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Pumped Storage Hydropower

The 2022 ATB data for pumped storage hydropower (PSH) are shown above. Base Year capital costs and resource characterizations are taken from a national closed-loop PSH resource assessment completed under the U.S. Department of Energy (DOE) HydroWIRES Project D1: Improving Hydropower and PSH Representations in Capacity Expansion Models. Resource assessment and cost assumptions are documented by (Rosenlieb et al., 2022). This effort considered only closed-loop systems due to their relatively lower environmental impacts, so open-loop and other configurations are not included in these estimates. Operation and maintenance O&M costs and round-trip efficiency are based on estimates for a 1,000-MW system reported in the 2020 DOE Grid  Energy Storage Technology Cost and Performance Assessment(Mongird et al., 2020). Projected changes in capital costs are based on the DOE Hydropower Vision study (DOE, 2016) and assume different degrees of technology improvement and technological learning. 

The three scenarios for technology innovation are:

  • Conservative Technology Innovation Scenario (Conservative Scenario): no change from baseline CAPEX and O&M costs through 2050
  • Moderate Technology Innovation Scenario (Moderate Scenario): no change from baseline CAPEX and O&M  costs through 2050, consistent with the Reference case in the DOE Hydropower Vision study (DOE, 2016)
  • Advanced Technology Innovation Scenario (Advanced Scenario): CAPEX reductions of 12% by 2050 based on improved process and design improvements along with advanced manufacturing, new materials, and other technology improvements, consistent with Advanced Technology in the DOE Hydropower Vision study (DOE, 2016); no changes to O&M.

Resource Categorization

Resource categorization from a national closed-loop PSH resource assessment is described in detail by (Rosenlieb et al., 2022). Individual sites are identified using geospatial algorithms to delineate potential reservoir boundaries, exclude reservoirs that violate technical potential criteria (e.g., protected land, critical habitat), find all possible reservoir pairings, and then eliminate overlapping reservoirs to produce the least-cost set of non-overlapping reservoir pairs. Underlying data are site-specific, but for the ATB, resource classes are binned by capital cost such that each class contains a roughly equal amount of total national PSH capacity potential. Binning is done at the national level for the data tables below, and other representations use region-specific cost bins to better represent the distribution of site characteristics in each region. Physical characteristics and capital cost statistics for each ATB class are included in the table below. 

Resource Class Capacity and Capital Costs

ATB ClassTotal Number of Sites IdentifiedTotal Generating Capacity (GW)Site Generating Capacity (MW)Capital Cost
(2020$/kW)
AverageMinMaxAverageMinMax
Class 13401895571823,328$1,912$1,200$2,138
Class 24721883971631,500$2,292$2,138$2,416
Class 35231883591531,382$2,526$2,416$2,620
Class 4629188298138959$2,729$2,621$2,831
Class 56531872861201,028$2,923$2,831$3,006
Class 67361882561061,036$3,089$3,006$3,171
Class 77601872471011,203$3,256$3,171$3,341
Class 8810187231105965$3,423$3,341$3,501
Class 987418721487790$3,584$3,501$3,667
Class 10927187202901,028$3,755$3,667$3,839
Class 11960188195821011$3,931$3,839$4,023
Class 121,00118718789811$4,123$4,023$4,232
Class 131,07118817582720$4,357$4,233$4,486
Class 141,06918717571609$4,654$4,486$4,850
Class 1594418819967619$5,266$4,850$6,981
Totals11,7692,814 

Resource Class Design Values

ATB ClassReservoir Volume (gigaliters)Hydraulic Head (m)Distance Between Reservoirs (m)
AverageMinMaxAverageMinMaxAverageMinMax
Class 14.31.043.569333215313,70011844498
Class 23.61.320.65853009403,69513424497
Class 33.61.421.65203008003,6789944498
Class 43.21.315.04803007513,64910804499
Class 53.31.316.14423006693,6486744497
Class 63.11.215.14183006243,6477134497
Class 73.11.217.64003005663,6408114498
Class 83.01.214.13783005923,6457004497
Class 92.91.012.73693005533,6825604499
Class 102.81.116.03553005103,6737804499
Class 112.71.114.43463005103,7059664499
Class 122.71.213.13353004983,7656614499
Class 132.61.211.63273004713,8159114499
Class 142.71.19.83193004693,89811284499
Class 153.11.19.13123004303,98018604499

Scenario Descriptions

Cost reductions in the Advanced Scenario reflect various types of technology innovations that could be applied to PSH facilities. These potential innovations, which are discussed in the DOE Hydropower Vision Roadmap (DOE, 2016), are largely similar to technology pathways for hydropower without pumping.

Summary of Technology Innovation: Advanced Scenario

 ModularityNew MaterialsEco-Friendly Pumps and TurbinesInnovative Closed-Loop Concepts
Technology DescriptionsDrop-in systems that minimize civil works and maximize ease of manufactureAlternative materials for water diversion (e.g., penstocks)Innovative approaches to improved environmental performanceOff-river designs allowing better combined economic and environmental performance
ImpactsReduced civil works costReduced construction material costsReduced environmental mitigation costsReduced environmental costs and increased modularity and standardization
References(DOE, 2016)(DOE, 2016)(DOE, 2016)(DOE, 2016)

Representative Technology

The resource assessment procedure requires several design specifications to be defined up front, and for the resource included in the ATB, these include a fixed 30-m dam height, a minimum 300-m hydraulic head height, and a maximum reservoir distance of 15 times the head height (Rosenlieb et al., 2022). Upper and lower reservoir volumes are also assumed to be within 20% of each other. Given the resulting technical specifications of each reservoir pair, the powerhouse (turbine, generator, and electrical equipment) can be sized flexibly for a given reservoir pair, and here all data assume the powerhouse is sized for exactly 10 hours of storage duration (i.e., a maximum of 10 hours generating at rated capacity). 

Methodology

This section describes the methodology to develop assumptions for CAPEX, O&M, and round-trip efficiency. 

Capital Expenditures (CAPEX)

Capital costs are first calculated for each site using the PSH cost model from Australia National University (Andrew Blakers et al., 2019) adjusted to use a 33% project contingency factor instead of the base 20% assumption to better align with other technologies and U.S. industry practice. The cost model uses reservoir and powerhouse characteristics as inputs to generalized equations for PSH overnight capital cost. These raw costs are then further calibrated to more closely match hydropower industry expectations by multiplying site costs by a factor equal to the ratio of the central CAPEX estimate in (Mongird et al., 2020) for a 1,000-MW, 10-hour facility to the median CAPEX of all sites in the capacity range of 900–1,100 MW (Mongird et al., 2020). This factor is equal to 1.51, and due to the limited amount of available cost data, this factor is applied uniformly to all sites. Grid connection costs are then added based on the distance from the powerhouse location (assumed at the lower reservoir) to the nearest high-voltage transmission line node  (Maclaurin et al., 2021). Cost assessment is described in greater detail in (Rosenlieb et al., 2022).

The maps below plot median CAPEX in each state for each of 15 resource classes when individual sites are binned by cost separately for each state. Some states have zero sites identified, largely due to insufficient elevation differences to meet a 300 m minimum head height criteria. The ratio of distance between reservoirs to head height (L/H ratio) is also shown for individual sites. The display also includes links to a bar chart and a tabular display. The bar chart shows more granular data for each balancing area defined in the Regional Energy Deployment System (ReEDS) capacity expansion model (Ho et al., 2021) along with the state average PSH capital cost. The table allows the data to be filtered by class and balancing area to view region- or class-specific data.

Regional PSH Capital Cost by Class

 

Operation and Maintenance (O&M) Costs

(Mongird et al., 2020) characterize PSH O&M costs using a literature review of recently published sources of PSH cost and performance data. For the 2022 ATB, we use cost estimates for a 1,000-MW plant, which has lower labor costs per power output capacity than a smaller facility. O&M costs also include component costs for standard maintenance, refurbishment, and repair. O&M cost reductions are not projected because the relevant technical components are assumed to be mature, so they are constant and identical across all scenarios.

Round-Trip Efficiency

Round-trip efficiency is also based on a literature review by (Mongird et al., 2020), who report a range of 70%–87% across several sources. The value of 80% is taken as a central estimate, and no improvements are projected either in (Mongird et al., 2020) or here because the relevant technical components are assumed to be mature. Thus, round-trip efficiency is constant and identical across all scenarios. 

References

The following references are specific to this page; for all references in this ATB, see References.

DOE. “Hydropower Vision: A New Chapter for America’s Renewable Electricity Source.” Washington, D.C.: U.S. Department of Energy, 2016. https://doi.org/10.2172/1502612.

Mongird, Kendall, Vilayanur Viswanathan, Jan Alam, Charlie Vartanian, Vincent Sprenkle, and Richard Baxter. “2020 Grid Energy Storage Technology Cost and Performance Assessment.” Washington, D.C.: U. S. Department of Energy, December 2020. https://www.energy.gov/energy-storage-grand-challenge/downloads/2020-grid-energy-storage-technology-cost-and-performance.

Rosenlieb, Evan, Donna Heimiller, and Stuart Cohen. “Closed-Loop Pumped Storage Hydropower Resource Assessment for the United States.” Golden, CO: National Renewable Energy Laboratory, 2022. https://www.nrel.gov/docs/fy22osti/81277.pdf.

Maclaurin, Galen, Nicholas Grue, Anthony Lopez, Donna Heimiller, Michael Rossol, Grant Buster, and Travis Williams. “The Renewable Energy Potential (ReV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling.” Golden, CO: National Renewable Energy Laboratory, 2021. https://doi.org/10.2172/1563140.

Ho, Jonathan, Jonathon Becker, Maxwell Brown, Patrick Brown, Ilya (ORCID:0000000284917814) Chernyakhovskiy, Stuart Cohen, Wesley (ORCID:000000029194065X) Cole, et al. “Regional Energy Deployment System (ReEDS) Model Documentation: Version 2020.” Golden, CO: National Renewable Energy Laboratory, June 9, 2021. https://doi.org/10.2172/1788425.

Andrew Blakers, Matthew Stocks, Bin Lu, Kirsten Anderson, and Anna Nadolny. “Global Pumped Hydro Atlas.” Australian National University, 2019. http://re100.eng.anu.edu.au/research/phes/.

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