The overall aim of this project was to assess the potential of Seasonal Thermal Energy Storage (STES) in Switzerland’s future energy system. Seasonal Thermal Energy Storage generally reduces winter electricity imports, methane and die-sel imports, the need for additional winter generation from thermal power plants, and total sys-tem costs. Depending on the scenario, the average thermal energy storage capacity is in the range of 6 to 10 TWh, leading to a significant annual reduction in winter electricity imports of up to 3.6 TWh. In addition, the inclusion of STES reduces total system costs by 400 to 900 million CHF per year. There-fore, full scale pilot plants have to be fostered now to fully benefit from these experiences in the wave of nationwide implementation from 2035 onwards. A GIS-based analysis was conducted to estimate the potential locations for STES deployment in Swit-zerland by identifying suitable areas under geographic and infrastructural constraints. Suitable storage sites were identified in most of the analysed cases. However, multifunctional PTES coverage is usually a necessary prerequisite for approval. To achieve these results, detailed models of archetypal implementation variants were developed for both PTES and borehole thermal energy storage (BTES). For PTES, different storage sizes and heating demands were analysed to determine key performance indicators such as effective storage capacity and efficiency. For BTES, a wider range of operating cases was investigated, since storage capacity and power strongly depend on charging and discharging strategies. Based on these detailed simula-tions, simplified performance relationships were derived for the implementation on the overall system model level. These results were used to implement STES in the Swiss Energy Scope model (SES-ETH), a linear optimization model of the Swiss energy system. The enhanced model was applied to evaluate the role of STES across a range of future scenarios, including different assumptions on climate targets, interna-tional integration, and technological innovation, while accounting for uncertainty through Monte Carlo analysis. In this way, the model shows under which conditions STES is deployed cost-effectively and at what scale, while minimizing the total annual cost of the overall Swiss energy system.