For short-term storage in a 100% renewables grid, thermal energy storage located at concentrated solar power plants could compete with batteries, found a study using an idealized grid model. Seasonal storage needs could best be met with power-to-gas-to-power technology.
Concentrating solar power plus thermal energy storage (CSP+TES) could be cost-competitive with battery storage for achieving a least-cost 100% renewables grid in the continental US, researchers have found.
For seasonal storage, power-to-gas-to-power had lower costs than CSP+TES.
Researchers used a simple model of the grid that assumed free lossless transmission across the continental US. They said their results could guide future studies using more detailed models, and expected that a model realistically representing transmission would not fundamentally alter the relative roles of the generation and storage technologies they evaluated. The study was published in the journal Advances in Applied Energy.
With CSP, mirrors direct sunlight onto receivers containing a heat transfer fluid, and the heat is used to run a steam turbine. Adding thermal energy storage allows electricity to be generated later.
CSP alone costs more than solar PV, and when researchers ran the model without TES, it selected no CSP. But TES costs less than battery storage, and when TES is added to CSP, the technology combination becomes competitive with batteries, the study found. The system cost savings of adding CSP+TES to a system with batteries was slight, at only 0.07 cents/kWh.
For increased uptake of the combined technology, cost reductions for CSP would be more important than cost reductions for TES. Yet ongoing cost reductions for batteries, as projected by NREL, would counter increased uptake of CSP+TES.
With power-to-gas-to-power (PGP) technology, renewable power is used to generate hydrogen from water, using electrolyzers. The hydrogen is later used to generate electricity using fuel cells, in the approach modeled by the researchers.
Seasonal storage needs in a 100% renewables grid were met with PGP storage capacity reaching 89,000GWh, the model showed. Short term-storage, typically with daily cycling, was met with far less capacity: 620GWh of thermal energy storage capacity, supplying 0.6% of total electricity supply, and 350GWh of battery storage capacity.
Demand response was represented in the model by allowing the system to supply less than the historical use profile “by paying a high cost.” Efforts to increase demand flexibility “could minimize the value” of CSP+TES, the authors said.
When methane gas units at varying percentages of generation were permitted, in a sensitivity analysis, the model selected batteries when methane gas generation was reduced to 5%, selected PGP with methane gas at or below 2%, and selected CSP+TES with methane gas at or below 0.1%.
The open-access article presents all cost elements for the generation and storage technologies considered, and is titled “The role of concentrated solar power with thermal energy storage in least-cost highly reliable electricity systems fully powered by variable renewable energy.”
The role of concentrated solar power with thermal energy storage in least-cost highly reliable electricity systems fully powered by variable renewable energy
Utility of CSP+TES stems from the TES storage function, not CSP generation.
CSP+TES may have a small role associated with adding flexibility to a VRE grid.
Penetration of CSP+TES is limited by high CSP costs and decreasing battery costs.
LCOE comparisons of CSP+TES vs PV+batteries disguise important interactions.
Policies in the US increasingly stipulate the use of variable renewable energy sources, which must be able to meet electricity demand reliably and affordably despite variability. The value of grid services provided by additional marginal capacity and storage in existing grids is likely very different than their value in a 100% variable renewable electricity system under such policies. Consequently, the role of concentrated solar power (CSP) and thermal energy storage (TES) relative to photovoltaics (PV) and batteries has not been clearly evaluated or established for such highly reliable, 100% renewable systems. Electricity generation by CSP is currently more costly than by PV, but TES is much less costly than chemical battery storage. Herein, we analyze the role of CSP and TES compared to PV and batteries in an idealized least-cost solar/wind/storage electricity system using a macro-scale energy model with real-world historical demand and hourly weather data across the contiguous United States. We find that CSP does not compete directly with PV. Instead, TES competes with short-duration storage from batteries, with the coupled CSP+TES system providing reliability in the absence of other grid flexibility mechanisms. Without TES, little CSP generation is built in this system because CSP and PV have similar generation profiles, but PV is currently cheaper on a dollar-per-kWh basis than CSP. However, CSP with TES can provide grid flexibility in the modeled least-cost system under some circumstances due to the low cost of TES compared to batteries. Cost-sensitivity analysis shows that penetration of CSP with TES is primarily limited by high CSP generation costs. These results provide a framework for researchers and decision-makers to assess the role of CSP with TES in future electricity systems.
The United States is setting more ambitious renewable energy goals each year, with 30 states and 3 territories adopting renewable portfolio standards, including eight with 100% renewable electricity generation targets . Dozens of other cities and counties have also committed to 100% renewable energy goals . These policies necessitate greater use of variable renewable energy (VRE) sources, which introduces new challenges to satisfy goals and requirements for grid reliability . The North American Electric Reliability Corporation (NERC) resource adequacy planning standard specifies that hourly averaged electricity demand must be met in full except for, at most, one hour in a decade [3,4]. An analysis based on historical weather data shows the dominant VRE generation technologies, solar photovoltaics (PV) and wind turbines, can likely only meet ∼80% of US electrical demand reliably without auxiliary technologies and/or extensive curtailment . Therefore, methods of improving grid flexibility and dispatchability are important to cost-effectively implementing VRE technologies while maintaining resource adequacy.
Full decarbonization of 100% VRE-based power grids is challenging because compensation for the variability of generation cannot be performed by dispatchable fossil fuel generation, specifically natural gas generators . Without firm generators, increased long-distance transmission to connect variable renewable resources across wide geographies can reduce, but not eliminate, resource variability . Strategies to reliably meet demand include overbuilding of generation capacity while incurring substantial curtailment of generation; cross-sector couplings to enhance flexibility; extensive demand management; and/or grid-scale energy storage technologies [5,7,8]. The California Energy Commission denotes long-duration storage as having a duration of greater than 10 h and short-duration storage as having a duration of less than 10 h . Long-duration storage at seasonal timescales can substantially decrease the cost of idealized 100% reliable electricity systems based on 100% VRE generation . However, short-duration storage remains a costly necessity for VRE-based grid services such as day-night cycling, peak-shaving, and energy arbitrage [11,12].
Two frequently cited options that combine VRE generation with short-term storage are solar PV with battery storage and concentrated solar power (CSP) with thermal energy storage (TES). Despite decades of commercial usage, the cost of CSP generation remains high compared to solar PV generation, which has been experiencing substantial cost reductions over at least two decades , , , . In contrast, current TES costs are low compared to storage in chemical batteries, which suggests a role for CSP with TES (i.e., CSP+TES) relative to PV+batteries, due to favorable storage costs for TES despite the disadvantage in generation costs for CSP [16,17]. Levelized costs of electricity for marginal addition of CSP with overnight TES storage are often compared favorably to marginal addition of PV and overnight battery storage, where marginal addition refers to adding new capacity to an existing electricity grid [18,19].
Concentrated solar power utilizes mirrors, referred to as a “solar field,” to concentrate sunlight onto receivers that contain a heat transfer fluid and generate thermal energy . The heat transfer fluid can then be used to run a steam turbine and generate electricity . When combined with TES, either the heat transfer fluid itself can be stored in what is known as “direct” storage, or the heat can be transmitted to another medium for “indirect” storage, allowing electricity to be generated later . The four main types of CSP are Parabolic Trough Collector (PTC), Solar Power Tower (SPT), Linear Fresnel Reflector (LFR), and Parabolic Dish Collector (PDC), with PTC and SPT accounting for most of the global installed capacity [20,21]. This analysis focuses primarily on PTC because it is the most mature CSP technology.
The first commercial CSP plant was built in the US in the 1980s, and CSP has been used continuously ever since . However, global CSP capacity has grown slowly over that period, with development occurring in just a few select nations . CSP and TES are currently enjoying renewed interest, particularly among solar belt countries in Africa [22,23] and the Middle East, as well as in China, which leads the world in planned new CSP capacity . Concentrated solar power offers several potential benefits to a VRE-based electricity system. The primary advantage arises from coupling CSP with TES to provide built-in energy storage, which can substantially increase the capacity factor to > 90% [20,24]. Recent innovations suggest that TES performance can be improved even further through careful design, and that TES may have synergies with second-life utilization of batteries , , . Life-cycle analyses suggest that CSP has lower emissions than solar PV [28,29]. CSP plants can be hybridized to use biofuels, fossil fuels, or geothermal energy to drive the steam turbine when insufficient solar energy is available [30,31]. The cogeneration of heat and electricity from CSP also provides opportunities to supply heat directly for industry, or for use in other coupled processes such as desalination , , .
The impacts of CSP with TES in an electrical grid have been explored in a range of studies across a variety of geographical areas [22,, , , , , ]. One study on the Brazilian electricity system found that adding CSP with TES was a cost-effective way to add marginal dispatchable capacity that complemented wind and PV generation, and added flexibility to the grid in the winter when Brazil’s large hydrological resources were less available . Another study similarly found that CSP improved flexibility in the Chilean electricity system, with low-cost scenarios leading to CSP with TES accounting for approximately one third of dispatched energy by 2037 . In the US, a study of the Western Interconnect comparing CSP+TES to renewable generators without other storage technologies found that CSP+TES could reduce the need for costly start-up and operation of high ramp-rate fossil fuel peaker plants .
Additional studies have emphasized the potential synergies between wind generation and CSP. A hybrid CSP-wind plant with TES and batteries designed to meet electrical, thermal, and transport needs was modeled for the Greek island Skyros . This configuration provided better exergetic efficiency while requiring less land than the two other configurations considered, which consisted of mixes of PV, wind, batteries, electrolyzers, and pumped hydropower storage . Another study on hybridizing wind and CSP in a Minnesota plant found that although costs at the time favored using only wind power, adding CSP provided valuable grid services by improving load-matching within the system . A third study focused on the Texas panhandle, the region with the largest wind resource in Texas . Extensive wind development has led to an increasingly large mismatch between demand and resource availability in the region, but a ratio of ∼2/3 wind generation and ∼1/3 CSP with 6 h of TES provided value by improving load-matching across the annual, monthly, and hourly timescales considered . In the Andalusia region of Spain, models suggested that careful siting of wind and CSP+TES could enable baseload renewable power .
The value of grid services provided by marginal addition of capacity and storage in existing grids, especially as measured by the levelized cost of marginal electricity, is likely very different than the value of generation and storage technologies in a fully VRE-powered electricity system. Consequently, in regions that require 100% electricity generation by renewable resources while maintaining statutorily mandated reliability and resource adequacy, the role of CSP and TES relative to PV and batteries has not been clearly evaluated. Herein, we analyze the role of CSP and TES in an idealized electricity system powered solely by variable renewable energy from solar and wind, using real-world historical demand and hourly weather data across the contiguous United States (CONUS). Under favorable assumptions that minimize the impacts of resource variability, specifically assuming lossless transmission from generation to load over the contiguous U.S., we assess the conditions under which CSP+TES would play a substantial role relative to other technologies such as PV and batteries. The base case technology mix in our analysis includes wind, PV, CSP with TES, batteries, and power-to-gas-to-power using hydrogen gas for seasonal storage. The base case uses current asset costs, and we then parameterize costs to perform a sensitivity analysis with no bias as to actual future costs of a specific generation or storage technology. We find that adding CSP+TES results in a decrease of only 0.0007 $/kWh in system costs, due to a relatively high TES capacity (1.45 kWh/kW of mean demand) that was used when an unusually high level of flexibility was needed.
Using a least-cost linear optimization model, our study focuses on dynamical relationships and system characteristics without attempting to predict future costs or detailed future electricity system architectures. The flexibility and low computational cost of this idealized system allows exploration of a large number of system compositions and facilitates parameterization of costs over a wide range of values to ascertain the robustness of our results. The breadth of analysis offered by this approach could provide potentially interesting parameter spaces for more detailed models to explore. The ability to investigate a wide range of scenarios is important due to uncertainties in cost projections for current renewable generation technologies as well as in the development of future technologies. Thus, this model provides a framework for analysis and decision-making based on fundamental trade-offs and technology niches inherent to a highly reliable, fully decarbonized, VRE-based electricity system.
2.1. Model formulation, costs, and assumptions
This analysis was performed using an idealized macro-scale electricity system  represented by the Macro-Energy Model (MEM) [6,8,10,, , ]. Each technology in the model was represented by a fixed cost and a variable cost. Wind, solar photovoltaic (PV), and natural gas with carbon capture and storage costs were taken from the EIA’s 2020 Annual Energy Outlook and are based on current cost estimates . Costs for concentrated solar power (CSP) and thermal energy storage (TES) were based on NREL’s System Advisory Model 2020.2.29 [15,16,, , ]. Parabolic trough collectors (PTC) were used as the base case in the model because they are the most mature CSP technology, and they allowed for facile comparison with single-axis tracking PV generation due to similar tracking geometry . Solar power tower (SPT) costs were used for comparison in certain cases. Costs and technology assumptions for the generation technologies are provided in Table 1.
Table 1. Model inputs for generation technologies. All cost values are represented in 2019 US dollars. Additional details provided in SI Section 2. O&M = Operations and Maintenance.
|Generation Technologies||Wind||Photovoltaics||CSP – PTC||CSP – SPT||Natural Gas|
|Technology Description||Onshore wind turbines||Single-axis tracking solar panels||Single-axis tracking parabolic trough CSP||Solar power tower CSP||Combined cycle with multi shaft configuration|
|Total Overnight Cost ($/kW)||1319 ||1331 ||2383.38 ||3432.17 ||954 |
|Lifetime (years)||25 ||25 ||30 ||30 ||30 |
|Capital Recovery Factor (%/year)||8.58%||8.58%||8.06%||8.06%||8.06%|
|Fixed O&M Costs ($/kW/yr)||26.22 ||15.19 ||67.32 ||67.32 ||12.15 |
|Variable O&M Costs ($/kWh)||0||0||0||0||1.86 × 10−3 |
|Fuel Cost ($/kWh)||–||–||–||–||1.91 × 10−2 [46,50]|
|Annualized Hourly costs|
|Fixed Cost ($/kW/h)||1.59 × 10−2||1.48 × 10−2||2.96 × 10−2||3.93 × 10−2||1.02 × 10−2|
|Variable Cost ($/kWh)||0||0||0||0||2.097 × 10−2|
Battery costs, capacity, and lifetimes were taken from the financial advisory firm Lazard . Costs for electrolyzer facilities (stack, compressor, and balance of plant (BoP)) and power-to-gas-to-power (PGP) underground storage were based on NREL’s H2A model , , , . Fuel cell costs were taken from the EPA’s Catalog of CHP Technologies . Storage technologies were assumed to be operational at all times, with costs and technology assumptions for storage provided in Table 2.
Table 2. Model inputs for storage technologies. All cost values are represented in 2019 US dollars. Additional details provided in SI Section 2. O&M = Operations and Maintenance.
|Storage Technologies||Battery Storage||PGP Storage||PGP Electrolysis Plant||PGP Fuel Cell||TES – PTC||TES – SPT|
|Technology Description||Li-ion battery||Underground hydrogen storage in caverns||PEM electrolyzer plant with compressors||Molten carbonate||Two-tank indirect||Two-tank direct|
|Units for Capacity Costs||$/kWh||$/kg/hr*||$/kg/hr*||$/kW||$/kWt -hr||$/kWt-hr|
|Total Overnight Cost||365.77 ||6.86 ||63,008 ||5000 ||77.82 ||27.61 |
|Lifetime (years)||10 ||30 ||7 stack, 40 BoP, 15 compressor ||20 ||30 ||30|
|Capital Recovery Factor (%/year)||14.24%||8.06%||18.56% stack, 7.5% BoP, 10.98% compressor||9.44%||8.06%||8.06%|
|Fixed O&M Costs ($/kW/yr)||12.32 ||0.537 ||1822.13 plant, 182.33 compressor ||43 ||0||0|
|Efficiency||90% ||–||61.4% ||70% ||98.5% ||98.5% |
|Self-Discharge Rate (1/h)||1 × 10−5 51||1.14 × 10−8 55||–||–||3.60 × 10−4 47||2.9 × 10−4 |
|Energy/Power Ratio (h)||4 ||–||–||–||6 ||6 |
|Annualized Hourly costs|
|Fixed Cost ($/kWh, $/kW)||7.35 × 10−3||3.73 × 10−6||3.46 × 10−2||5.88 × 10−2||7.16 × 10−4||2.54 × 10−4|
Values for H2 storage and the electrolysis plant were converted from kg to kWh’s for model inputs using the lower heating value (LHV) of 33.33 kWh/kg for hydrogen.
The model optimized for the least-cost solution with the constraint that electrical sources and demand were balanced on an hourly basis. No restrictions were placed on the resources based on societal complications such as technology acceptance or local preferences. The cost of electricity was internally calculated at each hourly time step based on the availability of generation capacity, wind and solar resources, stored energy, and demand. The time varying price of electricity for the 2017 base case year is shown in Fig. S1. Directional flows for each technology are represented below in Fig. 1. Batteries and PGP could accept inputs from any technology that supplied the main node, or electrical grid, whereas energy into TES could only be supplied by generation from CSP because CSP and TES are physically coupled as a single system in this analysis. A simple demand response mechanism that allows the system to supply less than the historical use profile by paying a high cost was used to represent load shedding, referred to here interchangeably as lost load.
2.2. Solar and wind data
Hourly capacity factors for solar and wind data were generated using the Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2) reanalysis data . These data have a grid-cell resolution of 0.5° latitude by 0.625° longitude. Solar capacity factors, used for both photovoltaics and concentrated solar power, were calculated for a single-axis tracking system capable of tilting 0°–45°. The calculated solar capacity factors ranged from 0 to 0.925, with a mean of 0.279. Wind capacity factors were calculated for a GE 1.6–100 turbine with a 1.6 MW nameplate capacity, using methods described in Refs , , . The calculated wind capacity factors ranged from 0.021 to 0.998, with a mean of 0.430. Greater detail for these calculations is provided in Section S3. The geographic regions with the top 25% generation potential were used to create model inputs. The base case year used for solar and wind resource data was 2017.
2.3. Demand data
Demand inputs for the model were generated from hourly data drawn from balancing authorities in the contiguous US, accessed through the EIA’s data portal . Previously published methods were used to clean the data and replace missing values using multiple imputation by chained equations (MICE) . The validity of this technique was verified by testing against known values within the dataset. The mean absolute percentage error (MAPE) across all balancing authorities was calculated to be 3.5%, with a relatively small bias of 0.33% . The base case year used for demand data was 2017.
3.1. Increased grid flexibility through CSP+TES
Fig. 2 shows dispatch curves in a least-cost electricity system for which the solar, wind, and storage resources were built to meet 2017 demand data on an hourly basis. Positive values indicate sources of electricity being provided to the grid, and negative values indicate sinks in which energy is flowing out of the grid. The dispatch curves represent the base case technology mix with generation from PV, wind, and CSP, and storage from batteries, TES, and PGP. The full year is shown in Fig. 2(a), while 4-day panels in Fig. 2 (b–d) represent the periods of maximum hourly dispatch from each storage technology. These panels show that batteries and TES filled short-term gaps in resource that generally lasted less than 24 h, whereas PGP filled multi-day resource gaps that had a continuous deficit in generation relative to demand. CSP is used primarily to charge TES instead of directly providing electricity to the grid. Therefore, the combined impact of CSP+TES was primarily to add flexibility to the grid through TES’s storage role.
Without TES, no CSP generation was built. This behavior results from the favorable fixed capacity cost of 0.0148 $/kW/h for solar PV in the model, approximately half of the 0.0296 $/kW/h fixed cost for CSP, given that both technologies as implemented share the same capacity factor resource characteristics. This relationship is reversed for the associated storage technologies, with the battery storage fixed capacity cost of 0.00735 $/kWh/h being an order of magnitude higher than 0.000716 $/kWh/h for TES. The cost advantage of TES allowed the combined CSP+TES technology to play a role in the idealized VRE-dominated electricity system. Further investigation of the balance between cheap PV generation and cheap TES storage is provided in Fig. S2, which displays a system based solely on solar resources. In this system, some CSP+TES was built in addition to PV+batteries. However, the addition of long duration PGP storage sharply increased the share of demand supplied by TES from ∼0.2% to ∼17%, indicating that the presence of long-duration storage improved the utility of CSP with TES. None of the solar-only systems used CSP for direct generation.
The capacities and system costs for the base case (Fig. 2 (a–d)), and for the case in which TES was removed, are given in Table 3, to show the impact on the full technology mix. Figures throughout this analysis are shown normalized to the mean hourly electrical demand, but the values in Table 3 are scaled up to the 2017 hourly average of 453 GW to provide context for comparing the model results to real-world capacities. Removal of TES resulted in no CSP capacity but caused substantial increases in deployed battery capacity (from 354 GWh in the base case to 523 GWh in the case without TES), along with marginal increases in power-to-gas-to-power technologies (electrolyzer: 50.6 to 58.2 GW, fuel cell: 191 GW to 221 GW, storage: 89,400 GWh to 97,400 GWh). Only slight increases occurred in the generation capacities for PV (424 GW to 428 GW) and wind (1290 GW to 1290 GW). This behavior again demonstrates the primacy of the role of TES storage for the combined CSP+TES technologies, and shows that CSP+TES competes for short-duration storage rather than for long-duration storage. The tradeoffs of building additional capacity of other technologies when CSP+TES was removed resulted in an essentially constant system cost of 0.10 $/kWh for both cases, with a decrease of only 0.0007 $/kWh when CSP+TES was built. This behavior suggests that purely in terms of cost, adding CSP+TES to the grid is a choice rather than a necessity to reach a low-cost system in this idealized electricity system model.
Table 3. Built capacities and system costs for base case and base case without TES for 2017. Capacities for base case system for years 2016–2019 given in Fig. S3. When TES is not included, no CSP is built. Energy and power values are in units of equivalent electricity. PV refers to solar photovoltaics; CSP is concentrating solar power; TES is thermal energy storage; PGP is power-to-gas-to-power.
|Base Case||Base Case without TES|
|System Cost ($/kWh)||0.101||0.102|
|Average hourly demand (GW)||453||453|
|PV capacity (GW)||424||428|
|Wind capacity (GW)||1290||1290|
|CSP generation capacity (GW)||27.1||0|
|CSP turbine capacity (GW)||50.6||0|
|TES capacity (GWh)||623||–|
|Battery capacity (GWh)||354||523|
|Electrolyzer capacity (GW)||50.6||58.2|
|PGP storage capacity (GWh)||89,400||97,400|
|Fuel cell capacity (GW)||191||221|
Fig. 3 shows the temporal variation of the average charging and discharging behavior of each storage technology on a monthly and hourly basis for the base case. TES was utilized at similar levels year-round, with a slight increase during the summer months. Batteries had noticeably higher usage during June-Sep to compensate for a reduction in wind generation during the summer doldrums . Although the least-cost system contained a higher capacity of TES (1.45 kWh/kW of mean demand) than batteries (0.78 kWh/kW of mean demand), batteries showed a higher average usage. This behavior indicates that batteries were used for more routine storage, whereas TES was used when an unusually high level of flexibility was needed. The monthly distributions of TES and batteries show nearly identical charging and discharging, confirming that both storage technologies are mainly used for short-term storage across several days or weeks (Fig. 3 (a,c)). In contrast, PGP exhibited inverted monthly charging and discharging patterns, discharging the most power during the summer when wind resources were low, with a smaller discharge peak in the winter when the solar resource was low.
The hourly patterns in Fig. 3 (shown in Central Standard Time (CST)) indicate that TES had a clear cycle of charging determined by the solar resource, with a peak at mid-day. Batteries had the opposite pattern, with peak charging occurring overnight when the wind resource tends to be higher. This pattern for batteries was not substantially different when CSP+TES was removed from the system (Fig. S4). This behavior suggests that given the strong alignment of daytime solar PV generation with peak daytime demand, PV is preferentially used immediately as opposed to charging battery storage. CSP+TES introduces a cost-effective solar technology that has incentive to store the resource instead of providing direct generation, due to the higher cost of CSP generation compared to TES storage. Batteries and TES both had large discharging peaks in the morning before sunrise and smaller peaks in the evening after sunset, with little use during the day due to the availability of cheap solar PV generation during the daytime demand peak. PGP showed nearly constant charging throughout the day, with similar morning and evening discharge peaks.
The patterns observed for TES and batteries persisted even when long-duration storage was not available, as shown in Figs. S5 and S6. The absence of PGP led to deployment of excess generation, which decreased the need for frequent use of short-term storage to fill small gaps between resource availability and demand. However, at the times of seasonal lows in generation (summer for wind, winter for solar) a larger capacity of short-term storage was required to meet demand. Removal of PGP from the system consequently resulted in larger capacities of batteries and TES that were used less frequently throughout the year, as shown in Figs. S7–S11.
3.2. Grid flexibility from other sources
Several approaches can increase the flexibility of an electricity system. In one such approach, the system could occasionally, for a very high cost, supply less than the demand load. The potential effects of a few rare hours in which demand substantially exceeds supply were evaluated by relaxing the strict constraint that demand had to be met for all hours in the period of interest, instead assigning a cost to this “lost load”. The cost of lost load was based on the value of economic losses sustained when electrical demand is not met, with units of $/kWh. In the US, estimates place this value between 3 and 12 $/kWh for the entire economy . The cost of unmet demand was varied between 0 $/kWh and 20 $/kWh to understand how a system built with the base case mix of technologies responded to looser and tighter constraints on resource adequacy As shown in Fig. 4(a), beginning from the 20 $/kWh unmet demand case supplying 100% of demand, CSP with TES was the first technology to be eliminated from least-cost systems as the cost of unmet demand decreased, and was absent when the cost of unmet demand was ≤$7/kWh. At $7/kWh, only 0.055% of demand, or less than 5 h of mean hourly averaged demand out of the year, went unmet demonstrating that CSP+TES was primarily used to increase flexibility in the grid to meet a small fraction of demand over the course of a year. The overall cost of the system slowly decreased from 0.10 $/kWh in the configuration that met 100% of demand to 0.095 $/kWh when the cost of unmet demand was $3/kWh, before dropping precipitously as the cost of lost load decreased to the point where it was cheaper not to build a system at all.
In Fig. 4(b) the dispatch from natural gas was constrained to meet no more than a given percentage of demand, thereby requiring VRE generation to meet the remainder of the demand. Natural gas is dispatchable, and thus acted as a flexibility buffer for the system. Under these constraints and with our specific demand and resource characteristics, at ∼90% natural gas the renewable technology deployed preferentially in least-cost systems was the cheapest generation source, solar PV, followed by wind turbines. Flexibility provided by storage technologies first appeared when batteries entered the system at ∼5% natural gas followed by PGP which entered at ∼2% natural gas. CSP+TES was not built until natural gas was constrained to meet no more than ∼0.1% of demand, making CSP+TES the last technology required to meet the flexibility needs of the idealized VRE-dominated electricity system. However, in the base case system dispatch from TES actually accounted for ∼0.6% of demand with CSP direct generation offering the potential for another ∼0.4%. This behavior indicates that once the technology is deployed, it may be used and has value in these idealized least-cost systems beyond the thresholds shown in Fig. 4(b). Both the comparison in natural gas in Fig. 4(b) and the base case quantities affirm the role of CSP+TES as a “last 1%” technology focused on adding the most difficult and costly final degree of flexibility to the idealized, 100% reliable, 100% variable renewable electricity system. These patterns were also observed when low-carbon, load-following flexibility was added to the grid through natural gas with 90% carbon capture and storage (CCS), as shown in Fig. S12.
3.3. Technology combinations and interactions
Fig. 5 shows changes in idealized least-cost electricity systems as different combinations of generation and storage technologies were deployed. When TES was a storage option, CSP with TES was always present in the least-cost systems to add flexibility to the system. Moreover, when both batteries and CSP+TES were included, both technologies were always built simultaneously. More CSP+TES was built in systems without PGP long-duration storage, as seen when comparing the base case to the TES+Battery case in Fig. 5(a) and (b). Without the PGP buffer, more short-duration storage capacity is needed to meet demand during periods of low solar and wind resources. When PGP was not included in the system and only one short-duration storage technology was used, the TES-only case resulted in a lower system cost than the battery-only case.
In Fig. 5(b), a mid-range value of $10/kWh for lost load was used to facilitate comparisons between least-cost systems with (Fig. 5(a)) and without (Fig. 5(b)) a demand-response mechanism. In general, least-cost systems with lost load included slightly higher installed PV capacity, and lower installed CSP+TES, wind, and PGP capacities compared to cases in which 100% reliability was specified as a strict constraint. Battery capacity increased when lost load was allowed in the base case system but decreased when lost load was allowed in the Battery+PGP and TES+Battery cases. Capacity values for each case are provided in Table S4. When lost load is allowed, the substitution of PV for CSP is consistent with the lower asset costs of PV relative to CSP in the base case. Only 0.03% (2.76 h) of total demand was assigned to lost load when all generation and storage technologies could be deployed. When only one short-duration storage technology could be used, the battery-only system was cheaper than the TES-only system, but experienced twice the lost load (0.09% of demand for battery-only vs 0.04% for TES-only).
Fig. 5(c) shows the system assets after removing wind from the generation mix. In the absence of wind power, CSP+TES supplied electricity overnight, resulting in a doubling of system costs due to the higher cost of CSP generation compared to wind. PV remained in the mix to provide generation during the day, with batteries built to support the PV generation Fig. 5.(d) then compares this configuration to a system without CSP+TES. The overall system cost for the PV+batteries/PGP storage configuration is essentially the same as the cost for the PV/CSP with TES/batteries/PGP storage configuration, with only a marginal decrease in cost when CSP+TES is added, despite CSP+TES becoming a substantial part of the system. This behavior again suggests that, based on current cost estimates, addition of CSP+TES is a choice rather than a necessity to reach the least-cost system. However, in the absence of PGP, adding CSP+TES to the PV+batteries system decreased system costs by 0.02 $/kWh. Hence, cheap short-duration storage through CSP+TES became more valuable in the absence of seasonal-scale long duration storage.
3.4. Cost drivers for CSP+TES penetration in the grid
At current costs, the above analysis shows that CSP+TES fills a short-term storage role that is complementary to and compatible with simultaneous deployment of PV and batteries. Given that costs for many of these technologies are expected to change substantially in the timeframe over which electricity systems based predominantly on VRE resources are likely to be implemented, a cost sensitivity analysis was performed to analyze the robustness of these results. First, technology costs were varied individually while all other costs were held constant at base case values Fig. 6.(a) and (b) provides results when CSP and TES costs, respectively, are varied, with benchmark costs for Solar Power Tower (SPT) technologies given for each case. CSP cost reductions in (a) primarily resulted in reductions in wind and battery capacity. When CSP reached ∼0.6x of the base case cost, batteries were eliminated from the least-cost system. Further cost reductions in CSP reduced the deployment of PGP, with PGP capacity becoming minimal when CSP costs were ≤ 10% of the base case costs. PV was resilient against reductions in CSP cost, remaining in the system even slightly beyond the case in which CSP costs were assumed to reach parity with PV costs. Hence, both solar generation technologies operate within their own niches in providing the ability to meet demand, rather than purely competing with one another based solely on marginal generation capacity cost. Dispatch curves that demonstrated the behavior of least-cost systems as CSP costs decrease are given in Fig. S14.
Reductions in TES costs had a relatively small impact on the overall system cost (Fig. 6(b)). As TES costs decreased, the capacities in least-cost systems of batteries and PGP decreased, but neither was fully eliminated from the asset mix until TES costs neared zero. Even in that extreme limit, large capacities of PV and wind generation were deployed in these idealized least-cost electricity systems.
Fig. 6(c) and (d) show similar cost sensitivity analyses in these idealized least-cost systems as a function of assumed changes in PV costs or battery costs. Decreases in PV costs had a minimal effect on the characteristics of least-cost systems. The wind capacity and overall system cost decreased as PV costs decreased. The absolute cost contributions of PV to the system cost also decreased because the PV costs decreased faster than capacity increased. However, the capacities of other technologies remained nearly constant, with CSP and TES both remaining in least-cost systems even when PV generation was free. This behavior reflects the value of CSP+TES in providing the fundamental need for flexibility inherent in a VRE-based electricity system, even with cheap generation sources. Even setting PV costs to zero did not eliminate the need for additional flexibility in these idealized least-cost systems beyond that provided by batteries supplied by PV.
When battery costs were varied, CSP and TES were eliminated from least-cost systems when batteries reached 40% of their base case cost but CSP and TES costs were unchanged (Fig. 6(b)). Further decreases in battery cost resulted in a larger deployed capacity of PV in the least-cost systems, whereas the deployed wind capacity decreased slightly. This behavior further confirms that battery costs are the primary driver of combined PV and battery behavior in these idealized least-cost VRE-dominated electricity systems.
Technology costs were also varied in pairs to capture impacts on the overall system costs, as seen in Fig. 7. In Fig. 7(a), the cost of CSP+TES was varied as a single unit by simultaneously applying the same cost multiplier to each technology, and similarly PV+battery costs were also varied as a unit. The result was nearly symmetrical, with reductions in PV+battery costs exerting a slightly stronger influence on the overall system cost than reductions in CSP+TES costs, as shown by the steeper gradient. Hence these idealized least-cost systems experienced a trade-off between the two technology pairs, in accord with other results (Figs. 5 and 6). Although the technological mix in the least-cost system might change substantially depending on a choice to deploy CSP+TES instead of PV+batteries, both technology paths were capable of meeting demand at roughly the same overall system cost.
In Fig. 7(b) PV and battery costs were kept constant, and CSP and TES costs were varied separately. Although the overall diagonal shape of the contour plot suggests that improvements in both technologies were effective in decreasing system costs, the steeper vertical gradient shows that CSP is clearly the more important cost driver for total asset costs of these idealized least-cost electricity systems. Decreases in TES costs led to greater reductions in electricity costs when CSP costs were high, as demonstrated by the comparison between parabolic trough collector (PTC) and solar power tower (SPT) technologies. Generation costs for SPT were higher than PTC costs, but SPT has lower storage costs than PTC, resulting in an overall lower system cost when SPT was deployed in these idealized electricity systems relative to PTC.
Fig. 7(c) shows the opposite pattern for PV and batteries. Although the diagonal contour lines also show that improvements in both technologies decreased system costs, the steeper gradient in the horizontal direction indicates that battery storage was the primary cost driver, rather than generation from PV. This behavior can be understood intuitively from the higher cost of battery storage compared to the cost of PV generation. The costs of the two storage technologies were also varied, and system costs were more sensitive to reductions in battery costs than to reductions in TES costs (Fig. S14).
4.1. CSP with TES as a storage technology
Both dispatch behavior and cost sensitivity analysis suggest that the primary grid service value of CSP arises from coupling with cheap TES, rather than as a direct generation technology. CSP+TES provides valuable grid services mostly relative to batteries rather than relative to PV generation, with cost benchmarks tied to battery costs for when CSP+TES is a contributor to least-cost systems that meet 100% of demand. In the base case for these 100% reliable idealized least-cost electricity systems, the CSP + TES grid services niche would be eliminated if the ratio of battery costs to other technology costs decreased by 40%. NREL projects that battery costs will reach 40% of their current level by 2050, or even as soon as 2030 in the most aggressive projection .
Aggressive cost reductions would be required to allow parabolic trough CSP to be deployed in the modeled least-cost systems at those battery costs, but given the relative maturity of CSP technology, these reductions are considered unlikely [21,66,67]. The less mature solar power tower technology could potentially achieve more substantial cost reductions, with DOE SunShot goals calling for a 50% reduction of 2010 costs by 2030 . Although CSP generation costs include both the solar field and the steam turbine, the maturity of the steam turbine technology due to extensive usage in other contexts makes the solar field the most likely target for innovation . Turbine efficiency could be improved if higher temperatures could be obtained from the solar field, which would then lower the overall cost of CSP energy generation .
Even substantial cost reductions for solar power towers would only maintain CSP+TES’s role as a short-duration storage technology in these idealized least-cost VRE-dominated 100% reliable electricity systems. In our modeled systems, the 50% cost reduction called for in the SunShot goals was not sufficient to convert CSP into a bulk power provider. For example, Fig. S14 shows that CSP generation became a substantial contribution to the asset mix at 25% of base case costs. Reductions in the cost of CSP and TES also would not eliminate the need for long-duration storage such as PGP. The need for seasonal storage decreased in idealized least-cost reliable systems when CSP generation costs were very cheap, but PGP was not eliminated from the asset mix until either CSP or TES were nearly free. Overall, penetration of CSP+TES in these highly reliable variable renewable electricity systems is very limited, with CSP+TES costs struggling to compete with declining battery costs.
4.1.1. CSP with TES in a system without long-duration storage
Without PGP, the analysis shows that batteries and TES were used relatively infrequently, only using the full built capacity during the periods of seasonal resource lows (Figs. S5-S11). In this regime, the addition of CSP+TES to a battery-only idealized 100% VRE/storage system decreased system costs by ∼7% (Fig. 5). Notably, TES is the preferred technology in this analysis to add the final measure of flexibility needed to reach a reliable 100% VRE-based system. This behavior was seen both when gradually eliminating natural gas from the system (Figs. 4(b) and S11), and when allowing the system to include unmet demand (Figs. 4 and 5). When CSP+TES was removed to leave a battery-only VRE/storage system (Fig. 5), least-cost systems resulted in additional lost load as opposed to meeting the extra demand with batteries. The cheaper storage from TES made TES a more valuable technology for the highly infrequent use needed in idealized VRE-dominated least-cost systems that did not contain long-duration storage. The modeling indicates that the strongest incentives to build CSP+TES occur in systems without firm generators, long-duration storage, or other mechanisms to obtain grid flexibility.
4.2. Considerations for CSP and TES integration into renewable systems
Across a range of scenarios and costs, CSP with TES maintained a small role in idealized least-cost systems that met 100% of demand. This finding was also verified across multiple years of input data between 2016 and 2019 to ensure that the 2017 base case year was not an outlier year despite the expected inter-annual variability of wind, solar, and demand data (Fig. S2) . Unless CSP costs were assumed to reach less than 50% of current levels, CSP+TES primarily acted as a “last 1%” peaker technology. This behavior suggests that efforts to increase demand-side flexibility could minimize the value of CSP to satisfying resource adequacy planning constraints in such electricity systems. NREL’s Electrification Futures Study suggests the potential for large shifts in peak demand behavior, particularly in the case of widespread usage of electric vehicles . Although this analysis shows that CSP+TES has a limited role adding flexibility to a VRE grid with currently available technologies and costs, that role could be filled by other flexibility mechanisms in future electricity systems. In regions other than the US, short-duration storage should remain the main role for CSP+TES, but the size of that niche role would depend on the particular resource and demand characteristics for that region. Therefore, although the overall conclusions described here may be generally applicable, a quantitative assessment will require analysis based on the specific characteristics of each individual region.
4.2.1. Impact of firm generators
Firm generators with low- or zero-carbon emissions could also minimize the need for storage technologies, reducing the need for CSP+TES to contribute grid flexibility. The impact of adding such firm generators was evaluated by allowing for either natural gas with CCS (Fig. S12) or nuclear power (Fig. S16) to be included in the modeled least-cost electricity systems. For systems with natural gas with CCS, CSP+TES was present in the idealized least-cost system only if natural gas with CCS was limited to ≤ 3% of total dispatch. The inclusion of nuclear power reduced the role of CSP+TES, but CSP+TES was nevertheless used in combination with batteries to smooth out sharp demand peaks, supplying ∼0.1% of demand. Clearly, these firm generator technologies could play a role in meeting demand for electricity systems with large amount of generation from variable renewable resources, but such technologies are often limited from future electricity systems by regulation or mandate . Dispatchable hydropower was not considered here, but would be expected to have a similar impact in our idealized least-cost system to the other dispatchable technologies that were modeled, such as natural gas. Geographical and other limitations on hydropower generation also prevent it from fully eliminating the need for variable renewables, with limited hydropower growth expected in the US through 2050 . Regardless of firm generators, some amount of variable renewables are expected in future electricity systems, which will likely require either curtailment or storage of some of that variable generation. Our analysis indicates that under certain albeit limited conditions, CSP+TES is a viable option to provide such storage, and remains so even at relatively low penetration of variable renewables, as seen in Figs. 4(b), S12, and S16.
This model assumes free, lossless transmission across the contiguous US, without separation into more geographically constrained load-balancing regions. The hourly time resolution in the model assumes that load balancing and grid stabilization on shorter time scales will be provided by other currently available technologies. If transmission were limited, greater variability in the solar and wind generation profiles would be expected, as found in previously published work . The increased resource variability would increase the need for energy storage and/or curtailment of excess wind/solar generation, but is not expected to fundamentally alter the assessment of the relative roles and values of the generation and storage technologies evaluated herein. Constraining the geographic region could lead to different mixes of technologies, such as the increased PV and decreased wind deployment due to localized resource availability when only California was considered in previous work .
Each model run generates a single end state, so no learning rates were used in cost calculations. The modeled system was assumed to be built instantaneously using “overnight” costs, and the configurations of the modeled least-cost systems were determined using perfect foresight of future resource availability and demand. Consequently, the results herein represent a lower bound for the generation and storage capacity needed to meet electricity demand. The exclusion of offshore wind power from the model is an exception to this lower bound, as wind off the East coast of the US generally has higher capacity factors and less variability . However, offshore generation profiles still exhibit considerable variability,[74,75] and the conclusions of this analysis should not be substantially impacted by this exclusion. No life-cycle considerations for technologies were incorporated into the model.
This analysis does not consider the use of CSP for non-electrical cogeneration products such as heat, desalinated water, or hydrogen [67,76]. These uses might improve the economics of CSP implementation beyond what is evaluated here . Hybridization of CSP with biofuels, geothermal, or fossil fuels which could provide benefits such as increased capacity factor, increased efficiency, and cost-reductions from sharing equipment between technologies is also outside the scope of this analysis [30,77]. This analysis also did not consider any capacity credits, tax incentives, mandates, security of supply or geopolitical considerations. Thus, the results presented here represent a conservative estimate of the utility of CSP. Given the possibility for CSP to supply other products such as heat or hydrogen, future work could examine the potential of CSP to contribute to these other sectors rather than solely to electricity.
Concentrating solar power (CSP) with thermal energy storage (TES) occupies a small but persistent niche in an idealized highly reliable least-cost electricity system with 100% of generation from variable renewable resources. The low cost of TES allowed for a large capacity to be built, with TES cycled infrequently to meet the most difficult hours of demand throughout the year. The other storage technologies filled distinctly different roles. Batteries provided steady short-duration storage, cycling a lower capacity at high frequency compared to TES. Power-to-gas-to-power (PGP) provided seasonal-scale storage that reduced the need to overbuild generation and short-duration storage to meet demand during periods of low solar and wind resources. These behaviors resulted in greater competition between CSP+TES and battery usage rather than with photovoltaics (PV) deployment. These roles also suggest that in electricity markets other than the US, the utility of CSP with TES can be evaluated by considering the magnitude and frequency of generation supply gaps when the system is not able to meet demand. Batteries often provide storage services at lower cost up to a capacity that is expected to cycle at almost daily frequency. In the absence of other grid flexibility mechanisms, CSP+TES may have a small niche compensating for extremely high variability, large magnitude resource gaps in which a large capacity is cycled infrequently. Thus, by evaluating the historical profile of the variable resources in a given geographical region, the relative utility of the two storage technologies can be estimated.
Cost sensitivity analysis showed that deployment of CSP+TES would increase more in response to reductions in the cost of solar generation than to equivalent fractional reductions in the cost of TES technology in these idealized least-cost systems. These cost improvements should be benchmarked against utility-scale battery storage costs, however, which are declining more rapidly than CSP+TES costs have historically decreased. Thus, although CSP with TES offers a cost-effective approach to provide for the “last 1%” of demand in reliable deeply decarbonized electricity systems, technology improvement in other areas, along with improved demand management, may reduce its benefit to the overall system cost.
Data and code availability
The Macro-Energy Model (MEM) uses historical weather data with hourly time resolution over the contiguous U.S. for years 1980–2020 for wind and solar input data. The model incorporates demand data with hourly time resolution for 2015–2019 from the U.S. Energy Information Administration (EIA). The model code, hourly input data, and data visualization code are available on GitHub at https://github.com/carnegie/MEM_public/tree/Kennedy_et_al_2022. Output data for all modeled systems in this analysis can be found at https://zenodo.org/record/5541685.
CRediT authorship contribution statement
Kathleen M. Kennedy: Conceptualization, Formal analysis, Investigation, Writing – original draft, Visualization. Tyler H. Ruggles: Methodology, Writing – review & editing. Katherine Rinaldi: Writing – review & editing. Jacqueline A. Dowling: Writing – review & editing. Lei Duan: Methodology, Data curation, Writing – review & editing. Ken Caldeira: Conceptualization, Methodology, Writing – review & editing, Funding acquisition. Nathan S. Lewis: Conceptualization, Writing – review & editing, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
This work was supported by a gift from Gates Ventures LLC to the Carnegie Institution of Science and a fellowship from SoCalGas in support of Low Carbon Energy Science and Policy.
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