Any model informing the future behaviour of energy systems necessarily relies on heavy sets of assumptions regarding technology, consumption and production patterns. Modelling assumptions of the alternative decarbonisation pathways are split into two sections: grid flexibility and GHG emissions.
3.1.1. Grid Flexibility
Integrating high levels of variable renewable energy (VRE), such as the electricity produced by wind turbines or solar photovoltaic (PV) panels, into the grid requires an increase in the flexibility of the grid. Grid flexibility can be achieved in various ways. Kondziella and Bruckner [
12] identified seven possible measures on the production and on the consumption side: highly flexible power plants, large-scale energy storage, curtailment of renewable surplus, demand-side management, grid extension, virtual power plants and linkage of energy markets. These measures, either individually or in unison, can ensure that electricity demand is met at all times. Here, focus was given to the production-side measures of large-scale energy storage and of the curtailment of renewable surplus.
Existing studies [
12,
19,
20,
21,
22,
35,
36] show that increasing grid flexibility becomes essential when the share of VRE fed into the grid reaches levels of 40 to 50%. Grid flexibility can be increased through large-scale energy storage. In this case, surplus electricity generated by VRE when production is higher than demand can be converted into gravitational, thermal or electrochemical energy and fed back into the system when production is lower than demand. Curtailment of renewable surplus, on the other hand, relies on the installation of more renewable infrastructure than what is needed to cover average yearly demand (also known as backup power plants). When the combined electrical output of the renewable infrastructure is higher than the demand at a given point in time, the output of VRE plants is curtailed. As a result, curtailment of renewable surplus as a means to improve grid flexibility has an impact on the utilisation factor (UF) of renewable plants. The review paper by Kondziella and Bruckner [
12] provided a thorough overview of existing studies assessing grid flexibility requirements for high renewable integration.
Steinke [
19] examined the interplay between storage, curtailment and grid extensions for a 100% renewable electricity system in Europe. Denholm and Hand [
21] and Denholm and Margolis [
22] modelled, respectively, the electricity grids of Texas and California to provide an assessment of how grid flexibility can be achieved in low and high storage scenarios. The models of US case studies, produced for the National Renewable Energy Laboratory (NREL), have not been replicated in the EU to this level of detail and are the most comprehensive reference points for assessments of grid flexibility. Building on these studies [
21,
22], we hypothesised two pathways for decarbonisation: a low storage high curtailment (LSHC) pathway and a high storage low curtailment (HSLC) one. In the former, we assumed that no extra storage technologies were added to the EU’s electricity system, therefore the only storage services available up to 2050 were those produced by current PHS facilities (at a storage capacity of approximately 600 GWh [
37]). To ensure grid flexibility, renewable back up power was added and renewable generation was assumed to be curtailed. In the latter, curtailment was greatly decreased by the addition of storage services. Both scenarios adapted the curtailment and flexibility rates from the comprehensive model of the Texas grid [
21]. The relations between curtailment and flexibility in the EU depend on specific geographies and grid configurations. However, for the purpose of these pathways—i.e., to point towards a problem in GHG accounting rather than to provide accurate predictions—this approximation was considered satisficing.
The total amount of storage required by 2050 was calculated following Steinke [
19] and Renner and Giampietro [
35]. Assuming that grid expansions were limited to the national scale and that no backup generation was provided, Steinke estimated that the EU would require between 7 and 30 days of storage to accommodate shares of 90% or more of VRE. Analysing data for Germany and Spain over an 84 months and 132 months, with a resolution of 60 minutes and 10 minutes respectively, Renner and Giampietro estimated that the two countries would require approximately one week of storage capacity in a 100% intermittent penetration scenario. The study used the comprehensive datasets available for the two countries to check “the extent of the predicted worst annual hypothetical ‘failure event’ (where the guaranteed level of intermittently sourced electricity is not met)”. The results by Renner and Giampietro for Germany are in line with the analysis by Kuhn [
36], predicting a requirement of installed storage charging power in Germany of the order of 53 GW by 2050. Similar values apply to the case of Japan [
20], where, despite a different energy mix and configuration, it was also found that storage requirements are on the order of a week of average electricity supply.
Thus, storage capacity requirements for 2050, where gross electricity production is assumed to grow to approximately 5140 TWh, were assumed to be on the order of a week of average daily demand. It was then assumed that PHS, the most implemented and mature storage technology in the EU and worldwide, increased up to its viable potential in the EU, following the analysis by Gimeno-Gutiérrez and Lacal-Arántegui [
38]. Then, battery energy storage (BES) was introduced to cover the gap between the maximum PHS potential and the total storage capacity needed. The relevant assumptions shared across pathways and those differing for each pathway are collected in
Table 1 and
Table 2 respectively, at ten-year snapshots between 2020 and 2050.
Following the EU high RES pathway, gross electricity consumption increased in both pathways, despite an overall reduction in energy consumption—mirroring the trend of electrification. Hydropower (excluding PHS) was assumed to remain unchanged throughout the years, therefore as electricity generation increased its share in the electricity mix decreased. Nuclear power was assumed to gradually decrease in absolute and relative terms. All other non-renewable power plants were grouped under the umbrella term fossil plants and eliminated by 2050. The share of wind and solar power rose gradually until reaching 90% of the total generation share in 2050. The relative contribution of wind and solar power remained fixed at 70 and 30% respectively, mirroring their 2016 relative contribution in the EU. This was also in line with what was identified by Denholm and Hand [
21] as the optimal balance between the two types of generation technologies to ensure minimum curtailment.
Given the higher curtailment rate in the LSHC scenario, although the gross electricity consumption was the same as in the HSLC scenario, a higher amount of wind and solar power were assumed to be generated (see the second and third row of
Table 2). The surplus generation was not assumed to enter the grid but was curtailed. The curtailment rates, also included in
Table 2, were taken from Denholm and Hand [
21], by assuming that curtailment rates as a function of VRE penetration can be generalised. Contrary to storage requirements, which tend to increase linearly as VRE integration increases, curtailment increases exponentially, meaning that it becomes less and less favourable to rely on curtailment at higher rates. The amount of storage capacity, in GWh of installed capacity, did not increase throughout the years for the LSHC scenario. Eurostat does not provide statistics on storage capacity, and the value of 600 GWh of PHS in the EU was taken from Kougias and Szabó [
37]. In the HSLC scenario, the storage capacity increased up to a week of average demand. The curtailment rates in both scenarios led to a gradual decrease in the utilisation factors (UF) of wind and solar power, calculated as the amount of time throughout the year when electricity generated by wind and solar (GWh
used) was fed into the grid:
where GW
installed is the installed power capacity, and 8760 is the number of hours in a year.
3.1.2. GHG Emissions of Renewable Infrastructure, Storage and Fossil Plants
The values of lifetime GHG emissions of renewable infrastructure, and their associated ranges, were adjusted from the comprehensive meta-review by Nugent and Sovacool [
32]. For GHG emissions of storage technologies, values were taken from Denholm and Kulcinski [
39].
Table 3 summarises the main technological assumptions of both studies. For renewable infrastructure, Nugent and Sovacool provide intensive data derived from a number of studies, each with different technical specifications. Therefore, the values do not refer to specific technological characteristics. This enlarges the range of the estimated values, but also their robustness. Storage infrastructure values, similarly, refer to a review of various existing plants, with the range of technological characteristics included in
Table 3. As the GHG emissions associated with battery energy storage (BES) were an important variable for the results, the values of Denholm and Kulcinski, dating to 2004, were cross-checked against a recent study referring specifically to lithium-ion batteries [
40]. and were found to be consistent. Since the scenarios were modelled at the EU level, they did not take into account differences across member states. The values taken from literature, associated with a range of technological characteristics, reflect the heterogeneity of infrastructure required across the EU.
The GHG emissions from the review studies were adjusted as the renewable share of the electricity mix in the pathways increased, since the electricity mix strongly affects GHG emissions. As we were singling out the power sector, emissions associated with the use of fuels and other forms of thermal energy remained invariant. To adjust the values throughout the years, the carbon intensity of the EU’s electricity mix was estimated each year, starting from 320 g/kWh in 2016 [
41] and reaching almost zero in 2050. The contribution of the electricity mix to the overall GHG emissions of wind power infrastructure was estimated by comparing existing studies which made a direct link between the carbon intensity of the electricity production system and the GHG emissions associated with infrastructure (see Reference [
42] for a comparison of Germany and China, Reference [
43] for Brazil and Reference [
44] for different values of carbon intensities). The effect of different electricity mixes on the construction on the lifetime of solar panels was assessed directly by Reich [
45] in relation to the CO
2 emission factor of electricity supply. Varying GHG emissions for CFC and Operation are included in
Table 4.