Advanced exergy analysis of Rankine-based pumped thermal energy storage systems: Methodology and theoretical analysis

Large-scale electricity storage is crucial for balancing renewable energy supply and demand. Pumped thermal energy storage (PTES) systems present a promising solution by converting electrical energy to thermal energy for storage and subsequent reconversion to electricity. This study contributes to the advancement of PTES technology through two primary contributions. Firstly, it presents a novel methodology for advanced exergy analysis that integrates the rigor of the decomposition method with cycle-based simulation approaches. Secondly, it employs this methodology to analyze different PTES configurations, facilitating a comparative analysis of systems with pressurized and atmospheric thermal energy storage across varying temperature levels. The methodology is implemented in a dedicated Python code that solves all real, ideal, and hybrid cases within a unified Newton–Raphson framework. This code is distributed together with the PTES models and input data. The findings indicate that, while heat exchangers exhibit the highest exergy destruction rates, turbomachinery components offer greater potential for optimization. The high-temperature PTES configuration achieves superior round-trip efficiency (up to 43.2%) in comparison to the low-temperature design (below 40%). The results of this study indicate that open-source frameworks can support the conduction of comprehensive exergy analyses, thereby establishing a foundation for future research endeavors aimed at incorporating economic considerations and more complex process designs. • Different configurations of Rankine-based pumped thermal energy storage systems are modeled and analyzed. • High-temperature water storage reaches round-trip efficiency up to 43.2%. • A new methodology for advanced exergy analysis combining prior approaches is presented. • Advanced exergy analysis shows mainly endogenous, partly avoidable losses. • An open-source, Python-based framework is used for conducting the analysis.