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The design space for long-duration energy storage in decarbonized power systems


Long-duration energy storage (LDES) is a potential solution to intermittency in renewable energy generation. In this study we have evaluated the role of LDES in decarbonized electricity systems and identified the cost and efficiency performance necessary for LDES to substantially reduce electricity costs and displace firm low-carbon generation. Our findings show that energy storage capacity cost and discharge efficiency are the most important performance parameters. Charge/discharge capacity cost and charge efficiency play secondary roles. Energy capacity costs must be ≤US$20 kWh–1 to reduce electricity costs by ≥10%. With current electricity demand profiles, energy capacity costs must be ≤US$1 kWh–1 to fully displace all modelled firm low-carbon generation technologies. Electrification of end uses in a northern latitude context makes full displacement of firm generation more challenging and requires performance combinations unlikely to be feasible with known LDES technologies. Finally, LDES systems with the greatest impact on electricity cost and firm generation have storage durations exceeding 100 h.

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Fig. 1: System cost percentage reduction in the Northern System for LDES parameter combination.
Fig. 2: System cost percentage reduction in the Northern System with electrified load for LDES parameter combination.
Fig. 3: Firm capacity percentage reduction in the Northern System for LDES parameter combination.
Fig. 4: Firm capacity percentage reduction in the Northern System with electrified load for LDES parameter combination.
Fig. 5: LDES duration in hours in the Northern System for the optimal deployment of LDES.

Data availability

The data that support the figures and other findings of the study are available from the corresponding author upon reasonable request given the size of the data sets generated for this research. Input data and sources can be found in the Supplementary Information.

Code availability

The code used to generate and analyse the data that support the findings of this study are available from the corresponding author upon reasonable request. The CEM model ‘GenX’ used in this research is being prepared for open-source release.


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N.A.S. contributed to this study while funded by the National Science Foundation under grant OAC-1835443. D.M. and A.E. contributed to this study while supported by the Low-Carbon Center on Electric Power Systems at the MIT Energy Initiative.

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Authors and Affiliations



N.A.S. and J.D.J. conceptualized the study. N.A.S., J.D.J. and A.E. implemented the required model modifications. N.A.S., J.D.J., A.E. and D.S.M. developed the experimental design. N.A.S. and A.E. performed the model evaluations. N.A.S. developed the formal analysis, visualization and investigation, and produced the figures. N.A.S. and J.D.J. drafted and finalized the manuscript. D.S.M. and R.K.L. advised on the analysis and reviewed and revised the manuscript. N.A.S., J.D.J. and D.S.M. responded to reviewer comments and revised the manuscript for re-submission.

Corresponding author

Correspondence to Nestor A. Sepulveda.

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Competing interests

N.A.S. and J.D.J. are partners in DeSolve LLC which provides consulting and analytical services for for-profit and non-profit clients, including (within the last 12 months) CorPower Ocean, Westinghouse Electric Corporation, Qvist Consulting Limited, Environmental Defense Fund and Clean Air Task Force. R.K.L. serves on the Scientific Advisory Council of Engie. A.E. works at the Cadmus Group, a strategic and technical consulting firm where she works on clean and renewable energy planning projects for public, non-profit and private sector clients.

Additional information

Peer review information Nature Energy thanks Meihong Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Intersection between LDES Technology Space and Future Technology Projections.

Data from Table 1. Each column represents a specific Energy Capacity Cost [$/kWh] assumption in the ‘LDES Technology Space’. Within each subplot the x-axis represents the Weighted Power Capacity Cost and the y-axis the Round-Trip Efficiency. In a, Dash-dotted lines depict technologies subject to geological and geographic constraints. In (b) feasibility lines in black correspond to the convex-hull of the lowest weighted power cost and highest round-trip efficiency regions of different geological and geographic constrained and unconstrained LDES projected technologies. For cases with the unconstrained feasibility line reaching higher efficiency and lower power cost levels than the constrained one, only the unconstrained line is shown.

Extended Data Fig. 2 Effect on Average Cost of Electricity due to Changes in Weather (VRE Availability) Conditions in Northern System.

The figure shows the perturbation effect of VRE profile changes on average cost of electricity, the solid line marks the region of no perturbation (points in the line) in average cost of electricity cost as VRE availability changes. Each data point on the plot corresponds to a specific set of LDES design space parameters, the x-axis value is the result obtained under base weather assumptions (Scenario 5 in Table 2), while the the y-axis value is the result obtained when changing the weather conditions (Scenarios 10 and 11 in Table 2). The space above the line corresponds to the region of increased average cost of electricity and the space below the line corresponds to the region of reduced average cost of electricity. Panels going left-right indicate different energy capacity cost levels and panels going bottom-up indicate different weighted power cost levels.

Extended Data Fig. 3 Distribution of Discharge and Charge Power Capacities Normalized as Percent of Peak Demand in Northern system.

Discharge power capacity and charge power capacity are both normalized by the peak demand. The resulting values range between 0% and 100% of peak demand and the hexbins (2D bins) have a width of 2%. The dotted line indicates balanced or symmetrical charge and discharge power capacities and separates the space into two diagonal sub-spaces: the upper diagonal sub-space contains systems with more charge power capacity than discharge power capacity, and the lower diagonal space contains systems with more discharge power capacity than charge power capacity.

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Supplementary discussions, figures, tables and methods.

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Sepulveda, N.A., Jenkins, J.D., Edington, A. et al. The design space for long-duration energy storage in decarbonized power systems. Nat Energy 6, 506–516 (2021).

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