Climate variability on Fit for 55 European power systems

interactions between climatic conditions and power systems operations. The use of climate information derived from ERA5 atmospheric reanalysis makes it possible to introduce interan- nual variability consistently for all the regions analysed [37]. We analyse the interannual vari- ability of the selected inputs in the S1 File. The simulations in this study are conducted using the METIS power system model [38, 39]. While the model is based on proprietary software, all the data that we have used–as well as the results–are freely available with an open license. The METIS model can simulate the hourly operations of energy assets over a year, minimising the system cost maintaining the supply/ demand in each node. Each node of the model represents a country and the interconnector between countries can exchange electricity according to their net transfer capacity. METIS solves a linear economic dispatch problem including all the generation technologies specified in the MIX scenario (as described in [31]). More details on the METIS modelling framework can be found in [38–40]. The MIX scenario was originally developed using PRIMES (Price-Induced Market Equilib- rium System) [41]. The scenario covers all the EU plus the United Kingdom and is focused on the long-term energy transition including technology changes and policy measures. The model based on METIS, used in this article, was created following the methodology docu- mented in [31]. Model inputs and outputs are available online with open license [42, 43]. 3. Results

Our simulations generate results on the operations of power system models for 34 countries using 34 different climatic years (1982–2015). To enhance readability, we group the countries in seven regions: Central Eastern Europe (CEE), Central Western Europe (CWE), Iberia, Italy, Northern, South Eastern Europe (SEE) and UK & Ireland (Fig 1). This classification, as visual- ized in Fig 1, aims to group together the countries with similar characteristics, and follows the approach used in the “Gas and electricity market reports” published by the European Commission. To characterise power system operations, we use the following six indicators calculated at hourly level: 1. CO2 intensity: the amount of CO2 emissions emitted per MWh of electricity generated (gCO₂eq/kWh). 2. Inflow: the quantity of water that a hydropower plant can use to be stored or converted into electricity 3. Net export: the annual difference between cross-border exports of electricity and imports. 4. Peak net demand: the peak demand of electricity minus the variable renewable generation 5. Solar generation: the amount of electricity generated by solar power in a year 6. Wind generation: the amount of electricity generated by wind power in a year The chosen metrics were selected to provide an overview of the power systems’ general characteristics, rather than using more detailed metrics (e.g. as in [10]) that would inevitably increase uncertainty due to the challenge of characterising electricity dispatching and power markets dynamics in future policy scenarios. Below, we discuss the impact of the natural vari-

ability of meteorological parameters on these parameters considering three different perspec- tives: (1) measuring power system variability, (2) measuring the co-variability between regions and (3) identifying patterns in the impact of weather regimes. PLOS ONE Climate variability on Fit for 55 European power systems PLOS ONE | https://doi.org/10.1371/journal.pone.0289705 December 20, 2023 4 / 16