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Photovoltaic Potentials in Austria

Austria's photovoltaic expansion pathway is shaped by land availability, building stock, grid infrastructure, and societal acceptance. The study Erneuerbare Energiepotenziale in Österreich 2030 & 2040, conducted on behalf of the Austrian Climate and Energy Fund (KLIEN), quantifies these constraints at municipality level and derives realisable PV potentials for three area categories, two target years, and three ambition storylines.

PyPSA-AT uses these potentials to constrain the optimised expansion of PV generators in Austrian regions. For the shared data pipeline, scenario dimensions, and configuration reference, see KLIEN Renewable Energy Potentials.

PV in the KLIEN Study

The KLIEN study distinguishes three area categories for PV deployment, each governed by different technical, economic, and social constraints:

Buildings (rooftop and façade)

The technical potential for roof and façade installations is derived from GIS datasets of the Austrian building stock. System efficiency is assumed to improve over time — from 19.0 % in 2030 to 20.4 % in 2040 — reflecting expected advances in module technology. The long-term 2040 potential extends to roof areas with global irradiation of at least 550 kWh/m²·a, unlocking a larger share of the building stock compared to 2030. Realisable potentials are derived by applying economic and social realisation factors to the technically usable area, differentiated between pitched and flat roofs and weighted by three storylines (Low, Medium, High).

Ground-mounted PV on sealed surfaces

This category covers areas where land is already sealed or otherwise compromised, including car parks, landfills, industrial sites, and linear technical infrastructure (e.g., motorway verges, railway embankments). These sites combine PV deployment with minimal additional land-use conflict.

Ground-mounted PV on unsealed surfaces

Unsealed open land is assessed across 26 land-use categories. Areas unsuitable for ground-mounted PV — including forests, protected natural areas, and settlement zones — are excluded from the analysis. Realisable potentials are determined by applying economic and social realisation factors in a two-step procedure, with individual usability factors assigned per land-use class.

The three storylines represent distinct assumptions about the pace of barrier removal:

  • Low: Permitting, grid connection, and supply chain constraints persist. PV remains concentrated on buildings using the best-suited areas. Ground-mounted PV falls significantly short of its area potential.
  • Medium: Gradual removal of administrative and grid-related barriers enables continued growth on buildings, complemented by increasing ground-mounted and agrivoltaic installations.
  • High: Most barriers are resolved. Grid access is widely available. Storage, energy communities, and digital energy management enable high system flexibility, activating the full potential on buildings and open land.

New and renovated buildings are assumed to be PV-optimised in line with EU building regulations.

Input Data

The study provides three GeoJSON files at municipality level, each containing C_-prefixed scenario columns:

GeoJSON file Area category
pv_buildings_EEPOT_W23.geojson Buildings (rooftop and façade)
pv_ground_mounted_sealed_EEPOT_W23.geojson Ground-mounted, sealed surfaces
pv_ground_mounted_unsealed_EEPOT_W23.geojson Ground-mounted, unsealed surfaces

These files are fetched and processed by the build_klien_potentials rule (scripts/pypsa-at/build_klien_potentials.py). The sealed and unsealed ground-mounted files are summed element-wise to form the combined ground-mounted output (nuts3_pv_ground.csv, at10_pv_ground.csv).

Carrier Mapping

The three area categories map to PyPSA carriers as follows:

Area category PyPSA carrier
Buildings solar rooftop
Ground-mounted (sealed + unsealed combined) solar, solar-hsat

solar (fixed-tilt ground-mounted) and solar-hsat (single-axis horizontal tracking) are treated as distinct technologies but share the same physical land area. Both are capped against the combined ground-mounted potential.

Shared Land-Use Constraint for solar and solar-hsat

Because solar and solar-hsat compete for the same land, setting the same p_nom_max on both carriers would allow the model to install up to twice the available potential. apply_klien_potential_limits() therefore sets identical p_nom_max values on both carriers as a first-order bound.

The precise combined limit is then enforced at solve time by the constraint solar_potential in scripts/solve_network.py (add_solar_potential_constraints()):

solar_p_nom + solar_hsat_p_nom × 1.13 ≤ total_solar_potential

The factor 1.13 is the ratio of land-use intensities:

solar.capacity_per_sqkm / solar-hsat.capacity_per_sqkm = 5.1 / 4.43 ≈ 1.13

Single-axis tracking systems require wider row spacing to avoid self-shading, so a given land area yields less installed capacity in MW for solar-hsat than for fixed-tilt solar. The constraint corrects for this by penalising solar-hsat capacity relative to the shared land budget.