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Carnot batteries are interesting flexibility options to address the integration challenges posed by intermittent renewable energies, and to combine heat and power. Using thermodynamic cycles, these systems convert surplus electricity into heat, store it in thermal reservoirs, and later reconvert it into electricity. To enhance their adoption, Carnot batteries must balance the competing objectives of maximising efficiency and minimising investment costs, which means identifying the optimal system complexity. Additionally, designs must accommodate specific design preferences, such as limiting storage pressurisation and using generic components. However, current design methods often struggle to identify designs that achieve high efficiency and cost-effectiveness, while offering flexibility for varying design preferences and constraints. Moreover, methods based on economic indicators (e.g., minimisation of levelised cost of storage) are generally not robust to uncertainties. This study therefore introduces modelling to generate alternatives (or near-optimal design) to the thermodynamic design of Carnot batteries, leveraging genetic algorithms to explore a wide range of alternative configurations. The focus is on Rankine cycle based Carnot batteries with sensible heat storage. The analysis reveals that subcritical recuperated cycles are essential for maximising efficiency (reaching up to 33.3% for the selected technological parameters), whereas broader configuration options, including transcritical cycles, can maximise energy density (reaching up to 3.74 kWhel/m³). Trade-offs are inevitable: designs that meet certain preferences, such as low pressurisation or specific temperature ranges, often require compromises in efficiency or density. The near-optimal method elucidates these trade-offs by identifying key drivers, ‘must-have’, and ‘must-avoid’, enabling designers to make informed decisions that balance priorities. It provides a robust framework for adaptable Carnot battery designs, achieving tailored solutions while maintaining performance near theoretical maxima. Future work could extend this methodology and include advanced working fluids or multi-stage configurations to further enhance system performance and adaptability.
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Laterre, A., Coppitters, D., Vincent Lemort, & Contino, F. (2026). Designing small-scale Rankine Carnot batteries that suit your preferences: A near-optimal approach. Journal of Energy Storage, 141(118650), 25. https://doi.org/10.1016/j.est.2025.118650 (Original work published 2025)