An open-data QGIS-workflow for lot-scale shallow geothermal planning

Cities aiming to decarbonize space heating increasingly consider ground source heat pumps, but in densely built-up urban areas permission and practical installation is spatially constrained, e.g., by property boundaries, minimum distances to buildings, exclusion zones, and subsurface conditions. Planners and permitting authorities therefore need transparent tools at the lot-scale for borehole heat exchanger (BHE) array design in accordance with the building’s heat demand, and in compliance with local regulations. We present an open-data workflow implemented in QGIS and python to estimate the technical shallow geothermal potential for cities based on the lot level under relevant regulatory rules. The workflow is designed to rely on public datasets as far as possible, i.e. cadastral lots, building footprints, transport and land use restrictions, tree locations, and subsurface thermal conductivity. Lot-level annual heat demand is estimated based on LOD2 data by assigning standard residential building archetypes to 3D building models, scaling specific demands with floor area. All key distance and BHE array design parameters are considered as user-defined inputs, which facilitates sensitivity and scenario analyses.

The workflow comprises four major steps. First, available installation space is derived for each lot by assigning buffer zones around buildings, lot boundaries, tree locations and other restricting features in order to exclude the placement of BHEs in their direct vicinity. Furthermore, all exclusion zones are subtracted, e.g. drinking water protection zones or natural reserves. Second, within each available space polygon, candidate BHE positions are placed on a rotated and shifted hexagonal grid to approximate the densest location of BHEs for a given minimum separation distance. Third, thermal conductivity along the BHE length is sampled at every BHE position and combined with design tables for vertical BHE systems to estimate specific heat extraction rates and annually extractable geothermal energy. Finally, potentials are aggregated within lots and compared to lot-level heat demand. An energy index is derived to quantify the fraction of demand that can be covered on each lot.

The workflow was exemplarily applied to a city district, containing 1823 lots with a total annual heat demand of about 98 GWh. In a base-case scenario with all distances in compliance to local guidelines, roughly two-thirds of all lots (accounting for 88% of the district’s total heat demand) are suitable for BHE installation. The total technical potential exceeds total demand by about a factor of 1.5, but when limited to the demand per lot, only about half of the district’s heat demand can be met by BHEs on the same lot, and only about one quarter of lots with a non-zero heat demand are self-sufficient. Scenario analyses show that the geothermal potential is most sensitive to borehole depth, spacing between BHEs and distance to neighboring lots, while building and tree distance buffers have smaller effects. A scenario using deeper BHEs and slightly relaxed spacing rules increases district-wide demand coverage to about three quarters and more than doubles the number of self-sufficient lots.