This research delves into the integration of Thermal Energy Storage (TES) and Supercritical Carbon Dioxide (s-CO) in an innovative Energy Recycling System (ERS) that aims to improve overall system efficiency. The combination of TES and s-CO 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.

中文翻译：

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利用人工智能技术对热能储存和超临界二氧化碳循环进行创新集成的热经济优化


这项研究深入探讨了热能储存 (TES) 和超临界二氧化碳 (s-CO) 在创新能源回收系统 (ERS) 中的集成，旨在提高整体系统效率。 TES 和 s-CO 的结合是应对现代能源挑战和促进可持续、高效能源未来的一个有前途的解决方案。在本研究中，TES 系统基于填充石床 (PBS)。这种方法有几个好处，例如使用廉价且容易获得的材料进行存储、工作温度范围广、允许直接传热以及能够实现较高的最高温度。 TES 的模拟方法基于舒曼方法，并使用能量和火用分析进行热力学建模。此外，系统的经济建模采用了平准化成本。通过严格的热经济建模和模拟，使用遗传算法和神经网络技术优化运行参数，以平衡热力学效率、能量存储和经济可行性。研究结果揭示了卓越的能源和火用效率，超出了预期，并表明具有电网规模储能的 ERS ​​是一种具有成本效益的解决方案。模拟结果显示，能源效率和火用效率分别为 92.15% 和 49.66%，平均供热成本为 104.69 欧元/兆瓦时，电力为 135.20 欧元/兆瓦时，存储成本为 65.7 欧元/兆瓦时。