## 6 Sensitivity Analysis  
Results indicate that activation time for units providing primary frequency control services and thus the needed overall system inertia is one of the main drivers for increased overall system costs. However, carbon dioxide emissions increase at the same time. This is because it is more cost-efficient from a systems perspective to dispatch carbon-dioxide-emitting fossil fuel power plants than to invest in storage units providing synchronous inertia or SI. A market-driven solution would be to increase the costs for CO\({}_{2}\) emissions, e.g., by decreasing the number of tradeable certificates. Figure 9 depicts the development of increasing prices for CO\({}_{2}\) emissions for the NT scenario and 1 Hz/s, 2 s, parameter combination. For the analysis, price increments of 25 EURO/t\({}_{\text{CO}_{2}}\) are assumed. With increasing prices for CO\({}_{2}\) emissions, as expected, emissions decrease. The highest change is visible for increased prices from 200 EURO/t\({}_{\text{CO}_{2}}\) to 225 EURO/t\({}_{\text{CO}_{2}}\). Thereafter, carbon dioxide emissions do not decrease any further. In this case, fossil fuel generators are dispatched to meet power demand and not system inertia demand, hence, in times when power demand cannot be met by carbon-dioxide-emission-free generation or storage units.  
As already introduced, the scenarios in this research are based on the TYNDP of the ENTSO-E and the e-Highway 2050 study. Since the designed scenarios are characterised by high installed capacities of weather-driven generation sources such as onshore wind, solar PV or run-of-river units, the respective weather input is of high influence to the results. The 2020 TYNDP applies three different climate years from the Pan-European Climate Database, namely the climate years from 1982, 1984 and 2007 [48, 61]. The climate years are considered to be representative by the ENTSO-E [23]. To reduce complexity, the previous results are all modelled applying the 1984 climate year data sets. However, the impact of the climate years 1982 and 2007 on the overall system costs is analysed in this sensitivity section as well. Table 8 provides an overview of the climate years and their influence. For example, considering the factor **Demand**, it is highest for the climate year of 1984 compared to the climate years of 1982 or 2007. For **Hydro Inflow**, inflow is highest for the climate year 2007 compared with the climate years of 1982 and 1984.  
Each scenario and the parameter combination of 1 Hz/s, 2 s, has been analysed with respect to the three climate years. The results of the overall system costs are illustrated as a bar plot in Figure 10. For the climate year of 1982, system costs are highest since the potential for onshore and offshore wind is low. Hence, less SI is provided by such  
Figure 8: Illustration of additional system costs with respect to the additionally provided inertia due to the integration of inertia constraints into the dispatch model. The costs are indicated in EURO per provided inertia in kg-m\({}^{2}\).  
sources. Additionally, hydro inflow is in the middle region. Overall, this results in highest system costs compared to the other climate years and scenarios, since more inertia has to be provided by fossil-fuel-fired power plants and storage sources providing inertia. For the climate year of 1984, system costs are equally high. SI provision potential by onshore wind turbines in the middle region and inertia provision potential by hydro units is low. For the 2007 climate year, system costs are significantly lower, since the SI provision potential of onshore wind turbines and hydro units is highest.