*3.1. Definition of the Future Grid Mix Scenario in 2030*

The modelling of the future scenario for the California grid mix in the year 2030 was carried out as described below. Firstly, full hourly-resolution net generation profiles were generated for each technology using the OASIS data collected for 2018 (i.e., the latest available complete datasets at the time of writing). Figure 2 shows the resulting stacked contributions to the total delivered power (black line, which is equal to the demand profile) for a typical day in spring (2nd April). From bottom to top: (I—purple) nuclear (which as expected is almost constant, i.e., a "baseload" provider"), (II—green) other renewables (i.e., the sum of hydro, biogas, biomass and geothermal), (III—blue) wind, (IV—yellow) solar (PV + CSP). Lastly, at any hour, the gap (grey arrow) between the top-most reported generation profile and the demand profile is supplied by a combination of natural gas generation (SCGT + NGCC) and electricity imports.

**Figure 2.** Historical hourly generation and demand profiles for the day of 2 April 2018 in California, from CAISO data.

In addition, the 2018 hourly "potential" PV output was also calculated, by adding back the reported 2018 hourly PV curtailment data to the corresponding net PV generation data. The purpose of this "potential" PV output profile was to provide the basis for the future extrapolation of the corresponding "potential" PV output profile for 2030 (see point 2 below), under the assumption that by then, the large-scale availability of energy storage would negate the need for all technical or economic curtailment other than that due to oversupply (see Section 2.1.3).

Starting from these historical power generation profiles, the corresponding projected profiles for the year 2030 were calculated, based on the modelling assumptions and calculations described below:

	- (a) On an hourly basis, the increased PV output in 2030 with respect to 2018 (more precisely: the difference between the "potential" PV output in 2030, calculated as per point 2 above,

and the net PV output in 2018) will first be compensated for by reducing NGCC output. This is deemed the preferred strategy since gas-fired electricity is the most carbon-intensive technology in the California grid mix, and it is also more carbon-intensive than the average mix of technologies used to generate the electricity imported by California [65].


**Figure 3.** Wind and PV installed capacities in California—historical data from CAISO to 2019 and authors' projections to 2030.

**Table 1.** Key California ISO (CAISO) grid mix parameters for years 2018 (historical data) and 2030 (projected).


<sup>1</sup> Assuming that the total yearly gross electricity demand (pre-transmission losses) remains the same, i.e., 226 TWh/yr.

<sup>2</sup> RE includes: Geothermal, Biomass, Biogas, Hydro, Wind, PV, and CSP. <sup>3</sup> VRE includes: Wind, PV, and CSP.

**Figure 4.** Parametric investigation of variable renewable energy (VRE) curtailment accounting for combinations of lithium-ion battery (LIB) power capacities per PV power and depth of storage (h) in California in the year 2030.

Figure 5 shows the expected demand profile (black line) for 2 April 2030, and the following stacked power generation profiles, from bottom to top: (I—green) other renewables (hydro + biogas + biomass + geothermal), (II—blue) wind, and (III—yellow) solar ("potential" PV + CSP).

**Figure 5.** Projected hourly generation and demand profiles for the day of 2 April 2030 in California.

The complete projected hourly electricity generation and demand profiles for the entire year 2030, broken down by month, are reported in the Supplementary Material (Figures S1–S12). Interestingly, because of the large demand for air conditioning in the hotter months in California, the most severe mismatch between the "potential" solar electricity generation and electricity demand profiles occurs in spring, and not in summer, when solar irradiation is highest.

Using the dynamic modelling approach described above, an overall projected year-end domestic grid mix can then be calculated for the California state in 2030. This is illustrated as a pie chart in Figure 6, and its most salient characteristics are compared to those of the corresponding 2018 domestic grid mix in Table 1.

**Figure 6.** California domestic electricity generation mix—projected data for 2030. Total domestic generation is expected to be 199 TWh. NGCC = natural gas combined cycles; PV = photovoltaics; CSP = concentrating solar power.

The modelled 2030 California grid mix is characterized by a large share of VRE, out of which 25% is not consumed directly but is instead routed into storage, while only 2.8% is curtailed; the resulting share of net (i.e., post-curtailment and storage) VRE in the domestic grid mix is thus 61%. It is also noteworthy that, even after completely phasing out SCGTs, the remaining required NGCC output is also reduced by 30% (relative to 2018). Also, the hypothesised large deployment of PV + storage yields a surplus of available renewable energy, which, when retrieved from storage, allows a significant reduction in electricity imports, and a corresponding surge in the domestic share of total electricity supply in California, from 73% in 2018 to 88% in 2030.

Lastly, a further interesting finding ensued from a separate sensitivity analysis on the key model assumptions and parameters. While, as mentioned in Section 2.1.1, all nuclear capacity is expected to be phased out in California by the mid-2020s, with no plans for new replacement reactors, it was deemed worthwhile to investigate the theoretical effect that retaining the existing nuclear capacity would have on grid stability, demand for storage and corresponding % VRE curtailment. A first alternative grid model run was then carried out for 2030, with the exact same PV and storage capacities as described above, but in the presence of the same nuclear electricity output as in 2018. This resulted in an increased VRE curtailment rate of 4.3%. Such result was found to be due to the inflexibility of nuclear output, which pushed the "potential" PV output peaks even higher with respect to the demand profile, thereby saturating the available storage capacity sooner. In an alternative model run, the storage duration was then adjusted upwards, so as to increase the total storage capacity and thus bring the % VRE curtailment back down to the same 2.8% as in the "baseline" scenario. This ended up requiring the

deployment of 7.3 h of LIB storage vs. 6 h in the "baseline" scenario without nuclear electricity in the mix. The details of this analysis are shown in the Supplementary Material (Table S1 and Figure S13).
