Component-based modeling of ground-coupled seasonal thermal energy storages

Abstract

Seasonal thermal energy storages are considered a central element of modern, innovative energy systems and help to harmonize fluctuating energy sources. Furthermore, they allow for an improved coupling between the electricity and heating sectors. Despite recent improvements of planning processes and enhanced models, significant discrepancies between projected and measured heat losses were revealed. Additional shortcomings of available tools relate to limitations in specifying geometry, internal design, or physical processes. Addressing these drawbacks, this study employs a revised, alternative approach by using a flexible, component-based, model (“STORE”). It allows variable flexible parameterizations to study diverse design scenarios. After introducing relevant seasonal thermal energy storage components, processes and mechanisms, datasets, and evaluation techniques, a plausibility test is presented that applies a common thermal energy storage model for benchmarking. In a test study, the re-use of a circa 1,000 m3 large swimming pool is simulated. STORE is used to investigate performance trends caused by different designs (e.g., insulation thicknesses, materials at individual interfaces). For the plausibility test, the results show a high degree of coverage and good applicability. Further, the results of the test study show a storage efficiency of 12.4% for an uninsulated base case, which can be improved to 69.5% in case of the most complex, highly insulated configuration. Critical trends are revealed, covering reduced peak capacity levels (26.5 to 23.5 MWh) and raised average filling temperatures (39.1 to 45.2 °C). Improved long-term behavior involves reduced environmental impacts due to reduced heating of the ambient soil (+7.9 K compared to +14.1 K after 2 years). General conclusions reveal that an optimal design should initially focus on an external cover of soil and top insulation. However, evaluations should base on multiple parameters depending on the target criteria. This is where the present model is highly useful. The capability of STORE to rapidly analyze a plethora of scenarios proves its high applicability for optimizing the planning processes of seasonal thermal energy storage projects.