Climate variability on Fit for 55 European power systems

2. Data & models This paper investigates the impact of climate variability on wind, solar, hydropower generation and electricity demand in 34 inter-connected European power systems using a power system model based on the European Commission MIX 2030 scenario [31]. The MIX scenario is one of the European Commission’s core policy scenarios from the Fit for 55 package [32]. It was produced as a common tool for analysis in the impact assessments of various initiatives in the European Green Deal policy package. This scenario is the central policy scenario, and it assumes an extended Emissions Trading System (ETS) in road transport and buildings, medium to high ambition for energy efficiency and renewable energy policies. It delivers emis- sion reductions of 55% compared to 1990, reducing 2030 energy demand by around 9% com- pared to the REF 2020 scenario and reaching a share of renewables of 38.4%. Climate variability is modelled using weather-driven inputs in the power system modeling (Table 1). The selected inputs are the most significant factors in explaining how weather affects supply and demand: they include all the most important renewable energy sources and the electricity demand. Hydropower plays–both today and in the EU 2030 scenarios–a critical role in providing the seasonal storage of electricity. For an extended discussion about the aspects of power systems affected by climate we refer to [33–35]. The dataset we use for solar, wind and hydropower modelling is the Pan-European Climate Database v3.1 (PECD) created by ENTSO-E (European Network of Transmission System

Operators for Electricity) and used in the European Resource Adequacy Assessment 2021 (ERAA 2021). The dataset consists of a statistical downscaling from the ERA5 atmospheric reanalysis [37] for the following energy variables: electricity demand (not used in this study), wind (on- and off-shore) and solar capacity factors, hydropower inflows and run-of-river generation. The electricity demand data we used has been built following the methodology described in ([31], Section 5.5). The methodology is based on the demand for all the European countries, disaggregating heating & cooling (assumed to be little affected by meteorological conditions). Similarly to the PECD dataset, the electricity demand uses meteorological variables from the ERA5 atmospheric reanalysis. The results shown in this study are based on 34 climate years: each climate year consists of a set of inputs for solar, wind, hydropower generation and electricity demand based on the his- torical meteorological data for the specific year. A climate year is a physically consistent set of synthetic time-series used as input for the power system model for solar generation, wind gen- eration, hydropower generation and electricity demand. Basically, a climate year represents a what-if scenario, assuming the same installed capacity and technology. The use of climate years can be considered the best way to address the uncertainty derived from meteorological conditions. For example, the work by Collins et al. [10] uses climate years between 1985 and

2014 to analyse the inter-annual variability across Europe. Our approach covers not only wind and solar but also hydropower and demand, increasing the possibility to explain the complex Table 1. Source data for the weather-driven inputs used in the power system modelling. Input Unit measure Original temporal resolution Data source Solar (PV) power Capacity factor (%) hourly [36] Wind power (on- and off-shore) Capacity factor (%) Hourly [36] Hydropower inflow Capacity factor (%) Weekly [36] Run-of-river Capacity factor (%) Daily [36] Electricity demand Hourly demand (MW) Hourly [31] https://doi.org/10.1371/journal.pone.0289705.t001 PLOS ONE Climate variability on Fit for 55 European power systems PLOS ONE | https://doi.org/10.1371/journal.pone.0289705 December 20, 2023 3 / 16