Climate variability on Fit for 55 European power systems

3.1 Power system variability We use the coefficient of variation (CV) to measure the year-on-year variability. The CV (also called relative standard deviation) is defined as the ratio between the standard deviation (σ) and the average (μ): CV = σ/μ. Table 2 shows the interannual variability, measured using the CV expressed as a percentage, for all the selected metrics. The table shows that among all the renewables, hydropower seems to be the most variable source, which is consistent with recent statistics [44]. The hydropower inflow in the Iberian region shows a very high variability (as investigated for Spain in [45]) with a clear correlation with its net electricity export (rs 0.72 CI 95% [0.51, 0.85]), highlighting the capability of the Ibe- rian region to provide low-carbon electricity to its neighbours (i.e., France, in the analysed Fig 1. Left: Countries modelled in the simulation and the grouping used for the analysis. Right: installed electrical capacity for each region according to the MIX 2030 scenario. https://doi.org/10.1371/journal.pone.0289705.g001 Table 2. CV (expressed as a percentage) of the year-over-year variation of the annual value for the selected variables. Shaded cells represent the highest value for each variable. Region CO2 intensity Hydropower Inflow Net export Net demand Solar gen. Wind gen. CEE 7.1% 13% 64.9% 1.8% 3.2% 5.3% CWE 10.7% 8.1% 15.7% 5% 3.2% 4.9% Iberia 13.6% 32.6% 84% 6.7% 2.2% 5.3% Italy 2% 11.9% 15.4% 2.2% 2.5% 5.5% Northern 17.5% 10.6% 51.4% 2.2% 3.2% 5.4%

SEE 5.3% 13.5% 33.9% 1.3% 2.5% 3.5% UK & Ireland 15.2% - 24.3% 5.9% 3% 5.7% https://doi.org/10.1371/journal.pone.0289705.t002 PLOS ONE Climate variability on Fit for 55 European power systems PLOS ONE | https://doi.org/10.1371/journal.pone.0289705 December 20, 2023 5 / 16