Spatial Solar PV & Rooftop Capacity

Foundations of Spatial Solar Energy Modeling

Spatial solar engineering couples physical solar geometry with geographic information systems (GIS) to evaluate photovoltaic energy yields across terrestrial terrain and urban built environments. Regional solar deployments require simultaneous optimization of astronomical tilt, shadow avoidance, structural load limits, and electrical grid hosting capacity.

1. Solar Radiative Components on Tilted Collector Planes

The total instantaneous solar power striking a photovoltaic collector surface ($G_{poa}$, $\text{W/m}^2$) is governed by the vector angle of incidence ($AOI$) and atmospheric scattering:

Where $DNI$ is Direct Normal Irradiance, $DHI$ is Diffuse Horizontal Irradiance, $GHI$ is Global Horizontal Irradiance, $\beta$ is the array tilt from horizontal, and $\rho$ is the ground albedo reflection coefficient. Optimizing $\beta$ balances direct beam capture during peak midday hours against diffuse sky reception and ground reflection.

2. Empirical Lessons from Regional Photovoltaic Geodatabases

Geospatial engineering research and empirical distribution data demonstrate that photovoltaic facilities naturally organize into distinct physical scale and grid interconnection tiers:

• Low Tension Distributed Generation (< 200 kW): Accounts for over 98% of total individual plant records, with a mean installation capacity of 12 kW. These represent residential and light commercial rooftop units configured for on-site self-consumption.
• Medium Tension Commercial and Industrial (0.2 - 5.0 MW): Accounts for approximately 26.7% of total regional generation capacity, with a mean unit capacity of 850 kW. These facilities connect to local distribution utility networks via dedicated step-up transformers.
• High Tension Centralized Utility (> 5.0 MW): Comprises large-scale ground-mounted solar farms with a mean capacity of 11.2 MW, delivering power directly into the high-voltage transmission grid at 150 kV.

3. 3D GIS Rooftop Potential Assessment Workflow

Evaluating urban rooftop potential across municipal cadastres requires high-resolution LiDAR and building footprint analysis:

1. Height Normalization: Generate a normalized Digital Surface Model ($nDSM = DSM - DTM$) to isolate building structures from bare ground.
2. Morphological Filtering: Extract roof facet slope and aspect rasters using GDAL terrain processing utilities.
3. Suitability Screening: Exclude north-facing facets ($315^\circ - 45^\circ$) and steep slopes ($> 45^\circ$).
4. Obstacle Derating: Apply a usable area factor ($0.55 - 0.75$) to account for HVAC units, access pathways, parapet setbacks, and inter-row pitch spacing.
5. Electrical Modeling: Compute peak DC capacity ($kW_p$) and simulate annual AC generation ($MWh$) using the standard IEC 61724 Performance Ratio model ($PR \approx 0.78$).