A Dynamic 2d Borehole Thermal Energy Storage (Btes) Model for Enhanced Computational Efficiency

Abstract

Progressing towards a future that becomes increasingly dependent on renewable energy sources, the identification of effective, durable energy storage solutions becomes paramount. In the evolving energy landscape, Borehole Thermal Energy Storage (BTES) technology plays a significant role in enhancing the integration of renewable energy systems. Integrating Ground Source Heat Pumps (GSHP) with BTES systems in district energy networks offers an efficient and sustainable solution, enhancing the overall energy performance and providing stable heating and cooling. Although traditional three-dimensional (3D) modeling of BTES offers a thorough analysis, it also requires substantial computational resources, posing a significant obstacle. This study introduces an advanced two-dimensional (2D) modeling approach specifically designed to substantially reduce computational demands without significantly compromising the accuracy of simulations. This method meticulously simulates the complex processes of heat collection, preservation, and retrieval within a BTES framework, spanning a seven-month period. A key innovation of this research is the incorporation of the thermal-mass weighted-average temperature parameter, providing valuable insights into the operational mechanism of BTES systems. This innovative model highlights a considerable enhancement in BTES system efficiency, marking a significant advancement in the development of renewable energy storage solutions. The optimized model runs at a speed seventeen times faster than conventional Computational Fluid Dynamics (CFD) models with a mean absolute percentage error of 2% during the charging cycle and 4% during the discharging cycle, achieving a good balance between computational efficiency and precision. This investigation not only validates the feasibility of this innovative 2D approach but also paves the way for future developments in BTES technology, crucial for advancing toward a sustainable energy future.

Keywords: Renewable energy storage, 2D Modeling, Seasonal energy storage, Computational efficiency, Sustainable energy solutions.