Published July 15, 2024 | in press
Journal Article Open

The Influence of Regional Geophysical Resource Variability on the Value of Single- and Multistorage Technology Portfolios

Abstract

A stylized macro-scale energy model of least-cost electricity systems relying only on wind and solar generation was used to assess the value of different storage technologies, individually and combined, for the contiguous U.S. as well as for four geographically diverse U.S. load-balancing regions. For the contiguous U.S. system, at current costs, when only one storage technology was deployed, hydrogen energy storage produced the lowest system costs, due to its energy-capacity costs being the lowest of all storage technologies modeled. Additional hypothetical storage technologies were more cost-competitive than hydrogen (long-duration storage) only at very low energy-capacity costs, but they were more cost-competitive than Li-ion batteries (short-duration storage) at relatively high energy- and power-capacity costs. In all load-balancing regions investigated, the least-cost systems that included long-duration storage had sufficient energy and power capacity to also meet short-duration energy and power storage needs, so that the addition of short-duration storage as a second storage technology did not markedly reduce total system costs. Thus, in electricity systems that rely on wind and solar generation, contingent on social and geographic constraints, long-duration storage may cost-effectively provide the services that would otherwise be provided by shorter-duration storage technologies.

Copyright and License

© 2024 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY 4.0.

Acknowledgement

A.X.L., E.V., J.A.D., T.R., and A.W. acknowledge financial support from a gift by Gates Ventures LLC to the Carnegie Institution for Science. J.A.D., N.R., D.C., and N.S.L. acknowledge financial support, in the form of fellowships at Caltech, from SoCalGas in support of Low Carbon Energy Science and Policy. The authors appreciate Lei Duan’s assistance in preparing the wind and solar capacity factor data used in this study.

Contributions

A.X.L., E.V., and J.A.D. are equally contributing first authors to this study. The manuscript was written through the contributions of all authors. All authors have given approval to the final version of the manuscript. A.X.L. wrote the manuscript, E.V. led the submission process, and J.A.D. led the major revision. A.X.L., J.A.D., E.V., N.S.L., and K.C. contributed to conceptualization; A.X.L., E.V., J.A.D., A.W., D.C., and T.R. contributed to data curation; A.X.L., E.V., J.A.D., T.R., N.S.L., and K.C. contributed to formal analysis; N.S.L. and K.C. contributed to funding acquisition; A.X.L., E.V., J.A.D., A.W., D.C., T.R., N.S.L., and K.C. contributed to investigation; A.X.L., J.A.D., and K.C. contributed to methodology; E.V., J.A.D., N.S.L., and K.C. contributed to supervision; A.X.L., E.V., J.A.D., A.W., and D.C. contributed to visualization; A.X.L., E.V., and J.A.D. contributed to writing─original draft; A.X.L., E.V., J.A.D., T.R., A.W., D.C. N.R., N.S.L., and K.C. contributed to writing─review and editing.

Data Availability

  • Describing the model parametrization, costs, and presenting additional results; electricity sources, sinks, and storage technologies within the macroscale energy model (Figure S1); base case costs and efficiencies assumed for the short-, mid-, and long-duration storage technologies considered in this study (Figure S2); base case energy- and power-capacity total overnight costs of energy storage technologies modeled, where Li-ion total costs are shown as a dashed line (Figure S3); CONUS system costs for combinations of short-, mid-, and long-duration storage using predicted wind and solar costs for the year 2050 (Figure S4); if the cost of Li-ion batteries were much lower than current costs, deployment of Li-ion batteries would be much more effective for reducing total system costs, and would replace utilization of two mid-duration storage technologies (gravity energy storage and PSH) as compared to the base case (Figure S5); role of redox-flow battery (RFB) energy storage in CONUS (Figure S6), CAISO (Figure S7), ERCOT (Figure S8), ISO-NE (Figure S9), MISO (Figure S10) systems, role of pumped-storage hydropower (PSH) energy storage in CONUS (Figure S11), CAISO (Figure S12), ERCOT (Figure S13), ISO-NE (Figure S14), MISO (Figure S15) systems, role of gravity energy storage in CONUS (Figure S16), CAISO (Figure S17), ERCOT (Figure S18), ISO-NE (Figure S19), MISO (Figure S20) systems, role of thermal energy storage in CONUS (Figure S21), CAISO (Figure S22), ERCOT (Figure S23), ISO-NE (Figure S24), MISO (Figure S25) systems, role of compressed-air energy storage (CAES) in CONUS (Figure S26), CAISO (Figure S27), ERCOT (Figure S28), ISO-NE (Figure S29), MISO (Figure S30) systems, and role of metal–air battery energy storage in CONUS (Figure S31), CAISO (Figure S32), ERCOT (Figure S33), ISO-NE (Figure S34), MISO (Figure S35) systems with different combinations of short-, mid-, and long-duration storage; roles of storage technologies in least-cost CONUS systems with up to two storage options available (Figures S36 and S38); Figure 6 with the x-axes zoomed in to energy-capacity costs (Figure S37); storage technologies present in least-cost regional ISO systems and system cost reductions in systems with up to two storage options available (Figures S39 and S40); energy in storage over one year when hydrogen energy storage is the only storage technology available in a least-cost CONUS electricity system that relies on wind and solar generation (Figure S41); storage technologies present in least-cost CONUS systems and system cost reductions in systems with up to three storage options available (Figures S42 and S43); analogous plot to Figure S42 (Figure S44); the value of short-duration storage (Figure S45); when firm generators were available, the storage capacity required in least-cost systems decreased substantially (Figure S46); energy-capacity costs and power-capacity costs of energy storage technologies (Table S1); regional average wind and solar capacity factors for 2018 (Table S2); costs and assumptions for generation technologies (Table S3); base case costs and assumptions for storage technologies (Table S4); optimized discharge times (hours) (Table S5); equivalent annual discharge cycles (Table S6); least-cost system results when various single storage technologies are available (Table S7); least-cost system results when two storage technologies were available (Tables S8 and S9); least-cost system results when three storage technologies were available (Table S10); model nomenclature (Table S11) (PDF)

 

Conflict of Interest

The authors declare the following competing financial interest(s): T.R. is currently a Senior Analytics and Modeling Engineer at Powertech USA. T.R. produced most of his contributions to this work while he was affiliated full-time with Carnegie Institution for Science.

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Additional details

Created:
July 16, 2024
Modified:
July 16, 2024