## 2 Techno-Economic Assessment of CSP Based on sCO\({}_{2}\)
### Economic Assessment  
The calculations presented in Figure 2b above account for uncertainty regarding the capital costs of components. Using a Montecarlo analysis, each component cost is assigned a probability \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Net Power Output** & \(P_{max,co_{2}}\) & **TTT** & TES\({}_{capacity}\) & **SM** & DN\({}_{nom}\) & **T\({}_{amb,nom}\)** \\
**[MWe]** & **[MPa]** & **[ \({}^{\circ}\)C]** & **[hour]** & **[-]** & **[W/m\({}^{2}\)]** & **[ \({}^{\circ}\)C]** \\ \hline
50 & 30 & 750 & 10 & 2.4 & 850 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specifications of the reference sCO\({}_{2}\) power plant. Figure 2: Effect of maximum cycle pressure on thermal efficiency and Overnight Capital Cost per kilowatt. Legend: Simple Recuperated (SR), Transcritical (TC), Partial Cooling (PC), Recompression (R), Pre-compression (PrC) and Allam (AI). Data adapted from [10]. density function whereby costs are randomly taken from a given range of values with different probability: the cost of components with a lower maturity are allowed to change in a wider range whereas this range is much narrower for well established technologies. According to these calculations, uncertainty can potentially change the capital cost of the power block by plus/minus one-third of the cost, which is aligned with the information provided in the work by [PERSON] et al. for the National Energy Technology Laboratory and Sandia National Laboratories in the USA. In this latter work, the authors relied on data provided by Original Equipment Manufacturers to estimate the installation costs of small and large power plants operating on gaseous and solid fossil fuels [21]. The results, which also accounted for uncertainty of the input data provided by vendors, were similar to those reported in [10] both in terms of the total values and the variability brought about by uncertainty. As discussed by [PERSON] et al., this is very likely due to the lack of a well established market that prevents engineering costs from being charged on very few clients [22]. The data presented in Figure 2 in the previous section are expanded in Table 3, the economic values of which are calculated for the 85% confidence interval based on the probability density functions discussed in [10]. The table provides information about the main thermodynamic features--thermal efficiency (\(\eta_{th}\)) and temperature rise across the solar receiver (\(\Delta T_{solar}\))--and about the Overnight Capital Cost of the plant and the contribution of each major equipment (TES, Solar Field, Tower/Receiver and Power Block). According to these data, the _Transcritical CO\({}_{2}\)_ (TC) and _Recompression_ (RC) layouts yield the lowest and highest installation costs, respectively, 5656 and 6867 \(\$/kW_{inst}\), while the Partial Cooling (PC) cycle seems to provide the best compromise between thermal and economic features, closely followed by the Allam (Al) cycle.  
The information shown so far confirms that the installation costs based on sCO\({}_{2}\) power cycles are comparable or even lower (for some layouts) than for steam turbines used in state-of-the-art CSP plants \(-\)5800 \(\$/kW_{inst}\) according to [23]. The large cost share of the Thermal Energy Storage system is also confirmed, which puts the temperature rise across the solar receiver stems forward as a critical factor involved in plant design, given its very strong impact on the inventory of salts that is needed to operate a plant of given output and storage capacity. Finally, heat exchangers stem as the most relevant individual component in the power block cost-wise, Figure 3, with a larger share than turbomachinery; this confirms earlier comments in this section. A closer look into the operation of the cycles compared in Table 3 reveals that the _Transcritical CO\({}_{2}\)_ cycle requires the most restricting boundary conditions in order to achieve the lowest installation costs in the list.