# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 3 Computational Results and Discussion
### Sensitivity Analysis: Effect on Simulation Accuracy, Location, Electricity Price, and Costs
#### 3.4.2 Effect of the Electricity Price  
A hypothetical RTP price is considered to see the effect of using electricity price with a considerably higher difference between peaks and valleys to compare the electricity price with that mentioned in Section 3.2.2 (RTP Spanish electricity price of 2023 for the first year). For the first year, we considered the selling price to be the hypothetical RTP price, as shown in Figure 26. Further, Figure 27 shows the difference between the peaks and valleys for each day of the year, and Figure 28 shows the probability density of these differences. The purchase prices were the same, and a fixed value of 0.01 EUR/kWh was used as the access charge. As in Section 3.2.2, the electricity price will be updated each year considering the inflation and the 'duck curve' effect. Figure 29 shows the daily average hourly electricity sale prices for different years, all referring to the beginning of the system (year 0). Comparing Figures 27-29 with Figures 11, 12 and 14 (sell price in Section 3.2.2) reveals that, for the hypothetical price, the difference between the maximum and minimum prices of each day is considerably higher. This situation is considerably better in terms of storage profitability. Table 7 presents a comparison of the main characteristics. In addition, we see that during daylight hours, the electricity price is higher than the Spanish price of 2023, and therefore, this situation is better for PV.  
Figure 24: Optimal systems found for the different locations: LSS systems.  
Figure 25: Optimal systems found for the different locations: PGS systems.  
Figure 28: Probability density of the daily maximum minus minimum sell price difference (EUR/kWh), first year. Hypothetical electricity price.  
Figure 26: Hourly sell electricity price of first year. Hypothetical electricity price.  
Figure 27: Daily maximum minus minimum sell price difference (EUR/kWh), first year. Hypothetical electricity price.  
Figures 30 and 31 show the configuration and NPC/NPV of the optimal systems (LSS and PGS systems, respectively) found for each project type, where we compare the results of Sections 3.2.5 and 3.3 (cases with electricity price of Spain, 2023, for the first year) with the results obtained in this section using the hypothetical RTP price. Even with the new hypothetical price (with a high difference between peaks and valleys), PHS is not worthwhile, because the results are worse than those of the systems without PHS. Compared to the results in Sections 3.2.5 and 3.3, the LSS optimal systems using the hypothetical electricity price include a much higher proportion of PV power because the selling electricity price during sun hours is higher than that in the case of the Spanish 2023 RTP price. The optimal systems without PHS do not include wind turbines.  
In both LSS and PGS, the differences between the optimal PHS type A and type B systems are low because the pump-turbine sizes of the optimal systems are the smallest.  
\begin{table}
\begin{tabular}{l c c} \hline
**RTP Electricity Price, First** & **Spain 2023** & **Hypothetical Price** \\
**Year Data** & **(Section 3.2.2)** & **Hypothetical Price** \\ \hline Hourly electricity price: & & \\ Average (EUR/kWh) & 0.0871 & 0.0598 \\ Standard deviation & 0.0414 & 0.056 \\ (EUR/kWh) & 0.221 & 0.3286 \\ Minimum (EUR/kWh) & 0 & 0.0009 \\ Average from 10 to 16 h: & 0.0661 & 0.0915 \\ Daily difference (max.–min.): & & \\ Average (EUR/kWh) & 0.0733 & 0.181 \\ Standard deviation & 0.0315 & 0.0485 \\ (EUR/kWh) & 0.191 & 0.3219 \\ Minimum (EUR/kWh) & 0.0043 & 0.0293 \\ \hline \end{tabular}
\end{table}
Table 7: First year electricity sell price data.  
Figure 29: Daily average hourly electricity price, referred to year 0. Hypothetical electricity price.