Technology Assessment of Solar-Assisted Underground Thermal Energy Storage Potential Using a Lithology-Resolved GIS–Laboratory Framework

Solar-assisted underground thermal energy storage (UTES) can reduce the seasonal mismatch between summer solar availability and winter heat demand. However, regional planning requires renewable charging resources and subsurface thermal performance to be evaluated jointly. This study developed a lithology-resolved GIS–laboratory framework for preliminary borehole thermal energy storage screening in the Euganean Hills, northeastern Italy. Thermal conductivity and volumetric heat capacity were characterized for 23 local specimens under dry and water-saturated conditions. Conductivity was measured parallel and perpendicular to the dominant rock fabric. Additional basalt measurements and clay/silt reference values were used where local sampling was insufficient. Normalized thermal effusivity was combined multiplicatively with normalized seasonal solar radiation to define a dimensionless Ground Thermal–Solar Suitability (GTSS) index. Property uncertainty was propagated using 10,000 non-parametric bootstrap realizations. Saturated conductivity generally exceeded 3.0 Wm−1K−1 in compact carbonate formations. From the conservative dry/perpendicular to the favorable saturated/parallel scenario, lithology-level GTSS increased by 2.6–9.4%. Approximately 30.1% of mapped cells increased by one suitability class and 0.5% by two classes. Relative 95% GTSS interval widths ranged from 2.5–16.9% in the conservative scenario and 1.1–13.5% in the favorable scenario. Maiolica and Scaglia Rossa provided the strongest combination of thermal performance and spatial continuity. At Turri, field-equivalent conductivity was 5.10 Wm−1K−1, or 2.50–3.45 times the laboratory-weighted estimates. This difference confirms that field-scale fracture, groundwater, and borehole effects lie outside the laboratory calibration domain. GTSS therefore supports uncertainty-aware regional prioritization, not site-specific performance prediction.