Climate variability on Fit for 55 European power systems

3.3 Patterns Weather phenomena affecting European power systems can have a large-scale impact on power systems, i.e., simultaneously affecting multiple countries or even the whole continent. These phenomena can be recurrent and persistent, with similar patterns leading to similar impacts on power systems. A way to assess those patterns is through the analysis of weather regimes. Weather regimes are a classification of recurrent weather states and have been used effectively in many applications, including the analysis of energy systems [23, 30, 49]. In this work, we use a classification introduced by [23], to study the deployment of wind energy in Europe and applied in consequent works, as for example in [24, 27]. We use the pat- terns identified by Grams for the first time for the analysis of power systems operations; previ- ous work based on this classification has focused mostly on wind. Fig 5 shows the seven different weather states over Europe in this classification, computed through a clustering algorithm on the observed meteorological daily data for the period 1979– 2015. While this classification has been identified for a winter period, it can be associated with year-round phenomena [23]. The regimes are calculated using the geopotential height anom- aly on ERA-Interim atmospheric reanalysis and make it possible to characterise large-scale weather conditions that might affect renewable energy generation and power systems opera- tions in general. Three of the seven identified regimes can be associated with the so-called

cyclonic-type regime: Atlantic trough (AT), Zonal regime (ZO) and Scandinavian trough (ScTr). A cyclonic regime is often associated with windy conditions in northern Europe and mild temperatures in general. The remaining four weather regimes are ‘blocking’ regimes: Atlantic Ridge (AR), European blocking (EuBL). Scandinavian Blocking (ScBL) and Green- land Blocking (GL). The blocking regimes normally bring calmer winds, and colder Fig 4. Year-over-year Spearman correlation for six variables considering the entire year. The colours and size of the edges between the nodes is based on the value of the Spearman correlation coefficient. We show only values above 0.4 and below -0.4. https://doi.org/10.1371/journal.pone.0289705.g004 PLOS ONE Climate variability on Fit for 55 European power systems PLOS ONE | https://doi.org/10.1371/journal.pone.0289705 December 20, 2023 8 / 16