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Inter-seasonal compressed-air energy storage using saline aquifers

Abstract

Meeting inter-seasonal fluctuations in electricity production or demand in a system dominated by renewable energy requires the cheap, reliable and accessible storage of energy on a scale that is currently challenging to achieve. Commercially mature compressed-air energy storage could be applied to porous rocks in sedimentary basins worldwide, where legacy data from hydrocarbon exploration are available, and if geographically close to renewable energy sources. Here we present a modelling approach to predict the potential for compressed-air energy storage in porous rocks. By combining this with an extensive geological database, we provide a regional assessment of this potential for the United Kingdom. We find the potential storage capacity is equivalent to approximately 160% of the United Kingdom’s electricity consumption for January and February 2017 (77–96 TWh), with a roundtrip energy efficiency of 54–59%. This UK storage potential is achievable at costs in the range US$0.42–4.71 kWh−1.

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Data availability

The data used to determine the predictive models are provided in the ‘TrainSet’ and ‘TestSet’ sheets of Supplementary Data 1. The conversion rates used for the costings are provided in the ‘Conversions’ sheet of Supplementary Data 1. The hydrocarbon volumes data are provided in Supplementary Table 4. The CO2 Stored data that support the findings of this study are available from the British Geological Survey and The Crown Estate, but restrictions apply to the availability of the data, which were used under licence for the current study, and so are not publicly available. Data are, however, available from http://www.co2stored.co.uk/ after registration, which grants free access. Full data download is considered on a case by case basis by the British Geological Survey and The Crown Estate. Other data and materials, not specified above, are available from the authors upon reasonable request.

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

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Acknowledgements

This work has been funded by the Engineering and Physical Science Research Council (EPSRC) of the United Kingdom and by the Energy Technology Partnership. The authors thank E.V. Hipkins for her proof reading of the draft, as well as the Geofluids research group from the University of Edinburgh for their suggestions.

Author information

J.M.-C. co-developed the methodological approach and the models and performed the sensitivity analysis, data set screening and Monte Carlo simulation. M.W. conceptualized the study, co-developed the methodology for the screening of the UK data set, proposed the conceptual design of the idealized store model and provided significant input into the research coordination. D.M. co-developed the combustor model and verified the compressor and turbine models, provided the costings of the turbine and compressors, and contributed to the development of the predictive models. C.M. co-developed the numerical store models in OpenGeoSys. R.S.H. contributed to the costing methodology and conceptualized the study. Z.K.S. contributed to the conceptual store model development, in particular the geomechanical limitations of it. All authors made significant contributions and revisions to the manuscript itself.

Competing interests

The authors declare no competing interests.

Correspondence to Julien Mouli-Castillo.

Supplementary information

Supplementary Information

Supplementary Figures 1–4, Supplementary Tables 1–8, Supplementary References

Supplementary Data 1

The “Costings” sheet contains the costings established using carbon capture and storage site costings as analogues, as well as hypothetical sites using areas highlighted by this research. The “Conversions” sheet presents the cost conversion from the original values reported in the literature to 2018 US dollars. The “TrainSet” sheet contains the results from the store, well and plant models used to determine the predictive models, along with the associated statistical summaries. The “TestSet” sheet contains the results from the store, well and plant models used to test the predictive accuracy of the predictive models, along with the reported Pearson coefficient between the modeled and predicted values

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Further reading

Fig. 1: Schematic of the key components of a conventional CAES system.
Fig. 2: Workflow to build predictive models of PM-CAES power output and roundtrip efficiency and its use to perform a nation-scale storage assessment.
Fig. 3: Conceptual model of a well in a porous rock store under PM-CAES operation.
Fig. 4: Map of the United Kingdom showing the formations identified as presenting storage potential.
Fig. 5: PM-CAES storage capacity in offshore UK aquifers.