Working fluid screening for ORC-based Carnot batteries by deterministic global optimization of design and nominal operation

Abstract The rising temporal mismatch between energy supply and demand increases the need for large-scale energy storage. Carnot batteries offer a location-independent energy storage option and rely on readily available components. However, the low round-trip efficiency is an issue. Also, Carnot batteries have many degrees of freedom, making their optimal design and operation challenging. Additionally, the process is highly dependent on the working fluids used for the charging and discharging process. We optimize the Carnot battery design and nominal operation, including the working fluid selection. We perform deterministic global optimization to maximize the round-trip efficiency. Extending our previous work, we formulate a hybrid mechanistic/data-driven model in reduced space. We propose a model formulation for the thermal energy storage that is tailored to our problem and demonstrate that it results in substantial computational savings. We also extend our previously used surrogate model training procedure, by choosing surrogate models based on their relaxation tightness, strongly improving worst-case computational performance. These model improvements allow us to screen working fluids for the charging and discharging process by enumeration, globally optimizing each flowsheet with MAiNGO v0.7.2. Optimal round-trip efficiencies vary between 30% and 60% and are typically found in the root node (multistart) but for some cases during branch-and-bound. However, the top working fluid combinations include fluids with a strong environmental impact, indicating the need to include environmental objectives.