## 6 Discussion  
The proposed optimization approach which consists of the two main modules for cost-function generation and the mathematical programming model allows for detailed cost analysis of the individual TES technologies. At the same time, the approach yields  
Figure 16: Cutout of the thermal storage and steam generator loads, steam demand and electricity price profiles for Case 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technology** & **Max. Heat Load (MW)** & **Capacity (MWh)** & **Investment Costs (k\(\epsilon\))** & **Energy Costs (k\(\epsilon\)/y)** \\ \hline Electric boiler & 3.8 & - & 950.0 & 187.8 \\ High-temperature heat pump & 1.2 & - & 1220.0 & 123.0 \\ Latent heat thermal energy & 3.2 & 13.2 & 286.0 & - \\ storage & 1.1 & 1.1 & 44.4 & - \\ Concrete storage & - & - & 2505.5 & 310.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Optimal system configuration and resulting costs for Case 2. Figure 15: Optimal P2H system for Case 2 including electric boilers, high-temperature heat pumps, LHTS and concrete storage. important decision-support when it comes to selection of cost-efficient TES for The minus sign on the y-axis is related to the font used for plotting in python matplotlib and cannot be easily changed at this point.a specific industrial plant but also to evaluate economic benefits that might emerge from a P2H-system including TES. The two presented cases and especially the cost structures for the different TES technologies show that case-specific cost estimations with special emphasis on the heat load and temperature requirements is necessary in order to identify the most cost-efficient TES solution. The data presented in Figures 9 and 15 are also shown in Tables 6-8 to facilitate the following discussion. availability of appropriate PCMs with both low costs and high volumetric energy density is a decisive factor regarding cost-effectivity. The volume-specific costs for the selected PCM in Case 2 (LDPE at 500 \(\epsilon\)/m\({}^{3}\)) is only half of the costs for Case 1 where a more expensive salt mixture had to be considered (KNO\({}_{3}\)-NaNO\({}_{3}\) at 1000 \(\epsilon\)/m\({}^{3}\)). Case 2 showed that heat load requirements can be a major cost driver for LHTS and concrete storages with approximately doubling costs with doubling maximum heat loads from 0.94 Mc and 1.08 Mc for the High Cap./Low HL scenario to 1.80 Mc and 1.96 Mc for the High Cap./High HL scenario respectively. In Case 2, the relatively low temperature differences for charging and discharging between the HTF and the storage material require large amounts of tubing to establish a sufficient heat transfer. This, in turn, increases the overall volume of the storage and thus increases insulation costs and adds costs for the container structure. In Case 2, for both LHTS and concrete storage tubing makes up for 57% and 46% of the overall storage costs in the High Cap./High HL scenario (Table 7) compared to Case 1 where in the High Cap./High HL scenario tubing costs make up for only 13.0% in the case of LHTS and 11.6% for the concrete storage (Table 6). Some cost drivers considered within the cost functions showed only minor impact to the overall storage costs; e.g. heat exchanger (HX) costs that were considered for molten salt storage made up for a maximum of only 3%. The motors considered for pumping of liquid salt showed even less impact with less than 0.5% of total storage costs. The molten salt costs were in both cases dominated by the storage material costs. In the proposed approaches for cost-function generation, some aspects that might have a significant effect on costs, were not fully considered. Economy of scales was only considered for steel plates but was not applied for storage material costs. For large-scale applications such as Case 1, this effect might change the cost structure of the individual storage, as well as the choice of cheapest storage technology. In Case 1, the storage material was responsible for 50-85% of the total TES costs for molten salt, LHTS and concrete TES.