Modelling and optimization of compressed air energy storage integrated with thermal energy storage

The increasing penetration of variable renewable energy sources intensifies the need for reliable long-duration energy storage technologies capable of providing grid flexibility and system stability. Compressed Air Energy Storage (CAES), particularly in its adiabatic configurations, represents a promising solution due to its scalability, long operational lifetime, and potential for low environmental impact when integrated with thermal energy storage (TES). This thesis investigates the modelling and optimization of an advanced adiabatic CAES (AA-CAES) system integrated with high-temperature TES. A detailed thermodynamic model is developed and implemented in Python using a modular, open-source framework. The system layout includes multi-stage air compression with intercooling, underground air storage, staged air expansion with reheating supplied by TES, and waste-heat recovery through an ORC. The model is based on first-law energy balances and incorporates finite heat-exchange effectiveness to ensure physically realistic behavior. The developed model is validated against reference literature data, demonstrating good agreement in terms of compressor work, turbine output, and key performance indicators such as Energy Storage Efficiency (ESE) and Round-Trip Efficiency (RTE). A comprehensive sensitivity analysis is conducted to assess the robustness and thermodynamic consistency of the model under variations in compressor and turbine efficiencies, pressure ratios, mass flow rate, and TES temperature. The results confirm physically meaningful and stable system responses. To extend the analysis beyond validation, the model is further adapted for optimization-oriented studies. Pressure-ratio distribution, heat-exchange tuning, and operational constraints are introduced to explore improved operating conditions. In addition, the representation of the TES is refined to ensure energy-consistent coupling between parallel reheating stages, eliminating unphysical heat extraction and improving global performance indicators. The thesis provides a transparent and flexible modelling framework for hybrid CAES-TES systems and demonstrates its suitability for sensitivity analysis and preliminary optimization studies. The results highlight the critical role of thermal integration and operational design choices in enhancing CAES performance, supporting its potential application in future low-carbon and renewable-dominated energy systems.