Our results are presented in three sub-sections. We first discuss results on the least-cost combinations of wind, solar, and energy storage installations that meet simplified baseload, intermediate, and peaker power plant output shapes. We then estimate cost targets for energy storage that would enable these plants to reach cost-competitiveness with traditional electricity sources. Finally, we discuss cost features of current and future energy storage technologies as compared to these targets. Experimental Procedures are described in Section 4.

Cost-Minimized Wind, Solar, and Storage Installations for Baseload, Intermediate, and Peaker Power Plants

Table 1 Twenty-Year Average Capacity Factors for Wind and Solar Resources Location Wind Capacity Factor Solar Capacity Factor Arizona 38.1% 34.1% Iowa 51.7% 25.5% Massachusetts 39.8% 24.2% Texas 61.2% 31.0% Figure 1 Storage System Operation Show full caption Twenty-year average storage operation for cost-minimizing systems for all locations, grid roles, and resources. The black line shows the output shape to be met while the dotted lines show storage charge and discharge operation when paired with solar (red) or wind (blue) resources. Power from both renewable generation and storage discharge combine to meet the output shape. Figures S2–S9 show additional detail, including the distribution of hourly storage behavior over the twenty-year period. The results in this figure are for cost-minimizing systems with generation costs of $1,500/kW for wind and $1,000/kW for solar and storage costs of $1,000/kW for power capacity and $20/kWh for energy capacity. These systems have have an equivalent availability factor (EAF) of 100%, meaning that the output shape is met during 100% of the hours simulated. A similar plot for storage power and energy capacity costs of $700/kW and $150/kWh, respectively, is also available ( Figure S1 ). Figure 2 Electricity Cost Dependence on Wind-Solar Mix Show full caption Levelized cost of shaped electricity (LCOSE, $/kWh) for the four grid roles versus various combinations of wind and solar resources in Arizona, Iowa, Massachusetts, and Texas. Cost minima are marked with circles (∘). The percent solar in the wind-solar mix is defined based on the installed power capacity of wind and solar generation (see Experimental Procedures ). The results in this figure are for cost-minimizing systems with generation costs of $1,500/kW for wind and $1,000/kW for solar and storage costs of $1,000/kW for power capacity and $20/kWh for energy capacity (Tech I). These systems have have an equivalent availability factor (EAF) of 100%. The results for Tech II ($700/kW, $150/kWh) are shown in Figure S10 while the impact of lowering the EAF to 99.9% is shown in Figures S11 (Tech I) and S12 (Tech II). Lowering the EAF to 99.9% in Texas causes a sizable change to the optimal wind-solar mix due to one large solar shortage event discussed in Experimental Procedures . Results for alternative generation costs are also available ( Figures S56–S59 ). Here we examine how wind and solar energy and storage can be used to provide baseload, intermediate, and peak power outputs for twenty years across four locations representing different combinations of high and low resource availability ( Table 1 ): Arizona, Iowa, Massachusetts, and Texas. In each location, we solve for the cost-minimizing operation ( Figure 1 ) and sizing of wind and solar energy generation along with storage while varying technology costs and the installed capacity of wind and solar power ( Figure 2 ).

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Staffell I. The future cost of electrical energy storage based on experience rates. Figure 3 Optimal Wind-Solar Mixes and Electricity Costs Show full caption Minimum levelized cost of shaped electricity (LCOSE, $/kWh) for the four grid roles (horizontal axis) and two different storage technologies (bar outline) in Arizona, Iowa, Massachusetts, and Texas. Bar color shading denotes the optimal resource mix as defined by the installed power capacities. To highlight the different sensitivities of the overall renewables and storage system cost to storage power and energy capacity costs, we selected two technologies with high/low cost combinations: Tech I (solid bar outline, $1,000/kW and $20/kWh) and Tech II (dotted bar outline, $700/kW and $150/kWh). Lower energy capacity costs yield lower LCOSE for all wind-solar mixes despite higher power capacity costs. The results in this figure are for cost-minimizing systems with generation costs of $1,500/kW for wind and $1,000/kW for solar and an equivalent availability factor (EAF) of 100%. Results for alternative generation costs are available in Figures S60 and S61 Various factors affect the levelized cost of shaped energy (LCOSE, in $/kWh), including the location, degree of solar and wind mixing, output shape, and technology costs. The costs of energy from optimized systems are summarized in Figure 3 for two different storage technology cost structures, with power and energy capacity costs of $1,000/kW and $20/kWh (Tech I) and $700/kW and $150/kWh (Tech II). For both technology cost structures, round-trip efficiencies are 75%, and projected wind and solar total costs of ownership are $1,500/kW and $1,000/kW, respectively (see Experimental Procedures ). Examples of technologies with costs similar to Tech I are pumped hydroelectric storage (PHS), compressed air energy storage (CAES), and proposed flow battery technologies using highly abundant and low-cost elemental constituents.Examples of Tech II might include future Li-ion batteries after further cost reduction, and possibly other closed battery technologies, flywheels, and supercapacitors.Results for other solar and wind costs are also available ( Figures S56–S61 ).

We find that across locations and output shapes the least-cost resource portfolio draws on a combination of wind and solar energy ( Figure 2 ). Consistent with their energy resource profiles ( Figures S70 and S71 ), systems in Arizona favor solar in the wind-solar mix, while those in Texas favor wind. In Iowa and Massachusetts, a relatively balanced mix is preferred. For Tech I, the least-cost portfolio LCOSE values are lowest in Arizona and Texas and highest in Iowa and Massachusetts across all output shapes, while in all locations the peaker and bipeaker output shapes are roughly 1.3 times more costly than the intermediate and baseload shapes ( Table S1 ). For Tech II, Arizona has the lowest LCOSEs across output shapes, and Massachusetts the highest, while Iowa and Texas have nearly identical LCOSEs for all roles except bipeaker ( Table S2 ). In addition, for Tech II, peaker and bipeaker output shapes are 1.2 times as costly to produce as baseload and intermediate shapes are. In comparison, energy from competing technologies (e.g. natural gas) can be almost twice as expensive for peaker plants than for baseload.

Figure 4 Trends in Cost-Minimizing System Characteristics Show full caption Storage operation excerpts (A) and optimal sizing characteristics (B, C, and D) for a solar power plant that provides 1 MW of baseload power in Iowa. We optimize the solar power plant generation capacity (B), storage energy capacity (C), and storage power capacity (D), for three pairs of storage capacity costs (upper right). These different pairs of storage cost intensities were examined to demonstrate how minimizing LCOSE leads to different optimal combinations of renewable and storage system characteristics and operation. State-of-charge profiles (A) depict how storage operation changes for different storage sizes. As storage energy capacity costs increase, the solar power plant size increases (B), optimal storage duration decreases (C), and storage power capacity relative to output power increases (D). Solar cost of ownership is estimated as $1,000/kW for all three cases, and the EAF is 100%. Plots showing results in Texas, and when the EAF is lowered to 99.9%, are also available Figures S14–S16 Technology costs also impact the cost-minimized system sizes and LCOSEs of wind and solar plants with storage. As expected, the LCOSE rises with the costs of storage ( Figure 3 ) and wind and solar energy technologies ( Figures S56–S61 ). The effects of varying the wind and solar costs on the cost-optimal mix are shown in Figures 2 and S56–S59 , with wind or solar emphasized in the installed power capacity mix as the costs of each technology drops. As storage energy capacity costs rise, the installed capacity of wind or solar generation relative to both storage energy capacity and plant output power generally increases for cost-minimized systems ( Figures 4 and S49–S51 ). This is because for higher storage energy capacity costs, it is less expensive to install more renewables generation than to increase storage capacity, even if this leads to the renewables plant generating energy that is in excess of the energy used as baseload, intermediate, bipeaker, or peaker output. Figure 4 shows the drop in the ratio of renewable power to output power as storage energy capacity costs fall for the case of a baseload solar energy system in Iowa, where the solar power capacity ranges from 7.1 to 17 times greater than the output power.

Sizing renewables to have greater power capacity than the output shape power is a cost-reducing measure that is used in almost all of the cost-minimized systems across the locations considered, and particularly for baseload and intermediate output shapes. This oversizing of renewables is typically reduced for an optimal wind-solar mix as compared to wind- or solar-only systems ( Figure S51 versus Figures S49 and S50 ). The systems with lower storage power and energy capacities and higher renewables capacities generate greater amounts of excess energy ( Figures S52–S54 ). In our model, this excess energy is curtailed and not assigned any economic value. However, we acknowledge that such value might be created in the future, thereby effectively lowering the cost of electricity.

Figure 5 Storage State-of-Charge Profiles for Iowa-Based Baseload Systems Show full caption Storage state of charge (SOC) over twenty years for least-cost systems that provide baseload power using Tech I energy storage and only solar (A), only wind (B) and a cost-minimizing wind-solar mix (C) in Iowa. Storage SOC is the percentage of storage energy capacity available for discharge and can be used as a proxy for resource availability. Markers denote severe solar (•), wind (◆), and optimal wind-solar mix (►) resource shortages. These systems minimize LCOSE with generation costs of $1,500/kW for wind and $1,000/kW for solar and an EAF of 100%. Additional profiles are available in Figures S17–S28 The variability in resource availability over the twenty-year period also influences system characteristics and therefore the total plant cost. The impact of resource variability on the storage energy level is shown in the periods of deep discharge ( Figure 5 ). The inter-year variability is particularly important in determining required system sizes and therefore costs, and this observation demonstrates the importance of studying system performance and energy cost using data over time spans longer than a year or two.

Figure 6 Electricity Cost Dependence on Equivalent Availability Factor for Tech I Show full caption Levelized cost of shaped electricity (LCOSE, $/kWh) plotted against equivalent availability factor (EAF) for baseload and peaker roles using only wind (A, D), only solar (B, E), or an optimal wind-solar mix (C, F) across four locations and Tech I energy storage. Reducing EAF lowers system LCOSE. LCOSE data for Tech I are shown in Table S1 . Corresponding system characteristics such as storage power, storage duration, storage size, and installed renewable power are shown in Figures S29–S36 and the data are presented in Tables S3–S10 . For solar-only systems in Texas, lowering the EAF from 100% to 99.9% has a large impact on LCOSE due to a solar shortage event described in Experimental Procedures Figure 7 Electricity Cost Dependence on Equivalent Availability Factor for Tech II Show full caption Levelized cost of shaped electricity (LCOSE, $/kWh) plotted against equivalent availability factor (EAF) for baseload and peaker roles using only wind (A, D), only solar (B, E), or an optimal wind-solar mix (C, F) across four locations and Tech II energy storage. Reducing EAF lowers system LCOSE. LCOSE data for Tech II are shown in Table S2 . Corresponding system characteristics such as storage power, storage duration, storage size, and installed renewable power are shown in Figures S29–S36 and the data are presented in Tables S3–S10 . For solar-only systems in Texas, lowering the EAF from 100% to 99.9% has a large impact on LCOSE due to a solar shortage event described in Experimental Procedures We find that allowing for periods of unmet demand relative to the desired output shape during infrequent but significant resource shortages can substantially reduce the costs of supplying electricity in baseload, intermediate, bipeaker, and peaker output shapes. A useful metric to quantify plant downtime is the equivalent availability factor (EAF): the fraction of time during which the output shape is met over the time period considered (twenty years in this study). (See Experimental Procedures for the definition of EAF used here.) We present the effect of varying EAF from 100% to 80% in Figures 6 and 7 . For both Tech I and Tech II this effect is greatest when wind or solar are used alone. Dropping the EAF from 100% to 95% for a wind or solar plant reduces the LCOSE by 33% on average when using Tech I and 55% when using Tech II. When the optimal mix of solar and wind are used, a reduction in EAF from 100% to 95% cuts the LCOSE by 25% on average for Tech I and 48% for Tech II.

In the case of a solar-only plant in Texas, dropping the EAF from 100% to 99.9% has a large impact on the LCOSE, as well as the optimal wind-solar mix, which for a Tech I system incorporates more solar for an EAF of 99.9% ( Figure S11 ). Unmet demand hours for an EAF of 99.9% occur during a single large solar shortage event in Texas ( Figure S20 versus Figure S28 ), whereas for an EAF of 100%, demand is met during this shortage event by increasing the storage duration significantly. This reported event in the Texas data highlights the importance of data validation (see Experimental Procedures ) and careful study of rare events, which may not appear in every twenty-year period, when sizing systems for a particular EAF.

The periods of unmet demand associated with a reduction in EAF might be met by a combination of demand-side management (e.g. avoided demand) and supplemental generation (e.g. gas turbines). Alternatively, the resource shortages might be mitigated by spreading generation plants across a larger geographical area and expanding and improving the electricity transmission infrastructure. At a minimum, such systems could achieve costs comparable to those available in the best locations considered here, but only up to a limit imposed by resource constraints in the best locations. Understanding the potential for these mitigating technologies requires further study, but the results here suggest the significant impact they might have.