Thermal Energy Storage (TES) systems, integrated with Heat Pumps (HPs) and Combined Heat and Power (CHP) units, improve heating efficiency and grid stability. However, optimizing HP and CHP control remains challenging. Digital models offer valuable insights for control strategies, but their accuracy is crucial. A key issue in TES modeling is the limited consideration of sensor dynamics, leading to discrepancies between simulations and real-world performance, preventing reliable control optimization. This study improves TES model accuracy by incorporating sensor behavior into the simulation framework. A 10-layer stratified TES model in Hysopt is improved by integrating thermal inertia, offset errors, and optimal sensor positioning into the simulation. These enhancements were validated against real-world data, yielding multiple improvements in RMSE and MAE, with the most notable reduction in RMSE from 3.19 K to 1.14 K and in MAE from 2.83 K to 0.89 K, significantly enhancing model reliability for control strategies. Results highlight the impact of sensor height deviations and installation-related issues, revealing that inaccurate sensor placement introduces artificial response delays that distort control. Despite these advances, manual calibration was time-intensive and tailored to one control strategy. Future work will focus on automating calibration to improve efficiency and accuracy. Additionally, validating the model under varied control settings and across multiple TES cases will ensure robustness and help distinguish TES model limitations from sensor installation effects. By improving TES model fidelity and enabling automated calibration, this research supports more effective TES control strategies, enhancing efficiency and flexibility in heating systems.