Data-driven thermal characterization of a 1-D model for sensible stratified thermal energy storage

Thermal Energy Storage (TES) facilities have a wide range of applications in thermal networks. They enable adaptable functionality by effectively and timely managing the supply and demand of thermal energy. This practice offers both environmental and economic benefits, such as reduced CO2emissions, lower energy generation costs, and decreased operational expenses. Although there are various TES technologies available in the market with higher energy density, such as those based on latent heat and phase change materials, hot water TES units are predominantly used due to their cost-effectiveness, simplicity, and favourable thermal properties. Although many two-dimensional and three-dimensional simulation approaches have been employed to assess the fluid dynamic behaviour of TES components, there has been growing interest in one-dimensional models, as they are better suited for time-dependent, plantscale optimization studies. However, 1-D models often sacrifice accuracy for computational efficiency, leading many researchers to focus on improving their precision. In this study, field data acquired in an industrial solar thermal power plant are employed to validate and thermally characterize a sensible stratified thermal energy storage using pure water as Heat Transfer Fluid (HTF). This approach aims to bridge the gap between qualitative 1-D modelling and practical applications. The results demonstrate the advantages of data-based approaches while also highlighting some of the inherent limitations of 1-D modelling methods.