Usually, the calculation results of such analysis depend on initial parameters and modelling techniques. In any case, having quite uncertain parameters, the influence of the main initial parameters to the main results may be investigated performing uncertainty and sensitivity analysis. In particular case, the analysis focuses on how the calculation results could change, when the initial parameters describing IRIS-like technology in MESSAGE model are changed. In addition, the most important parameters for the precision of calculation results were also indentified.

The main parameters and their possible values (describing the IRIS-like technology in the model) for different scenarios are presented in Table 3.

The reference values of some parameters presented in this table are taken from (Alzbutas & Maioli, 2008). The possible variations of these parameters are based on calculation assumptions. For instance, in the calculations it was assumed that the EPZ could change from 0 to 30 kilometers. In the model this is represented by the length of additional heat supply pipe in order to connect IRIS-like NPP with cogeneration option to the existing district heating network. In addition, the number of IRIS units in the MESSAGE model is adapting depending on specific conditions in the modelled energy system.

Following the uncertainty analysis the sensitivity measure PCC (see Fig. 9) describes how the initial conditions and model parameters (see Table 3) influence the result. From the sensitivity analysis we can see that the 3rd parameter (discount rate) has the largest (negative) influence on the total system costs (main modelling result). When this parameter increases, the considered model result decreases most significantly. Alternatively, the increase of nuclear fuel price (the increase of 6th parameter) in the considered range has the lowest influence.

In general, a high discount rate gives more weight or importance to present expenditures than to future ones, while a low discount rate reduces these differences and thus favours technologies that have high investment cost but low operation costs (for example NPPs).

I parameter

Fig. 9. Sensitivity measure and determination coefficient (R**2) for total system cost

In this case study, the sensitivity measure, which is a product of the statistical analysis, shows which sources of uncertainty are contributing most to the uncertainty in the predicted energy system performance (see Fig. 9). But it is possible that sensitivity measure, in this case a Partial Correlation Coefficient (PCC), explains too small a fraction of the variability of the model output values, for instance, if coefficient of determination is less than 0.5. However, for analyzed case the coefficient of determination is 0.99. Thus, in this case the sensitivity measure PCC in a very good way express the relation in variability and analyst can easily determine which model parameters should be controlled better in order to decrease unfavourable changes of results. Alternatively, the analyst can determine which parameters could be less precise without substantially affecting results.

2. Conclusions

1. While innovative design solutions are possible in an early design stage to cope with extreme internal events, the need for integrating external events considerations on a probabilistic basis at a relatively early design stage is going to be another challenge for effective and balanced use of PSA as a support of the design phase.

2. Further progress of PSA application and EPZ definition could be achieved via discussion with national regulatory authorities in those IAEA Member States that are considering performance-based and risk-informed licensing approaches for future NPPs.

3. Construction of SMR units is very attractive option (looking from economical point of view) for the future electricity and heat generation. The option with SMR cogeneration mode may cause the lowest total discounted cost among the scenarios analyzed.

4. In the case, when IRIS cogeneration unit should be installed away from existing district heating networks (due to EPZ), the attractiveness of this unit is decreasing gradually with distance, because of investment cost and heat losses in addition district heating pipelines.

5. The sensitivity analysis may be essential as it shows how particular parameter is important to the modelling results and where the accuracy of primary data could be increased (in order to decrease the uncertainty of the results). Alternatively, the analyst can determine which parameters could be less precise without substantially affecting results.

6. In our case, the discount rate has the highest influence on the total system costs, while the increase of nuclear fuel price in the considered range has the lowest influence to the total system costs.

3. Acknowledgment

Due to the international nature of the IRIS project, the cooperation can be treated as a trade mark of the IRIS project. This was even truer for the IRIS PRA. The authors wish to acknowledge the large support and valuable assistance of the IRIS heads M. D. Carelli and

B. Petrovic as well as of other members of the IRIS PRA team, especially D. J. Finnicum and

C. L. Kling. We also want to acknowledge the advises and useful discussions with L. E. Conway and L. Oriani from the IRIS design team. And last but not least we would like to extend thanks to J. Augutis and M. Ricotti for the great personal support provided during the initial stage of PRA related research. The publication of this chapter was funded by Westinghouse Electric Company, LLC.