. A first approximation to this may result if the plant level goal is prorated amongst each initiating event category, and their frequencies and their overall contribution to the plant level goal used to define system or function level goals.

The PSA and cost models are then used to reapportion train and component reliability and unavailability to maintain the system level goals at its nominal value, while simultaneously evaluating total cost. An optimization algorithm will provide the first optimal solution which in turn will identify the preliminary set of system and component level goals and the preliminary design configuration, whose uncertainty will be used to adjust their single point values to levels which are both feasible and challenging.

Successful completion of this task will result in the definition of a baseline plant configuration and a set of front line system performance goals which can be used by the design team to further focus their efforts on achieving a design which exhibits the greatest degree of safety for the lowest possible cost. Performance goals for the support system infrastructure will be assigned later during the plant design process, however, the design team will again use the results and qualitative insights from the PSA to identify those support system structures and interconnections which preserve the defined front line system goals at minimum cost.

A process by which the preliminary system goals can be allocated for both generating and safety systems is shown in Fig. 5-4.

FIG. 5-4. System goal allocation (preliminary).

5.4.3. Optimization of the performance goals

The design team will exploit the capabilities of the preliminary RAM and PSA models, which have themselves evolved with the design, to identify the optimal SSC performance goals. In principle, this requires the goal setting team to:

- Perform sensitivity analyses to identify the potential worth, from improved plant generation or safety, for the range of feasible system “upgrades” or enhancements,

- Estimate the costs to achieving the reliability which would result from the implementation of each configuration used in the sensitivity analyses,

- Use the resultant system reliability-cost-benefit curves as input to an optimization algorithm to find the optimal plant configuration,

- Confirm that the resultant design meets all applicable design criteria and is suitable as the basis for the plant reference design,

- Calculate the default values for system reliability and availability and assign them to be the optimized plant performance goals,

- Incorporate the effects of uncertainty on the goals to ensure that they are challenging, - Comparing the new design with past designs and the magnitude of the proposed goals

with past operating experience, and use judgment to confirm the feasibility of the goals.

The generation and safety System goal Optimization process is shown in flow chart form by Fig. 5-5.

FIG. 5-5. System goals of optimization and allocation.

5.5. ANALYTICAL MODELS