Thermo-economic optimization of an innovative integration of thermal energy storage and supercritical CO2 cycle using artificial intelligence techniques

This research delves into the integration of Thermal Energy Storage (TES) and Supercritical Carbon Dioxide (s-CO2) in an innovative Energy Recycling System (ERS) that aims to improve overall system efficiency. The combination of TES and s-CO2 is a promising solution to address modern energy challenges and promote a sustainable and efficient energy future. For this research, the TES system is based on a packed bed of stones (PBS). This approach has several benefits, such as the use of inexpensive and readily available materials for storage, a broad range of operating temperatures, allowing for direct heat transfer, and the ability to achieve high maximum temperatures. The simulation methodology for TES is based on Schumann's approach, and energy and exergy analysis are used for thermodynamic modeling. Additionally, levelized costs are employed for the economic modeling of the system. Through rigorous thermos-economic modeling and simulation, operational parameters are optimized using genetic algorithms and neural network techniques to balance thermodynamic efficiency, energy storage, and economic feasibility. The findings reveal exceptional energy and exergy efficiencies, exceeding expectations, and suggest the ERS, with grid-scale energy storage, as a cost-effective solution. The simulations demonstrate remarkable energy and exergy efficiencies of 92.15% and 49.66%, respectively, with levelized costs of 104.69 €/MWh for heat, 135.20 €/MWh for electricity, and 65.7€/MWh for storage.